domain: sde spi

text:
Dr Ing Sam

Van Haute

Prof. Dr. ir Jo Dewulf

Prof. Dr. ir Sami Ghnimi

Prof. Dr. ir Van Marc

Meirvenne

Prof. Dr. ir Rik Van De Walle

Prof. Dr. ir Filip

Dr Ing Imca Sampers

Prof. Dr. ir A Kurt

Prof. Heekwan Rosentrater

Prof. Dr. ir Heekwan Lee

Prof. Dr Tanja Cirkovic Velickovic

Prof. Dr. ir Mieke Uyttendaele

Prof Sam Jo Van Haute

Prof Dewulf

Ghnimi Sami

Prof Filip Tack

Prof Leee

Cirkovic Tanja

Prof. Imca Velickovic

Sampers

PROMOTERS AND ACADEMIC MEMBERS IN GHENT UNIVERSITY

This chapter provides relevant literature regarding the application of life cycle assessment (LCA) in food supply chain, the studied impact categories by LCA, hybrid LCA, and the potential contribution of LCA in the agri-food sector.

viii

Chapter 3

A version of this chapter was published in the Journal of Science of the Total Environment: [START_REF] Nikkhah | Application of analytical hierarchy process to develop a weighting scheme for life cycle assessment of agricultural production[END_REF]. Application of analytical hierarchy process to develop a weighting scheme for life cycle assessment of agricultural production, Science of the Total Environment. 665, 538-545.

The objective of this chapter was to develop a weighting scheme for the agricultural production LCA using an analytic hierarchy process.

Chapter 4

A version of this chapter has been published in the Journal of Foods:

Nikkhah, A., Firouzi, S., Dadaei, K., Van Haute, S. 2021. Measuring circularity in food supply chain using life cycle assessment; refining oil from olive kernel. Foods.

10(3), 590.

The aim of this chapter is to develop LCA methodology as a powerful tool for measuring the circularity of the food supply chain. This chapter investigated the circularity of a food waste valorization system (refining oil from olive kernel), and an inventory of an industrialbased olive kernel oil production system is also provided in this chapter.

Chapter 5

A version of this chapter has been published in Scientific Reports-Nature:

Ghnimi, S., Nikkhah, A., Dewulf, J., Van Haute, S. 2021. Life cycle assessment and energy comparison of aseptic ohmic heating and appertization of chopped tomatoes with juice. Scientific Reports-Nature, 11(1), 1-12.

The aim of this chapter is to develop LCA by extending its application in an innovative food technology (ohmic heating). In this regard, the life cycle environmental impact assessment and energy consumption of chopped tomatoes with juice (CTwJ) processing and packaging upon appertization and aseptic ohmic heating systems were investigated.

ix Chapter 6

A version of this chapter has been published in Scientific Reports-Nature:

Nikkhah, A., Van Haute, S., Jovanovic, V., Jung, H., Dewulf, J., Cirkovic Velickovic, T., Ghnimi, S. 2021. Life Cycle Assessment of Edible Insects (Protaetia brevitarsis seulensis larvae) as a future protein and fat source. Scientific Reports, 4(9), 1653-1674.

The aim of this chapter is to develop LCA as a decision support tool for moving toward a sustainable future food system, considering edible insects as a future protein and fat source. Moreover, the protein, fat content, and fatty acid profile of the investigated insect are provided in this chapter to determine its potential food application and use these nutritional properties as functional units for LCA analysis.

Chapter 7

A version of this chapter has been written and will be submitted to the Journal of Cleaner Production:

Optimization of resource allocation in agri-food sector using coupled life cycle assessment, genetic algorithm and Delphi methodology.

The objective of this chapter is to use LCA for the optimization of FSC, not only evaluation.

For this purpose, this chapter provides a framework for optimization of resource allocation in the agri-food sector based on a hybrid LCA, genetic algorithm, and Delphi.

Chapter 8

Summarizes the outcomes of the presented dissertation.

x Outline of this PhD dissertation, entitled: "Development of life cycle assessment in food supply chain" xi Summary -Samenvatting xii

Summary

Life cycle assessment (LCA) is the core methodology used by the European Union to evaluate the carbon footprint of products. LCA is one of the best, most practical, and most standardized approaches for evaluating the environmental consequences of a food supply chain (FSC). It covers a comprehensive range of impact and damage categories.

The aim of this dissertation is to develop LCA methodology throughout FSC by (i) food safety and nutrition content inclusion in LCA, (ii) a weighting scheme development for LCA, and (iii) measuring circularity using LCA. This PhD thesis also extends the application of LCA to emerging food technology and future food systems. In addition, this PhD provides a novel framework for hybrid LCA and artificial intelligence (AILCA) for FSC optimization. Chapter 1 focuses on the relevant literature regarding the application of LCA in the FSC, hybrid LCA, and the potential contribution of LCA in the agri-food sector.

In Chapter 2, a food safety-based impact category (microbiological safety of cookedchilled foods) for LCA was developed. Moreover, the potential inclusion of multiple functional units (focusing on nutritional content) in the LCA was considered. As a case study, a Ghent University developed microbial risk model was applied to quantify the damage by microbial hazards (Bacillus cereus as case) in cooked-chilled foods, and then it was added to the human health damage assessment. Chapter 3 provides a weighting scheme for LCA based on a multi-criteria decision-making tool. Many agricultural systems could benefit from using the developed weighting factors for LCA studies. Chapter 4 focused on developing the LCA as a tool to measure the circularity of the FSC. For this purpose, the circularity of a food waste valorization system (refining oil from olive kernel) was investigated using LCA. An inventory of an industrial olive kernel oil production system is also provided in this study. The results indicated that natural gas consumption had the highest impact of the investigated impact categories. The global warming potential of 1 kg of produced oil from olive kernel was 0.32 kg CO2 eq. Moreover, the damages of 1 kg of oil production from olive kernel to human health, ecosystem quality, and resource depletion were 5.13 × 10 -7 DALY, 7.73 × 10 -2 PDF•m 2 •yr., and 24.26 MJ, respectively. Chapter 5 extends the LCA methodology to emerging food technologies.

As a case study, chopped tomatoes with juice (CTwJ) processing and packaging upon xiii appertization and aseptic ohmic heating systems were evaluated. The results clearly showed that the environmental impacts of tomato juice processing and packaging upon appertization were higher than those of ohmic heating systems. The uncertainty analysis results indicated that the global warming potential for producing one kg of packaged CTwJ with appertization ranges from 4.13 to 4.44 kg CO2eq, while for the ohmic heating system it ranges from 2.54 to 2.78 kg CO2eq. Likewise, the application of LCA was extended to a future food system (edible insects as a future protein and fat source) in Chapter 6. The protein, fat content, and fatty acid profile of the investigated insect (Protaetia brevitarsis seulensis larvae) were analyzed to determine their potential food applications and to be used as nutritional functional units for LCA analysis. The results revealed that the studied edible insect production system has beneficial environmental effects on various impact categories (ICs). This food production system can mitigate the negative environmental effects of these ICs but has a negative environmental impact on some other ICs, such as global warming potential. In Chapter 7, a framework was developed to optimize resource allocation in the agri-food sector based on a hybrid LCA, genetic algorithm, and Delphi in order to apply LCA for optimization, not only modeling. Chapter 8 discusses the general conclusions of this PhD thesis and perspectives for future research.

xiv Samenvatting Levenscyclusanalyse (LCA) is de kern-methodologie die gebruikt wordt door de Europese Unie om de carbon footprint van producten te evalueren. LCA is een van de beste, meest praktische, en meest gestandardiseerde benaderingen voor het evalueren van de impact van een food supply chain (FSC) op het milieu. LCA omvat een waaier van impact-en schade-categorieën. Het doel van deze dissertatie is het ontwikkelen van een LCA methodology voor FSCs door (i) inclusie van voedselveiligheid en nutritionele factoren in LCA, (ii) ontwikkelen van weighting scheme voor LCA, (iii) het meten van circulariteit met LCA. Deze dissertatie breidt de toepassing van LCA uit tot opkomende voedingstechnologieën en future food systemen. Tevens wordt in deze dissertatie een nieuw raamwerk voor hybrid LCA en artificiële intelligentie (AILCA) voor FSC optimalisatie. Hoofdstuk 1 focust op de relevante literatuur betreffende de toepassing van LCA in FSC, hybrid LCA, en de mogelijke bijdrage van LCA in de agri-food sector. In Hoofdstuk 2 werd een LCA impact categorie geïntroduceerd betreffende voedselveiligheid (microbiële veiligheid van gekookte-gekoelde levensmiddelen). Tevens werd de mogelijke inclusie van meerdere nutritionele functionele eenheden behandeld.

Als case-study werd een door de UGent ontwikkeld microbieel-risico model toegepast om de schade door een microbieel gevaar (Bacillus cereus in deze casus) in gekooktegekoelde levensmiddelen te kwantificeren en dit werd toegevoegd aan de bestaande beoordeling van schade aan menselijke gezondheid. Hoofdstuk 3 behandelt een weighting-scheme voor LCA gebaseerd op een multi-criteria decision-making tool. Het zou voordelig kunnen zijn voor vele landbouwsystemen om weighting-factors voor LCA te gebruiken. Hoofdstuk 4 focust op het ontwikkelen van LCA als een tool om de circulariteit van FSCs te meten. De circulariteit van een voedingsafval valorisatie-systeem (raffineren van olijfpitolie) werd onderzocht met LCA. Tevens werd een inventaris van een industrieel olijfpitolie productiesysteem weergegeven. De resultaten suggereren dat aardgas consumptie de grootste impact had van de onderzochte impact categorieën. Het global warming potentieel van 1 kg geproduceerde olijfpitolie was 0.32 kg CO2eq. De productie van 1 kg olie leidde tot een berekende schade aan de menselijke gezondheid, ecosysteem kwaliteit, en depletie van grondstoffen van 5.13 × 10 -7 DALY, 7.73 × 10 -2 xv PDF•m 2 •yr., and 24.26 MJ, respectievelijk. In Hoofdstuk 5 werd de LCA methodologie toegepast op emerging voedingstechnologieën. Als case-study werden versneden tomaten met tomatensap (CTwJ) verwerkt en verpakt en vervolgens werden appertiseren en ohmic heating geëvalueerd. De resultaten tonen aan dat de milieu-impact van appertiseren hoger was dan deze van ohmic heating. Onzekerheids-analyse gaf aan dat het global warming potential van 1 kg CTwJ met appertiseren tussen de 4.13 en 4.44 kg CO2eq varieerde, terwijl dit voor het ohmic heating systeem tussen 2.54 to 2.78 kg CO2eq varieerde. In Hoodstuk 6 werd LCA toegepast op een future food systeem (eetbare insecten als toekomstige eiwit en vet bron). Het eiwit en vetgehalte en de vetzuursamenstelling van het onderzochte insect (Protaetia brevitarsis seulensis larvae)

werden geanalyseerd om mogelijke voedingstoepassingen te bepalen en om te gebruiken als nutritionele functionele eenheid voor de LCA analyse. De resultaten toonden aan dat het bestudeerde eetbare insectenproductiesysteem gunstige effecten heeft op bepaalde impact categorieën (ICs). Dit voedselproductiesysteem kan de negatieve milieu-impact van deze ICs verminderen maar heeft een negatieve milieuimpact op sommige andere ICs, zoals global warming potential. In Hoofdstuk 7 werd een raamwerk ontwikkeld voor het optimaliseren van grondstof-allocatie in de agri-food sector, door middel van hybrid LCA, genetic algorithm, en de Delphi methode en dit om LCA toe te passen voor optimalisatie en niet enkel voor modeleren. In Hoofdstuk 8

tenslotte worden de algemene conclusies van de dissertatie weergegeven en perspectieven voor toekomstig onderzoek toegelicht. In recent years, because of the population growth and increasing demand for food, some keywords, such as "achieving food security" and "sustainable agriculture" have become trends for policymakers, authorities, producers, and consumers (Ala and Ridwan, 2020;Ulian et al., 2020). Additionally, the second aim of the United Nations (UN) is to "end hunger, achieve food security, improve nutrition and promote sustainable agriculture."

List of Figures

One solution to achieve the second goal of the UN is resource conservation and management. In this regard, decision support tools play a key role in resource management. So far, this has been applied by policymakers and producers to achieve a more sustainable production system. They can be used for modeling, prediction, or optimization of different production systems.

Decision support tools for analyzing sustainability

Decision support tools can contribute to the identification, evaluation, and optimization of multiple control variables potentially leading to improvements in the performance of supply chains in general and sustainability of the system in particular (Taticchi et al., 2015). To date, various decision support tools have been proposed and developed in the agri-food sector to improve the sustainability of food production systems. The sustainable development of new alternative technologies in agri-food systems needs to focus thorough attention to resource management within production systems and achieve a better understanding of its multiple impacts (Nikkhah et al., 2016). In this respect, life cycle assessment (LCA) is a well known standardized methodology for examining the sustainability of the food production systems due to its cradle-to-grave perspective.

Life cycle assessment

The procedures of LCA are standardized by ISO 14040-44 (Huang et al., 2018).

LCA is a methodology to comprehensively evaluate the environmental consequences of human activities from the extraction of raw materials, through processing/manufacturing, transportation, consumption, and to waste (cradle to grave) (Saba et al., 2020). The LCA study comprises four phases, which are described in the subsequent subsections.

Goal and scope definition

Goal and scope definition are considered the first phase of each LCA application. It is also needed to determine the functional units (FUs) considered by the system and system boundaries in the first phase of an LCA study (Elhami et al., 2017). Various FUs are considered to be evaluated by LCA, including mass-based (Bacenetti et al., 2020), landbased (Pishgar-Komleh et al., 2020a), energy-based, nutritional-based (McAuliffe et al., 2020), and economic-based (Van der Werf and Salou, 2015) FUs. The choice of FU can significantly affect the process of decision-making toward determining a sustainable food system (Masset et al., 2015). However, mass-based FUs are usually one of the common FUs in LCA studies in the food sector. However, nutritional FUs are more appropriate in some food systems and thus, certain studies have applied nutritional properties as FUs, such as protein quality for human diets (Sonesson et al., 2017) and the contribution of foods to the nutrient composition of a weekly diet (Tyszler et al., 2014).

After the selection of appropriate FUs, the system boundary should be specified. A system boundary may be chosen as the cradle of the process, for instance, from the production of raw materials to the grave (the treatment of the waste). Some LCA studies have specified the system boundary from cradle to gate, gate to gate, or gate to grave.

Inventory Analysis

The aim of the life cycle inventory analysis is to determine the inputs and outputs of the investigated system. The emissions of the system (to air/soil/water) are divided into the background (off-site) and foreground (on-site). Background emissions consist of the pollutants from the production of inputs (usually off-site emissions); while foreground emissions consist of the direct emissions from the consumption of inputs, such as on-site CO2 emissions from the burning of diesel fuel in a food industry engine. Several LCA databases have been developed since the release of environmental management standards (ISO14040 and ISO14044) (ISO, 2066a,b), including Agri-Footprint, Ecoinvent, ELCD, and the Swiss input-output database (Durlinger et al., 2014;Martínez-Rocamora et al., 2016;Wernet et al., 2016;Famiglietti et al., 2019). SimaPro, Gabi, Umberto, and

OpenLCA software can also be used for LCA analysis (Emami et al., 2019;Silva et al., 2019).

Impact assessment

The selection of impact categories and calculation of the characterization factors of the selected impact categories are mandatory in the impact assessment of LCA studies. On the other hand, the normalization of the investigated impact categories and weighting of the damage categories are optional in LCA analysis. Several impact assessment methodologies have been proposed for LCA analysis, such as CML-IA baseline, Ecological Scarcity 2013, EDIP 2003, EPD (2018), EF Method, IMPACT 2002+, and ILCD 2011.

Interpretation of results

The last LCA phase is the interpretation of the obtained results. In this phase, the results are explained and discussed in comparison with other relevant studies.

Impact categories in LCA

The different impact categories that are considered in various impact assessment methodologies are shown in Table 1.1. LCA covers a wide variety of impact categories in which global warming potential (GWP) is the main impact category included in almost all LCA's impact assessment methodologies. Therefore, LCA has been applied to investigate the GWP of various FSCs worldwide. Some other impact categories are (directly/indirectly) connected to GWP, such as ozone depletion and formation. GWP characterization factors of food products are expressed as kg CO2-eq, usually calculated according to IPCC guidelines (IPCC, 2013) in most LCA impact assessment methodologies (Huijbregts et al., 2016). The obtained GWP characterization factor can be normalized and weighted. Several methods have been applied for weighting LCA results, including panel-based, monetization, and distance-to-target (Castellani et al., 2016). * Source: SimaPro 9.0 Software [START_REF] Pre | SimaPro 9.0 software. simapro[END_REF].

The impact categories considered by LCA methodology is explained as follows:

Auatic acidification, terrestrial acid/nutria, terrestrial ecotoxicity, and land occupation are the impact categories considered by the ecosystem quality damage category. Land occupation: the damage take into account the result of either conversion of land or occupation of land [START_REF] Restrepo | A life cycle assessment of the Brazilian coal used for electric power generation[END_REF], Aquatic acidification: the impacts to the water ecosystem affected by the acid rain due to some acidic gases such as nitrogen oxides (NOx) and sulfur dioxide (SO2) [START_REF] Zhu | Developing an empathy educational model (EEM) for undergraduate nursing students: A Delphi Technique[END_REF], Terrestrial ecotoxicity: the impacts of toxicsubstances on the terrestrial ecosystems (Van der Werf, 2004), Terrestrial eutrophication: the deposition onto the terrestrial environment of compounds that promote biomass formation (Huijbregts et al.,200), and Aquatic ecotoxicity: the impact on water ecosystems due to the emissions to air, water, and soil (Yoshanti and Dowaki, 2017).

Carcinogens, non-carcinogens, respiratory organics, respiratory inorganics, ionizing radiation, and ozone layer depletion are the impact categories considered under the human health damage category. Carcinogens: the emissions of carcinogenic substances to air, soil and water [START_REF] Restrepo | A life cycle assessment of the Brazilian coal used for electric power generation[END_REF], Non-carcinogens: the chemicals that do not cause cancer, but they are still considered harmful [START_REF] Alsaleh | Life Cycle Assessment of Greenhouse Gas Emissions, Traditional Air Pollutants, Water Depletion, and Cumulative Energy Demand from 2-MW Wind Turbines in Texas[END_REF], Respiratory organics:

the smog caused by emissions of dust, sulfur and nitrogen oxides to air [START_REF] Morsali | A novel perspective to bitumen refineries life cycle assessment and processes emissions[END_REF],

Respiratory inorganics: the winter smog caused by emissions dust, sulphur and nitrogen oxides to the atmosphere [START_REF] Russo | Are biofuels sustainable? An LCA/multivariate perspective on feedstocks and processes[END_REF], Ionizing radiation: the particles that have a high enough energy to interact with the atoms of a target [START_REF] Neira | Status of Levels and Effects of Non-Ionizing Radiation[END_REF], Ozone layer depletion: the increased UV radiation as a result of emission of ozone depleting substances to air [START_REF] Rai | Ozone Layer Distribution, Depletion and Its Effects[END_REF].

Global warming is the impact category under the climate change damage category.

Global warming: the increase in average temperatures of planet earth due to the greenhouse effect of greenhouse gases released by human activities (Yilmaz and Can, 2020).

Non-renewable energy and mineral extraction are the two impact categories which are categorized under the resoursec damage category. Non-renewable energy: the energy content of the consumed inputs [START_REF] Lopes | Application of life cycle assessment to the Portuguese pulp and paper industry[END_REF], and Mineral extraction: the excavation and recuperation of mineralization and from the crust of the Earth [START_REF] Revuelta | Mineral Resource Extraction[END_REF].

Food supply chain

Food supply chains (FSC) are networks in which food products move from farm to fork/waste, including the production/agricultural phase, food processing, packaging, consumption, retail, and waste (Nikkhah et al., 2020). Several factors influence FSCs, including sustainability, which refers to the quality of life improvement, for the current as well as subsequent generations (Tümenbatur and Tanyaş, 2016). FSCs are one of the main contributors to global warming; for instance, in Europe, FSCs are responsible for 31% of the global warming potential (Jonkman et al., 2019). However, LCA can be applied by policymakers, farmers, companies, and consumers to mitigate FSC greenhouse gas emissions. The following paragraphs explain how LCA can be applied to different phases of FSCs to contribute to sustainability.

LCA contribution toward sustainable food production

Different phases of an LCA study within the FSC are shown in Figure 1.1. LCA is the core methodology in the European Union used to evaluate the carbon footprint of products (Van der Werf et al., 2020). Lu and Halog, (2020) investigated the frequency of LCA application in different stages of FSC. They highlighted that the agricultural phase is the most frequently included, while the retail, preparation, consumer level, and waste phases are included fewer times.

Application of LCA by farmers

Over the last few years, several studies have been conducted to investigate the environmental impacts of the agricultural phase of food production. These results could be effectively used by farmers for making decisions regarding the mitigation of environmental impacts of agricultural processes. For example, many studies indicate that nitrogen-based chemical fertilizer has the largest environmental adverse effects in some agricultural production systems due to its contribution to impact categories, including global warming and eutrophication (Bacenetti et al., 2020;Mostashari-Rad et al., 2020).

Accordingly, this can encourage producers to mitigate the environmental impact of nitrogen-based fertilizers through (i) nutrient content monitoring of soil and (ii) the selection of an environment friendly fertilizer, such as bio-fertilizer or a chemical fertilizer with lower environmental consequences (Graham and Vance, 2000;Nikkhah et al., 2017). In this regard, using green manures, bio-fertilizers, and farmyards can be considered as alternatives to supply the nitrogen required by crops (Firouzi et al., 2017).

Another issue with the agricultural phase of the FSC is the substantial fossil fuel footprint that has been reported as one the largest contributors to the environmental burdens of food production systems (Pishgar-Komleh et al., 2020a;Saber et al., 2020). In this case, the replacement of fossil-based fuels with biofuel could remarkably mitigate the environmental impacts of the entire system (Esmaeilpour-Troujeni et al., 2020).

Uses of LCA in the food processing and packaging

Although environmental data related to the agricultural phases of FSCs are increasingly becoming available, there is a gap regarding the LCA data on food processing (Sanjuán et al., 2014). However, LCA can be effectively used on a company scale to evaluate the overall environmental impacts of food production systems, identification of environmental hotspots, and development of various scenarios toward moving to a more environmentally-friendly food processing/packaging system. The methodology is also applicable to have a decent comparison of application of different technologies in a food processing and packaging system in order to find the most environmentally-efficient system. The comparison of LCA results obtained from a specific company with available relevant LCA studies can contribute to have a better picture of overall environmental impacts of the production system. Moreover, the hybrid application of LCA methodology can also contribute to modeling and optimizing the food production systems.

Environmental legislation could also significantly contribute to reducing the environmental impacts of FSC (Nikkhah et al., 2017). For example, a law in France and also a recommendation from the Swiss Federal Office for the Environment encourage producers to label their food products with environmental indicators like greenhouse gas emissions [START_REF] Cros | The French initiative on environmental information of mass market products[END_REF]De Menna et al., 2015). There are already some food manufacturing companies that compute the environmental footprint of their products, mentioning these data on the food product packages. In this regard, LCA as a powerful tool can be used by the food processing industry to examine their environmental footprints.

LCA results for consumers

Consumers can play a key role in moving to sustainable food production systems. Gruber et al., (2016) studied the LCA of the customer stage of FSCs, i.e., shopping, storage, preparation, and disposal. The authors indicated that the shopping phase has the largest adverse effect on the selected impact categories and highlighted that if consumers act carelessly regarding environmental protection, then the customer stage will appear as an environmental hotspot for FSCs. However, information on the environmental life cycle of the food that customers eat is not always available to them (Jones et al., 2008). For instance, the consumer awareness of the environmental impacts of the meat industry has been studied in Belgium, Germany, Finland, Portugal, the Netherlands, and the United States, with percentages of aware participants ranging from 23% to 35% (Sanchez-Sabate and Sabaté, 2019). However, replacing meat with alternatives could mitigate the environmental impacts of human diets. The following section explains the GWP of meat alternatives.

LCA methodology development

The first LCA study was done over 50 years ago. Since then, this methodology kept evolving and this development has led to standardization by the International Organization for Standardization (ISO) in the ISO-14040 series (ISO 14040:1997, ISO 14041:1999, ISO 14042:2000, ISO 14043:2000) [START_REF] Finkbeiner | From the 40s to the 70s-the future of LCA in the ISO 14000 family[END_REF]. The abovementioned standards have been updated, and the LCA is explained within ISO 14040 and 14044 standards (ISO, 2006a,b). However, the same LCA standards are being used to perform LCA studies in various sectors. In addition, there are still open questions and concerns regarding the application and extension of this methodology. The LCA standards are explained broadly and there is no specification regarding its application in food systems. Moreover, the extension of this methodology to emerging food technologies and future food systems can promote its role in mitigation of environmental impacts of food sector which is one of the main drivers of sustainability challenges, including climate change, and human health.

LCA of emerging food technologies

Scientific studies have shown that most FSCs are not sufficiently sustainable [START_REF] Notarnicola | Life Cycle Assessment in the agri-food sector: an overview of its key aspects, international initiatives, certification, labelling schemesand methodological issues[END_REF]. Thus, in recent years, various new technologies have been proposed and developed to address this issue. Sustainable development of new alternatives technologies in agri-food systems requires a thorough consideration of resource management in the production systems and a greater understanding of multiple impacts.

The term "clean technologies" is defined as "manufacturing processes or product technologies that reduce pollution or waste, energy use, or material use in comparison to the technologies that they replace". As it was mentioned in section 1.10, the extension of LCA methodology to emerging food technologies can promote its role as a decision support tool toward moving to a sustainable food system which can contribute to the mitigation of environmental impacts. However, emerging technologies pose unique concerns for the LCA community, due to the lack of available data, and inherent uncertainties regarding their development [START_REF] Miller | Framework for analyzing transformative technologies in life cycle assessment[END_REF]. The uncertainties of parameter are often higher for emerging technologies since the production system, inputs and outputs have not been completely established (Bartek et al., 2021)

LCA of future protein sources

There is a need to discover alternative sources of protein due to the increasing demand for meat. In addition, many studies have shown that most common meat production systems, such as beef (Biswas and Naude, 2016;Perez-Martinez et al., 2018), chicken (Kalhor et al., 2016;Skunca et al., 2018;López-Andrés et al., 2018), pork (Reckmann et al., 2013;Winkler et al., 2016), and fish (Buchspies et al., 2011;Dekamin et al., 2015), are not sufficiently environment friendly. Hence, the development of new alternative sustainable food sources is essential. LCA has been applied to compare the environmental consequences of protein alternatives. For instance, Halloran et al. (2017) compared the environmental impact of cricket farms to that of chicken farms in Thailand.

The results revealed that protein from insects is more environmentally efficient compared to that of chicken. Likewise, Smetana et al. (2019) reported that insect biomass is twice as environmentally efficient compared to chicken meat. One study investigated the LCA of cell-cultured chicken ovalbumin. The results showed lower GHG emissions compared to the traditionally produced dried egg white powder [START_REF] Maljanen | Life Cycle Assessment of Cell-Cultured Chicken Ovalbumin. 12th International Conference on Life Cycle Assessment of Food: LCA Food conference 2020[END_REF]. Another study, investigated the LCA of milk production within the conventional dairy systems, and two plant-based alternatives, i.e., soy milk and almond milk. They indicated that the selection of FU has a strong influence on their LCA results, and they suggested that multiple FUs are necessary for LCA analysis in food systems [START_REF] Grant | Comparative life cycle assessment of milk and plantbased alternatives[END_REF].

Table 1.3 summarizes the published studies on the LCA of future protein sources. *Abbreviations: global warming potential (GWP), abiotic depletion (AD), human toxicity (HT), acidification (AC), cumulative non-renewable energy demand (CEDnr), aquatic ecotoxicity (AET), aquatic eutrophication (AEU), carcinogens (C), climate change (CC), eutrophication (EP), fossil resources depletion (FD), respiratory inorganics (RI), fossil resources depletion (FRD), fresh water aquatic ecotoxicity (FE), land occupation (LO), terrestrial ecotoxicity (TE), ionizing radiation (IR), land use (LU), marine aquatic ecotoxicity (ME), mineral extraction (MEx), photochemical oxidation (PO), non-carcinogens (NC), non-renewable energy (NRE), ozone layer depletion (OLD), phosphate resources depletion (PhRD), Potash resources depletion (PRD), respiratory organics (RO), and water depletion (WD).

In this chapter, the frequency of hybrid LCA applications with various models and with different purposes in FSCs was investigated. Twenty recently published papers (from 2018 to now) on hybrid LCA in FSCs were investigated; the results are shown in Figure 1.2. The results indicate that the data envelopment analysis and adaptive neuro-fuzzy inference system are the most frequently applied approaches. The results shown in Figure 1.3 indicate that the hybrid applications of LCA with multi-criteria decision-making models have been included fewer times in hybrid LCA studies in FSCs, compared to modeling and optimization.

Hybrid LCA for climate change mitigation of FSC

LCA has been coupled with mathematical and artificial-based intelligence approaches to model, optimize, and make decisions in various food production systems (Figure 1.4). In this regard, a hybrid application of LCA with regression, artificial neural networks, and/or adaptive neuro-fuzzy inference systems can be applied to model a FSC. The integration of data envelopment analysis, particle swarm optimization, and genetic algorithms with LCA can contribute to the optimization of a system in terms of its mitigation of the environmental impacts. Hybrid LCA and multi criteria decision making (analytic hierarchy and fuzzy analytic hierarchy processes) is a tool which can be used by policymakers toward designing/moving toward a more environment friendly system.

As an example of hybrid application of LCA, the LCA + DEA framework was applied for the environmental optimization of grocery stores. The proposed framework could decrease approximately 9% of the GWP of the investigated grocery stores (Álvarez- Rodríguez et al., 2019). In another study, Pishgar-Komleh et al., (2020b) applied a hybrid LCA+DEA model to optimize the wheat production system in Poland. They indicated that there is significant potential for the mitigation of global warming caused by the system. In another study, Pishgar-Komleh et al. (2020a) modeled the obtained LCA data using artificial neural networks and subsequently applied two optimization algorithms: namely, the multi-objective genetic algorithm (MOGA) and multi-objective particle swarm optimization (MOPSO). The potential decreases of GWP according to MOGA and MOPSO were 43% and 39%, respectively. [START_REF] Elsoragaby | Applying multi-objective genetic algorithm (MOGA) to optimize the energy inputs and greenhouse gas emissions (GHG) in wetland rice production[END_REF] applied a hybrid LCA and genetic algorithm to optimize the overall environmental impacts of rice production.

They indicated that there is a potential to mitigate the GHG emissions by 20% and 46% within the transplanting and broadcast seeding methods, respectively.

Hybrid LCA shows potential in mitigating the environmental impacts of food production systems. However, three major issues need to be considered in hybrid LCA applications in FSCs. Few studies have been conducted on hybrid LCA + multi-criteria decision making. However, the combination of LCA+MCDM models could effectively be applied for decision-making in FSC, such as choosing an appropriate line/technology for food processing considering multiple criteria. Another issue with the hybrid application of LCA in FSCs is the data collection limitation from food processing and packaging industries.

Therefore, studies concerning hybrid LCA applications of food processing and packaging are highly valuable toward designing a sustainable FSC. Additionally, to the best knowledge of the authors, there is no published document on the practical application of the recommendations based on the optimization models/algorithm of hybrid LCA studies in the food sector. Thus, further research is needed focusing on implementing the recommendations of the developed models/algorithms and evaluating their accuracies in real-life conditions.

Conclusions

Recent studies highlight that most food production systems are not sufficiently sustainable. In this regard, moving toward sustainable food production is essential to address the UN development goals. Several methodologies have been proposed and developed to contribute to moving towards sustainability. Among them, the LCA methodology has been widely applied to evaluate various production systems. It can also be used by policymakers as a decision support tool to select an environment-friendly production system. Hybrid LCA is a promising tool that contributes to designing an environment friendly FSC. Nevertheless, four issues need to be addressed in future hybrid LCA applications in FSC: i) LCA methodology development, ii) developing LCA in future foods and emerging food technologies systems, iii) investigating the further potentials of the coupled implementation of LCA and multi-criteria decision making; iv) focusing on the neglected phases of FSC, such as food processing; v) application of LCA as an optimization tool, not only evaluation.

Yang, D., Jia, X., Dang, M., Han, F., Shi, F., Tanikawa, H. and Klemeš, J.J., 2020 Food safety and nutritional value, the neglected aspects in life cycle assessment of food production (ready ro submit).

Summary

LCA covers a limited number of human health-related impact categories besides climate change, ecosystem quality, and resources damage categories. However, the food safety and nutritional value-as the essential aspects for selecting the appropriate food production system-have been neglected in the life cycle assessment of FSC.

Therefore, in this chapter, the inclusion of these aspects in LCA methodology is discussed. More specifically, a framework for the inclusion of a food safety indicator as the disability-adjusted life year (DALY) value of the produced product to the human health damage category was developed. As a case study, a model called Perfect Model was applied to quantify the microbiological exposure of cooked-chilled meals to be included in human health damage category.

Introduction

The LCA objective is addressing all relevant environmental impacts occurring along with complete product life cycles, from raw material extraction (cradle), and manufacturing to end-of-life (grave), and determining the hotspots as well (Ögmundarson et al., 2020).

LCA as a "cradle-to-grave" approach consists of four steps of goal definition, inventory analysis, impact assessment and interpretation [START_REF] Agrawal | Life Cycle Assessment of an Additive Manufactured Automotive Component[END_REF]. The impact The effects of various impact categories on the damage assessment categories (Jolliet et al., 2003)

LCA of food systems

Food systems are a significant contributor to GHG emissions, with a share of around 30% of the global total [START_REF] Clark | Global food system emissions could preclude achieving the 1.5° and 2° C climate change targets[END_REF]. Food supply chain (FSC) refers to a network of food-based business enterprises where the food products transfer from farm to fork, involving pre-farm and on-farm operations, processing, packaging, food service, and consumption level (see Chapter 1). LCA is a powerful tool to select an environmentalefficient food system. It has been used for environmental impacts assessment of food systems throughout its life cycle, from agriculture (Król-Badziak et al., 2021), food processing (Sieti et al., 2019), packaging [START_REF] Humbert | Life cycle assessment of two baby food packaging alternatives: glass jars vs. plastic pots[END_REF], distribution (Álvarez- Rodríguez et al., 2019), consumer-level (Parajuli et al., 2021), to waste management [START_REF] Behrooznia | Comparative life cycle environmental impacts of two scenarios for managing an organic fraction of municipal solid waste in Rasht-Iran[END_REF]. A study reviewed the frequency of LCA application in different phases of food systems and they concluded that the agricultural is the most frequently phase included in the LCA. In contrast, the retail, preparation, consumer-level, and waste phases have been included fewer times (Lu and Halog, 2020).

Climate change, ecosystem quality, human health and resources are the four pillars of an LCA study (See Figure 2.2). LCA is rapidly evolving and the recent environmental concerns have been included as the new impact categories in this methodology. For instance, land use is considered as an impact category in Ecological Scarcity 2013 [START_REF] Frischknecht | Ecological scarcity 2013-new features and its application in industry and administration-54th LCA forum, Ittigen/Berne[END_REF], ILCD 2011 (Chomkhamsri et al., 2011), EF Method (Huijbregts et al., 2008), and IMPACT 2002+ impact assessment methodologies. Another recent example is the framework suggested for the inclusion of microplastic, which may end in our food (Saling et al., 2020).

Human health is an essential endpoint impact category for LCA since a number of midpoint categories potentially cause impacts to the human health [START_REF] Kobayashi | Assessing burden of disease as disability adjusted life years in life cycle assessment[END_REF]. However, food safety is an essential aspect for selecting the appropriate food production system (López-Gálvez et al. ( 2021)) -have been neglected in the LCA of FSC.

Food safety

Food production and consumption are remarkable drivers of sustainability challenges, including climate change, freshwater scarcity, and human health. There is a vital need for a more robust method (LCA) and metrics (Disability-Adjusted Life Years) to quantitatively examine the sustainability of the food supply chain in terms of nutritional-healthenvironmental perspective [START_REF] Green | Assessing nutritional, health, and environmental sustainability dimensions of agri-food production[END_REF]. In this regard, there are some impact categories that are included in human health damage categories, such as carcinogens, non-carcinogens, respiratory organics, respiratory inorganics, ionizing radiation, and ozone layer depletion. However, microbial exposure of the final product is neglected in this damage category. For example, several studies have reported the lower environmental impacts of insect-based protein [START_REF] Oonincx | Environmental impact of the production of mealworms as a protein source for humans-a life cycle assessment[END_REF]Smetana et al., 2019Smetana et al., ,2020) ) compared to the conventional meat production systems. However, there are some food safety concerns (such as microbiological risk) associated with the consumption of certain insects (Imathiu, S., 2020), which need to be included in the LCA analysis of meat and its alternatives. Another example can be the difference between the bacterial contamination in greenhouse and open field of fresh produce-based products, which is not considered by LCA studies addressing the comparison LCA of open-field, and the greenhouse based agricultural production systems (Romero-Gámez et al., 2019;[START_REF] Bartzas | Life cycle assessment of open field and greenhouse cultivation of lettuce and barley[END_REF][START_REF] Mujkic | Life Cycle Assessment of Lettuce Production Systems: A Case Study Performed at Hollbium[END_REF]. Nevertheless, food safety is rarely considered in LCA and the human health-related impact categories in LCA of food mainly express the exposure to chemicals, such pesticide residues, rather than microbial exposure [START_REF] Thoma | Chapter 1:Principles of Green Food Processing (Including Lifecycle Assessment and Carbon Footprint)[END_REF]. There is only one study addressing the microbiological exposure in LCA of FSC in which a high-pressure treated ham production system compared to the traditional one using LCA with the inclusion of potential human health damage by Listeria monocytogenes [START_REF] Villamonte | Consideration of the product quality in the life cycle assessment: case of a meat product treated by high pressure[END_REF]. However, there are few studies addressing the inclusion of microbial exposure in other sectors, such as wastewater treatment [START_REF] Harder | Including pathogen risk in life cycle assessment of wastewater management[END_REF], and urban water and sanitation infrastructures [START_REF] Harder | Using quantitative microbial risk assessment and life cycle assessment to assess management options in urban water and sanitation infrastructures: Opportunities and unresolved issues[END_REF].

Human health-related impact categories are expressed as a single disability-adjusted life years (DALY) based on human-health end-point damage category in LCA studies. DALY was developed by the World Health Organization and comprises both the years of life lost (YLL) and the years lived with disability (YLD) as a result of a given health problem [START_REF] Al-Hemoud | Disability adjusted life years (DALYs) in terms of years of life lost (YLL) due to premature adult mortalities and postneonatal infant mortalities attributed to PM2. 5 and PM10 exposures in Kuwait[END_REF]. In other words, DALY is a measure of overall disease burden, expressed as the number of years lost due to ill-health, disability or early death. The human health related problems are expresses as DALY in LCA and quantitative microbial risk assessment [START_REF] Harder | Including pathogen risk in life cycle assessment of wastewater management. 1. Estimating the burden of disease associated with pathogens[END_REF], which is an opportunity to include the DALY amount caused by microbial risk in LCA of food systems.

In the case of food industry, ready-to-eat meals are interesting given the growing demand for ready meals consumption in many high-income and middle-income countries [START_REF] Hillier-Brown | The impact of interventions to promote healthier ready-to-eat meals (to eat in, to take away or to be delivered) sold by specific food outlets open to the general public: a systematic review[END_REF]. Cooked-chilled foods, ready meals, microwave dinners, and refrigerated and processed foods of extended durability (REPFEDs), are all synonyms for a growing group of complex food products. There are concerns regarding the three bacteria of psychrotrophic Clostridium botulinum, Listeria monocytogenes, and Bacillus cereus in the case of REPFEDs consumption [START_REF] Daelman | Quantitative microbiological exposure assessment of Bacillus cereus in cooked-chilled foods[END_REF]. It is noticeable that the consumption of REPFEDs is also linked to higher fat and energy and lower micronutrient intake [START_REF] Hillier-Brown | The impact of interventions to promote healthier ready-to-eat meals (to eat in, to take away or to be delivered) sold by specific food outlets open to the general public: a systematic review[END_REF]. The microbiological risk of the produced ready to eat meals has not been included in the LCA analysis of those products.

Microbiological risk inclusion in LCA

Bacillus cereus are heat resistant endospore forming Gram-positive bacteria. It can also grow at low temperatures, and it is ubiquitous in the environment as well as in REPFEDs.

However, the symptoms caused by this bacterium are usually mild (diarrhea or vomiting).

This study suggested a framework for the inclusion of microbiological risk caused by

Bacillus cereus associated with the consumption of REPFEDs in the LCA results. More specifically, an approach based on the application of microbiological risk as DALY value to the human health damage category was developed in this study. As a case study, a model called Perfect Model was applied to quantify the microbiological exposure of REPFEDs and then, it was added to the human health damage assessment. In other words, this study has developed LCA through adding a new and essential impact category to human health damage category. The percentage of packages with a too high Bacillus cereus concentration (i.e., ≥ 10 5 CFU/g) was 0.43% or 4313 packages per million packages, according to the Perfect Model [START_REF] Daelman | Quantitative microbiological exposure assessment of Bacillus cereus in cooked-chilled foods[END_REF]. Moreover, an additional 0.04% of packages can contain the emetic toxin.

There were some assumptions in the Perfect Model [START_REF] Daelman | Quantitative microbiological exposure assessment of Bacillus cereus in cooked-chilled foods[END_REF] that could lead to an overestimation, including the assumption that all the B. cereus strains existing in the products have both the ability to produce diarrhoeal and emetic toxins. However, not all strains have the necessary genes to produce the emetic or the diarrhoeal toxins. Another assumption was that all B. cereus entering the model are spores, which is a worst-case assumption since only spores will survive the thermal processing. It was also assumed that no spoilage takes place which is another worst-case assumption. Regarding to host differences, in this Perfect model, units (batch/portion/package) with a B. cereus concentration of 10 5 CFU/g or more were considered 'risky' units. It is still not easy to determine which concentration of B. cereus will cause emetic or diarrhoeal symptoms given the diversity in B. cereus strains and the two B. cereus syndromes, complex bacteria-host cell interactions, complex regulation of toxin gene expression, lack of an easy-to-use method for enterotoxin quantification, etc. Therefore, the model is actually an exposure assessment and not a risk assessment.

In the next step, the DALY value of the microbiological risk of one package of ready-toeat meat was quantified. DALY is composed of years lived with disability (YLDs) and years of life lost due to premature mortality (YLL) and it is simply the sum of the YLD and YLL [START_REF] Devleesschauwer | Calculating disability-adjusted life years to quantify burden of disease[END_REF]:

𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 = 𝑌𝑌𝑌𝑌𝐷𝐷 + 𝑌𝑌𝑌𝑌𝑌𝑌 (1)
No Bacillus cereus cases attributed to industrially prepared REPFEDs have been reported. The death rate for Bacillus cereus is considered as zero [START_REF] Kirk | World Health Organization estimates of the global and regional disease burden of 22 foodborne bacterial, protozoal, and viral diseases, 2010: a data synthesis[END_REF].

Therefore, only the illnesses rate is needed to calculate the Daly value. The following equation shows how it can be calculated:

The DALY value of one package = (The duration of disease × disability weight ) × ( Risky packages One million packages )

(2-1)

The duration of the disease is 18 hours [START_REF] Griffiths | Bacillus cereus food poisoning[END_REF]. "A disability weight is a weight factor that reflects the severity of the disease on a scale from 0 (perfect health)

to 1 (equivalent to death). This value was considered as 0.2 for diseases caused by Bacillus cereus, according to [START_REF] Haagsma | Assessing disability weights based on the responses of 30,660 people from four European countries[END_REF]. Years Lost due to Disability (YLD) are calculated by multiplying the incident cases by duration and disability weight for the condition [START_REF] Haagsma | Assessing disability weights based on the responses of 30,660 people from four European countries[END_REF]. Equation 2-3 was used to quantify the DALY value:

𝑇𝑇ℎ𝑒𝑒 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 𝑣𝑣𝐷𝐷𝐷𝐷𝑣𝑣𝑒𝑒 𝑜𝑜𝑜𝑜 𝑜𝑜𝑜𝑜𝑒𝑒 𝑝𝑝𝐷𝐷𝑝𝑝𝑝𝑝𝐷𝐷𝑝𝑝𝑒𝑒 = � 0.75 365 × 0.2 � × ( 4750 1000000 )
(2-3)

Accordingly, the microbiological risk of one package of ready-to-eat meat was determined to be 1.95 × 10 -6 DALY. Many ready to eat-based LCA studies could benefit from using the developed microbiological risk impact category in this study.

DALY inclusion in IMPACT2002+

The developed microbiological risk impact category was included in the IMPACT 2002+

impact assessment methodology since it has been widely used in the food sector for LCA analyzing. It covers 15 impact categories including some human-health related impact categories (see Figure 2.2). Most impact assessment methodologies cover only the midpoints impact categories. However, IMPACT 2002+ impact assessment besides 15 impact categories covers also four damage categories (human health, ecosystem quality global warming, and resource depletion). In other words, 15 mid-points impact categories are evaluated by IMPACT 2002+, and then those impact categories are expresses based on four damage categories. In addition, this impact assessment methodology is being updated regularly, and the new version of this impact assessment has been published in 2019 as IMPACT World+ [START_REF] Bulle | IMPACT World+: a globally regionalized life cycle impact assessment method[END_REF].

The above-mentioned quantified DALY-based microbiological risk can be added to the human health damage category. Figure 2.3 shows the inclusion of quantified DALY-based microbiological risk to LCA using IMPACT 2002+ impact assessment methodology. In general for food products, inclusion in LCA of other categories of food safety could be done, including a number of chemical safety hazards of which the magnitude could change due to the applied resources and food processing technologies. This includes pesticide residuals, heavy metals, mycotoxins etc.

Nutritional risks is one of the aspects of human health risk which needs to be included in the LCA analysis of food systems. One example of nutritional risk is the possible connection between fat consumption and cardiovascular diseases [START_REF] Haukenes | Perceived health risks and perceptions of expert consensus in modern food society[END_REF].

The inclusion of the above-mentioned risks could be done in a similar fashion as for the microbial safety, i.e. quantifying based on the DALY value.

Table 2.1 also shows the damage factors characterization of various impact categories, including human health related impacts. In the next step of LCA, the human health damage category which includes the microbiological risk can be also normalized using the normalization factors of the damage categories (Table 2.2).

Inclusion of nutritional value in LCA

Functional unit provides a reference to which the input and output data are normalized, allowing the comparison with other LCA studies with the same functional unit (Del Borghi et al., 2020). The FU should be consistent with the goal and scope of the LCA study and it has to be well-defined and quantifiable, according to ISO 14044 (ISO, 2066a,b).

Biomass has been widely used as a FU for LCA of agricultural production systems, such as maize (Król-Badziak et al., 2021), potato and tomato [START_REF] Parajuli | Cradle to grave environmental impact evaluation of the consumption of potato and tomato products[END_REF], walnut (Khanali et al., 2020), tobacco (Mirkarimi et al., 2020), eggs (Zargar Ershadi et al., 2020), and sugarcane (Naseri et al., 2020). Land-based FU is the second-largest FU considered by LCA studies in agricultural sectors (Tricase et al., 2018;Alishah et al., 2019;[START_REF] Tassielli | Quantifying life cycle inventories of agricultural field operations by considering different operational parameters[END_REF]. Economic value-based FUs may also be considered as the integrated product quantity and quality single measure [START_REF] Cerutti | Life cycle assessment application in the fruit sector: state of the art and recommendations for environmental declarations of fruit products[END_REF]. The common FUs in LCA of FSC are biomass (mass, volume, number of final products), economic value, land usage, and energy generation/production. However, nutrition is the primary function of food (Saarinen et al., 2017). Therefore, more recently, nutrition is gaining attention as a FU, such as protein quantity and quality, fat (omega 3), and vitamin. Table 2.3 displays the application of nutritional-LCA in food supply chain. For instance, protein content is commonly used FU in LCA of alternative meat production systems.

Weidema and Stylianou, (2020) raised the question whether nutrition is a functional unit for LCA or if it is an impact? Given that a diet that is not nutritionally balanced may cause human health impacts, they proposed a hybrid nutrient balance indicator with the DALY Nutritional Index (DANI). The selection of FU can sometimes significantly influence the results/decisions coming from LCA analysis. For instance, the selection of FU can also affect the LCA results in comparing environmental impacts associated with food systems. In this regard, [START_REF] Cerutti | Environmental sustainability of traditional foods: the case of ancient apple cultivars in Northern Italy[END_REF] indicated that comparing the same fruit production systems with different functional units might lead to completely different scenarios. A review on LCA studies comparing agricultural products from conventional and organic farming indicated that impacts per area farmed land (1 hectare or m 2 ) are usually less in organic systems.

However, the environmental impacts per a unit of biomass production in organic systems are often higher [START_REF] Meier | Environmental impacts of organic and conventional agricultural products-Are the differences captured by life cycle assessment?[END_REF]. The results of a case on cattle, sheep, pigs, and poultry farming systems indicated that LCA final results associated with different systems may be dramatically altered when the nutrient content of meat replaces the mass of meat as the functional unit, due to differences of their nutritional properties [START_REF] Mcauliffe | Framework for life cycle assessment of livestock production systems to account for the nutritional quality of final products[END_REF]. Consequently, consideration of a single FU based on biomass or land could lead to results that may not be completely precise. Therefore, some LCA studies have compared multiple FUs in order to better understand the environmental impacts of food supply chain. However, the traditional single-FU or comparing different FUs are no longer able to handle these complex problems in the food systems, and the establishment of a framework for integrating multiple FUs in LCA of FSC remains mainly unexplored.

Integration of multiple FUs in LCA of FSC must be addressed in a multi-criteria context.

To overcome these constraints, this study suggests a framework for the hybrid application of multiple FUs in LCA of FSC as shown in Fig. 2.4. In this respect, multi-criteria decisionmaking (MCDM) as one of the advanced and practical methodologies can be applied to integrate various FUs in LCA of food systems. FUs in a food system can be integrated using an MCDM approach through a structured questionnaire and a panel of experts.

To date, several MCDM approaches have been developed. Analytical hierarchy process (AHP), multi-criteria optimization and compromise solution (VIKOR), the technique for order of preference by similarity to ideal solution (TOPSIS), elimination and choice expressing reality (ELECTRE), and preference ranking organization method for enrichment evaluation (PROMETHEE) are the common applied MCDM (Al [START_REF] Garni | A multicriteria decision making approach for evaluating renewable power generation sources in Saudi Arabia[END_REF]. A limitation of the MCDM employment for integrating FUs may come from the limited number of available experts on LCA of food systems and the results depend on the selection of panel, and thus, an objective result is not 100 percent attainable. Another limitation can be the limited number of criteria that can be considered by a food LCA study.

Conclusions

To establish a sustainable food supply chain, it is essential to consider the environmental impacts, human health and nutritional properties simultaneously. This the resource management of the process of kiwifruit production (Nikkhah et al., 2016).

Mousavi-Aval et al. (2017b) integrated multi-objective genetic algorithm and LCA. They emphasized that this integration could be used to optimize resource consumption of canola production.

Based on LCA procedure, any LCA study involves four steps of goal definition, inventory analysis, impact assessment and interpretation [START_REF] Tabatabaie | A regional life cycle assessment and economic analysis of camelina biodiesel production in the Pacific Northwestern US[END_REF]. The impact assessment in an LCA study is classified into three steps of characterization, normalization and weighting (Nikkhah, 2018). There are some methods for weighting the impact categories in an LCA study; three main groups of weighting methods are monetization, distance to target and panel-based (Castellani et al., 2016). In this regard, multi criteria decision-making is known as one of the most advanced and effective approaches for comparison and analysis [START_REF] Pourjavad | Analyzing RCM Indicators in Continuous Production Lines: A Case Study[END_REF]. Thus, it can be employed to compare between various impact categories of agricultural production.

Nevertheless, weighting and prioritizing of different impact categories of agricultural production using multi criteria decision-making has not been investigated yet. In this regard, Analytic Hierarchy Process (AHP) is one of the most common and widely used multi criteria decision-making tools [START_REF] Amini | Selecting the most appropriate tractor using Analytic Hierarchy Process-An Iranian case study[END_REF]. In this regard, the aim of this chapter of the PhD dissertation is to integrate the LCA and AHP for environmental evaluation of an agricultural system with tobacco production as a case study.

Materials and method

LCA application

As illustrated in Figure 3.1, and based on the ISO framework [START_REF] Iso | [END_REF], the procedure of LCA comprises four major steps including goal and scope definition, inventory analysis, impact assessment, and interpretation. The aim was to investigate the environmental burdens of tobacco production system. In this research, two mass and land based functional units (FUs) were considered. More specifically, one ton of tobacco and one hectare of tobacco farm were considered as FUs.

This step compiles all resources consumption and emissions of pollutants by a system based on the specific FUs. The amounts of resource consumption for the case study were gathered using face to face interviews. Then, the amounts of each pollutant were As mentioned before, the impact assessment is classified into three steps including characterization, normalization and weighting. In this study, six impact categories were considered, namely acidification potential, terrestrial eutrophication potential, global warming potential, depletion of phosphate source, depletion of potash source, and depletion of fossil sources. For each impact category, the characterization index was computed through the coefficient of each pollutant on that impact category. Then, the indices were normalized. The normalization indices were calculated through dividing the characterization indices by normalization factors based on the following equation:

NI i = CI i NF i (3-1)
where NIi is the normalization index of the impact category i, CIi is the characterization index of the impact category i, and NFi is the normalization factor of the impact category i [START_REF] Tayefeh | Environmental impact of rice production based on nitrogen fertilizer use[END_REF].

Weighting using analytic hierarchy process (AHP)

In this study, the weighted factors for the studied impact categories were determined using AHP method. The data for AHP analysis were gathered by surveying 12 LCA experts around the world. The names of the experts who were contributed to the AHP section are listed in Appendix C3.1. An AHP research has three main steps including hierarchy construction, priority analysis, and consistency verification [START_REF] Ho | Integrated analytic hierarchy process and its applications-A literature review[END_REF][START_REF] Yaghoubi | Identifying sustainable options for rice husk valorization using the analytic hierarchy process[END_REF]. The consistency ratio is employed to compute the degree of consistency index and it can be expressed according to [START_REF] Deng | Supplier selection using AHP methodology extended by D numbers[END_REF] as:

𝐶𝐶𝐶𝐶 = 𝐶𝐶𝐶𝐶 𝐶𝐶𝐶𝐶 (3-2)
where CR is the consistency ratio, CI is the consistency index, and RI is the random consistency index.

Then, the weighting indices were calculated through multiplying the normalization indices by weighted factors as:

WF i = NI i × WF i (3-3)
where WFi is the weighted index of the impact category i, NIi is the normalization index of the impact category i, WFi is the weighted factor of the impact category i (determined by AHP). Figure 3.2 illustrates the hybrid approach of LCA and AHP. The primary data were entered into Excel 2013 and then analyzed by Matlab2014b software.

Figure 3.2:

The hybrid approach of LCA and AHP.

The case study

The research was conducted in Guilan province, northern Iran, covering an area of 509.5 km 2 , of which arable land constitutes 333 km 2 . The province is the main tobacco production region in the Northern parts of Iran. Virginia tobacco is produced in tobacco farms of Guilan Province, Iran [START_REF] Zamani | Tobacco farming and curing[END_REF]. Figure 3.3 illustrates the off-farm and on-farm operations in the life cycle assessment of tobacco leaf production system. The required data for this study were collected from 225 tobacco farmers using a face to face interview. Each farmer was asked for explaining in detail the agronomic operations and preliminary curing of leaves in tobacco production. to electricity consumption which is mostly generated from fossil fuels (Ministry of Energy of Iran, 2017). In addition, greenhouse gas emissions per capita in Iran were raised from 6.0 tons per person in 2006 to 7.5 tons per person in 2014 (Nikkhah, 2018). In this case, using alternative green resources for electricity generation can be considered as a GHG mitigation strategy for Iran. In this regard, [START_REF] Mohammadi-Maghanaki | Potential of biogas production in Iran[END_REF] claimed that the potential of energy generation from agricultural and forest wastes, municipal wastes, livestock wastes, sewage and industrial wastes in Iran is equal 132-million-ton oil equivalent.

On the other hand, on-farm CH4, N2O and NH3 were accounted for 30, 93 and 99% of total emissions, respectively. Several studies suggested that the high on-farm emission of NH3 is due to the application of N-based fertilizer (Firouzi et al., 2017;Nikkhah et al., 2017;[START_REF] Tayefeh | Environmental impact of rice production based on nitrogen fertilizer use[END_REF]. Chapter 4 66 2015), 695 and 585 [START_REF] Khoshnevisan | Environmental impact assessment of open field and greenhouse strawberry production[END_REF], 1312, and 762 kgCO2 (Khoshnevisan et al., 2014b), respectively. Moreover, the characterization index of global warming potential was 3639 kgCO2eq per hectare (Table 3.4). This index for the production of rice in traditional and consolidated rice farms were reported to be 4864 and 4040 kgCO2eq per hectare, respectively (Khoshnevisan et al., 2014b). So, it can be concluded that the global warming potential of tobacco production was higher comparing to the other crop production systems in the region; however, the global warming potential of tobacco production was less than rice production based on the land-based FU. More specifically,

given that the yield of tobacco production is less than other products, the environmental of its production is higher based on the mass-based FU and is less according to landbased FU.

Terrestrial eutrophication characterization index for one ton tobacco production was 20 kgNOxeq. This index for the production of peanut was reported to be 16 kgNOxeq (Nikkhah et al., 2015). In addition, the characterization index of terrestrial eutrophication was computed 16 kgNOxeq per hectare (Table 3.4).

Characterization index of acidification potential was calculated to be 14 kgSO2eq. This index for the production of peanut, greenhouse and open field strawberry and rice in traditional and consolidated rice farms in Guilan province were reported to be 6 (Nikkhah et al., 2015), 2 and 3 [START_REF] Khoshnevisan | Environmental impact assessment of open field and greenhouse strawberry production[END_REF], 3 and 6 kgSO2eq, respectively.

Moreover, the acidification potential characterization index was 38 kgSO2eq per hectare (Table 3.2). This acidification potential for the production of rice in traditional and consolidated rice farms were reported 18 and 22 kgSO2eq per hectare, respectively (Khoshnevisan et al., 2014b).

LCA + AHP results

Figure 3.5 displays the normalized amounts of various impact categories which were calculated by AHP. If CR ≤ 0.10, the level of inconsistency can be considered as acceptable [START_REF] Dweiri | Production planning forecasting method selection in a supply chain: a case study[END_REF]. The CR in this study was 0.04, that is, within the acceptance range. The normalized values of different impact categories of global warming, terrestrial eutrophication, acidification, fossil resources depletion, phosphate resources depletion and potash resources depletion were 1, 0.79, 0.52, 0.68, 0.42, and 0.26, respectively. Several agricultural systems could benefit from using the developed weighting factors for LCA studies in the agri-food sector. source and depletion of potash source, and for one ton production of tobacco were 0.19, 0.41, 0.24, 1.04, 0.23, and 0.20, respectively. In addition, the weighted indices of the aforementioned impact categories per one hectare of tobacco farm were 0.37, 0.79, 0.46, 2.01, 0.45, and 0.38, respectively. This implies that fossil resources depletion impact category had the largest negative environmental impacts of tobacco production followed by terrestrial eutrophication. In similar studies which have been conducted in the same region, the highest negative impacts on the environment during peanut production have been reported to be related to the fossil resources depletion and terrestrial eutrophication impact categories (Nikkhah et al., 2015), and for kiwifruit production related to the phosphate resources depletion and terrestrial eutrophication (Nikkhah et al., 2016).

Curing of fresh Virginia tobacco leaves constitutes the last process of tobacco production system in the studied case (Figure 3.3). A large amount of energy is required to perform this process [START_REF] Cao | Analysis on efficiency optimization of tobacco leaf flue-curing process[END_REF]. More than 90 percent of the total energy input in tobacco production system belongs to the leaves curing stage [START_REF] Reed | Curing Tobacco. Flue-cured Tobacco Production Guide[END_REF]. Therefore, it is the main responsible for fossil fuel depletion in tobacco production system. The natural gas is the dominant fossil fuel used for warming up the tobacco leaf curing barns in northern

Iran. Using a controlled temperature and humidity system to perform the process and utilization of the maximum curing barns capacity are among the applied approaches to mitigate the fuel consumption in tobacco leaves curing process and then lessen the total fossil resources depletion in tobacco production. More than 50 percent of tobacco farms of the study region are less than 1 ha in size. Therefore, aggregation of small tobacco farms to economize using the modern large tobacco curing barns equipped with the automatic control facilities and isolated walls and roofs also are among the useful ways to diminish the fossil energy consumption of the process. Researche on the replacement of some of fossil fuel use with the renewable energy sources like the solar energy and biomass should be considered to cut down much more amount of fossil energy use.

Conclusion

Weighting is an optional step of the impact assessmet phase in an LCA study, and it is not specified in the ISO 14040 and ISO 14044. However, there are different methodologies toward weighting of LCA results. This study applied Analytic Hierarchy Process (as a novel methodology) to develop a weighting scheme for LCA of agricultural production. The data related to AHP analysis were collected from experts in the field of LCA of agricultural production. The integration of LCA and AHP indicated that the weighted factors of impact categories of acidification potential, terrestrial eutrophication, global warming, fossil resources depletion, phosphate resources depletion and potash resources depletion were 0.52, 0.79, 1, 0.68, 0.42 and 0.26, respectively. Many agricultural systems could benefit from using the developed weighting scheme for LCA studies in the agricultural sector.

In the second step of this study, the developed weighted factors were applied to a specific agricultural system. Iranian tobacco production system was considered as the agricultural case study. The results indicated that the weighted indices of acidification potential, terrestrial eutrophication potential, global warming potential, depletion of fossil sources, depletion of phosphate source, and depletion of potash source for one ton production of tobacco were 0.41, 0.19, 0.24, 1.04, 0.23 and 0.20, respectively. This implies that fossil resources depletion impact category had the largest negative environmental impacts for tobacco production followed by terrestrial eutrophication.

Chapter 4

Methodology development; measuring circularity using LCA Partially redrafted after:

Nikkhah, A., Firouzi, S., Dadaei, K., Van Haute, S. 2021. Measuring circularity in food supply chain using life cycle assessment; refining oil from olive kernel. Foods. 10(3), 590.

Summary

Valorization of food waste is a potential strategy toward a circular food supply chain. In this regard, measuring the circularity of food waste valorization systems is highly important to better understand multiple environmental impacts. Therefore, this study investigated the circularity of a food waste valorization system (refining oil from olive kernel) using a life cycle assessment methodology. An inventory of an industrial-based olive kernel oil production system is also provided in this study. The system boundary was the cradle to the factory gate of the production system. The results indicated that natural gas consumption was the highest contributor to most of the investigated impact categories. The global warming potential of one kg of oil produced from olive kernel was calculated to be 1.37 kg CO2eq. Moreover, the calculated damages of 1 kg oil production from olive kernel to human health, ecosystem quality, and resource depletion were 5.29

× 10 -7 DALY, 0.12 PDF•m 2 •yr., and 24.40 MJ, respectively.

Introduction

The circular economy concept is gaining growing attention as an alternative to the linear economy-"take, make, waste,"-which exists now [START_REF] Haupt | Measuring the environmental sustainability of a circular economy[END_REF][START_REF] Secondi | A Regression-Adjustment Approach with Control-Function for Estimating Economic Benefits of Targeted Circular Economy Practices: Evidence from European SMEs[END_REF]. In a linear economy, natural resources are transformed into goods that provide economic value; however, they come with a limited life span, and are disposed of in the environment with minimum recovery of resources [START_REF] Ruggieri | A meta-model of interorganisational cooperation for the transition to a circular economy[END_REF][START_REF] Babbitt | Closing the loop on circular economy research: From theory to practice and back again[END_REF]. This system puts enormous stress on the carrying capacity of the planet [START_REF] Scheepens | Two life cycle assessment (LCA) based methods to analyse and design complex (regional) circular economy systems. Case: Making water tourism more sustainable[END_REF]. The circular economy describes a system with minimum loss of resources by reusing, recycling, and recovering materials and energy (Ellen MacArthur Foundation 2013; European Environment Agency 2014; [START_REF] Haupt | How can LCA support the circular economy?-63rd discussion forum on life cycle assessment[END_REF][START_REF] Patwa | Towards a circular economy: An emerging economies context[END_REF]. Various strategies have been suggested for moving from a linear economy to a circular one, including R-based frameworks, such as the 3Rs strategy (reduce-reuse-recycle), the 4Rs (introducing "recover" as the fourth R), the 6Rs, and even the 9Rs [START_REF] Kirchherr | Conceptualizing the circular economy: An analysis of 114 definitions[END_REF][START_REF] Bassi | The use of circular economy practices in SMEs across the EU[END_REF].

The measurement of circularity is the first step in moving toward a circular system, as quoted by Peter Ducker: "what gets measured gets managed" [START_REF] Haupt | Measuring the environmental sustainability of a circular economy[END_REF].

There is not a unique approach for measuring a circular economy, since the understanding of a circular economy is still being explored [START_REF] Korhonen | Circular economy: The concept and its limitations[END_REF]. To date, some assessment indices have been applied to measure circularity, such as the material circularity indicator (Ellen MacArthur Foundation and Granta Design, 2015), the circular economy index [START_REF] Di Maio | A robust indicator for promoting circular economy through recycling[END_REF], material flow analysis [START_REF] Linder | A metric for quantifying product-level circularity[END_REF], food loss and waste [START_REF] Vilariño | Food loss and waste reduction as an integral part of a circular economy[END_REF], and life cycle assessment (LCA) methodology (ISO, 2006a,b). [START_REF] Corona | Towards sustainable development through the circular economy-A review and critical assessment on current circularity metrics[END_REF] reviewed the applied approaches for measuring circularity and found three assessment frameworks, seven measurement indices, and nine assessment indicators. In this regard, LCA has been used for decades for the evaluation of the environmental impact of products and services but, more recently, it has

shown to be a promising method to measure circularity. It is an appropriate method to investigate the environmental consequences of circular product designs and large-scale changes to move toward a more circular economy [START_REF] Haupt | How can LCA support the circular economy?-63rd discussion forum on life cycle assessment[END_REF]. However, in recent years, LCA has been applied to measure circularity in various sectors, such as bio-based materials [START_REF] Lokesh | Hybridised sustainability metrics for use in life cycle assessment of bio-based products: Resource efficiency and circularity[END_REF][START_REF] Dahiya | Biobased Products and Life Cycle Assessment in the Context of Circular Economy and Sustainability[END_REF], tourism [START_REF] Scheepens | Two life cycle assessment (LCA) based methods to analyse and design complex (regional) circular economy systems. Case: Making water tourism more sustainable[END_REF], and concrete production [START_REF] Colangelo | Comparative LCA of concrete with recycled aggregates: A circular economy mindset in Europe[END_REF]. There are also some published documents addressing the connection between LCA and the circular economy concept in the food supply chain [START_REF] Pauer | Assessing the environmental sustainability of food packaging: An extended life cycle assessment including packagingrelated food losses and waste and circularity assessment[END_REF][START_REF] Vilariño | Food loss and waste reduction as an integral part of a circular economy[END_REF][START_REF] Sadhukhan | Perspectives on "game changer" global challenges for sustainable 21st century: Plant-based diet, unavoidable food waste biorefining, and circular economy[END_REF].

In the case of the food supply chain, approximately one-third of the total global food production, which is equal to 1.3 billion tons per year, is wasted in the food production/consumption chain [START_REF] Dahiya | Food waste biorefinery: Sustainable strategy for circular bioeconomy[END_REF]. This includes food loss (such as losses and spoilage at the producer level before the market) or waste (such as losses at retailer and consumer levels) (Shafiee-Jood and Cai, 2016). In fact, food loss and waste (FLW) refer to a certain amount of food, nutrients, or calories that intentionally/unintentionally disappear from food systems [START_REF] Wesana | Measuring Food Losses in the Supply Chain through Value Stream Mapping: A Case Study in the Dairy Sector[END_REF]. A large part of FLW is avoidable, and could be decreased by implementing different strategies at each level of the life cycle of the production system [START_REF] Strotmann | A participatory approach to minimizing food waste in the food industry-A manual for managers[END_REF][START_REF] Principato | Adopting the circular economy approach on food loss and waste: The case of Italian pasta production[END_REF]. Although food waste has been understood as a critical global issue [START_REF] Muhammad | Comparison of global-warming potential impact of food waste fermentation to landfill disposal[END_REF], food waste has high potential for reuse or recovery in a circular economy prospective [START_REF] Ingrao | Food waste recovery into energy in a circular economy perspective: A comprehensive review of aspects related to plant operation and environmental assessment[END_REF].

In this regard, the olive-based products industry is an interesting case, as it is an economically important industry [START_REF] Negro | The biorefinery concept for the industrial valorization of residues from olive oil industry[END_REF]. As a globally energy-intensive sector, the olive processing industry faces sustainability challenges [START_REF] Khounani | Environmental life cycle assessment of different biorefinery platforms valorizing olive wastes to biofuel, phosphate salts, natural antioxidant, and an oxygenated fuel additive (triacetin)[END_REF]. Espadas-Aldana et al. ( 2019) studied 23 published papers on the LCA of olives and olive oil and concluded that the global warming potential (GWP) of one liter of olive oil production is equal to 1.6 kg CO2eq. The olive oil production supply chain also faces crucial challenges regarding waste management. For example, 80% of olive mass is composed of olive pulp and stones; therefore, waste production is four times higher than that produced within the extraction process [START_REF] Espadas-Aldana | Analysis and trends for Life Cycle Assessment of olive oil production[END_REF]. In this regard, the by-products and residues generated in olive processing are not commonly used and end up as waste [START_REF] Khounani | Environmental life cycle assessment of different biorefinery platforms valorizing olive wastes to biofuel, phosphate salts, natural antioxidant, and an oxygenated fuel additive (triacetin)[END_REF]. Thus, valorization of food waste could be considered as an effective strategy to make the supply chain of olive-based products more circular. In this regard, measuring the circularity of food waste valorization systems is highly important for improving understanding of multiple environmental impacts. One of these wastes is olive kernel (stone). Olive kernel is an important by-product generated in the pitted table olive industry [START_REF] Nunes | Olive by-products for functional and food applications: Challenging opportunities to face environmental constraints[END_REF]. The characterization and application of olive kernel are described in Figure 4.1. However, the current and main use of olive kernel is as direct solid feedstock for biofuel generation for domestic application (Gomez- et al., 2018). However, this currently may not be a realistic option for an oil-rich country. In this regard, establishing an environmentally efficient approach for olive kernel utilization could actually improve the overall sustainability of olive-based product supply chains. This PhD dissertation is the first report on the LCA of industrial-scale refining oil from olive kernel (as an olive processing waste valorization approach) system.

Martin

Materials and Methods

Refining Oil from Olive Kernel

The olive kernel oil production company investigated in this study is located in Iran. Olives (Olea europaea L.) were grown in Iran, and the uses of olive fruits in the studied region are: (i) raw material for extra virgin olive oil production, (ii) inside dishes (pitted table olive), and (iii) raw material for pickling. Solid-liquid olive pomace and olive mill wastewater are the two major by-products of the extra virgin olive oil production system [START_REF] Batuecas | Life Cycle Assessment of waste disposal from olive oil production: Anaerobic digestion and conventional disposal on soil[END_REF]. The kernel must be separated from the olive fruit in the second and third abovementioned olive fruit applications. Therefore, olive kernel is a common source of waste in olive fruit processing systems (Figure 4.2). Olive kernel can be used to produce oil. The characterization of olive kernel oil was described by [START_REF] Moghaddam | Characterization of different olive pulp and kernel oils[END_REF].

The industrial olive kernel oil production process is shown in Figure 4.3. Olive kernels are transported to the processing plant, and the factory is located in the olive oil production/processing area. A small amount of olive pulp is stuck to kernels because it cannot be completely separated from the olive kernels in olive processing (Figure 4.2b).

The received kernels are washed to remove impurities. Then, they are crushed to ease the release of the oil and are subsequently mixed. Afterward, the liquid (including oil and water) is separated through a decanter. In the next step, the oil is separated from the water and the olive kernel oil is extracted by a separator. Natural gas is consumed to heat the water in the boiler at a working temperature of 60 to 70 °C. Its circulation in the decanter's double-walled jacket heats the dough (the crushed olive kernel, oil, and water).

Mixer blades of the decanter provide a uniform spread of heat throughout the dough.

Heating the dough contributes more efficiently to separating the three phases of oil, water, and pulp through centrifuging at 4000-4200 rpm. Moreover, the remaining pulp needs to be warmed to flow easily through the discharge mono pump of the decanter. Warm water is also added to the oil entering the separator in order to maximize the oil extraction rate.

At the final step, a centrifuge rotating at a high rotational speed of 7000-7200 rpm separates the warm water and the olive kernel oil. LCA studies, the functional units (FUs) are defined as the quantity of olive oil produced in kg, and the energy content of the produced oil in MJ. Therefore, 1 kg of oil produced from olive kernel and 100 MJ energy generation were selected as the FUs for this LCA analysis to compare the environmental consequences associated with oil production from olive kernel with other vegetable-based oil production systems. The heating value (energy equivalent) of olive kernel oil was considered to be the same as for olive oil at 34.5 MJ/L [START_REF] Cappelletti | Energy requirement of extra virgin olive oil production[END_REF]. The system boundary of this LCA study was the cradle-to-factory gate olive kernel oil production.

The cradle-to-gate environmental impact for olive kernel oil production was evaluated using LCA. In this regard, the main primary inventory data for olive kernel production in the considered factory are shown in Table 1. It should be noted that the input and output amounts were measured and recorded at the factory level, not through surveys or interviews. The experiments were performed in 2019. Experiments were conducted three times and the mean values are reported (Table 4.1). The olive kernel was not included as an input for the LCA analysis, and it was considered as a burden-free input; this is because it is a by-product/waste of a food production system. Therefore, the environmental impacts of wastewater from the olive kernel production system are not included in the system boundary. In other words, the assumption of not considering the wastewater treatment is due to the fact that olive kernel was not included as an input for the LCA analysis, as it is actually a by-product/waste of an olive processing system.

Packaging is also excluded from the system boundary. The emitted pollutants were divided into off-site and on-site emissions. The emitted pollutants in the off-site phase (production of input materials) were adapted from the Ecoinvent 3.0 database using SimaPro 9.0.0.49 software. The datasets applied for calculation of emissions from off-site operations are shown in Appendix C7.5, Table A1. The on-site emissions of natural gas consumption (see Appendix C4-2) were calculated and added to the on-site emission section using SimaPro software. The inventory of emissions for refining one kilogram oil from olive kernel is provided at https://doi.org/10.3390/foods10030590. In the third step of the LCA study, IMPACT 2002+ was employed as the impact assessment (IA) methodology due to its hybrid application of IMPACT 2002, Eco-Indicator 99, CML, and IPCC, all of which cover various impact and damage categories.

This impact assessment (IA) methodology evaluates the environmental impacts based on the 15 impact categories, and also divides the impact categories into four damage categories: human health, ecosystem quality, GWP, and resource depletion. The human health damage category is represented as disability-adjusted life year (DALY), ecosystem quality as PDF•m 2 •yr., GWP as kg CO2eq, and resource depletion as MJ. The fourth and last step of conducting an LCA study is the interpretation of the LCA results, which are explained in the Results and Discussion section. The fourth step of an LCA study includes the determination of hotspots, the specification of areas with a potential for improvement, and recommendations [START_REF] Sundaram | Biorefineries and chemical processes: Design, integration and sustainability analysis[END_REF]. A detailed explanation of this IA methodology can be found in Jolliet et al. (2003). A flowchart of the utilization of LCA for measuring the circularity of olive kernel oil production systems is demonstrated in Figure 4.4. Edraw Max (ver. 9.1, 2018; Sheung Wan, Hong Kong, China) software was used for the representation of graphical items.

There are several sources of uncertainty in an LCA study [START_REF] Zargar-Ershadi | Comparative life cycle assessment of technologies and strategies to improve nitrogen use efficiency in egg supply chains[END_REF]. It is important to take into account the uncertainty of LCA results which can be due to a lack of accuracy in the collected data (Vásquez-Ibarra et al., 2020), the initial assumption [START_REF] Fathollahi | Comparative energy, economic and environmental analyses of forage production systems for dairy farming[END_REF], the allocation method [START_REF] Cherubini | Uncertainty in LCA case study due to allocation approaches and life cycle impact assessment methods[END_REF], the selection of functional units [START_REF] Noya | Environmental impacts of the cultivation-phase associated with agricultural crops for feed production[END_REF], the determination of on-site emissions [START_REF] Rezaei | Impact of agrochemical emission models on the environmental assessment of paddy rice production using life cycle assessment approach[END_REF], and the type of IA methodology (Paramesh et al., 2018). Therefore, to test the consistency of the LCA results obtained in this study, a quantitative uncertainty analysis was conducted to evaluate the effect of IA methodology selection on the final LCA results.

In this regard, the GWP impact category was selected for comparison, as it is the mutual impact category among the investigated IA methodologies. The selected impact assessment methodologies were IMPACT 2002+, ReCiPe 2016 (Dekker et al., 2019), CML-IA baseline (Guinée et al., 2002), EDIP 2003 (Hauschild and Wenzel, 1998), EF (Huijbregts et al., 2008), EN 15804 +A2 (EN, 2012), Environmental Prices [START_REF] De Bruyn | Environmental prices handbook 2017-methods and numbers for valuation of environmental impacts[END_REF], EPD (PRe, 2019) and ILCD (Chomkhamsri et al., 2011). The IA methodologies evaluated by this study are available on SimaPro 9.0.0.49-a widely used software for LCA analysis [START_REF] Herrmann | Does it matter which Life Cycle Assessment (LCA) tool you choose?-a comparative assessment of SimaPro and GaBi[END_REF][START_REF] Hiloidhari | Life cycle assessment of sugar and electricity production under different sugarcane cultivation and cogeneration scenarios in India[END_REF].

Results and Discussion

Interpretation of mid-point LCA results

The quantified amounts of the environmental impacts of olive kernel oil production, based on different impact categories, are presented in Table 2. The environmental impact of 1 kg of olive kernel oil production on GWP was calculated as 1.37 kg CO2eq. This value of the abovementioned impact category for 100 MJ energy generation was 4.32 kg CO2eq.

The GWP of one liter of olive oil production is equal to 1.6 kg CO2eq, according to [START_REF] Espadas-Aldana | Analysis and trends for Life Cycle Assessment of olive oil production[END_REF]. They also indicated that the agricultural phase is the most impactful phase in the olive oil supply chain, responsible for 0.46 kg CO2eq/kg of GWP.

Espadas

In the case of refining oil from olive kernel, olives are not used in the system, and the byproduct of the olive postharvest waste was used to produce oil. Then, the environmental impacts of the agricultural phase are not accounted for in this system. Therefore, the environmental impacts of oil production from olive kernel are low, making the conventional olive processing systems more circular through waste valorization (olive kernel) of the system. If it is assumed that the production of oil from kernels means less olive oil needs to be produced elsewhere for cosmetic and pharmacological purposes (Figure 4.1), then this process can save around 0.23 kg CO2 per each kg of oil produced from olive kernel in the investigated system. The single scores of the damage categories in olive kernel oil are shown in Figure 4.7.

Interpretation of end-point damage assessment

The total weighted environmental damage from refining oil from olive kernel was calculated as 395 µPt/FU. Natural gas was the largest contributor to the total environmental impacts of the studied system, with a share of 80% (including its background and on-site emissions). As explained in the Materials and Methods section, natural gas is used in olive kernel processing to heat water in order to (a) facilitate the separation of water and oil from pulp in the decanter, (b) contribute to the flow of the remaining pulp in the decanter, and (c) separate water from olive kernel oil in the separator. A preliminary experiment shows that the optimum dough temperature to extract purer oil is around 35 °C, which is associated with the circulating water temperature at around 60-70 °C in the studied system. Moreover, a temperature of around 35 °C for the water added to the separator might be efficient. Nevertheless, further research is needed to determine the optimum water temperature to avoid extra natural gas/heat consumption. [START_REF] Khounani | Environmental life cycle assessment of different biorefinery platforms valorizing olive wastes to biofuel, phosphate salts, natural antioxidant, and an oxygenated fuel additive (triacetin)[END_REF] showed that the total environmental burdens of olive oil systems can be reduced by 12% by applying agro-biorefinery strategies based on olive cultivation and the extraction of fruit and pomace oil. There are also other ways to valorize olive processing wastes, such as energy generation. One LCA study highlighted significant greenhouse gas emission savings through olive husk (the solid portion remaining after pressing olives) application in a mobile pyrolysis process [START_REF] El Hanandeh | Carbon abatement via treating the solid waste from the Australian olive industry in mobile pyrolysis units: LCA with uncertainty analysis[END_REF]. In another LCA study, [START_REF] Intini | Life cycle assessment (LCA) of an energy recovery plant in the olive oil industries[END_REF] reported the environmental advantages of using de-oiled pomace and waste wood as feedstock for biofuel production, in terms of greenhouse gas emissions reduction. Multiple environmental measures could be applied in order to improve the sustainability of olive processing. In this regard, Martinez-Hernandez et al.

(2014) indicated how integrated process schemes can be used to develop a sustainable Jatropha-based biorefinery system.

Conclusions

This study applied LCA to measure the circularity of a food waste system-in this case, refining oil from olive kernel. The damages of 1 kg of oil production from olive kernel to GWP, human health, ecosystem quality, and resource depletion were 1.37 kg CO2eq, 5.29 × 10 -7 DALY, 1.21 × 10 -2 PDF•m 2 •yr., and 24.40 MJ, respectively. The results highlighted that the studied system is relatively circular, resulting in low environmental impacts. Olive processing systems could be made more circular through food waste valorization. By managing the consumption of energy sources, such as natural gas, olive kernel oil production systems can become environmentally efficient. In this regard, further research is needed to determine the optimum temperature of olive kernel dough in the decanter, as well as the optimum temperature for warm water when added to the separator in the process of oil extraction from olive kernel. Further research is also required to explore the human health and microbiological risks of the oil produced from olive kernels, which were not considered in this study.

The environmental impacts of the agricultural phase of tomato production have been widely investigated in different parts of the world, such as Australia [START_REF] Page | Carbon and water footprint tradeoffs in fresh tomato production[END_REF],

Albania [START_REF] Canaj | LCA of tomato greenhouse production using spatially differentiated life cycle impact assessment indicators: an Albanian case study[END_REF], Colombia [START_REF] Bojacá | Life cycle assessment of Colombian greenhouse tomato production based on farmer-level survey data[END_REF], Iran (Khoshnevisan et al., 2014), Italy [START_REF] Garofalo | Environmental sustainability of agri-food supply chains in Italy: The case of the whole-peeled tomato production under life cycle assessment methodology[END_REF][START_REF] Ronga | Carbon footprint and energetic analysis of tomato production in the organic vs the conventional cropping systems in Southern Italy[END_REF], Spain [START_REF] Muñoz | Comparing the environmental impacts of greenhouse versus open-field tomato production in the Mediterranean region[END_REF][START_REF] Antón | Improvement of agricultural life cycle assessment studies through spatial differentiation and new impact categories: case study on greenhouse tomato production[END_REF][START_REF] Neira | Energy use and carbon footprint of the tomato production in heated multi-tunnel greenhouses in Almeria within an exporting agri-food system context[END_REF], and Canada [START_REF] Maham | The environmental impacts of organic greenhouse tomato production based on the nitrogen-fixing plant (Azolla)[END_REF]. More information regarding the LCA of the agricultural phase of tomato production systems can be found in [START_REF] Pineda | Review of inventory data in life cycle assessment applied in production of fresh tomato in greenhouse[END_REF]. However, there are a few published documents related to the LCA of different tomato processing systems. Table 5-1 displays a summary of the relevant literature on the LCA of tomato processing systems.

Industrial food processing is the second most notable phase of the food supply chain, accounting for 28% of the total energy use. Moreover, industrial production, together with logistics and packaging (beyond the farm gate), are responsible for approximately half of the total energy consumption in the food chain [START_REF] Monforti-Ferrario | Energy use in the EU food sector: state of play and opportunities for improvement[END_REF]. The EU food industry is making significant contributions to improve energy efficiency while optimizing production processes through different approaches: (i) energy and heat recovery, (ii) selection of renewable energy sources to minimize the impact of energy consumed, and (iii) development and application of new sustainable "green and innovative" techniques in food processing. In comparison to conventional food processing technologies, green and innovative food processing technologies involve less processing time, reduced solvent and energy consumption, and a lower CO2 footprint [START_REF] Misra | Landmarks in the historical development of twenty first century food processing technologies[END_REF]. The most common innovative thermal and non-thermal technologies in the food industry are ohmic heating, microwave heating, radiofrequency, high-pressure processing, and pulsed electric fields. Ohmic heating provides a rapid and uniform heating, consequentially reducing thermal damage compared to conventional heating and allows manufacturers to obtain high-quality products with minimum nutritional, sensorial, and structural changes (Ghnimi et al., 2014a) high-pressure processing (HPP), and pulsed electric field (PEF) technologies for tomato and watermelon preservation. The results indicated that the tomato juice farm-to-gate environmental impact was higher than that of watermelon juice, and the largest energy uptake was documented for HPP, followed by PEF and traditional thermal processing.

Italy leads tomato production in the EU with a share of 36%; the amount of total tomato production was 5.6 million tons (96% for processing) in 2015 [START_REF] Costa | The global tomato industry[END_REF]. Reviewing the related relevant literature, it has been highlighted that in-depth research has not yet been conducted on the environmental impacts of chopped tomatoes with juice (CTwJ) processing by conventional processing, including appertization and innovative electro-technology such as ohmic heating. Similarly, for energy efficiency, research studies focus only on specific unit operations and not on the entire processing line to determine energy consumption. Thus, this study aimed to perform an energy efficiency comparison (global line and thermal unit operations) and LCA of ohmic heating and appertization for CTwJ processing.

Materials and Methods

Tomato processing

Tomatoes (Solanum lycopersicum var. Hybrid) were grown in the Italian Puglia region.

They were transported to processing plants located 160 km away and then processed at a local Italian factory. For the purpose of this study, the industrial appertization and aseptic ohmic heating units were selected as described in Figures 5.1 and 5.2, respectively. The preparation processes (grading, washing, peeling, and dicing) were the same for both industrial lines. The tomatoes were peeled using a steam system (Cavalieri, Italy) under pressurized steam at 110 °C for 10 s, followed by mechanical separation of the peels. A linear dicer was used to cut tomatoes (Cavalieri, Italy). The tomatoes were first cut into 2 cm thick slices, and afterward to 1.7 cm-side cubes. Diced tomatoes and juice were mixed in a tank for approximately 10 min for further processing by appertization or ohmic heating. A volumetric pump was used to feed the ohmic heating and appertization units.

This pump minimizes product damage and enables moving fluid-solid mixtures containing approximately 60% of diced tomatoes in 40% tomato juice. Figures 5.1 and 5.2 illustrate the system boundaries in CTwJ processing and packaging upon appertization and ohmic heating systems.

Appertization line

Diced tomatoes and juice were mixed in a tank for 10 min. They were pre-heated (ca.

60 °C) using direct steam injection before hot filling in tinplate cans. Cans sterilization was done using a tunnel sterilizer (Manzini, Italy) at 95 °C for 30 min with a production rate of 6 t/h of canned diced tomatoes with juice. The steam production system consists of firetube steam boilers (PB 100 model, Mingazzini, Parma, Italy) with a steam output of 10,000 kg/h and rated pressure of 12 bar.

Aseptic ohmic heating line

The process is based on the electrical resistance of the chopped tomato with the juice that is treated. Dissipation of the electrical energy when electric current flows through food leads to heat release (Joule effect) [START_REF] Ghnimi | Axial temperature profile of ohmically heated fluid: analytical model and experimental validation[END_REF]. Ohmic heating was conducted in an industrial-scale unit (Emmepiemme, Italy), consisting of one stage for pre-heating the CTwJ up to 75 °C and a subsequent stage for heating them up to 102 °C.

It then enters the insulated holding tubes before being cooled in tubular heat exchangers (Tetra Pak, Denmark) up to 20 °C. After cooling, the product is pumped into the aseptic storage vessel prior to aseptic packaging in multi-layer plastic pouches. The total electrical power of the ohmic system was 240 kW, and the average product flowrate was 4 t/h. The flowrate was recorded using an electromagnetic flowmeter (EMC, Auckland, New Zealand) with a precision of within 1% of the full range. The bulk temperatures were measured using platinum resistance probes (Pt 100 Ω to 0 °C with a ± 0.1 °C accuracy) placed at the inlet and outlet of each zone. Relative pressure was measured with manometers (JUMO, type 4AP30, Fulda, Germany) at the inlet and outlet of each zone with a precision of 0.1%. The power supply delivers bipolar potential pulses, and electrolysis is prevented by using a high-frequency alternating voltage [START_REF] Ghnimi | Design and performance evaluation of an ohmic heating unit for thermal processing of highly viscous liquids[END_REF].

Energy auditing

Energy requirements were experimentally measured through energy auditing. Both processing lines were instrumented to determine thermal and electrical energy inputs and consumption.

The energy requirement for the appertization line encompasses the electricity used by conveyers, pumping systems, and line dicers; thermal energy is required for peeling, preheating prior to appertization, sterilization and cooling of the product.

The specific energy required for heating the cans (𝐸𝐸 𝑐𝑐 ) and their contents (𝐸𝐸 𝑝𝑝 ) was calculated using equations (5-1) and (5-2), where n c is the number of cans per cycle, m c is the weight of empty cans, m p is the weight of the product per can, C pc is the specific heat of cans, C pp is the specific heat of the product, T c is the temperature of the cans, T p is the temperature of the product, and T s is the steam temperature inside the sterilizer:

(5-1)

(5-2)

The energy input in the tunnel sterilizer (𝐸𝐸 𝑠𝑠 ) was calculated using equation (5-3), with as the steam mass flowrate, and h vs is the specific enthalpy of steam:

(5-3)

Ohmic heating occurs due to the electrical conductivity and ability of the electrical current to flow through it. The power clamp meter was connected to the electrical power supply to measure the given electrical power (P g ). The fraction of electrical power converted to thermal energy and dissipated in the product was calculated using equation

(5-4), with as the mass flowrate of the product in the ohmic heater, C pp is the specific heat of the product, T pi is the inlet temperature of the product, and T po is the outlet temperature of the product.

(5-4) of inputs in the investigated factory, for instance, emissions released within the combustion of natural gas. The background's emissions coefficients, such as the emissions of electricity generation and distribution, were adapted from the Ecoinvent database.

In the third phase of an LCA study, impact category selection and characterization are mandatory; however, normalization and weighing are optional [START_REF] Dong | A life cycle assessment model for evaluating the environmental impacts of building construction in Hong Kong[END_REF]. The IMPACT 2002+ methodology was applied as the baseline impact assessment methodology, given its inclusion of various impact and damage categories. It is also a combination hybrid IMPACT 2002, Eco-Indicator 99, CML, and IPCC.

Uncertainty analysis of the impact assessment

There are some sources of uncertainties affecting the LCA results, including data quality, scenarios, and mathematical models underlying the impact assessment methods [START_REF] Rafiee | Sustainability evaluation of pasteurized milk production with a life cycle assessment approach: An Iranian case study[END_REF]. As shown in Table 6.1, studies on the LCA of tomato processing have employed different impact assessment methodologies. In this chapter of the PhD dissertation, an uncertainty analysis was performed to investigate the effect of impact assessment selection on the LCA results of the case study. For this purpose, six impact assessment methodologies, i.e., EDIP 2003, CML-IA baseline, EDP 2013, ILCD 2011 Midpoint, ReCiPe midpoint, and IMPACT 2002+ were considered to be evaluated by LCA.

The analyses were conducted using SimaPro V8.0.3.14.

Results and discussion

Energy auditing

An overview of the energy and water requirements for the aseptic ohmic heating and appertization lines is presented in Table 5.2. The global specific energies for appertization and ohmic heating industrial lines for preprocessing and processing of CTwJ were 156 and 54 kWh/t, respectively. The energy requirement for the appertization line encompasses the electricity consumed by conveyers, pumping systems, and line dicers; the thermal energy required for peeling, pre-heating prior to appertization, and the energy required for sterilization, cooling, and packing of the product. Energy uptake for the ohmic heating includes the electricity used by conveyers, pumping systems, and line dicers; thermal energy required for peeling, ohmic pre-heating and ohmic sterilization; and energy consumption for cooling and aseptic packaging of the final product. The energy saving of the ohmic heating line compared to the appertization line is 102 kWh/t of the product (or 65% energy saving).

The energy efficiencies of the appertization and ohmic heating systems are 25% and 77%, respectively. For ohmic heating, the electrical energy input is converted to thermal energy by the Joule effect, where the chopped tomato with juice behaves like a resistor in an electrical circuit. Energy losses in the ohmic heating system are mainly due to the preprocessing step and the cooling of the product during the holding phase between ohmic pre-heating and ohmic heating steps of the product; this temperature gradient was around 4.5 °C. Adding thermal insulation to this holding zone will improve the energy efficiency of the ohmic heating system.

Energy losses in the appertization line are mainly due to the lack of insulation, lack of reuse of steam condensate, and non-condensation of part of the injected steam. Steam condensate could be reused to heat feedwater for the steam boiler, pre-heat utilities, or clean equipment. Another major reason is the non-synchronization of the cans flow rates between the filling and retorting levels, which induces a continuous injection of steam in the retort even with a low load of cans. This situation leads to large energy losses due to the high steam consumption. This low efficiency can be improved by installing a steam regulation in the retort, which will provide the required steam flow depending on the load of the cans.

Interpretation of LCA results

Table 5.3 displays the characterization indices for CTwJ production. The global warming potential, ozone layer depletion, and non-renewable energy consumption for 1 kg production of packaged CTwJ for the appertization system were determined to be 4.38 kg CO2eq, 1.34 × 10 -7 kg CFC-11eq, and 52.15 MJ, respectively. However, the aforementioned amounts for 1 kg of packaged CTwJ production upon the ohmic heating system were 2.52 kg CO2eq, 4.00 × 10 -8 kg CFC-11eq, and 24.94 MJ, respectively. The results clearly showed that the environmental impacts of CTwJ processing and packaging on appertization were higher than those of ohmic heating systems. In other words, CTwJ production by the ohmic heating system was more environmentally efficient. The global warming potential of tomato sauce production was reported to be 1.5 kg CO2eq [START_REF] Parajuli | Cradle to grave environmental impact evaluation of the consumption of potato and tomato products[END_REF]. indicated that the tin production phase was the hotspot in CTwJ production by the appertization system. The agricultural phase was the second main contributor to the most impacted categories. More specifically, the agricultural phase of tomato production and tin packaging accounted for 54.33% and 45.00% of the total global warming potential of CTwJ production, respectively. The results are in line with the study of [START_REF] Manfredi | Life cycle assessment of a packaged tomato puree: a comparison of environmental impacts produced by different life cycle phases[END_REF], which indicated that packaging was the main contributor to most impact categories for tomato purée production in a glass jar. Arnal et al. (2018) also highlighted that canning was the largest contributor to the total industrial-scale environmental impacts of packaged peeled tomato production systems. Packaging was reported to be responsible for the environmental impacts within all selected impact categories of tomato purée production, except for acidification and eutrophication [START_REF] Shahvarooghi Farahani | Comparison of different tomato puree production phases from an environmental point of view[END_REF][START_REF] Del Borghi | An evaluation of environmental sustainability in the food industry through Life Cycle Assessment: the case study of tomato products supply chain[END_REF] showed that packaging and agricultural phases had the highest adverse impacts on the environment during the production of 13 different tomato processed products. Many studies have also reported that the packaging phase is an environmental hotspot during the production of some other processed food products, such as dairy products [START_REF] Djekic | Environmental lifecycle assessment of various dairy products[END_REF] and canned sardine [START_REF] Almeida | Environmental life cycle assessment of a canned sardine product from Portugal[END_REF].

The results highlight the important contributions of the agricultural phase in the ohmic heating system in the most impacted categories. The agricultural phase accounted for 94.44% of the total global warming potential of CTwJ production by the ohmic heating system. In other words, the consumption of inputs, such as diesel fuel and chemical fertilizer cause a huge negative environmental impact during the CTwJ production supply chain. packaging for appertization and ohmic heating systems. The normalized damage assessment of CTwJ processing and packaging for the appertization system was higher than the ohmic heating system in all impact categories. So, the packed CTwJ for the ohmic heating system was more environmentally friendly. An LCA study on tomato processing also indicated that the application of PEF technology could mitigate environmental impacts (Arnal et al., 2018).

Mitigation strategies

As previously mentioned, packaging and agricultural phases were the environmental hotspots in both systems. One solution to reduce the environmental burdens of tomato product processes is related to the packaging phase; it could be weight reduction and transitioning to packaging materials with less environmental impacts, such as bio-based packaging [START_REF] Del Borghi | An evaluation of environmental sustainability in the food industry through Life Cycle Assessment: the case study of tomato products supply chain[END_REF]. Therefore, the mitigation of the environmental impacts of tomato cultivation, replacing the packaging materials with bio-based materials, and using the by-products of cultivation and processing for supplying a part of the energy requirement can be considered as the main strategy for environmental impact mitigation of the CTwJ production supply chain. In the case of agricultural phase of CTwJ supply chain, [START_REF] Muñoz | Comparing the environmental impacts of greenhouse versus open-field tomato production in the Mediterranean region[END_REF] highlighted that the environmental impacts of 1 kg of tomato production in a greenhouse is less than that of an open field system due to the efficient use of water, fertilizers, and pesticides. A study compared three tomato farming systems (open field, greenhouse, and hydroponic) in terms of energy use patterns and concluded that the hydroponic system was the most environmentally friendly system [START_REF] Platis | Analysis of energy and carbon and blue water footprints in agriculture: a case study of tomato cultivation systems[END_REF]. [START_REF] Bojacá | Life cycle assessment of Colombian greenhouse tomato production based on farmer-level survey data[END_REF] believed that the implementation of integrated pest management programs could mitigate the environmental impacts of Colombian greenhouse tomato production. [START_REF] Bacenetti | Mitigation strategies in the agrofood sector: the anaerobic digestion of tomato puree by-products. An Italian case study[END_REF] compared the two scenarios of the tomato purée production supply chain. In the first scenario, tomato by-products were sent back to the farms as bio-fertilizers; in the second scenario, the by-products were used in terms of biogas generation. The results showed that the second scenario was more environmentally efficient. A study focusing on the valorization of tomato by-products (tomato seeds and peels) highlighted the potential application of whole tomato byproducts for valuable compound recovery and sequential low-cost biosorbent production [START_REF] Azabou | Towards sustainable management of tomato pomace through the recovery of valuable compounds and sequential production of low-cost biosorbent[END_REF].

Moreover, there are some measures which can be taken into consideration in order to mitigate the environmental impacts of CTwJ processing. For instance, a study focusing on the valorization of tomato by-products (tomato seeds and peels) highlighted the potential application of whole tomato by-products for valuable compound recovery and sequential low-cost biosorbent production [START_REF] Azabou | Towards sustainable management of tomato pomace through the recovery of valuable compounds and sequential production of low-cost biosorbent[END_REF]. [START_REF] Winans | Life cycle assessment of California processing tomato: An evaluation of the effects of evolving practices and technologies over a 10year (2005-2015) timeframe[END_REF] applied LCA to study the diced tomatoes and paste production systems in California over a 10-year timeframe. They showed that the introduction of renewables in the life cycle of the production systems, such as solar-powered irrigation pumps, and on-site solar energy generation for facilities can mitigate the GWP impacts by 9-10%.

Given this study was conducted on an industrial scale, the effect of influencing variables in the manufacture process of CTwJ production was not optimized. Therefore, further research is needed to optimize the processes in terms of energy and environmental impacts. In addition, replacing the packaging materials with bio-based materials, and using the by-products of cultivation and processing for supplying a part of the energy requirement, on the final energy and environmental impacts of CTwJ production should be further explored.

Conclusions

This chapter of this dissertation is the first study to compare the energy requirement and life cycle environmental impact of a novel food processing technology (ohmic heating) with a conventional method (appertization) in the tomato processing industry, considered one of the largest food processing industries worldwide. Moreover, uncertainty analysis was performed through the application of six different impact assessment methodologies.

The energy requirement evaluation showed the highest energy consumption for appertization (156 kWh/t of product). The energy saving of the ohmic heating line compared to the appertization line was 102 kWh/t of the product (or 65% energy saving).

The energy efficiencies of the appertization and ohmic heating systems are 25% and 77%, respectively. There are opportunities for energy optimization of the investigated processes while maintaining the potential quality benefit. In the appertization system, a more energy-efficient process could be obtained by reducing steam non-condensation and installing a steam regulation, which will give the necessary steam flow depending on the load of the cans. In the case of ohmic heating, adding thermal insulation to the holding zone will improve the energy efficiency of the system.

From the LCA perspective, the uncertainty analysis results suggested that the global warming potential of the production of 1 kg of packaged CTwJ ranges from 4.13 to 4.44 kg CO2eq. In addition, the global warming potential of the ohmic heating system ranges from 2.54 to 2.78 kg CO2eq. Overall, CTwJ production by the ohmic heating system was more environmentally efficient than traditional retort canning.

Hence, the development of new alternative sustainable food sources is very important. In recent years, insects have been recommended as a novel food source for human consumption and the amount of insect production units has been increasing worldwide.

Insect production can be considered as a solution for two problems, namely, the increasing demand for food by the production of edible insects, and waste management by composting food waste [START_REF] Salomone | Environmental impact of food waste bioconversion by insects: application of life cycle assessment to process using Hermetiaillucens[END_REF]. The following sections discuss the current status of edible insect production, small scale insect production systems, and the literature review on sustainability assessment of edible insect production.

Current status of edible insect production

Approximately 2,000 insect species are consumed as food around the world, particularly in tropical countries [START_REF] Van Huis | Edible insects contributing to food security?[END_REF]. Based on the European Food Safety Scientific Committee report, nine different insect species are currently recorded as being farmed for feed and food production (Halloran et al., 2017). For instance, there are approximately 20,000 cricket farms in Thailand, which produce 7,500 metric tons of insects per year which are used for domestic consumption and the rest for market (Van Huis et al., 2015).

Currently, two billion people across the world eat insects, and insects are being consumed as food in approximately 80 countries [START_REF] Lombardi | Willingness to pay for insect-based food: The role of information and carrier[END_REF].

In South Korea (one of the main consumers), edible insects were previously more widespread in the populations' nutrition. Under the economic development plan implemented in the 1970s, the production and also the consumption of edible insects decreased. In recent years, the consumption is on the rise again, and the value of the edible insect market in south Korea has increased from 143 million in 2011 to 259 million in 2015 [START_REF] Shin | Edible Insects Uses in South Korean Gastronomy:"Korean Edible Insect Laboratory" Case Study[END_REF].

There are many small scale edible insect startups and production units around the world.

For example, most insect producers in Thailand are small and medium size enterprises which require relatively low land usage and capital investment [START_REF] Durst | Small-scale production of edible insects for enhanced food security and rural livelihoods: experience from Thailand and Lao People's Democratic Republic[END_REF]. Moreover, environmental issues are a major factor with regard to the sustainable development of food production systems. Thus, this study applied Life Cycle Assessment (LCA) to estimate the environmental impact of small scale edible insect production in South Korea, as an example of small scale edible insect production and the nutritional value was assessed with a focus on proteins and fats.

Literature review

Various studies have been conducted with regard to the LCA of the production of insects for use as feed [START_REF] Van Zanten | Can the environmental impact of livestock feed be reduced by using waste-fed housefly larvae[END_REF][START_REF] Le Feon | Life Cycle Assessment of fish fed with insect meal: Case study of mealworm inclusion in trout feed[END_REF][START_REF] Thévenot | Mealworm meal for animal feed: Environmental assessment and sensitivity analysis to guide future prospects[END_REF][START_REF] Roffeis | Ex-ante life cycle impact assessment of insect based feed production in West Africa[END_REF]. However, in recent years, researchers have focused on the environmental life cycle impacts of edible insect production systems. Table 6.1 shows the summary of the relevant literature on the LCA of edible insect production systems.

Oonincx and De Boer (2012) compared the protein production from two species of mealworms (Tenebrio molitor and Zophobasmorio) with conventional sources of protein like beef, milk, chicken, and pork. The results revealed that lower GHG emissions and land use are required for mealworm production, while the required amount of energy is similar to that of conventional and mealworm protein production systems. Thus, they concluded that mealworms are a more sustainable source of edible protein. Halloran et al. (2017) compared the environmental impact of cricket farms to that of chicken farms in Thailand. The results revealed that protein from insects is more environmentally efficient compared with that of chicken. One study reported that the direct CO2eq emissions from bio-waste conversion using insects (black soldier flies) were 47 times lower compared with the emissions produced by an open windrow composting facility (Mertenat et al., 2019). Smetana et al. (2019) reported that the insect biomass is twice more environmentally efficient compared with that of chicken meat.

As an example, this study investigated the environmental sustainability of small scale edible insect production in South Korea using LCA methodology. The current study is the first research on the environmental impacts of Protaetia brevitarsis seulensis (PBS). In addition, fatty acid profile, protein and fat contents of PBS were determined to assess the nutritive value of PBS edible insect.

Materials and methods

Figure 6.1 shows the LCA of PBS production. Accordingly, the inputs and yield of edible insects in South Korea were determined. Then, the obtained data were used to conduct cradle-to-gate environmental impact evaluation for the production systems. As a strong and standardized methodology, LCA was used to conduct the environmental consequences of edible insect as a future protein and fat source.

Insect production system

This study was conducted at Gwangmyeong-si in South Korea, which is located in the Mid-West region of Gyeonggi-do, and at a metropolitan area in Korea (the central part of the Korean Peninsula). The investigated region consists of 38.8% of mountainous area and 28.9% of arable land, where in rice was predominantly cultivated but as the agricultural population decreased, the agricultural activities shifted to the production of high-value vegetable and fruit crops. Gwangmyeong-silies in an average agro-climatic zone in South Korea with four distinct seasons, and average rainfall of 1,556 mm per annum; the temperature ranges from an average low of -1.1 °C to an average high of 25.9

°C.

The investigated insect species is PBS larvae. PBS larvae is one of the five species which are consumed in South Korea [START_REF] Ghosh | Nutritional composition of five commercial edible insects in South Korea[END_REF]. The investigated production unit uses mushroom waste to feed insects, and banana waste to feed immature insects.

Temperature is kept around 25 °C throughout the year. However, the relative humidity of the farm was not managed during the process. The volume of the breeding box was 36 liters (600mm × 450mm × 200mm). On average, the investigated insects lay eggs every seven to ten days. PBS has four life stages: egg, larva, pupa, and adult. It takes 10 weeks for an egg to become a larva and then it is ready to be collected. The investigated farm was a small insect farm with the capacity of 960 kg larvae (dry basis) production per year.

The insects produced in the studied system are available legally for consumption on Korean markets. The investigated insect species (PBS) is relatively expensive in Korea because of its medicinal properties. Korean food law has limits in PBS larvae on the presence of heavy metals (lead, cadmium and arsenic) and microbial indicators of hygiene/food safety (coliforms, E. coli) (Ministry of Food and Drug Safety, 2019).

Sample preparation

Air dried PBS larvae were collected from the insect farm located in Gwangmyeong-si, South Korea. The dried sample was homogenized using mortar and a pestle, and stored in a plastic box at -20°C until further analysis.

Determination of protein and fat content

The fat and protein content of PBS larvae were determined using standard methods. The nitrogen (N) and proteins were investigated by the Kjeldahl method (ISO 1871(ISO , 2009)).

The protein content was determined through multiplying the N content by the coefficient of 6.25.

The fat content was determined after ethyl ether extraction in a Soxhletapparatus for six hours. Subsequently, the ethyl ether was removed through a rotary evaporator. After that, the extracted sample was weighed until a constant sample weight was reached.

Fatty acid methyl esters preparation

The fatty acid composition was calculated by the GC/EI-MS of the fatty acid methyl esters (FAME), which were prepared by transmethylation based on the following procedure. In short, 25 mg of dried PBS larvae powder were measured in a Pyrex® test tube with a Teflon lined screw cap. Next, 3.3 mL of methanol/hydrochloric acid (2M in methanol) mixture were added into the tube. After vigorous vortexing for 5 to 10 s, 0.3 mL of chloroform, which contained an internal standard (13:0) and antioxidant (BHT), were added and the tube was tightly sealed. After vigorous vortexing for 30 s, the tube was heated at 90°C for 2 h. When it gets cooled off to room temperature, the FAME were extracted by adding 0.9 mL of miliQ water into the tube. The mixture in the tube was vortexed for 5 to 10 s, and then 1.8 mL of n-hexane were added and vortexed again for 20 to 30 s. The n-hexane layer containing the FAME was separated by centrifugation for 5 min at 4000 rpm. The uppern-hexane phase was drawn off and transferred to a sample vial for GC/EI-MS analysis. The preparation of FAME was performed in two duplicates.

protein and fat sources of human nutrition. The system boundary of this research was the cradle to farm gate insect production system, including inputs (electricity, water, mushroom production wastes, banana peels, and transportation) as well as on-farm operations (see Figure 6.1).

Inventory analysis

The cradle-to-gate environmental impact for edible insect production was evaluated using LCA. Table 6.2 presents the main primary inventory data for small scale edible insect production. The emitted pollutants were classified as the cradle-to-gate (background) and gate-to-gate (foreground) emissions. The emitted pollutants in the background phase (production of input materials) were adapted from the Ecoinvent 3.0 database using the Simapro 8.2.3.0 software. The foreground (on-site) emissions were the pollutants emitted during the composting process, such CO2, N2O, and CH4. The amounts of emitted pollutants during composting process were calculated based on EPA, 2010. Feed is a major factor with regard to the total environmental impacts of insect production either as a burden or as avoided impacts in case of waste treatment (Smetana et al., 2021). In this study, the emissions within composting process were included as on-site emissions (Table 6.2), and the bio-waste treatment was considered as an avoided product. (Jolliet et al., 2003). This IA methodology is the hybrid application of IMPACT 2002, Eco-Indicator 99, CML, and IPCC. Additionally, five other IA methodologies, namely, the ReCiPe midpoint, CML-IA baseline, EDIP 2003, EDP 2013, and ILCD 2011 Midpoint, were also evaluated for comparison with the baseline IA methodology, that is, IMPACT 2002+. The above-mentioned impact categories were compared in terms of the characterization indices of global warming potential and ozone layer depletion since those are the mutual impact categories considered by the six studied impact assessment methodologies.

Results and discussion

Protein and fat content of Protaetia brevitarsis seulensis

Because one of the objectives of this study was to compare the environmental effects of insect protein with those of protein produced from conventional human nutrition sources, the protein content of larvae was determined and the LCA results are expressed per kg of insect protein. The protein and fat contents of the dried larvae of PBS were determined as 50.5% and 13.5%, respectively. The results are in good agreement with the results of previous studies, and revealed that the dried larvae contained more than 50% protein and between 10% and 25% fat [START_REF] Yeo | Fatty acid composition and volatile constituents of Protaetia brevitarsis larvae[END_REF][START_REF] Kim | Proximate composition and mineral content of five edible insects consumed in Korea[END_REF]. The insects' protein content is generally similar to that of beef, pork, and chicken, and contains more polyunsaturated fatty acid with higher contents of various minerals, such as zinc and iron (Van Huis et al., 2015). There might be an overestimation of protein content measurement of the studied edible insect since the conversion rate of nitrogen to protein was considered as 6.25. In this regard, [START_REF] Boulos | Nitrogen-to-Protein Conversion Factors for Edible Insects on the Swiss Market: T. molitor, A. domesticus, and L. migratoria[END_REF] claimed that the application of this conversion rate may lead to an average 17% overestimation of protein contents. But it is not considered in this PhD dissertation since the above-mentioned study was done on mealworms, crickets, and locusts, and there is not such a data available for PBS.

PBS larvae is one of the seven types of dried edible insects that are currently available in the Korean market. The larvae stage of PBS is also currently being used in traditional Chinese medicine [START_REF] Suh | Antioxidant activity of aqueous methanol extracts of Protaetia brevitarsis Lewis (Coleoptera: Scarabaedia) at different growth stages[END_REF] since it may produce therapeutic effects for the treatment and prevention of various types of diseases (inflammatory disease, liver cirrhosis, and hepatitis) and cancers (hepatic and breast cancer) [START_REF] Yoo | Isolation of fatty acids with anticancer activity from Protaetia brevitarsis larva[END_REF][START_REF] Kang | Effects of fermented aloe vera mixed diet on larval growth of Protaetia brevitarsis seulensis (Kolbe) (Coleopteran: Cetoniidae) and protective effects of its extract against CCl4-induced hepatotoxicity in Sprague-Dawley rats[END_REF]. Therefore, in the near future, it can be used as a potential source of protein and fat.

The GC-MS (Gas Chromatography Mass Spectrometry) analysis of the fatty acid profile revealed the presence of 18 fatty acids, whose spectra overlap with the spectra from the NIST base (Table 6.3) with a probability of more than 93%. Amongst the 18 identified FAs, six of them were saturated FAs (SFA) (two of them has an odd number of C atoms:C15:0 and C17:0); eight were unsaturated FAs (four monounsaturated FAs (MUFA) and four polyunsaturated FAs (PUFA)); four were methyl FAs. The most abundant FA was oleic acid (60.38%), which along with palmitic, palmitoleic, and linoleic acid contributed to 90% of the total FA content. These results are in agreement with the results reported by [START_REF] Yeo | Fatty acid composition and volatile constituents of Protaetia brevitarsis larvae[END_REF].

The contribution of MUFA to the total FA content was 71.70%, which resulted in a very high MUFA value in the calculated SFA:MUFA:PUFA ratio of 3.3:12:1.This exhibits a discrepancy with the recommended SFA:MUFA:PUFA ratio for a healthy nutrition(1.25:1.5:1). Therefore, it can be concluded that fats from PBS are a good source of MUFA. Moreover, according to the literature, it is known that MUFAs promote a healthy blood lipid profile and improve blood pressure, insulin sensitivity, and glycemic control [START_REF] Garg | High-monounsaturated-fat diets for patients with diabetes mellitus: a metaanalysis[END_REF][START_REF] Pm | Monounsaturated fatty acids and risk of cardiovascular disease[END_REF]Ros et al., 2013).

The effectiveness of PBS larvae in traditional medicine for the treatment and prevention of various diseases (inflammatory disease, liver cirrhosis, and hepatitis) and cancers (hepatic and breast cancer) can be explained by the presence of various FAs, such as palmitic (16.28%), palmitoleic (8.32%), and oleic acid (60.38%) at a very high concentration in the fats of these larvae. Additionally, palmitoleic acid has been associated with increased insulin sensitivity and decreased lipid accumulation in the liver [START_REF] Cao | Identification of a lipokine, a lipid hormone linking adipose tissue to systemic metabolism[END_REF]. [START_REF] Yoo | Isolation of fatty acids with anticancer activity from Protaetia brevitarsis larva[END_REF] demonstrated that the dichloromethane extract from PBS, which contains FAs (palmitic and olelic acid), has anti-cancerogenic effects. For the first time, our study revealed the presence of four methyl-FAs. Branched-chain FAs are common constituents of bacteria and animal lipids. Amongst them, 13methyltetradecanoic acid is the most abundant (1.28%), and has been well known to induce the apoptosis or programmed cell death of certain human cancer cells (Klein et al., 2013;[START_REF] Cai | 13-Methyltetradecanoic acid exhibits anti-tumor activity on T-cell lymphomas in vitro and in vivo by down-regulating p-AKT and activating caspase-3[END_REF]. According to the total fat, protein content, and FA analysis results, it can be concluded that the PBS larvae fed with banana waste can be used as a potential source of protein and fat.

LCA results

Table 6.4 presents the characterization indicators of edible insect production in South Korea. The results revealed that the investigated edible insect production system has beneficial environmental effects on certain ICs, such as land occupation, mineral extraction, aquatic and terrestrial ecotoxicity (4 ICs out of 15). In other words, this food production system can mitigate the environmental impacts of the abovementioned ICs, due to utilization of biowaste (mushroom production waste and banana peels) to feed insects by turning something harmful for environment into compost. Previous studies on the LCA of chicken (Kalhor et al., 2016;Skunca et al., 2018;López-Andrés et al., 2018), beef (Biswas and Naude, 2016;Perez-Martinez et al., 2018), milk (Soltanali et al., 2015;[START_REF] Depping | Integrating environmental impact assessment into new product development and processing-technology selection: Milk concentrates as substitutes for milk powders[END_REF][START_REF] Üçtuğ | The Environmental Life Cycle Assessment of Dairy Products[END_REF], and crop production [START_REF] Habibi | Life cycle assessment of rice production systems in different paddy field size levels in north of Iran[END_REF] have shown that the production of these protein sources has negative environmental effects on all investigated ICs.

Moreover, negative environmental effects on some ICs, namely, ozone layer depletion, non-renewable energy, aquatic eutrophication, ionizing radiation, carcinogens, aquatic acidification, non-carcinogens, respiratory inorganics, respiratory organics, terrestrial acid/nutria, and global warming, have been observed. The environmental impacts of 1 kg of dried insect production on global warming, ozone layer depletion, and renewable energy were calculated as 8.05 kgCO2eq, 1.58 × 10 -7 kg CFC-11 eq, and 32.46 MJ, respectively. Moreover, the values of the abovementioned ICs for 1 kg of protein produced from insects were 15.93 kgCO2eq, 3.12 × 10 -7 kg CFC-11 eq, and 64.63 MJ, respectively. The same values for 1 kg of fat produced from insects were calculated 59.60 kgCO2eq, 1.17 × 10 -6 kg CFC-11 eq, and 241.75 MJ, respectively (Table 6.4). The GWP of farming 1 kg of insects and producing 1 kg of protein from insects in Thailand has been reported as 4.0 and 3.9 kgCO2eq, respectively (Halloran et al., 2017). The GWP of 1 kg of protein and lipids produced from the Hermetia illucens insect has been reported as 2.1 and 2.9 kgCO2eq, respectively [START_REF] Salomone | Environmental impact of food waste bioconversion by insects: application of life cycle assessment to process using Hermetiaillucens[END_REF]. Some insect species (Hermetia illucens and Tenebrio molitor) have been shown a promising potential to be used as an alternative for animal and plant-based lipids products, such as butter or margarine (Smetana et al., 2020). The results revealed that the electricity consumption was the environmental point of interest in the production system. The on-site emissions accounted for the largest proportion of the environmental impact pertaining to global warming and respiratory inorganic ICs. Bio-waste consumption, which is used to feed insects, exerted beneficial environmental effects on all investigated ICs. In cricket production, the environmental Chapter 6

152 point of interest is related to the feed production (Halloran et al., 2017). Food waste has a promising nutrient properties that can be valorized for feeding edible insects [START_REF] Varelas | Food wastes as a potential new source for edible insect mass production for food and feed: A review[END_REF]. The single scores of the damage categories in edible insect production are presented in Table 6.5. Based on the beneficial environmental impacts of edible insect production in various ICs, edible insects can become an environment efficient food production system for human nutrition by managing certain consumption inputs. In fact, only 40-50% of the produced biomass of cattle, poultry, and pigs are used directly as food. In contrast, the entire body of edible insects can be used as food (Heckmannet al., 2018). Moreover, insects as mini-livestock have many environmental benefits and similar nutritional quality compared with conventional livestock production systems [START_REF] Van Huis | Edible insects contributing to food security?[END_REF]. The primarily studies show that edible insect cell culture also may provide a more cost-efficient platform of cell-based meat system, according to the unique properties of insect cells (Rubio et al., 2019a,b;[START_REF] Rubio | Plant-based and cell-based approaches to meat production[END_REF]. Edible insects have the potential to be the future food given to their positive nutritional properties and relatively low environmental impacts; however, there are still food safety concerns associated with the consumption of insects, namely, the microbiological and chemical health risk [START_REF] Garofalo | Current knowledge on the microbiota of edible insects intended for human consumption: A state-of-the-art review[END_REF]Imathiu, S., 2020). The current study, investigated the environmental impacts (climate change, resource depletion, human health, and ecosystem quality) associated with the PBS production system, and the microbiological and chemical health risk of the final product was not included in the LCA. Further research are needed to look at the microbiological, chemical health risk, and potential allergy related issues of this species toward moving to a sustainable edible insect-based production system.

Uncertainty analysis

The total characterization indices of PBS edible insects are presented in Table 6.6, as determined using various IA methodologies. These results may help in gaining agreement with the findings of relevant LCA studies on edible insects. Moreover, the results revealed that the global warming potential of farming 1 kg of insects ranges from 8.05 kgCO2eq to 12.52 kgCO2eq. The amount of ozone layer depletion caused by production of 1 kg of insects ranges from 1.57 × 10 -7 to 1.58 × 10 -7 kgCFC-11eq. The results pertaining to global warming potential was obtained using the IMPACT 2002+ midpoint and is remarkably different to the results obtained using other IA methodologies. ReCiPe midpoint 12.52 -* The base scenario is shown in bold numbers.

Conclusions

The development of sustainable food production systems is highly important for achieving food security. Moreover, environmental efficiency is one of the main pillars of sustainability. However, many conventional food production systems are not sustainable.

Thus, this study investigated the life cycle environmental sustainability of small scale PBS production. The obtained results revealed that edible insect production systems can be considered as a sustainable food production system owing to their positive environmental effects on 8 out of the 15 investigated ICs. Moreover, according to the total protein and fat, content, and fatty acid analysis results, it can be concluded that the PBS larvae fed with banana waste can be used as a potential source of protein and fat source. However, various negative environmental impacts were observed in some categories. For example, One important step in an optimization problem is to determine the bounds for the optimization variables. As it can be seen in Table 7.1, previous papers used the minimum and maximum of the obtained data as the bound for optimization of food systems in terms of environmental impacts. Normally, the data for LCA analysis in food systems are collected through face to face interviews, questionnaire or limited measurement in the food industry. An error can affect the final results of the optimization process. In this regard, Kanali et al., (2021) also believed that the lack of experiments to calculate the amount of fertilizers required, was a limitation for their optimization algorithm and the accurate range of that variable could contribute to provide more accurate results.

Although some studies (Khoshnevisan et al., 2015;[START_REF] Soltanali | Energy audit of Iranian kiwifruit production using intelligent systems[END_REF] reported that the values for bounds were verified by local experts, but there is still a need for a standardized approach to determine the bounds of variables in an optimization problem in food supply chain. In this regards, Delphi can be applied as a validated scientific method in order to solve complex problems through expert consensus [START_REF] Zhu | Developing an empathy educational model (EEM) for undergraduate nursing students: A Delphi Technique[END_REF].

This methodology has been extensively used in the literature as an effective group-based judgment and decision-making [START_REF] Belton | Improving the practical application of the Delphi method in group-based judgment: a six-step prescription for a well-founded and defensible process[END_REF]. This methodology selects a panel of experts to go through a series of questionnaire-based interviews in order to achieve a consensus on a specific topic [START_REF] Mahdavi | Drivers and barriers for organic rice (Oryza sativa L.) production in northern Iran: experts' consensus using the Delphi method[END_REF]. Thus, in this study, a hybrid LCA, genetic algorithm was applied to optimize a food production system and the variables' bound for the optimization problem were determined using Delphi methodology.

nutrient-based FUs are also investigated in this study, i.e., daily requirement of vitamin C, intake of energy, and dietary fibre. A comprehensive review of the inclusion of nutritional properties in LCA was provided in the second chapter of this PhD dissertation.

The average daily requirement of vitamin C is assumed to be 55 mg/day (Nordic Council of Ministers, 2014). Likewise, 9.9 MJ/day (2365 kcal/day) was considered as the reference values for daily energy intake, according to Nordic reference values for male and females aged 31-60 (NordicCouncil of Ministers, 2014, p. 34). 30 g/day was assumed as the recommended intake of dietary fibre [START_REF] Svanes | Environmental life cycle assessment of production, processing, distribution and consumption of apples, sweet cherries and plums from conventional agriculture in Norway[END_REF]. The system boundary of this study was the cradle to orchard gate of the system, including the environmental consequences of the extraction of raw materials as well as on-orchard operations.

In the second step of LCA, the inventory data of cradle-to-farm gate pomegranate production was provided. The raw data of inputs-output were adapted from the literature [START_REF] Esmailpour-Troujeni | Sensitivity analysis of energy inputs and economic evaluation of pomegranate production in Iran[END_REF]. The data were collected from 65 rain-fed orchards using face to face interviews. Off/on-orchard emissions were calculated using the data provided by each orchard. The emissions' coefficients in the off-site phase (production of input materials) were adapted from the Ecoinvent 3.0 database using the SimaPro 9.0.0.49 software. The datasets applied for calculation of emissions from off-site operations are shown in Appendix C7.1. The coefficients for fertilizers' on-orchard emission were provided in Figure 7.1.

The calculation of on-orchard emissions from pesticides was done based on the [START_REF] Margni | Life cycle impact assessment of pesticides on human health and ecosystems[END_REF] model. Accordingly, the amounts of pesticides emitted to the soil, air, water, and crop are 76.5%, 10%, 8.5%, and 5%, respectively. The carbon sequestration of pomegranates trees and soil were not included in LCA analysis as there is not a welldefined procedure for its inclusion in LCA [START_REF] Goglio | Accounting for soil carbon changes in agricultural life cycle assessment (LCA): a review[END_REF]. The on-orchard emission from fertilizers were provided in the appendix C7. 2.

IMPACT 2002+ was applied as the impact assessment methodology within the third step of the LCA analysis. This methodology covers 15 impact mid-pint categories into four endpoints level categories, i.e., climate change, resource depletion, human health, and ecosystem quality (Jolliet et al., 2003). The LCA analysis was done using Simapro 9.0.0.49 software. In the fourth step of LCA, the results were interpreted and the discussion is provided in the results and discussion section. In this study, LCA was performed two times, first one for investigating the environmental impacts of the real condition and one more time for evaluating the environmental impacts under optimized condition.

Figure 7.1: The primary inputs consumption for pomegranate production and their on-orchard emissions calculation [START_REF] Rohani | A novel soft computing model (Gaussian process regression with K-fold cross validation) for daily and monthly solar radiation forecasting (Part: I)[END_REF]. Normally, the k value is chosen as 5 or 10, as these values have been shown a better estimations of the capacity of models [START_REF] Baldo | Stiffness modulus and marshall parameters of hot mix asphalts: Laboratory data modeling by artificial neural networks characterized by cross-validation[END_REF]. In this study, 5-fold cross validation is applied, and 100 runs were done for each training-test configuration.

Model performance evaluation

The following criteria were used to assess the performance of the developed ANN models: root mean square error (RMSE), coefficient of determination of the linear regressions equation between the measured and predicted values (R 2 ). The equations of the above-mentioned performance criteria are provided as follows [START_REF] Taki | Application of machine learning for solar radiation modeling[END_REF]:

𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 = � 1 𝑛𝑛 �(𝑌𝑌 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 -𝑌𝑌 𝑚𝑚𝑝𝑝𝑚𝑚𝑚𝑚𝑚𝑚𝑝𝑝𝑝𝑝𝑝𝑝 ) 2 𝑛𝑛 𝑝𝑝=1 (1)
𝑅𝑅 2 = ∑ (𝑌𝑌 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 -𝑌𝑌 𝑚𝑚𝑝𝑝𝑚𝑚𝑚𝑚𝑚𝑚𝑝𝑝𝑝𝑝𝑝𝑝 ) 2 𝑛𝑛 𝑝𝑝=1 ∑ (𝑌𝑌 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 -𝑌𝑌 𝑚𝑚𝑝𝑝𝑚𝑚𝑛𝑛 ) 2 𝑛𝑛 𝑝𝑝=1
(2)

𝑅𝑅𝑀𝑀𝑀𝑀𝑅𝑅 = 1 n � � 𝑌𝑌 𝑚𝑚𝑝𝑝𝑚𝑚𝑚𝑚𝑚𝑚𝑝𝑝𝑝𝑝𝑝𝑝 -𝑌𝑌 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 𝑌𝑌 𝑚𝑚𝑝𝑝𝑚𝑚𝑚𝑚𝑚𝑚𝑝𝑝𝑝𝑝𝑝𝑝 � × 100 n i=1 𝑅𝑅𝐸𝐸 = 1 - ∑ �𝑌𝑌 𝑚𝑚𝑝𝑝𝑚𝑚𝑚𝑚𝑚𝑚𝑝𝑝𝑝𝑝𝑝𝑝 -𝑌𝑌 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 � 2 n i=1 ∑ �𝑌𝑌 𝑚𝑚𝑝𝑝𝑚𝑚𝑚𝑚𝑚𝑚𝑝𝑝𝑝𝑝𝑝𝑝 -𝑌𝑌 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 ������������ � 2 n i=1
(3) where Y measured is measured amount of pomegranate yield, and Ypredicted is the predicated value by the developed ANN model [START_REF] Bolandnazar | Energy consumption forecasting in agriculture by artificial intelligence and mathematical models[END_REF]. A model with the largest EF and R 2 and the smallest MAPE is considered as the best [START_REF] Soltanali | Energy audit of Iranian kiwifruit production using intelligent systems[END_REF]. selection procedure. Uniform crossover procedure was used to generate a new offspring and the next generation. The mutation rate was 30%. 20% of the bits in the new generation were randomly changed from 0 to 1, or vice versa. The production of new generation continued until a better solution was found. The modelling and optimization analysis were done by MATLAB R2019b software.

The limitation of this research comes from its limited number of investigated orchards.

Another limitation of this study was the lack of available experiments results for determining the reasonable range of some inputs consumption, such as chemical fertilizers to be used for optimization problems. The limited number of available experts on choosing the appropriate ranges of inputs consumption within pomegranate production can be also considered as a limitation for the current study.

Results and discussions

The results are explained under four subsections of i) LCA of current condition, ii) modeling, iii) optimization, iv) LCA of optimized scenarios.

LCA of current condition

Table 7.2 shows the inventory of on-orchard emissions in pomegranate production. The quantified amounts of environmental impacts of pomegranate production under current condition based on the selected impact categories are displayed in Table 7.2. The environmental impact of 1 ton of pomegranate production in a rain-fed system on GWP was determined 68.97 kg CO2eq. This value for 1 hectare pomegranate orchard was 427.24 kg CO2eq. For comparison, the GWP of one ton of apple was 89 kg CO2eq [START_REF] Bartzas | Comparative life cycle assessment of pistachio, almond and apple production[END_REF], kiwifruit was 152 kg CO2eq, peach was 172 kg CO2eq (Nikkhah et al., 2017) and sweet cherries was 201 kg CO2eq [START_REF] Tassielli | Environmental life cycle assessment of fresh and processed sweet cherries in southern Italy[END_REF].

The GWP of fulfilling reference daily requirement of vitamin C, energy intake, and dietary fibre for 1 person from pomegranate were calculated 0.04, 0.20, and 0.05 kg CO2eq, respectively. The same values for apple were reported as 0.3, 2.3, and 0.6 kg CO2eq, respectively [START_REF] Svanes | Environmental life cycle assessment of production, processing, distribution and consumption of apples, sweet cherries and plums from conventional agriculture in Norway[END_REF]. Figure 7.3 illustrates the share of inputs to the environmental impacts of the investigated agricultural system. Chemical fertilizer consumption was the major contributor to the most investigated impact categories. The contributions of on-farm emissions to the aquatic ecotoxicity, non-carcinogens, terrestrial ecotoxicity impact categories were over 99%.

Moreover, the contributions of on-site emissions to the GWO impact category was 61%. The total weighted environmental damage of one kg pomegranate production was determined to be 2.68 PtFU -1 (Figure 7.4). Chemical fertilizers were the highest contributor to the total environmental impacts of the studied system, with a share of 80%

(including its background and on-site emissions). The results are in line with a study on apple production that indicated the environmental impacts of production of chemical fertilizers was the largest contributor to environmental impacts [START_REF] Bartzas | Comparative life cycle assessment of pistachio, almond and apple production[END_REF].

Modeling

The performance index (RMSE) of the developed models based on various numbers of neuron using K-fold cross validation are shown in Table 7.5. To find the best developed models, the best developed models were selected from K-fold analysis which can be seen in Table 7.6. The best model among the extracted models was selected to serve as the fitness function for Genetic algorithm. The selected model has the RMSE of 528 kg and R 2 of 0.91, an indication that the pomegranate output can be accurately predicted using MLP artificial neural networks.

Optimization

The optimized amounts of variables calculated using ANN+GA are shown in Table 7.7.

Accordingly, the optimized amounts of diesel fuel and human labor were 46.59 L, and 333.58 h, respectively. These values for insecticides, and fungicides were 0.11, and 0.20 kg. The optimized amount of value for nitrogen chemical fertilizer we estimated as 21.82 kg.

LCA of optimized scenarios

The coupled application of LCA + ANN + Delphi + GA indicated the reduction of environmental impacts within various impact categories. The measured and optimized values of environmental impacts of pomegranate production are shown in Table 7.8.

Accordingly, the environmental impacts of GWP can be saved by 86%. This value for non-renewable energy impact category was calculated as 80%. Figure 7.5 illustrates the share of environmental impacts of inputs within measured and optimized scenario.

Overall, the total weighted environmental damage of one ton pomegranate production was decreased to 0.32 PtFU -1 . framework was applied for a rain-fed pomegranate production system. The results highlighted that a significant amount of environmental impacts could be potentially mitigated using the application of the proposed framework. Many agri-food production systems can benefit using this framework for optimization purposes. Further research is required to evaluate the proposed framework in order to optimize other aspects of sustainability of food systems, including economic and social.

LCA, past, trend, and the future direction

Recent studies highlight that most food production systems are not sufficiently sustainable. In this regard, moving toward sustainable food production is essential to address the UN development goals. Several methodologies have been proposed and developed to contribute to moving towards sustainability. Among them, the LCA methodology has been widely applied to evaluate various production systems. LCA has been widely used for evaluation of environmental impact of various food systems. It is well known as one of the best, most practical, and most standardized approaches in food supply chain. This methodology covers a comprehensive ranges of impact categories including human-health related impact categories. An LCA study consists of four phases of goal and scope definition, inventory analysis, impact assessment and interpretation.

This method is still evolving and researchers are trying to optimize each phase of LCA and also extend the number of impact categories covered by LCA. There is a need for methodology development in food systems, extending LCA to emerging and novel food technologies. Moreover, LCA is not only an evaluation tool anymore, it can be used as a powerful decision tool for process improvement and optimization. It can also be used by policymakers as a decision support tool to select an environment-friendly production system. LCA is a promising tool that contributes to designing an environment friendly FSC. Nevertheless, three issues need to be addressed in LCA applications in FSC: (i) methodology development, focusing on the neglected aspects of LCA of FSC, including food safety and nutritional value and application of LCA as a tool to measure circularity toward moving to a circular FSC; (ii) development of LCA in innovative food technologies and future foods; and iii) LCA application not only as an evaluation tool, but also for optimization.

LCA methodology development

The first LCA application was done in 1969 in the packaging industry. Since then, this methodology has been used for decades for evaluation of environmental impact of products and services. This methodogy kept evolving and even though it is now considered as a powerful tool for assessing environmental impacts, but still it needs to be improved in order to respond to growing concerns regarding the environmental issues.

This PhD developed this methodology for the application in FSC. Given that the food safety and nutritional value (as the important aspects for selecting the appropriate food production system) have been neglected in life cycle assessment of FSC, a framework based on the application of nutritional values as the functional units and also adding food safety indices as the disability-adjusted life year (DALY) value of the produced product to human health damage category was developed. In fact, this study has developed LCA through adding a new and important impact category to human health damage category.

Many food supply chains could benefit from using this framework in their LCA application.

Moreover, the circular economy concept is gaining growing attention as an alternative to the linear economy "take, make, waste," which exists now. The measurement of circularity is the first step in moving to a circular system. There is not a unique approach for measuring circular economy in FSC since the understanding of circular economy is still being explored. In this PhD thesis, LCA was applied to measure circularity in food supply chain using LCA for the case of refining oil from olive kernel.

processing and packaging by appertization were higher than those of ohmic heating systems.

Application of LCA highlighted the beneficial environmental effects of the investigated edible insect on various impact categories (ICs), i.e., land occupation, mineral extraction, and aquatic and terrestrial ecotoxicity, due to utilization of bio-waste to feed insects. In addition, the entire body of some edible insects can be used as food, however, less than 50 percent of biomass of cattle, poultry, and pigs are being directly used as food. This food production system can mitigate the negative environmental effects of those ICs, but has negative environmental impact on some other ICs such as global warming potential.

Multiple functional units (biomass, protein and fat) were applied to study the system. The environmental hotspot of the systems was indentified and the recommendations in order to enhance the sustainability of the systems were provided. Edible insects have the potential to be the future food given to their positive nutritional properties and relatively low environmental impacts; however, there are still food safety concerns associated with the consumption of insects, namely, the microbiological and chemical health risk. Further research is needed to look at the microbiological and chemical health risk of this species toward moving to a sustainable edible insect-based production system.

AI-LCA

Recent studies show that the hybrid application of LCA is more powerful than single LCA applications. LCA can be combined with other methodologies to model, and optimize processes. In this regard, LCA has been coupled with mathematical approaches (regression, Cobb-Douglas, and data envelopment analysis), artificial-based intelligence (artificial neural networks, adaptive neuro-fuzzy based interface, particle swarm optimization, and genetic algorithms), and multi-criteria decision making (analytic hierarchy process, and fuzzy analytic hierarchy process).

Optimization of resource allocation in agri-food sector, in order to find the most appropriate mix of inputs, could contribute to mitigation of environmental impacts and moving toward a more circular food supply chain. In this regard, environmental LCA is a well known standardized methodology toward sustainability assessment. LCA has been coupled with several artificial intelligent based optimization techniques in order to optimize food systems in terms of environmental impacts. One important step in an optimization problem in food system is to determine the bounds for the optimization variable. This study developed an optimization bound determination scheme through application of Delphi methodology. The developed framework (LCA + multilayer perception artificial neural network + Delphi methodology + genetic algorithm) in this PhD was applied for a rain-fed pomegranate production system and the results indicated that a significant amount of environmental impacts could be mitigated using the application of the proposed framework.

Uncertainty in LCA results

As it was discussed throughout this PhD dissertation, there are various sources of uncertainty in LCA studies. Therefore, the LCA results reported in this dissertation are not the absolute values, and they are obtained according to some assumptions in the LCA analysis. The LCA results can be affected by the following assumptions: accuracy in the collected data, the allocation strategy, functional unit, database, software, on-site emissions coefficients, and impact assessment methodology selection. Various approaches can be applied in order to to examine the consistency of the obtained LCA results. This PhD dissertation focused on the uncertainty coming from the impact assessment methodology selection. The uncertainty analysis indicated that the selection of impact assessment methodology is an influential factor on the LCA results. It is therefore recommended to develop and employ a harmonized impact assessment methodology in which the most important/relavante impact/damage categories are included that can eventually contribute to produce a better and more comparable LCA results.

Future direction for LCA of food systems

LCA is trusted by academia and industry all over the world for decades for evaluation of environmental impact of products and services but more recently it has shown to be a promising method to measure sustainability/circularity. However, there is a great opportunity for this methodology to become the dominant approach toward evaluation and optimization of sustainability of food supply chain. The LCA methodology is explained in ISO 14040 and 44 only broadly, however many important design choices such as the selection of functional unit, system boundary, impact assessment methodology, and uncertainty analysis are not specified in the standards. It leads to a remarkable variation in the selection of functional units, systems boundary, impacts assessments and consequently the LCA results of studies in food systems. Thus, further research is needed to harmonize the LCA methodology in the food systems, address the uncertainty of the results, and determine the relevant impact categories for food systems. Novel artificialbased intelligent systems coupled with LCA methodology can also be further explored in order to model and optimize the food production systems. LCA has already been shown to be a promising methodology for measuring circularity and it can be further boosted as

Figure 1 . 1 .Figure 1 . 2 . 20 Figure 1 . 3 . 20 Figure 1 . 4 .Figure 2 . 1 .Figure 2 . 2 .Figure 2 . 3 .Figure 3 . 1 .Figure 3 . 2 .Figure 3 . 3 .Figure 3 . 4 .Figure 3 . 5 .Figure 4 . 1 .Figure 4 . 2 .Figure 4 . 3 .Figure 4 . 4 .Figure 4 . 5 .Figure 4 . 6 .Figure 4 . 7 .Figure 4 . 8 .Figure 5 . 1 .

 1112201320142122233132333435414243444546474851 Figure 1.1. Four phases of an LCA study within the food supply chain ………………..………..… 10 Figure 1.2. Frequency of coupling other models with LCA ……………………….……..…………… 20

Figure 5 . 2 .Figure 5 . 3 .Figure 5 . 4 .Figure. 5 . 5 .Figure 5 . 6 .Figure 5 . 7 .Figure 6 . 1 .Figure 6 . 4 .Figure 7 . 1 .Figure 7 . 3 .Figure 7 . 4 .Figure 7 . 5 .

 525354555657616471737475 Figure 5.2. Aseptic Ohmic heating line with 4 t/h capacity ….……………………………………… Figure 5.3. Inputs contribution to the environmental impact upon appertization ….……..…….. 121 Figure 5.4. Inputs contribution to the environmental impact upon ohmic heating ….……..…… 121 Figure. 5.5. Normalized values of input damages in CTwJ processing and packaging ……… Figure 5.6. Normalized values of inputs damages ….……………...……………………..…..……… 123 Figure 5.7. Normalized damage assessment of tomato juice processing and packaging …… 124 Figure 6.1. Life cycle assessment of Protaetia brevitarsis seulensis production ………..……

Figure 1 . 1 :

 11 Figure 1.1: Four phases of an LCA study within the food supply chain.

Figure 1 . 2 :

 12 Figure 1.2: Frequency of coupling other models with LCA.

Figure 1 . 3 :

 13 Figure 1.3: Frequency of hybrid LCA application with different purposes.

Figure 1 . 4 :

 14 Figure 1.4: Hybrid LCA for analyzing GWP of food supply chains.

  categories which are considered by various LCA impact assessment methodologies are shown in Figure2.1. Some impact assessment methodologies also cover the end-points damage assessment. For instance, IMPACT 2002+ impact assessment covers four damage categories of human health (disability-adjusted life year), ecosystem quality (PDF*m2*yr), global warming (kgCO2eq), and resource depletion (MJ) (Figure2.2).

Figure 2 . 1 :

 21 Figure 2.1: Impact categories considered by different impact assessment methodologies.*The bold impact categories are the human health-based impact categories

Figure 2 . 3 :

 23 Figure 2.3: Hybrid application of food safety indicator with LCA

  Figure 2.4: A framework for the hybrid application of multiple functional units in LCA of FSC

Figure 3 . 1 :

 31 Figure 3.1: Life Cycle Assessment (ISO Framework)

Figure 3 . 3 :

 33 Figure 3.3: Off-farm and on-farm practices in the life cycle assessment of tobacco leaf production system

Figure 3 .

 3 Figure 3.4 illustrates the contribution of each pollutant to different phases of cured

Figure 3 . 4 :

 34 Figure 3.4: Contribution of each pollutant to different phases of cured tobacco leaf production chain Table3.2 shows the characterization indices of tobacco production system. Global

Figure 3 . 5 :

 35 Figure 3.5: Normalized amounts of various impact categories, calculated by AHP

Figure 4 . 1 :

 41 Figure 4.1: Characterization and application of olive kernel[START_REF] Rodríguez | Olive stone an attractive source of bioactive and valuable compounds[END_REF]

Figure 4 . 2 :Figure 4 . 3 :

 4243 Figure 4.2: (a) different parts of an olive fruit[START_REF] Martins | Olives and Olive Oil as Functional Foods: Bioactivity, Chemistry and Processing[END_REF][START_REF] Kiritsakis | Bailey's Industrial Oil and Fat Products[END_REF], (b) the parts of the olive which were used to produce oil in the case study. Note: In this study, the term "kernel" refers to "kernel, pit, and pulp stuck to kernel"

  One limitation of this research comes from the limited number (one industrial-scale company refining oil from olive kernel) of investigated olive kernel oil factories. Another limitation of this study is that, compared with studies on the LCA of extra virgin olive oilsthe highest quality of olive oils-this olive kernel study does not consider the olive quality indices, as suggested by[START_REF] Salomone | Life Cycle Assessment in the Olive Oil Sector[END_REF].

Figure 4 . 4 :

 44 Figure 4.4: Measuring the circularity of olive kernel oil production using LCA methodology

Figure 4 .

 4 Figure 4.5 demonstrates the proportion of inputs to the environmental effects of the olive

Figure 4 . 5 :

 45 Figure 4.5: Contribution of consumed inputs to the environmental impact of the olive kernel oil production system.

Figure 4 .

 4 Figure 4.6 displays the damage assessment of the olive kernel oil chain. According to the results, the production of 1 kg of oil from olive kernel led to 5.29 × 10 -7 DALY damage to

Figure 4 . 6 :

 46 Figure 4.6: Damage assessment of olive kernel oil production (per 1 kg produced oil).

Figure 4 . 7 :

 47 Figure 4.7: Single scores of damage categories in the olive kernel oil production chain (unit = µPt)

Figure 4 . 8 :

 48 Figure 4.8: Effect of impact assessment methodology selection on the global warming potential (GWP) results.

Figure 5 . 1 :

 51 Figure 5.1: Appertization line with 6 t/h capacity, including pre-processing, canning, sterilization and cooling.

Figure 5 . 3 :Figure 5

 535 Figure 5.3: Inputs contribution to the environmental impact of tomato juice production upon appertization

Figure 5 . 5 :Figure 5 . 6 :

 5556 Figure 5.5: Normalized values of input damages in CTwJ processing and packaging for the appertization system

Figure 5 . 7 :

 57 Figure 5.7: Normalized damage assessment of tomato juice processing and packaging for appertization and ohmic heating systems

Figure 6 . 1 :

 61 Figure 6.1: Life cycle assessment of Protaetia brevitarsis seulensis production.

Figure 6 .

 6 Figure 6.2 shows the proportion of inputs in the environmental effects of PBS larvae.

Figure 6 . 2 :

 62 Figure 6.2: Proportion of consumed inputs in environmental impact of Protaetia brevitars seulensis.

Figure 6 .

 6 Figure 6.3 shows the normalized damage assessment of the investigated production

Figure 6 . 3 :

 63 Figure 6.3: Normalized damage assessment of production system based on different consumption inputs.

Figure 6 . 4 :

 64 Figure 6.4: Normalized values of different edible insect damage categories.

Figure 7 . 2 :

 72 Figure 7.2: A hybrid life cycle assessment, genetic algorithm and Delphi framework

Figure 7 . 3 :Figure 7 . 4 .

 7374 Figure 7.3: Percentage of environmental impacts of consumed inputs within pomegranate productionThe damage assessment of the pomegranate production is demonstrated in Figure7.4.The production of 1 ton of pomegranate led to 7.26 × 10 -4 DALY damage to human health, 1.68 × 10 4 PDF•m 2 •yr. to ecosystem quality, 69 kg CO2eq to GWP, and 719 MJ to

Figure 7 . 5 :

 75 Figure 7.5: Share of environmental impacts of inputs within measured and optimized scenario

Table 1 .

 1 1. Impact categories considered by different IA methodology ………………...………… 6

Table 1 .

 1 2. Recently published studies on the LCA of emerging food technologies …………… 15 Table 1.3. Recently published studies on the LCA of future protein sources …………………… 17 Table 1.4. Recently published studies on the use of hybrid LCA in FSC ……………...………… 18 Table 2.1. Damage factors characterization of various impact categories ……………...……..… 44

Table 2 .

 2

2. Normalization factors of the studied damage categories for Western Europe ……… 45 Table 2.3. Application of nutritional-LCA in food supply chain ………………….……......……… 46

Table 3.1. Inputs consumption for tobacco production …………………………………………… 64

Table 3.2. The characterization indices of tobacco production …………………..……………… 67 Table 3.3. Normalization and weighted indices of tobacco production ………………..…..….… 70 Table 4.1. Main primary inventory data for the olive kernel oil production system ……….....… 89 Table 4.2. Environmental consequence of the olive kernel oil production system ……..……… 93 Table 4.3. Environmental impacts based on different damage categories ………………..….… 95 Table 5.1. Summary of the literature on the LCA of tomato processing ………..…….….….… 109 Table 5.2. Overview of energy and water consumption for producing 1 kg of CTwJ ………… 117 Table 5.3. Characterization indices for tomato juice production ……………………………..… 119 Table 5.4. Total characterized indicators of CTwJ within the selected IA methodologies …....125 Table 6.1. The relevant literature on LCA of edible insect-based food …………….……….… 138 Table 6.2. Main primary inventory data ………………………………………………….……..… 145 Table 6.3. Fatty acid profile of Protaetia brevitarsis seulensis larvae ………....…………….… 148 Table 6.4. Characterization indices of Protaetia brevitars seulensis production …………...… 151 Table 6.5. Single score of damage categories in edible insect production …………...…….… 155 xxv Table 6.6. Characterization indices of edible insects determined ……………………..…….… 156

Table 7 .

 7 1. Recently published papers on the use of LCA based AI-based optimization ….… 173

Table 7 .

 7 2. On-orchard emissions in pomegranate production …………………..……..…….…

Table 7 .

 7 3. Environmental impacts of pomegranate production according to different IC . .…. Environmental impacts of the production system under current condition ……..…

	Table 7.4.

Table 7 .

 7 5.Average and standard deviation of RMSE for the developed models ……….….… 179 Table7.6. The model performance of the best developed models extracted from K-fold …...

Table 7.7. The optimized amounts of variables calculated using ANN+GA …………….….… 181 Table 7.8. Measured and optimized values of environmental impacts of the production …… 182 Introduction

Table 1 . 1 :

 11 Impact categories considered by different IA methodology

	CML-IA	Ecological	IMPACT	EDIP 2003	ILCD 2011	EPD (2018)	EF Method
	baseline	Scarcity 2013	2002+
	Abiotic depletion	Water	Carcinogens	Global warming	Freshwater	Ozone layer	Cancer human
		resources			ecotoxicity	depletion	health effects
	Abiotic depletion	Energy	Aquatic	Ozone	Ozone depletion	Eutrophication	Ozone depletion
	(fossil fuels)	resources	eutrophication	depletion
		Mineral	Respiratory	Resources (all)	Ionizing radiation	Abiotic	Ionising radiation
	Acidification	resources	organics			depletion, fossil
						fuels
	Ozone layer	Radioactive	Non-	Ozone	Human toxicity,	Photochemical	Photochemical
	depletion	waste to deposit	carcinogens	formation	cancer effects	oxidation	ozone formation
				(Human)
	Photochemical	Heavy metals	Ionizing	Ecotoxicity	Particulate matter	Abiotic	Climate change -
	oxidation	into soil	radiation	water chronic		depletion,	fossil
						elements
	Fresh water	Ozone layer	Land	Terrestrial	Climate change	Acidification	Non-cancer
	aquatic ecotox.	depletion	occupation	eutrophication			human health
							effects
	Global warming	Main air	Aquatic	Aquatic	Marine	Water scarcity	Eutrophication
		pollutants and	ecotoxicity	eutrophication	eutrophication		freshwater
		PM
	Eutrophication	Carcinogenic	Global	Aquatic	Photochemical	Global warming	Acidification
		substances into	warming	eutrophication	ozone formation		terrestrial and
		air					freshwater
	Marine aquatic	Heavy metals	Terrestrial	Human toxicity	Acidification		Climate change
	ecotoxicity	into air	acid/nutri	air
	Terrestrial	Water	Non-renewable	Human toxicity	Terrestrial		Water scarcity
	ecotoxicity	pollutants	energy	water	eutrophication
	Human toxicity	POP into water	Aquatic	Human toxicity	Freshwater		Climate change -
			acidification	soil	eutrophication		biogenic
		Heavy metals	Mineral	Acidification	Ionizing radiation		Ecotoxicity
		into water	extraction				freshwater
		Pesticides into	Ozone layer	Ecotoxicity	Human toxicity,		Land use
		soil	depletion	water acute	non-cancer
					effects
				Continued on next page

Table 1 .

 1 1-continued from previous page

	CML-IA	Ecological	IMPACT	EDIP 2003	ILCD 2011	EPD	EF Method
	baseline	Scarcity 2013	2002+			(2018)
		Global warming	Terrestrial	Ecotoxicity soil	Land use		Eutrophication marine
			ecotoxicity	chronic
		Land use	Respiratory	Hazardous	Water resource		Resource use, energy
			inorganics	waste	depletion		carriers
		Radioactive		Slags/ashes	Mineral, fossil &		Resource use,
		substances into			ren resource		mineral and metals
		water			depletion
		Radioactive		Bulk waste			Respiratory
		substances into					inorganics
		air
		Non radioactive		Radioactive			Eutrophication
		waste to deposit		waste			terrestrial
		Noise		Ozone formation			Climate change -land
				(Vegetation)			use and transform.

Table 1 .2: Recently

 1 published studies on the LCA of emerging food technologies

	. The

Table 1 .3: Recently published studies on the LCA of future protein sources Future food The studied region Functional unit Impact assessment methodology Environmental hotspot Reference

 1

	Seaweed	NA	Dried seaweed with a protein content of one ton	CML 200	drying the harvested seaweed	Van Oirschot et al. (2017)
				IPCC 2013 100a and
	Edible insect (Black Soldier Fly)	Indonesia	One ton of bio-waste	ReCiPe Midpoint Hierarchist	Electricity consumption	Mertenat et al. (2019)
				(H)
			One kg of dried and
			pelletized organic
	Edible insect (Black Soldier Fly)	Not available	fertilizer, one kg of fresh insect used as pet food, one kg of protein, and	IMPACT2002+	Feed production and energy use	Smetana et al. (2019)
			one kg of insect fat used
			as feed
	Edible insect (Black soldier fly and mealworm)	Poland	One kg	IMPACT2002+	Raw materials consumption	Smetana et al. (2020)
	Microalgae	Germany	1 ton docosahexaenoic acid	ReCiPe 2016	Aquaculture	Bartek et al. (2021)
	Plant-based burger patties	Beef from Irish and Brazilian origins	a single 4 oz burger patty and nutrient density unit	product environmental footprin	NA	Saget et al. (2021)
	Seaweed (Saccharina latissimi)	Netherlands	1 kg of edible product	ReCiPe Midpoint	Cultivation phase	Slegers et al. (2021)
			Raw burger patties
	Meat substitution in burgers	Germany	(weight 113 g) and ready to eat cooked burger	IMPACT 2002+	NA	Smetana et al. (2021)
			made of burger patty

.13. Hybrid LCA in FSC

	Recent studies show that the hybrid application of LCA is more powerful than single LCA
	applications. LCA can be combined with other methodologies to model, optimize
	processes, and enhance decision-making purposes. In this regard, LCA has been
	coupled with mathematical approaches (multiple regression, and data envelopment
	analysis), artificial-based intelligence (artificial neural networks, adaptive neuro-fuzzy
	based interfaces, particle swarm optimization, and genetic algorithms), and multi-criteria
	decision making (analytic hierarchy process). Table 1.4 shows a few recently published
	papers on the use of hybrid LCA in FSC.

Table 1 .4: Recently

 1 published studies on the use of hybrid LCA in FSC (from 2018 to now)

Food/service Numbe r(s) of farms/ sites Selected impact categories Decision making/modeling/optimizat ion Joint LCA with Authors

	Chicken meat	One farm	GWP, OLD, TE, AC, MEx, NRE, AEU, LO, AET, RO, IR, RI, NC, and C	Optimization	Genetic algorithm	López-Andrés et al. (2018)
	Arecanut	70 farms	GWP, C, AC, AET, EU, IR, LO, MEx, NC, NRE, OLD, RI, RO, and TE	Optimization	Data Envelopment Analysis	Paramesh et al. (2018)
					Artificial
	Paddy rice	240 farms	GWP, AD, AC, EP, TE, HT, PO, OLD, and ME, and FE	Modeling	neural networks and adaptive neuro-fuzzy inference	Nabavi-Pelesaraei et al. (2018)
					system
			Continued on next page

Table 1 .4 -continued from previous page Food/service Number(s) of farms/sites Selected impact categories Decision making/modeling/optim ization Joint LCA with Authors

 1

	Grape	58 orchards	GWP, AC, HT, AD, EP, ME, OLD, FE, TE, and PO	Optimization	Data Envelopment Analysis	Mohseni et al. (2018)
						Artificial neural
			GWP, AC, AD,		networks and
	Sugarcane	120 farms	TE, PO, EP, OLD, HT, ME,	Modeling	Adaptive neuro-fuzzy	Kaab et al. (2019)
			and FE			inference
						system
	Rice milling factories	60 milling factories	GWP, AD, OLD, AC, PO, EP, HT, TE, ME, and FE	Modeling	Adaptive neuro-fuzzy inference system	Nabavi-Pelesaraei , et al. 2019
	Rice	Multiple years and farm sizes	GWP and EP	Optimization	Data envelopment analysis	Masuda (2019)
	Grocery stores	30 groceries	GWP CEDnr	and	Optimization	Data envelopment analysis	Álvarez-Rodríguez et al. (2019)
		Field	GWP,	ODP,		Fuzzy analytic	Król-
	Maize	experiment	POCP, AP, EP,	Decision making	hierarchy	Badziak et
		al design	and AD			process	al. (2021)

Table 2 .

 2

1: Damage factors characterization of various impact categories

(Jolliet et al., 2003)

Impact category Damage factor Unit

	Carcinogens	1.45E-06	DALY/kg chloroethylene
	Non-carcinogens	1.45E-06	DALY/kg chloroethylene
	Respiratory organics	2.13E-06	DALY/kg ethylene
	Respiratory inorganics	7.00E-04	DALY/kg PM2.5
	Ionizing radiation	2.10E-10	DALY/Bq carbon-14
	Ozone layer depletion	1.05E-03	DALY/kg CFC-11
	Aquatic ecotoxicity	8 .86E-05	PDF.m2.yr/kg.tdethylgelynceo l
		Continued on next page

Table 2 .

 2 1 -continued from previous page

	Impact category	Damage factor	Unit
	Terrestrial ecotoxicity	8 .86E-05	PDF.m2.yr/kg.tdethylgelynceo l
	Terrestrial acid/nutri	1.04	PDF.m 2 .yr/kg SO2
	Land occupation		PDF.m 2 .yr/m 2 .organic arable
		1.09	land.yr
	Aquatic acidification
	Aquatic eutrophication
	Global warming	1	kg CO2/kg CO2
	Non-renewable energy	45.6	MJ/kg crude oil
	Mineral extraction	5.10E-02	MJ/kg iron

Table 2 .

 2

	Damage category	Unit	Normalization factor
	Human health	DALY/person/year	0.0077
	Ecosystem Quality	PDF.m2.yr/person/year	4650
	Climate Change	kg CO 2 /person/year	9950
	Resources	MJ/person/year	152000

2: Normalization factors of the studied damage categories for Western Europe

(Jolliet et al., 2003)

Table 2 .

 2

	Product	Nutritional functional unit	Impact assessment	Focus of study	Reference
	Chicken, farmed salmon and wild cod	A normalized factor (nutrients gained from a kg of product are divided by daily nutrients) required amount of	Literature data	Comparing the food products environmental impacts of aquaculture and agri-	Mungkung et al. (2007)
	Pork	protein	Simplified LCA	Comparison of organic and conventional systems	Teixeira et al. (2013)
	Gryllusbimaculatus			Comparing
	De Geer (field cricket) and Acheta domesticus	Protein	ILCD	environmental impacts of insect with chicken	Halloran et al. (2017)
	(house cricket)
		Continued on next page

3: Application of nutritional-LCA in food supply chain

Table 2 .

 2 3 -continued from previous page

	Product	Nutritional functional unit	Impact assessment	Focus of study	Reference
	Whole-wheat bread, chicken fillet, minced pork, minced beef, pea soup drinking milk, and	Quality and quantity of proteins	IPCC for global warming	PQI-based, g development protein, and g digestible protein nutritional FU	Sonesson et al. (2017)
			CML 2 baseline
			2000, IPCC
	Hermetia illucens (Black Soldier Fly)	Protein and lipid	2007, Cumulative energy demand	Food waste bioconversion by insect	Salomone et al. (2017)
			method, and
			CML 2001
	29 food products	Mass and multiple recommended nutrients including vitamin	Global warming potential	Taking nutrition into account in LCA of food	Saarinen et al. (2017)
	Dairy products	Fat and Protein Corrected Milk	ILCD 2011	Traditional food manufacturing	Bava et al. (2018)
	Spanish agri-food system	The daily energy requirement of adult 2000 kcal of an	ILCD/PEF	Efficiency food system of Spanish agri-	Laso et al. (2018)
				Vegetable
	Vegetable multi cropping system	Combined nutritional equivalent unit	A combination of methods	multi-cropping environmental system	Li et al. (2018)
				impacts
		100 g meat, 1 g
	Beef, chicken, lamb, pork	omega-3 fatty acids, 1 g non-competing omega-3 fatty acids, 1%	Global warming potential	Nutritional functional units for meat production	McAuliffe et al. (2018)
		nutrient density
		score
	Black soldier fly
	(Hermetia illucens) and mealworm	One kg insect margarine	IMPACT2002+	Insect margarine	Smetana et al. (2020)
	(Tenebrio molitor)
	Regional-average diets in Spain	Nutritional and the socio-economic dimensions	Not available	Inclusion of food in LCA affordability and nutritional values	Batlle-Bayer et al. (2020)

  include other aspects of food safety (chemical hazards, physical hazards) and ultimately food quality and nutrition can be introduced as impact in LCA studies in FSC. sectors: methods of meaningful comparisons. Rome. FAO.FAO Fisheries Proceedings, (10), 87-96. Naseri, H., Parashkoohi, M.G., Ranjbar, I. and Zamani, D.M. 2021. Energy-economic and life cycle assessment of sugarcane production in different tillage systems. Energy, 217, p.119252. Nikkhah, A. and Van Haute, S. 2020. Energy flow modeling and optimization trends in food supply chain: a mini review. Current Opinion in Environmental Science & Health, 13, 16-22. Nikkhah, A., Firouzi, S., Assad, M.E.H. and Ghnimi, S. 2019. Application of analytic hierarchy process to develop a weighting scheme for life cycle assessment of agricultural production. Science of the Total Environment, 665, 538-545. Ögmundarson, Ó., Herrgård, M.J., Forster, J., Hauschild, M.Z. and Fantke, P., 2020. Addressing environmental sustainability of biochemicals. Nature Sustainability, 1-8. Oonincx, D.G. and De Boer, I.J. 2012. Environmental impact of the production of mealworms as a protein source for humans-a life cycle assessment. PloS one 7 (12), p.e51145 (2012). Parajuli, R., Matlock, M.D. and Thoma, G. 2020. Cradle to grave environmental impact evaluation of the consumption of potato and tomato products. Science of The Total Environment, p.143662. Parajuli, R., Matlock, M.D. and Thoma, G. 2021. Cradle to grave environmental impact evaluation of the consumption of potato and tomato products.

	Science of The Total
	Environment, 758 (1), 143662.
	Romero-Gámez, M., Audsley, E. and Suárez-Rey, E.M. 2014. Life cycle assessment of
	cultivating lettuce and escarole in Spain. Journal of Cleaner Production, 73, 193-
	203.
	Saarinen, M., Fogelholm, M., Tahvonen, R. and Kurppa, S. 2017. Taking nutrition into
	account within the life cycle assessment of food products. Journal of Cleaner
	production, 149, 828-844.
	Saling, P., Gyuzeleva, L., Wittstock, K., Wessolowski, V. and Griesshammer, R. 2020.
	Life cycle impact assessment of microplastics as one component of marine plastic
	debris. The International Journal of Life Cycle Assessment, 25(10), 2008-2026.
	Salomone, R., Saija, G., Mondello, G., Giannetto, A., Fasulo, S. and Savastano, D. 2017.
	Environmental impact of food waste bioconversion by insects: application of life
	cycle assessment to process using Hermetiaillucens. Journal of Cleaner
	Production, 140, 890-905 (2017).
	Sieti, N., Rivera, X.C.S., Stamford, L. and Azapagic, A. 2019. Environmental sustainability

study developed a food safety-based impact category (microbiological exposure of cooked-chilled foods) for LCA. Moreover, the inclusion of multiple functional units (with the focus of nutritional content) in LCA is discussed in this study. Further research is needed to assessment of ready-made baby foods: Meals, menus and diets. Science of The Total Environment, 689, 899-911.

Table 3

 3

	Off-farm Practices		On-farm Practices
	Raw Materials Extraction + Production &Transport of Farm Inputs	Tillage Raising Seedlings	Transplanting	Crop management -Irrigation -Weeding -Fertilizing -Crop protection -Topping	Harvesting & Curing Tobacco Leaves	Cured Tobacco Leaves + Emissions
		.1 displays the inputs consumption of tobacco production systems. The
	gasoline, diesel and natural gas, and electricity consumption for one ton production of

tobacco were calculated to be 65.01 L, 26.26 L, 1114.55 m 3 , and 1852.10 kWh, respectively. The aforementioned inputs consumption for producing tobacco in each

Table 3 . 2 :

 32 The characterization indices of tobacco production

		Consumption	Standard deviation
	Impact category	(Unit per one ton tobacco)	(Unit per one hectare)	(Unit per one ton tobacco)	(Unit per one hectare)
	Acidification (kg SO 2 eq)	14	38	1.26	4.70
	Global warming (kg CO 2 eq)	1884	3639	167.87	417.879
	Terrestrial eutrophication (kg NO x eq)	20	27	2.13	3.16
	Fossil resources depletion (MJ)	59659	115444	7832.36 18477.53
	Phosphate resources depletion (kg P 2 O 5 ) Potash resources depletion (kg K 2 O)	4 6	8 12	0.49 0.67	1.14 1.39

Table 3 . 3 :

 33 Normalization and weighted indices of tobacco production

		Normalization index	Weighted index
		(Unit per one ton	(Unit per one	(Unit per one	(Unit per one
	Impact category	tobacco)	hectare)	ton tobacco)	hectare)
	Acidification potential	0.37	0.71	0.19	0.37
	Terrestrial
		0.52	1.00	0.41	0.79
	eutrophication
	Global warming	0.24	0.46	0.24	0.46
	Fossil fuel depletion	1.52	2.95	1.04	2.01
	Phosphate sources
		0.55	1.06	0.23	0.45
	depletion
	Potash sources
		0.75	1.45	0.20	0.38
	depletion

Table 4 . 1 :

 41 Main primary inventory data for the olive kernel oil production system

	Inputs and Outputs	Unit	Quantity
	Inputs		Per 1 kg Produced Oil Per 100 MJ Produced Oil
	-Olive kernel	kg	47.72	150.83
	-Water consumption	m 3	0.05	0.14
	-Natural gas	m 3	0.52	1.65
	-Electricity	kWh	0.10	0.31
	-Human labor	h	0.16	0.50
	-Transportation of the produced oil ton × km 0.34	1.07
	Outputs
	-Olive kernel oil		1 kg	100 MJ
	-Efficiency (oil/olive kernel)	%	2.09	2.09

Table 4

 4

	.2: Environmental consequence of the olive kernel oil production system based on
	different impact categories
	Impact Category	Unit		Quantity
			Per 1 kg Produced Oil	Per 100 MJ Produced Energy
	Global warming	kg CO2eq	1.37	4.32
	Non-renewable energy	MJ primary	26.40	83.44
	Mineral extraction	MJ surplus	0.005	0.02
	Ozone layer depletion	kg CFC-11eq	1.57 × 10 -7	4.97 × 10 -7
	Non-carcinogens	kg C2H3Cl eq	0.02	0.05
	Carcinogens	kg C2H3Cl eq	0.08	0.24
		Continued on next page

Table 4 .

 4 2 -continued from previous page

	Impact Category	Unit		Quantity
			Per 1 kg Produced Oil	Per 100 MJ Produced Energy
	Ionizing radiation	Bq C-14 eq	1.94	6.14
	Respiratory organics	kg C2H4 eq	3.84 × 10 -4	1.39 × 10 -3
	Respiratory inorganics	kg PM2.5eq	4.40 × 10 -4	1.21 × 10 -3
	Aquatic ecotoxicity	kg TEG water	68.07	215.16
	Terrestrial ecotoxicity	kg TEG soil	12.71	40.16
	Aquatic eutrophication	kg PO4 P-lim	3.28 × 10 -5	1.04 × 10 -4
	Terrestrial acid/nutri	kg SO2eq	0.007	0.02
	Land occupation	m 2 org.arable	0.009	0.03
	Aquatic acidification	kg SO2eq	0.002	0.01

  Table 4.3.

	100%
	90%
	80%
	70%
	40% 60% 50%	Transportati on Electricity
	30%	Natural gas
	20%
	10%
	0%

Table 4 .3: Environmental

 4

	Off-Site

impacts of the olive kernel oil production system based on different damage categories

Table 5 . 1 :

 51 Summary of the literature on the LCA of tomato processing

	Product	The studied region	Functional unit	Impact assessment methodology	Focus of the research	Environmental hotspots	Reference
	Paste and diced tomatoes	United States	One kilogram of canned, consumer-ready tomato paste	Not available	The environmental consequences of regional and national-scale food systems	Processing and retail packaging	Brodt et al. (2013)
	Chopped tomatoes and
	peeled tomatoes in tomato juice and	Italy	One kg of packaged product	CML 2001	Environmental impacts of various tomato based products	Agricultural phase and packaging	Del Borghi et al. (2014)
	tomato purée
					Environmental impacts of various
	Packaged tomato puree	Northern Italy	700 g puree jar	CED, CML 2001 and ReCiPe	phases of packaged tomato puree production process	Packaging and agricultural phases	Manfredi and Vignali (2014)
	Tomato puree	Northern Italy	One kg of tomato purée	ILCD method	Anaerobic digestion of by-products	Packaging and agricultural phases	Bacenetti et al. (2015)
	Tomato juice	German institute of food Technologies, Germany	One kg of packaged tomato juice	Not available	Thermal, high pressure processing, and pulsed electric fields technologies compression	Packaging	Aganovic et al. (2017)
	Packaged peeled tomatoes	Italy	1 kg of processed tomato	ReCiPe.	Pulsed electric fields technology at an industrial scale	Canning	Arnal et al. (2018)
	Fresh and dried organic tomato	Southern Sweden	One ton	ReCiPe midpoint	Environmental impacts of fresh and dried tomato supply chain	Agricultural phase, packaging and drying	Bosona, and Gebresenbet, (2018)
	tomato puree	Iran	500 g packaged tomato puree in a steel can with a plastic cap	CML-IA baseline	Comparison of different tomato puree production phases	Packaging	Shahvarooghi Farahani et al. (2019)
	Tomato-pasta sauce	The USA	1 kg product eaten at the consumer level	ReCiPe 2016	Cradle to grave environmental impact assessment	Processing and the agriculture phase	Parajuli et al. (2020)
	Tomato ketchup	Austria	3.8 kg ketchup	Combination of a few methodologies	Packaging systems in the light of food waste	Packaging	Wohner et al. (2020)
				CML-IA baseline, ILCD
	Chopped tomatoes with juice	Italy	One kg of packaged CTwJ	2011 Midpoint, EDIP 2003, EDP 2013, ReCiPe midpoint, and IMPACT	Processing upon appertization and ohmic heating systems	-	Current Study
				2002+

Table 5 . 2 :

 52 Overview of energy and water consumption for producing 1 kg of CTwJ through industrial appertization and aseptic ohmic heating lines.

		Appertization line	Aseptic Ohmic heating
			line
	Pre-processing
	Electricity, Wh/kg	0.23	0.19
	Natural gas, m 3 /kg	7.92 10 -4	2.38 10 -5
	Water, m 3 /kg	0.37	0.34
	Processing
	Electricity, Wh/kg	3.19	53.97
	Natural gas, m 3 /kg	1.48 10 -2	-
	Water, m 3 /kg	1.32	1.35

Table 5 . 3 :

 53 Characterization indices for tomato juice production

	Impact category	Unit	Appertization	Ohmic heating
	Global warming	kg CO 2 eq	4.38	2.52
	Mineral extraction	MJ surplus	67.11	0.02
	Non-renewable
	energy	MJ primary	52.15	24.94
	Ozone layer		1.34×10 -7	4.00×10 -8
	depletion	kg CFC-11 eq
	Ionizing radiation	Bq C-14 eq	55.49	10.96
	Respiratory organics kg C 2 H 4 eq	0.0019	0.001
	Respiratory
	inorganics	kg PM 2.5 eq	0.01	0.002
	Non-carcinogens	kg C 2 H 3 Cl eq	0.11	0.03
	Carcinogens	kg C 2 H 3 Cl eq	0.08	0.03
	Aquatic ecotoxicity	kg TEG water	1943.03	505.51
	Terrestrial acid/nutri	kg SO 2 eq	0.15	0.029
	Terrestrial ecotoxicity kg TEG soil	526.11	161.90
	Aquatic acidification	kg SO 2 eq	0.06	0.01
	Aquatic
	eutrophication	kg PO 4 P-lim	0.0006	0.0003
	Land occupation	m 2 org.arable	0.20	0.09
	Figures 5.3 and 5.4 illustrate the share of inputs in environmental burdens of CTwJ
	production upon appertization and ohmic heating systems, respectively. The results

Table 5

 5

	.4: Total characterized indicators of CTwJ within the selected impact assessment
	methodologies
	Impact assessment	Global warming
		(kg CO2eq)		Ozone layer depletion
				(kg CFC-11 eq)
		Appertization	Ohmic heating	Appertization	Ohmic heating
	CML-IA baseline	4.44	2.50	1.34×10 -7	4.00×10 -8
	EDIP 2003	4.44	2.50	1.34×10 -7	4.00×10 -8
	EDP (2013)	4.44	2.50	1.34×10 -7	4.00×10 -8
	ILCD 2011 Midpoint	4.44	2.50	1.33×10 -7	3.99×10 -8
	ReCiPe midpoint	4.13	2.54	1.35×10 -7	4.03×10 -8
	IMPACT 2002+	4.38	2.52	1.34×10 -7	4.00×10 -8

Table 6 . 1 :

 61 The relevant literature on LCA of edible insect-based food

	Insect species	The studied region	Functional unit	Impact assessment methodology	Focus of the research	Environmental hotspots	Reference
			One kg of
			dried
	Not available	Germany	defatted insect powder and 1 kg of ready for consumption	ReCiPeV1.08 and IMPACT 2002+	Compare insect-based food product with other food products	Feed production	Smetana et al. (2016)
			fresh
			product
			without
			packaging
				CML 2
	Hermetia illucens (Black Soldier Fly)	Italy	One ton of food waste, one kg of protein and lipid	baseline 2000, method, and IPCC 2007, Cumulative energy demand	Food waste bioconversion by insect	Electricity consumption and transportation	Salomone et al. (2017)
				CML 2001
	Mealworm	Finland	One kg of mealworms	Not available	GWP of the future potential industrial scale	Feed crop production and direct heating energy	Joensuu and Silvenius, (2017)
			Continued on next page

Table 6 .

 6 1 -continued from previous page

	Insect species	The studied region	Functional unit	Impact assessment methodology	Focus of the research	Environmental hotspots	Reference
					Comparing
	Gryllusbimaculatus De Geer (field cricket) and Acheta domesticus (house cricket)	Thailand	One kg of edible mass and one kg of protein	ILCD	environmental impacts of insect with chicken	Feed production	Halloran et al. (2017)
	Hermetia illucens (Black Soldier Fly)	Indonesia	One ton of bio-waste	IPCC 2013 100a and ReCiPe Midpoint Hierarchist (H)	Bio-waste conversion using insect	Electricity consumption	Mertenat et al. (2019)
			One kg of dried
			and pelletized
			organic fertilizer,
			one kg
	Hermetia illucens (Black Soldier Fly)	Not available	of fresh insect used as pet food,	IMPACT2002+	Sustainably of insect production as feed and food	Feed production and energy use	Smetana et al. (2019)
			one kg of protein,
			and one kg of
			insect fat used as
			feed
	Black soldier fly (Hermetia illucens) and mealworm (Tenebrio molitor)	Poland	One kg	IMPACT2002+	Insect margarine	Raw materials consumption	Smetana et al. (2020)
	Protaetia brevitarsis seulensis	South Korea	One kg of dried insect, protein, and fat	IMPACT 2002+, CML-IA baseline, EDIP 2003, EDP 2013, ILCD 2002+ midpoint, and IMPACT 2011 Midpoint, ReCiPe	Protaetia brevitarsis and fat source a future protein seulensis larvae as	-	Current Study

Table 6 . 2 :

 62 Main primary inventory data for small scale Protaetia brevitarsis seulensis production.

	Inputs-outputs

.8. Impact assessment and uncertainty analysis The

  impact assessment (IA) methodologies of previous studies on the LCA of insects are presented in Table6.1. The selection of the IA methodology may significantly influence the obtained results of every LCA study on food production systems. In this study,

	IMPACT 2002+ was employed as the baseline IA methodology owing to its various impact
	(15 impact categories) and damage categories. IMPACT 2002+ divides the 15 impact
	categories into four damage categories (endpoints level), i.e., climate change, resource
	depletion, human health, and ecosystem quality

Table 6 .3:

 6 Fatty acid profile of Protaetia brevitarsis seulensis larvae; values are expressed as a mean value ± SD.

		Retention	Fatty acid		Content
		time (min)			(% of total fatty
					acids )
	1	9.720	Myristic acid	C14:0	0.58±0.003
	2	10.200	13-Methyltetradecanoic acid	C14:0 13 methyl	1.28±0.001
	3	10.664	Pentadecanoic acid	C15:0	0.11±0.005
			Continued on next page

Table 6 .

 6 3 -continued from previous page

		Retentio	Fatty acid		Content
		n time			(% of total fatty
		(min)			acids )
	4	11.200 14-Methylpentadecanoic acid C15:0 14 methyl	0.49±0.034
	5	11.718 Palmitic acid		C16:0	16.28 ± 0.143
	6	12.148 Palmitoleic acid		C16:19c	8.32±0.008
	7	12.250 11-cis-Hexadecenoic acid	C16:111c	1.17±0.052
	8	12.352 15-Methyhexadecanoic acid	C16:0 15 methyl	0.50±0.018
	9	12.550 14-Methylhexadecanoic acid	C16:0 14 methyl	0.27±0.018
	10	12.900 Heptadecanoic acid		C17:0	0.10±0.012
	11	13.300 6-cis-9-cis-12-cis-		C16:36c9c12c	0.31±0.010
		Hexadecatrienoic acid
	12	14.200 Stearic acid		C18:0	1.69±0.004
	13	14.600 Oleic acid		C18:19c	60.38±0.021
	14	14.725 11-cis-Octadecenic acid	C18:111c	1.83±0.023
	15	15.317 Linoleic acid		C18:29c12c	4.92±0.056
	16	15.800 γ-Linolenic acid		C18:36c9c12c	0.19±0.004
	17	17.000 Arachidic acid		C20:0	0.50±0.003
	18	19.050 Arachidonic acid		C20:45c8c11c14c	0.31±0.007

Table 6 . 4 :

 64 Characterization indices of Protaetia brevitars seulensis production.

	Impact category	Unit

Table 6 . 5 :

 65 Single score of damage categories in edible insect production (unit= µPt).

		Bio-waste
		treatment
			electricity	On-farm	Transportation	Total
		(avoided
		activity)
	Human health	-0.06	0.21	0.0002	0.04	0.19
	Ecosystem
		-0.13	0.03	0	0.01	-0.09
	quality
	Climate change	-0.03	0.19	0.63	0.03	0.81
	Resources	-0.05	0.23	0	0.03	0.21
	Total	-0.27	0.67	0.63	0.11	1.14
	* Negative values refer to savings and positive values refer to impacts.

Table 6 . 6 :

 66 Characterization indices of edible insects determined using various IA methodologies.

	Impact assessment	Global warming	Ozone layer
		(kg CO2eq)	depletion
			(kg CFC-11 eq)
	CML-IA baseline	11.49	1.58 × 10 -7
	EDIP 2003	11.42	1.58 × 10 -7
	EDP 2013	11.49	1.58 × 10 -7
	ILCD 2011 Midpoint	11.56	1.57 × 10 -7
	IMPACT 2002+	8.05	1.58 × 10 -7

Table 7 . 2 :

 72 On-orchard emissions in pomegranate production

	Emissions	Unit Quantity (per a	Quantity (for ton
			hectare of	produced pomegranate)
			pomegranate's
			orchard)
	Emissions form diesel fuel combustion
	to soil
	Cadmium (Cd)	kg	1.04 × 10 -5	1.69 × 10 -6
	Lead (Pb)	kg	4.59 × 10 -5	7.41 × 10 -6
	Zinc (Zn)	kg	2.81 × 10 -2	4.53 × 10 -3
	Emissions form diesel fuel combustion
	to air
	NH3	kg	1.16 × 10 -3	1.88 × 10 -4
	C6H6	kg	4.25 × 10 -4	6.86 × 10 -5
		Continued on next page

Table 7 .

 7 4. Environmental impacts of the pomegranate production system under current condition

			Human health	Ecosystem quality	Climate change	Resource depletion
	Total	Damage assessment	7.26 × 10 -7 DALY	16.77 PDF•m 2 •yr	0.07 kg CO 2 eq	0.72 MJ
		Normalized	0.10	1.22	0.01	4.73 × 10 -3
	On-site	Damage assessment	6.87 × 10 -7 DALY	2.27 PDF•m 2 •yr	4.22 × 10 -2 kg CO 2 eq	0
		Normalized	0.10	0.17	4.26 × 10 -3	0
	Diesel fuel	Damage assessment	3.59 × 10 -9 DALY	1.93 × 10 -2 PDF•m 2 •yr	2.97 × 10 -3 kg CO 2 eq	0.35 MJ
		Normalized	5.07 × 10 -4	1.41 × 10 -4	3.00 × 10 -4	2.28 × 10 -3
	Nitrogen chemical	Damage assessment	1.14 × 10 -8 DALY	3.29 × 10 -3 PDF•m 2 •yr	1.10 × 10 -2 kg CO 2 eq	0.21 MJ
	fertilizer	Normalized	1.61 × 10 -3	2.40 × 10 -4	1.11 × 10 -3	1.36 × 10 -3
	P2O5 chemical fertilizer	Damage assessment Normalized	1.55 × 10 -8 DALY 2.18 × 10 -3	2.83 PDF•m 2 •yr 0.21	7.25 × 10 -3 kg CO 2 eq 7.32 × 10 -4	0.11 MJ 7.27 × 10 -4
	K2O chemical fertilizer	Damage assessment Normalized	4.78 × 10 -9 DALY 6.74 × 10 -4	11.60 PDF•m 2 •yr 0.85	3.40 × 10 -3 kg CO 2 eq 3.44 × 10 -4	0.03 MJ 1.99 × 10 -4
	Farmyard manure	Damage assessment Normalized	2.42 × 10 -9 DALY 3.42 × 10 -4	0.07 PDF•m 2 •yr 4.99 × 10 -3	1.29 × 10 -3 kg CO 2 eq 1.30 × 10 -4	0.01 MJ 6.87 × 10 -5
	Pesticide	Damage assessment	1.12 × 10 -9 DALY	4.04 × 10 -4 PDF•m 2 •yr	8.25 × 10 -4 kg CO 2 eq	0.02 MJ
		Normalized	1.58 × 10 -4	2.95 × 10 -8	8.33 × 10 -5	1.03 × 10 -4

Table 7 . 5 :

 75 Average and standard deviation of RMSE for the developed models based on various numbers of neuron

	Training	Activation
	algorithm	function

Table 7 . 6 :

 76 The model performance indices of the best developed models extracted from K-fold analysis

	Train	Test	Total

*The bold values represent the selected model for optimization.

Table 7 .

 7 6 -continued from previous page

	Train	Test	Total

Table 7 . 7 :

 77 The optimized amounts of variables calculated using ANN+GA (per hectare)

	Input	Unit	Optimized value
	Diesel fuel	Lit	46.59
	Human labor	h	333.58
	Herbicides	kg	0
	Insecticides	kg	0.11
	Fungicides	kg	0.20
	Farmyard manure	kg	537.86
	Nitrogen (N) chemical fertilizer	kg	21.82
	Phosphate (P 2 O 5 ) chemical fertilizer	kg	0
	Potassium (K 2 O) chemical fertilizer	kg	0
	Agricultural machinery	h	11.97

Table 7 . 8 :

 78 Measured and optimized values of environmental impacts of pomegranate production

	Impact category	Unit	Quantity (Per 1 ton produced pomegranate)
			Measured	Optimized scenario
	Global warming	kg CO 2 eq	68.97	37.05
	Non-renewable energy	MJ primary	717.61	397.64
	Mineral extraction	MJ surplus	1.40	0.35
	Ozone layer depletion	kg CFC-11eq	8.05 × 10 -6	4.66 × 10 -6
	Non-carcinogens	kg C 2 H 3 Cl eq	209.14	124.51
	Carcinogens	kg C 2 H 3 Cl eq	0.97	0.43
	Ionizing radiation	Bq C-14 eq	352.97	151.99
	Respiratory organics kg C 2 H 4 eq	0.09	0.01
	Respiratory inorganics	kg PM2.5eq	0.20	0.11
	Aquatic ecotoxicity	kg TEG water	547557.35	323717.15
	Terrestrial ecotoxicity kg TEG soil	284724.90	162695.05
	Aquatic eutrophication	kg PO 4 P-lim	0.26	0.79
	Terrestrial acid/nutri	kg SO 2 eq	10.00	5.99
	Land occupation	m 2 org.arable	13285.20	36.68
	Aquatic acidification	kg SO 2 eq	1.38	0.01

Table C7 .

 C7 2. Coefficients for calculation of emissions from diesel fuel combustion (gram)

	Emissions	Unit Coefficients (EPA, 1998)
	Emissions to air
	NH 3	kg	4.44 × 10 -7
	C 6 H 6	kg	1.62 × 10 -7
	Benzo (a) pyrene	kg	6.68 × 10 -10
	Cd	kg	2.22 × 10 -10
	CO 2 , fossil	kg	7.00 × 10 -2
	CO, fossil	kg	1.30 × 10 -4
	Cr	kg	1.11 × 10 -9
	Cu	kg	3.78 × 10 -8
	N 2 O	kg	2.67 × 10 -6
	Heat, waste	MJ	1.01
	CH 4 , fossil	kg	2.87 × 10 -6
	Ni	kg	1.55 × 10 -9
	NO x	kg	8.66 × 10 -4
	NMVOC	kg	4.77 × 10 -5
	PAH	kg	7.29 × 10 -8
	Particulates, < 2.5 um kg	1.09 × 10 -4
	Se	kg	2.22 × 10 -10
	SO 2	kg	2.24 × 10 -5
	Zn	kg	2.22 × 10 -8
	Continued on next page

Table C7 .

 C7 5. Datasets applied for calculation of emissions from off-site operations

	Activity		Database	Category	Unit	Activity uuid
	Diesel fuel		Ecoinvent 3 GLO	kg	8033d635-116d-44c5-89ea-86d2fff8edbf
	Nitrogen	chemical	Ecoinvent 3 GLO	kg	bf6d000a-7e47-4348-8950-f4eacbc446ca
	fertilizer
	P2O5 chemical fertilizer	Ecoinvent 3 GLO	kg	64130ab8-5793-4481-ab94-1f3199d6dba2
	K2O chemical fertilizer	Ecoinvent 3 GLO	kg	a3de0bf6-de25-47b7-b59e-b7d9231ac123
	Farmyard manure	Ecoinvent 3 GLO	kg	a1867757-45f6-4ebb-bda0-c839892b28d0
	Pesticide		Ecoinvent 3 GLO	kg	629976ba-f1ca-4a56-930b-0a1e41e0f01c

Table C7 .

 C7 6. Average and standard deviation of R 2 for the developed models based on various numbers

	of neuron
	Training	Activation				Neuron number
	algorithm	function	5	10	15	20	25	30	35	40
		Logarithmic sigmoid	0.65±0.1 8	0.69±0. 13	0.69±0. 12	0.70±0.17	0.69±0.17	0.70±0.14	0.69±0.14	0.69±0.14
	Trainlm	Tangent sigmoid	0.61±0.1 9	0.68±0. 18	0.63±0. 21	0.62±0.21	0.63±0.21	0.60±0.22	0.61±0.20	0.59±0.21
	Trainbr	Logarithmic sigmoid	0.26±0.2 0	0.26±0. 22	0.36±0. 23	0.54±0.12	0.56±0.06	0.57±0.05	0.55±0.08	0.55±0.06
		Tangent sigmoid	0.52±0.1 6	0.55±0. 12	0.57±0. 07	0.58±0.04	0.55±0.12	0.55±0.11	0.56±0.10	0.56±0.10
										202

Table C7 .

 C7 7. The bound determination using Delphi methodology

		Unit	Min and max of	Delphi results
	Input		data
	Diesel fuel	Lit	10-93	40-90
	Human labor	h	93-1503	200-1500
	Herbicides	kg	0-1.98	0-2
	Insecticides	kg	0-2.40	0-2
	Fungicides	kg	0-4.20	0-2
	Farmyard manure	kg	0-2500	1000-2500
	Nitrogen (N) chemical fertilizer	kg	0-180.00	0-170
	Phosphate (P 2 O 5 ) chemical fertilizer	kg	0-184	0-120
	Potassium (K 2 O) chemical fertilizer	kg	0-180	0-100
	Agricultural machinery	h	4-31	10-30

Acknowledgements

I would like to express my sincere gratitude to my promoter, Prof. Sam Van Haute, for all of his supports and guidelines given to me throughout my PhD and it was a great opportunity for me to have his advices as a supervisor and a also a friend.

I would like to express my highest level of appreciation to my co-promotors Prof. Sami Ghnimi and Prof. Jo Dewulf for their guidance, expertise, valuable comments, and constructive suggestions.

Furthermore, I am genuinely grateful to all members of Department of Food Technology, Safety and Health, Faculty of Bioscience Engineering, Ghent University, specifically Prof Liesbeth Jacxsens, and Prof. Frank Devlieghere, for providing me with the training courses.

Author's Curriculum Vitae

Amin Nikkhah is a PhD student at the faculty of Bioscience Engineering, Department of Food technology, Safety and Health, and at the same time, he is also a teaching/research assistant in the Department of Environmental Technology, Food Technology and Molecular Biotechnology at Ghent University. He has published more than 50 scientific papers in some high impact journals, such as Renewable and Sustainable Energy Reviews (Impact factor=15), Journal of Cleaner Production (Impact factor=9), Science of the Total Environment (Impact factor=8), and Energy (Impact factor=7). As a young scientist, his research has been cited over 555 times and has an h-index of 16 and i10index of 19. Cumulative impact factor of Amin's publications stands at over 100.

Cumulative impact factor of Amin's publications during his PhD stands at over 55. In 2017, he was selected as an elite student by the BMN Elites Foundation. His current research interest is development of Life Cycle Assessment (LCA) methodology.

Chapter 3

Methodology development; Developing a weighting scheme for LCA Partially redrafted after: [START_REF] Nikkhah | Application of analytical hierarchy process to develop a weighting scheme for life cycle assessment of agricultural production[END_REF]. Application of analytical hierarchy process to develop a weighting scheme for life cycle assessment of agricultural production, Science of the Total Environment. 665, 538-545. (Impact factor = 6.5).

Summary

The impact assessment (IA) step in LCA studies is classified into three steps of characterization, normalization and weighting. In this chapter of the PhD dissertation, various impact categories were weighted using Analytic Hierarchy Process (AHP), as a multi criteria decision making tool. The tobacco production system was the example of agricultural system. The data for LCA analysis were collected from 225 farms. The data for AHP analysis were gathered by surveying 12 LCA experts. The results indicated that on-farm emissions of CO2, CH4, N2O, NH3, NOx and SO2 were accounted for 30, 93, 93, 99, 38 and 5% of the total emissions (cradle to farm gate), respectively. The LCA+AHP showed that the weighted factors of global warming, terrestrial eutrophication, acidification, fossil resources depletion, phosphate resources depletion and potash resources depletion were 1, 0.79, 0.52, 0.68, 0.42, and 0.26, respectively. Many agricultural systems could benefit from using the developed weighting factors for LCA studies in the agricultural sector. Based on the new developed weighted factors, the weighted indices of aforementioned impact categories for tobacco production were 0.24, 0.41, 0.19, 1.04, 0.23, and 0.20, respectively. This implies that fossil resources depletion impact category was attributed the highest negative environmental impacts of tobacco production followed by terrestrial eutrophication.

Introduction

Combination of LCA with other models is now an emerging topic in environmental impacts evaluation of agricultural production. For example, combination of LCA and artificial neural networks was employed to better predict the environmental burden of lentil production (Elhami et al., 2017). In a similar study, a coupled application of LCA and adaptive neuro-fuzzy inference system was applied to evaluate the environmental consequences of canola production supply chain (Mousavi-Avval et al., 2017a

Summary

The energy balance and life cycle assessment (LCA) of ohmic heating and appertization systems for processing of chopped tomatoes with juice (CTwJ) were evaluated. The data included in the study, such as processing conditions, energy consumption, and water use, were experimentally collected. The functional unit was considered to be one kg of packaged CTwJ. Six LCA impact assessment methodologies were evaluated for uncertainty analysis of selection of the impact assessment methodology. The energy requirement evaluation showed the highest energy consumption for appertization (156 kWh/t of product). The energy saving of the ohmic heating line compared to the appertization line is 102 kWh/t of the product (or 65% energy saving). The energy efficiencies of the appertization and ohmic heating lines are 25% and 77%, respectively.

Regarding the environmental impact, CTwJ processing and packaging by appertization were higher than those of ohmic heating systems. In other words, CTwJ production by the ohmic heating system was more environmentally efficient. The tin production phase was the environmental hotspot in packaged CTwJ production by the appertization system; however, the agricultural phase of production was the hotspot in ohmic heating processing.

The uncertainty analysis results indicated that the global warming potential for appertization of 1 kg of packaged CTwJ ranges from 4.13 to 4.44 kg CO 2 eq. In addition, the global warming potential of the ohmic heating system ranges from 2.50 to 2.54 kg CO 2 eq. This study highlights that ohmic heating presents a great alternative to conventional sterilization methods due to its low environmental impact and high energy efficiency.

Introduction

Tomato (Lycopersicon esculentum) is the second most important vegetable crop after potato, and the total worldwide tomato production was 182 million tons in 2017 [START_REF] Anwar | Tomatoes: A Model Crop of Solanaceous Plants[END_REF]. There are different tomato-based products, such as purée, paste, juice, chopped tomatoes, and peeled tomatoes in tomato juice, resulting in various processing systems.

The energy required for final cooling prior to aseptic packaging was calculated using equation (5-5), where is the mass flowrate of cooling water, C pw is the specific heat of water, T iw is the inlet temperature water, and T ow is the outlet temperature of water in the heat exchanger:

(5-5)

Software

EDraw Max (ver. 9.1, 2018; Sheung Wan, Hong Kong) was used for the representation of the appertization and ohmic heating flowcharts. The LCA analysis was performed using the professional SimaPro software (ver. 8.1.0 Analyst) and adapted Ecoinvent 3.2 database.

LCA application

The objective of this study was to perform an attributional life cycle environmental assessment of CTwJ processing and packaging upon appertization and ohmic heating systems. The functional unit (FU) was considered as one kg of CTwJ, which is a single reference of the product, requiring 1.6 kg of fresh tomato. Mass-based FU is common in food processing LCA (see Table 5.1). Figures 5.1 and 5.2 illustrate the system boundaries in CTwJ processing and packaging for the appertization and ohmic heating systems.

The inputs and outputs of the investigated system were quantified in the second phase of the LCA [START_REF] Rafiee | Sustainability evaluation of pasteurized milk production with a life cycle assessment approach: An Iranian case study[END_REF]. The cradle-to-grave emissions were classified into background (off-site) and foreground (on-site) emissions (Paramesh et al., 2018). The background emissions include the emitted pollutants from the production of material inputs, for instance, the emissions released within the generation of electricity and natural gas. However, foreground emissions comprise the direct emissions from the consumption

Summary

Because it is important to develop new sustainable sources of edible protein, insects have been recommended as a new protein source. This study applied Life Cycle Assessment (LCA) to investigate the environmental impact of small-scale edible insect production unit in South Korea. IMPACT 2002+ was applied as the baseline impact assessment (IA) methodology. The CML-IA baseline, EDIP 2003, EDP 2013, ILCD 2011 Midpoint, and ReCiPe midpoint IA methodologies were also used for uncertainty analysis. The protein, fat contents, and fatty acid profile of the investigated insect (Protaetia brevitarsis seulensis larvae) were analyzed to determine its potential food application. The results revealed that the studied edible insect production system has beneficial environmental effects on various impact categories (ICs), i.e., land occupation, mineral extraction, aquatic and terrestrial ecotoxicity, due to utilization of bio-waste to feed insects. This food production system can mitigate the negative environmental effects of those ICs, but has negative environmental impact on some other ICs such as global warming potential. By managing the consumption of various inputs, edible insects can become an environmentally efficient food production system for human nutrition.

Introduction

Owing to the increasing demand for meat, there is a need for discovering alternative sources of protein [START_REF] Van Huis | Edible insects contributing to food security?[END_REF]. Moreover, many studies have shown that most common food production systems, such as beef (Biswas and Naude, 2016;Perez-Martinez et al., 2018), chicken (Kalhor et al., 2016;Skunca et al., 2018;López-Andrés et al., 2018), pork (Reckmann et al., 2013;Winkler et al., 2016), fish (Buchspies et al., 2011;Dekamin et al., 2015), and plant-based products (bean, corn, soybean, and wheat) (Ghasempour and Ahmadi, 2019;[START_REF] Heusala | Carbon footprint and land use of oat and faba bean protein concentrates using a life cycle assessment approach[END_REF] are not environmentally efficient, and that the existing production systems of protein sources have enormous environmental disadvantages.

Analysis and identification of fatty acids using GC/EI-MS

The analysis of FAME was done according to [START_REF] Ristivojević | Rapid analytical approach for bioprofiling compounds with radical scavenging and antimicrobial activities from seaweeds[END_REF]. In short, an Agilent 6890 gas chromatograph equipped with a DB-23 capillary column (30 m×0.25 mm id;film thickness of 0.25 μm) was used (Aglient Technologies Inc., Santa Clara, CA, USA).

The capillary column was directly joined to an Agilent 5973 mass spectrometer (Agilent Technologies Inc.). The sample (1 μL) was injected into the capillary column with a split ratio of 10:1. Helium (purity of 5.0) was applied as the carrier gas with a flow rate of 0.6 mL/min. The temperatures of the detector and the injector were set to 230°C and 250°C, respectively.

The FAME were determined through comparing their retention times with those of the FAME standards (Supelco-37 FAME mix) under the same conditions, and through comparing their mass spectra with those stored in the Mass Spectral Library of the National Institute of Standards and Technology (NIST).

Objective

The objective of this study was to conduct an attributional life cycle environmental impact analysis of the small scale edible insect production in South Korea and assess the nutritional value of the investigated insect. Different functional units (FUs) for insect production systems have been considered for LCA, which means that all inputs and impact categories (ICs) in the assessment are normalized. As presented in Table 6.1, the mass-based FU is commonly used in LCA studies on edible insects. Therefore, in this study, 1 kg of dried insects was selected as the FU. Additionally, 1 kg of protein in insects as well as 1 kg fat were considered as the secondary FUs for comparing the environmental consequences of protein production from insects with other conventional the global warming potential in the production of 1 kg of insects ranged from 8.05 kgCO2eq to 12.52 kgCO2eq based on the application of different IA methodologies.

Finally, the environmental efficiency of the insect production system can be increased by managing certain inputs, such as electricity.

Chapter 7

Modeling and optimization (AI-LCA)

Partially redrafted after:

Optimization of resource allocation in agri-food sector using coupled life cycle assessment, genetic algorithm and Delphi methodology. Journal of Cleaner Production. (ready to submit).

Summary

Optimization of resource allocation in agri-food sector, in order to find the most appropriate mix of inputs, could contribute to mitigation of environmental impacts and moving toward a more circular food supply chain LCA has been coupled with several artificial intelligent-based optimization techniques in order to optimize food systems in terms of environmental impacts. One important step in an optimization problem in food system is to determine the bounds for the optimization variable. This study developed an optimization bound determination scheme through application of Delphi methodology. A hybrid LCA + multilayer perception artificial neural network + Delphi methodology + genetic algorithm was applied to optimize a food production system.

Five functional units were considered for this study: one ton of produced pomegranate, one hectare, daily energy intake, vitamin C, and dietary fibre for 1 person. The results indicated that a remarkable amount of environmental impacts could be mitigated using this approach in a case study of pomegranate production.

Introduction

Optimization of resource allocation in agri-food sector, in order to find the most appropriate mix of inputs, could contribute to mitigation of environmental impacts and moving toward a more circular food supply chain. The LCA methodology has been coupled with some optimization techniques in order to optimize food systems in terms of environmental impacts. More specifically, it has been coupled with data envelopment analysis, particle swarm optimization, imperialist competitive algorithm, and genetic algorithms. Some studies indicated that artificial intelligent based approaches are more powerful than mathematical ones for environmental optimization of food systems (Khoshnevisan et al., 2015;[START_REF] Elhami | Optimization of energy consumption and environmental impacts of chickpea production using data envelopment analysis (DEA) and multi objective genetic algorithm (MOGA) approaches[END_REF]Nabavi-Pelesaraei et al., 2017). Table 7.1 shows recently published papers on the use of LCA based artificial intelligent-based optimization in FSC.

Materials and Methods

Life cycle assessment

The aim of the study needs to be specified in the first step of an LCA analysis. The aim of this LCA analysis was to assess the environmental impacts of fresh pomegranate production system. Five functional units (FUs) were chosen for the studied system, including one kg of produced pomegranate, and one hectare of pomegranate orchard.

The above-mentioned FUs were selected to provide comparable results with other published LCA works on pomegranate and horticultural products [START_REF] Canals | Evaluation of the environmental impacts of apple production using Life Cycle Assessment (LCA): case study in New Zealand[END_REF][START_REF] Vinyes | Life Cycle Assessment of apple and peach production, distribution and consumption in Mediterranean fruit sector[END_REF]Alishah et al., 2019) which employed the same FUs. Moreover, three

Modeling

The relationship between the dependent variable (pomegranate yield) and independent variables was modeled using a multilayer perception (MLP) artificial neural network with back propagation algorithm. An MLP artificial neural network consist of at least three layers, including input, hidden, and output. The first layer is the input layer which has the same size as the independent variables. More specifically, in this study the variables for the input layer were diesel fuel, nitrogen, phosphorus, potassium, manure , agricultural machinery, human labor, herbicides, insecticides, and fungicides. In this study, the effect of the selection of different numbers of neurons in the hidden layer on the performance of MLP model was also investigated. Moreover, Logarithmic sigmoid and Tangent sigmoid were used as transfer functions for the hidden layer. In this study the output layer had one neuron (pomegranate yield). Two different training algorithm (Trainlm and Trainbr) were applied in this study.

K-Fold cross validation

The performance indices of ANN models can differ based on the data set chosen in in the training step [START_REF] Bowden | Optimal division of data for neural network models in water resources applications[END_REF][START_REF] Kalantar | Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN)[END_REF]. In other words, the random selection of training data set may affect the ANN results [START_REF] Mukti | Implementation of artificial neural networks for determining power transfomer condition[END_REF]. To overcome this issue, this study applied k-fold cross-validation method in order to a better estimation of ANN generalizability, consistently and robustness. This technique divides the original sample randomly into K subsamples [START_REF] Baykan | Mineral identification using color spaces and artificial neural networks[END_REF]. After that, a single sub sample is considered as the validation data for testing phase, and the remaining K-1 subsamples as training data [START_REF] Anand | K-fold cross validation and classification accuracy of pima Indian diabetes data set using higher order neural network and PCA[END_REF]. These processes are repeated K times and each of the K sub-samples in this technique used exactly one time as the validation data

Genetic Algorithm

Genetic algorithm was applied to optimize the pomegranate production system. More specifically, it is used to find the best mixture of the inputs to achieve a greater pomegranate yield. For this purpose, a binary GA procedure was applied (Figure 7.2).

The best developed model was employed as the fitness functions for GA optimization.

The promenade yield was the fitness, and the inputs were the optimization variables. The next step is the determining of optimization variables' bounds. This study developed an optimization bound determination scheme through application of Delphi methodology.

The Delphi technique was used to develop the optimization variables' bounds. Experts, questionnaire, iteration, controlled feedback, anonymity of technical panel, analysis of the results, consensus, time, and facilitating team are the main pieces of a Delphi-based study [START_REF] Samizadeh | The driving and inhibiting factors of mechanized tobacco production in Iran using the Delphi technique[END_REF]. Delphi is done based on the sequence of questionnaires distributed to a panel of experts. The coordinator of the experiment delivers an anonymous feedback to the panel after the first round of questions. After that, the participants may reconsider their opinions based on the provided feedback [START_REF] Hirschhorn | Reflections on the application of the Delphi method: lessons from a case in public transport research[END_REF]. This process continues until a coherent result is obtained. The appropriate selection of panellists is a key stage in the application of Delphi [START_REF] Belton | Improving the practical application of the Delphi method in group-based judgment: a six-step prescription for a well-founded and defensible process[END_REF]. In this study, the data for Delphi analysis were gathered by surveying 13 experts.

The population size of initial people was assumed as 100 samples. Chromosome refers to each solution in the population. The fitness of each chromosome was computed using the developed model. The chromosomes were arranged in an ascending order in terms of their fitness and 50% of their initial chromosomes were selected to produce the next generation. The parents from the remaining population were selected by tournament Chapter 8 Conclusions

LCA as a powerful tool for emerging and novel food systems

Various new technologies have been proposed and developed in the agri-food sector to manage cost, energy and environmental impacts of the production systems. It seems that some of these new developed technologies are not as green as we think. In this regard, in sustainability assessment of agri-food systems, various methods and models may be implemented. Sustainable development of new alternatives technologies in agri-food systems requires a thorough consideration of resource management in the production systems and a greater understanding of multiple environmental impacts. On the other hand, owing to the increasing demand for meat, there is a need for discovering alternative sources of protein. Moreover, many studies have shown that most common protein production systems are not environmentally efficient, and that the existing production systems of protein sources have enormous environmental disadvantages. Hence, the development of new alternative sustainable food sources is very important. In recent years, insects have been recommended as a novel food source for human consumption and the amount of insect production units has been increasing worldwide. Insect production can be considered as a solution for two problems, namely, the increasing demand for food by the production of edible insects, and waste management by composting food waste.

In this regard, in this PhD dissertation, an emerging food processing (ohmic heating), and a future food system (edible insect) were investigated using LCA. More specifically, the energy balance and LCA of ohmic heating (as an emerging food technology) for processing of chopped tomatoes with juice (CTwJ) were evaluated and the LCA results compared with the appertization system. Adverse environmental impact of CTwJ the core methodology to measure circularity of food systems toward moving to a circular and sustainable food system. Appendix C2.

Conferences with oral presentation

Nikkhah, A, Cirkovic Velickovic, T., Van Haute, S. 2021. An AI life cycle assessment toward a sustainable food supply chain. Waste Management and Valorisation for a Sustainable Future. Korea University, Seoul, South Korea Nikkhah, A., Esmaeilpour Troujeni, M., Kosari-Moghaddam, A., Bacenetti, J., and Van Haute, S. 2020