Valentin Reungoat

Morad Chadni
email: morad.chadni@agroparistech.fr

Louis M M Mouterde

Fanny Brunissen

Florent Allais

Hélène Ducatel

Irina Ioannou

Liquid-liquid extraction of sinapic acid from a mustard seed by-product using a hollow fiber membrane contactor

Keywords: Sinapic acid, hollow fiber membrane contactor, solvent extraction, mustard seed byproduct, liquid-liquid extraction

This study focuses on the recovery of sinapic acid using liquid-liquid extraction (LLE) assisted by a hollow fiber membrane contactor (HFMC) from an aqueous feed obtained through the hydrolysis of mustard bran. The hydrolyste contains 230 ± 36 mg/L of sinapic acid, with a natural pH of 4. A screening was performed with solvents of different chemical nature (alcohol, ester, ketone, ether, cyclic ether).

Data showed that all the solvents tested gave an extraction efficiency of more than 80% for pH < 5, whereas the initial concentration of sinapic acid in the aqueous feed has little impact on the extraction efficiency. Four of the solvents tested were selected for use in the HFMC: two volatile (CPME, MIBK) and two non-volatile (octanol, octyl acetate). The solubility of the volatile solvents in the feed phase was found to be an important factor to consider in evaluating the HFMC (2.6 ± 0.5 % and 1.07 ± 0.05 % v/v with MIBK and CPME, respectively). Mass transfer coefficients with volatile solvents (25 ± 1 x10 -6 and 15.3 ± 0.5 x10 -6 m/s for MIBK and CPME, respectively) exceeded those of non-volatile solvents (4.1 ± 0.2 x10 -6 and 4.5 ± 0.5 x10 -6 for octanol and octyl acetate, respectively) by 4-to 6-fold.

Extraction was intensified by increasing the initial concentration of sinapic acid in the feed phase and by increasing the feed-to-solvent ratio. CPME demonstrated optimal recovery efficiency at a phase ratio of 8:1 (v/v), yielding 0.9 g of sinapic acid per liter of CPME used.

Introduction

Researchers are increasingly interested in the potential uses of sinapic acid (a member of the phydroxycinnamic acids family) in various sectors, including health and cosmetics due to its antioxidant [START_REF] Horbury | Towards symmetry driven and nature inspired UV filter design[END_REF]. In addition, it holds promise in areas such as materials, serving as a fundamental component for monomers materials [START_REF] Diot-Néant | Biocatalytic Synthesis and Polymerization via ROMP of New Biobased Phenolic Monomers: A Greener Process toward Sustainable Antioxidant Polymers[END_REF], and in pharmaceuticals and food products as a preservative [START_REF] Nićiforović | Sinapic Acid and Its Derivatives: Natural Sources and Bioactivity[END_REF]. Sinapic acid can be readily synthesized through green synthetic routes [START_REF] Mouterde | Microwave-Assisted Knoevenagel-Doebner Reaction: An Efficient Method for Naturally Occurring Phenolic Acids Synthesis[END_REF][START_REF] Peyrot | Proline-Mediated Knoevenagel-Doebner Condensation in Ethanol: A Sustainable Access to p-Hydroxycinnamic Acids[END_REF][START_REF] Van Schijndel | Conversion of Syringaldehyde to Sinapinic Acid through Knoevenagel-Doebner Condensation[END_REF], using lignin-derived syringaldehyde.

Alternatively, a more environmentally-friendly production approach could involve extracting and hydrolyzing sinapic acid derivatives present in Brassicaceae seeds, such as rapeseed and mustard seeds, along with their processing by-products [START_REF] Sinichi | Recovery of phenolic compounds from the by-products of yellow mustard protein isolation[END_REF][START_REF] Odinot | A Two-Step Bioconversion Process for Canolol Production from Rapeseed Meal Combining an Aspergillus niger Feruloyl Esterase and the Fungus Neolentinus lepideus[END_REF][START_REF] Chadni | Improvement of Sinapine Extraction from Mustard Seed Meal by Application of Emerging Technologies[END_REF][START_REF] Chadni | Selective Extraction of Sinapic Acid Derivatives from Mustard Seed Meal by Acting on pH: Toward a High Antioxidant Activity Rich Extract[END_REF]. However, this approach yields an extract containing various metabolites (e.g., proteins, sugars, lipids) that must be separated from sinapic acid.

Consequently, the availability of affordable, sustainable, and highly pure sinapic acid is currently limited as the relatively low purity of sinapic acid obtained from the extraction/hydrolysis process highlights the necessity for the implementation of purification procedures (Figure 1).

Figure 1: Recovery flow chart of concentrated sinapic acid [11]

Currently, only a limited number of studies are dedicated to the purification of sinapic acid extracted from rapeseed and mustard seeds [START_REF] Reungoat | Extraction and Purification Processes of Sinapic Acid Derivatives from Rapeseed and Mustard Seed By-Products[END_REF]. Conventional methods employ purification technologies such as classical liquid-liquid extraction (LLE) [START_REF] Prapakornwiriya | Recovery of sinapic acid from a waste stream in the processing of yellow mustard protein isolate[END_REF], membrane filtration [START_REF] Sinichi | Recovery of phenolic compounds from the by-products of yellow mustard protein isolation[END_REF], or adsorption [START_REF] Odinot | A Two-Step Bioconversion Process for Canolol Production from Rapeseed Meal Combining an Aspergillus niger Feruloyl Esterase and the Fungus Neolentinus lepideus[END_REF][START_REF] Thiel | A novel integrated downstream processing approach to recover sinapic acid, phytic acid and proteins from rapeseed meal: A novel downstream processing approach for rapeseed meal[END_REF]. However, most of these investigations lack comprehensive sinapic acid purity data, and the scaling-up of these processes remains uncommon. Among the three technologies mentioned, LLE stands out as a promising avenue for recovering p-hydroxycinnamic acids using diverse bio-based solvents [START_REF] Cañadas | Evaluation of bio-based solvents for phenolic acids extraction from aqueous matrices[END_REF][START_REF] Combes | Solvent selection strategy for an ISPR (In Situ/In stream product recovery) process: The case of microbial production of p-coumaric acid coupled with a liquid-liquid extraction[END_REF]. Nonetheless, studies involving LLE have largely been conducted at the laboratory scale, using ethyl acetate/diethyl ether (1:1, v/v) as a typical solvent [START_REF] Prapakornwiriya | Recovery of sinapic acid from a waste stream in the processing of yellow mustard protein isolate[END_REF][START_REF] Morley | Antioxidant canolol production from a renewable feedstock via an engineered decarboxylase[END_REF]. The advancement of this purification process has encountered challenges in equipment design due to the presence of emulsions and phase dispersion [START_REF] Mohanty | Modeling of liquid-liquid extraction column: a review[END_REF].

In recent years, the development of membrane contactors has emerged as a potential solution to overcome these issues [START_REF] Chadni | Membrane contactors-assisted liquidliquid extraction of biomolecules from biorefinery liquid streams: A case study on organic acids[END_REF].

In membrane contactors, the membrane acts as a barrier between two phases that prevents mixing but not controling the rate of transport of different components between phases. It's typically composed of hydrophobic hollow fibers housed within a shell. In the context of LLE, this device facilitates the transfer of a solute between two immiscible liquids [START_REF] Chadni | Membrane contactors-assisted liquidliquid extraction of biomolecules from biorefinery liquid streams: A case study on organic acids[END_REF]. This transfer occurs through the porous membrane without causing the liquids to mix. Specifically, one of the liquids acts as an organic solvent, filling the pores of the fibers, while the back pressure from an aqueous phase in the shell side stabilizes the interface located at these pores [START_REF] Gabelman | Hollow fiber membrane contactors[END_REF]. This limits the dispersion of the solvent in the aqueous phase and facilitates the separation of the two fluids, enabling the implementation of a continuous process. LLE assisted by membrane contactors have been little studied for the recovery of phenolic acids, with the few existing studies using only model solutions [START_REF] Agrahari | Removal of Benzoic Acid from Water by Reactive Extraction Using Hollow Fiber Membrane Contactor: Experiment and Modeling[END_REF][START_REF] Reis | Removal of phenols from aqueous solutions by emulsion liquid membranes[END_REF][START_REF] Sciubba | Membrane-based solvent extraction of vanillin in hollow fiber contactors[END_REF]. To the best of our knowledge, this is the first application of a membrane contactor for the purification of a p-hydroxycinnamic acid from plant material.

The objective of this study is to investigate the utilization of a hollow fiber membrane contactor (HFMC) in LLE for the purification of sinapic acid from a hydrolysate derived from mustard bran, which is a byproduct of mustard seed processing. To achieve this, an initial solvent screening was conducted to identify the most effective extraction solvents. Subsequently, the chosen solvents were employed in a pilot-scale (HFMC) to assess the feasibility of sinapic acid extraction from the hydrolysate of mustard bran. The operational parameters were then optimized to determine the maximum quantity of sinapic acid extractable per unit volume of solvent.

Materials and methods

Chemicals

Sinapic acid (purity > 98%) was produced in-house following the procedure of [START_REF] Janvier | Syringaresinol: A Renewable and Safer Alternative to Bisphenol A for Epoxy-Amine Resins[END_REF]. HCl (~ 37%), NaOH (purity ≥ 97%), acetic acid (purity of 99.7%) and sodium acetate (purity ≥ 99%) were from Fisher (UK). Acetonitrile (purity ≥ 99.9%) was from VWR. The selection of solvents was based on various criteria, encompassing their bio-based production, safety and sustainability according to the GSK's solvent sustainability guide updated in 2016 [START_REF] Alder | Updating and further expanding GSK's solvent sustainability guide[END_REF], as well as their potential affinity for hydroxycinnamic acids [START_REF] Combes | Solvent selection strategy for an ISPR (In Situ/In stream product recovery) process: The case of microbial production of p-coumaric acid coupled with a liquid-liquid extraction[END_REF]. These solvents, characterized by differing chemical properties, were categorized based on their boiling points, with those boiling below 150 °C considered volatile and those boiling above 150 °C classified as non-volatile. A detailed description of these solvents is provided in Table 1.

Solvent screening on a model solution

Prior the use of a HFMC, a screening of solvents was first performed on a simple model solution using an adapted shake-flask method [START_REF] Berthod | Determination of liquid-liquid partition coefficients by separation methods[END_REF]. A model solution of sinapic acid (0.1 g/L) was prepared by dissolving sinapic acid in milli-Q water and adjusting the pH (Inlab Science Pro-ISM electrode, Mettler Toledo, Switzerland) from 3 to 7 with 0.1 M HCl or 0.1 M NaOH solutions. The shake flask method is described as follow: 5 mL of the model solution of sinapic acid was added in a 15 mL centrifuge tube.

An equal volume of solvent was added (aqueous phase to solvent ratio of 1 v/v). The system was manually shaken for 3 x 1 min with equilibration times of 4 min and a final quiescent time of 15 min.

The system was centrifuged (4000 g, 10 min) only when a persistent emulsion was observed. Finally, the aqueous feed was selectively taken and analyzed using HPLC. The HPLC method used for the quantification of sinapic acid was previously detailed [START_REF] Reungoat | Optimization of an ethanol/water-based sinapine extraction from mustard bran using Response Surface Methodology[END_REF].

The extraction efficiency of the solvents shown in Table 1 was first investigated by varying the pH (unbuffered) over a range of 3 to 7, at a temperature of 25 °C, while maintaining an initial sinapic acid concentration of 0.1 g/L in the feed phase. The extraction efficiency 𝐸 (%) represents the disappearance of sinapic acid from the feed phase. It was calculated using Eq. 1.

𝐸 (%) = 𝑚 0 (𝑎𝑞) -𝑚 𝑒𝑞 (𝑎𝑞) 𝑚 0 (𝑎𝑞) × 100 Eq. 1

Where 𝑚 0 (𝑎𝑞) and 𝑚 𝑒𝑞 (𝑎𝑞) are the mass of sinapic acid (mg) in the aqueous phase at initial and equilibrium times respectively.

Effect of the initial concentration of sinapic acid in the aqueous phase

In order to assess the effect of the initial concentration of sinapic acid in the aqueous phase on the extraction efficiency, different concentrations of sinapic acid in the model solution (0.01, 0.05 and 0.1 g/L) were prepared at pH 4 and at a temperature of 25 °C. The efficiency of sinapic acid extraction from a model solution was compared to the one from a mustard bran hydrolysate using the shake-flask method.

The hydrolysate was obtained as follows: first, mustard bran was subjected to aqueous extraction (at 100 °C and pH 4, using 100 mL of solvent per gram of dry mustard bran). Subsequently, enzymatic hydrolysis was carried out using a cocktail of enzymes (Deltazym VR AC-100). The resulting hydrolysate underwent centrifugation (at 4000 g for 20 minutes) followed by filtration through a 2 µm cellulose filter. In this phase of the study, the sinapic acid concentration in the hydrolysate was measured to be 55 ± 4 mg/L a pH of 4.3 and a temperature of 25 °C. The extractions were operated in crossflow countercurrent mode. Aqueous feed (0.5 L) was pumped through the fiber lumen side, while organic phase (0.5 L) was pumped through the module shell side at a flow rate of 50 ± 2 L/h for both phases. The aqueous phase pressure (Plumen) was maintained higher than the organic phase pressure (Pshell) to prevent the organic phase (the wetting phase) from permeating [START_REF] Dupuy | Stabilisation de l'interface liquide-liquide dans un contacteur membranaire: Application à l'extraction sélective de terpènes oxygénés d'huile essentielle d'agrumes[END_REF]. A constant pressure difference (ΔP) between the two phases was kept at 0.5 bar.

Kinetic study of sinapic acid extraction with solvents in HFMC

The sinapic acid is initially contained in the feed phase (42 ± 4 mg/L) consisting of the mustard bran hydrolysate produced as described in part 2.3. The corresponding dry matter content is 0.5% (w/w) with a sinapic acid purity of 0.4 ± 0.1 %DM. The purity of a compound on a dry matter (%DM) of a hydrolysate or a solvent phase was calculated using Eq. 2.

𝑃 (% 𝐷𝑀 ) = 𝑚 𝑐𝑜𝑚𝑝𝑜𝑢𝑛𝑑 𝑚 𝐷𝑀 × 100 Eq. 2

Where 𝑚 𝑐𝑜𝑚𝑝𝑜𝑢𝑛𝑑 and 𝑚 𝐷𝑀 are the mass of a compound and the mass of the dry matter in a hydrolysate or a solvent phase respectively.

Based on the screening step, four solvents were subjected to testing: two volatile solvents (MIBK and CPME) and two non-volatile solvents (octanol and octyl acetate). The extraction kinetics of sinapic acid were monitored by periodically collecting samples from the feed phase and subsequently analyzed using HPLC.

The extraction kinetics analysis enabled the determination of an apparent mass transfer coefficient, denoted as 𝐾 𝑎𝑝𝑝 (m/s). This coefficient quantifies the speed at which sinapic acid is extracted from the aqueous phase within the HFMC. To calculate 𝐾 𝑎𝑝𝑝 , an adapted equation from the work of Dahuron and Cussler in 1988 [START_REF] Dahuron | Protein extractions with hollow fibers[END_REF] was employed. It's important to note that while Dahuron's relationship was initially established for a parallel f-current flow, the HFMC configuration used in our case involves a cross-current flow, thereby rendering 𝐾 𝑎𝑝𝑝 an apparent transfer coefficient. The detailed procedure for calculating 𝐾 𝑎𝑝𝑝 is presented in the supplementary material file.

Determination of mutual phase solubility

Certain solvents exhibit partial solubility in water, and conversely, water can also partially dissolve in some solvents. This occurrence becomes particularly notable when dealing with solvents characterized by shorter carbon chain lengths. Information about mutual phase solubilities proves valuable in understanding phase diffusion-induced losses, preventing phase emulsions through interfacial tension reduction, and facilitating the design of liquid-liquid extractors [START_REF] Stephenson | Mutual solubility of water and aliphatic alcohols[END_REF].

Consequently, after extraction within the HFMC, the mutual phase solubility was determined using diverse methodologies. To ascertain the solubility of water in the organic solvent, the Karl-Fischer method was employed with the 899 KF Titrino Plus and 899 coulometer instruments (Metrohm, Switzerland). The titrating reagents employed were HYDRANAL™ -Composite 5 (Honeywell, USA) and Aqualine complete 5K (Fisher Scientific, USA).

On the other hand, the solubility of solvents in the aqueous phase required distinct approaches.

Specifically, the solubility of MIBK in the aqueous phase was quantified using HPLC-UV at 210 nm.

Meanwhile, the solubility of CPME and octanol was assessed through Gas Chromatography-Mass Spectrometry (GCMS) coupled with Solid Phase Microextraction (SPME). In this procedure, a SPME fiber (Carboxen/PDMS) was immersed in 10 mL of water containing up to 10 -3 % (v/v) of solvents for 60 seconds at 50 °C. Subsequently, solvent desorption occurred in a Gas Chromatograph (7890A, Agilent) at 300 °C for 600 seconds under a hydrogen flow rate of 2 mL/min. Separation took place in a ZB-5MSplus column (Phenomenex) (30 m x 250 µm x 0.25 µm) using a temperature gradient of 10 °C/min from 35 °C to 200 °C, followed by 20 °C/min to 300 °C for 5 minutes. Detection was performed by a mass spectrometer MS (5975C, Agilent). Calibration curves were established based on reference solvent standards diluted in osmosis water.

The final volume of each phase having been in contact depends on their mutual solubility. It was calculated using Eq. 3 for the aqueous phase and Eq. 4 for the solvent phase.

𝑉 𝑒𝑞 (𝑎𝑞) = 𝑉 0 (𝑎𝑞) -𝛾𝑉 0 (𝑜𝑟𝑔𝑎) + 𝛿𝑉 0 (𝑎𝑞) Eq. 3

𝑉 𝑒𝑞 (𝑜𝑟𝑔𝑎) = 𝑉 0 (𝑜𝑟𝑔𝑎) + 𝛾𝑉 0 (𝑜𝑟𝑔𝑎) -𝛿𝑉 0 (𝑎𝑞) Eq. 4

Where 0 and 𝑒𝑞 represent the initial and the equilibrium states, 𝑎𝑞 and 𝑜𝑟𝑔𝑎 represent the aqueous and solvent phases, 𝑉 is the volume of a phase, 𝛾 is the solubility of water in solvent (% v/v), and 𝛿 is the solubility of the solvent in water (% v/v). According to the final volumes determined, the volume loss can then be calculated using Eq. 5.

∆𝑉 = 𝑉 0 -𝑉 𝑒𝑞 Eq. 5

2.4.4. Study of the effect of the initial concentration of sinapic acid in the feed phase

In order to intensify the extraction in the HFMC, the effect of the initial concentration of sinapic acid in the hydrolysate was studied. Higher dry matter content in the hydrolysate could have an impact both on the extraction efficiency and the mass transfer coefficient due to its possible accumulation in the aqueous boundary layer of the membrane of the fibers.

Therefore, the feed phase studied consists of different batches of hydrolysate having a sinapic acid concentrations of 43 ± 2, 87 ± 1 and 274 ± 22 mg/L, with a dry matter content of 0.5%, 1% and 5% (w/w), respectively. The pH was around 4.2 for all batches. The sinapic acid purity in the hydrolysate remains constant at 0.4 ± 0.1 %DM.

For this study, MIBK was selected as the solvent due to its notable extraction efficiency and high mass transfer coefficient. The extraction kinetics of sinapic acid were monitored through periodic sampling of the hydrolysate from the feed phase and subsequently quantified using HPLC.

Study of the effect of the feed-to-solvent ratio

To enhance the extraction process by achieving higher sinapic acid recovery with a reduced solvent quantity, the impact of the feed-to-solvent ratio was investigated using LLE assisted by HFMC. Various feed-to-solvent ratios were examined: 1:1, 2:1, 4:1, and 10:1 (v/v). While maintaining a constant initial volume of the solvent phase at 0.5 L, the volumes of the feed phase were set at 0.5, 1, 2, and 5 L, respectively.

The aqueous feed phase encompassed either the hydrolysate (characterized by a sinapic acid concentration of 230 ± 36 mg/L, purity of 0.4 ± 0.1 %DM, and a dry matter content of 5% w/w) or a buffered model solution of sinapic acid (with a concentration of 250 mg/L, pH 4, and acetate buffer at 30 mM) for comparison. The extraction kinetics of sinapic acid was monitored through periodic sampling of the feed phase, and the measurements were performed using HPLC.

To assess the purity of the sinapic acid extracted within the solvent, our focus was solely on examining volatile solvents, specifically MIBK and CPME. The determination of purity requires the evaporation of the solvent, a process that was achieved utilizing a Rotavap (Büchi, France) with an overnight vacuum ramp.

The material balance was established through the procedure of obtaining a solvent phase sample upon completion of the extraction. This sample was diluted in acetonitrile at a tenfold ratio and subsequently subjected to HPLC analysis. This analytical approach enabled the computation of the recovery yield of sinapic acid present in the solvent, expressed as a percentage. This computation was described by Eq.

6, wherein the mass of sinapic acid obtained within the organic phase was compared to the initial mass present in the aqueous feed phase.

The material balance was calculated by taking a sample of the solvent phase at the end of the extraction, diluting it in acetonitrile to the tenth, and analyzing it by HPLC. This allowed the calculation of a recovery yield of sinapic acid in the solvent (%), described by Eq. 𝑅𝑒𝑐𝑜𝑣𝑒𝑟𝑦 𝑦𝑖𝑒𝑙𝑑 (%) = 𝑚 𝑒𝑞(𝑜𝑟𝑔𝑎) 𝑚 0 (𝑎𝑞) × 100 Eq. 6

Where 𝑚 𝑒𝑞(𝑜𝑟𝑔𝑎) is the mass of sinapic acid in the organic phase at equilibrium, 𝑚 0 (𝑎𝑞) is the initial mass of sinapic acid in the aqueous phase. The recovery yield in function of the feed-to-solvent ratio (F/S) can be predicted by a linear regression detailed Eq. 7.

𝑅𝑒𝑐𝑜𝑣𝑒𝑟𝑦 𝑦𝑖𝑒𝑙𝑑 * (%) = 𝑎 × 𝐹/𝑆 + 𝑏 Eq. 7

Where 𝑅𝑒𝑐𝑜𝑣𝑒𝑟𝑦 𝑦𝑖𝑒𝑙𝑑 * is the predicted recovery yield in function of the feed-to-solvent ratio (F/S), 𝑎 and 𝑏 are the slope and the intercept respectively. From the predicted recovery yield, a recovery capacity (g/L) can be predicted by Eq. 8. It represents the mass of sinapic acid that can be obtained in the solvent for a same initial volume of solvent used during the LLE.

𝑅𝑒𝑐𝑜𝑣𝑒𝑟𝑦 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦 * (𝑔/𝐿) = 𝑚 0 (𝑎𝑞) × (𝑎 × 𝐹/𝑆 + 𝑏) 𝑉 0 (𝑜𝑟𝑔𝑎) Eq. 8

Where 𝑅𝑒𝑐𝑜𝑣𝑒𝑟𝑦 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦 * (𝑔/𝐿) is the predicted recovery capacity in function of the feed-to-solvent ratio and depending on the parameters of the linear regression of the recovery yield, on 𝑚 0 (𝑎𝑞) the initial mass of sinapic acid in the feed phase, and on 𝑉 0 (𝑜𝑟𝑔𝑎) the initial volume of solvent. In this study, this prediction is obtained for an initial concentration of sinapic acid in the feed phase set at 230 mg/L, using a closed-loop extraction and one extraction cycle.

Statistical analysis

The data were analyzed statistically by one-way ANOVA. Differences between data were considered significant when p < 0.05. Error bars on the figures represent the standard deviation. Experiments were conducted at least in duplicate. The number of replicates performed is indicated by n.

Results

A screening approach for the selection of effective solvents and pH values for the extraction of sinapic acid by simple LLE is first described. Next, extraction kinetics using HFMC are presented with four selected solvents. Finally, the improvement of sinapic acid mass recovery by two volatile solvents by acting on the initial concentration in the feed phase and on the feed-solvent ratio is presented.

3.1. Solvent screening using simple LLE

Effect of solvent and pH on extraction efficiency

In this first part, the objective was to select the most efficient organic solvents to extract sinapic acid from a model solution (100 mg/L) at different pH. The extraction efficiencies are shown in Table 3. Table 3 shows that high extraction efficiencies (> 80%) are obtained for pH 3 and 4. Thus, solvents from the class of alcohols, esters, ether, cyclic ether and ketone can be considered efficient for the extraction of sinapic acid under acidic conditions. These results are in accordance with those obtained by [START_REF] Cañadas | Evaluation of bio-based solvents for phenolic acids extraction from aqueous matrices[END_REF][START_REF] Combes | Solvent selection strategy for an ISPR (In Situ/In stream product recovery) process: The case of microbial production of p-coumaric acid coupled with a liquid-liquid extraction[END_REF] in their studies on similar p-hydroxycinnamic acids. Additionally, the solvents used in this study exhibit an intermediate level of polarity, which makes them well-suited for dissolving sinapic acid. This aligns with the predictive findings of Galanakis et al [START_REF] Galanakis | A Knowledge Base for The Recovery of Natural Phenols with Different Solvents[END_REF], which suggested that solvents with intermediate polarity, like alcohols, esters, and ketones, are particularly effective for dissolving hydroxycinnamic acids. Hydroxycinnamic acids have a low activity coefficient, and data predicted by the UNIFAC model indicate that natural phenols including sinapic acid tend to favor solvents with intermediate polarity, such as alcohols and acetone, over more polar options like water, or less polar solvents like dichloromethane and diethyl ether. The current study supports this prediction, suggesting that the solvents used here should also be effective for dissolving other phenolic acids.

Increasing the pH results in a decrease in the extraction efficiency of sinapic acid, which is related to a lower proportion of its protonated (undissociated) form (Figure 3). Indeed, the proportion of the protonated form of sinapic acid is less than 1% (m/m) at two points above its first pKa (4.5) [START_REF] Smyk | Spectroscopic investigation of the equilibria of the ionic forms of sinapic acid[END_REF]. As a result, a notable decline in extraction efficiency becomes obvious at pH levels equal to or greater than Their effects on the extraction efficiency of sinapic acid were studied at pH 4 with different solvents (Table 4). Table 4 shows that the increase of the initial concentration of sinapic acid tends to lower the extraction efficiency. Indeed, the extraction efficiencies with CPME are 93.9 ± 1.9 %, 90.1 ± 0.4 % and 87.1 ± 1.9 % with concentration of sinapic acid of 10, 50 and 100 mg/L in the model solution, respectively. However, despite the slight loss of extraction efficiency when increasing the concentration of sinapic acid 10-fold, it remains above 80%. Therefore, it is considered in this study that the initial concentration of sinapic acid in a simple model solution has a low impact on the extraction efficiency in the range of concentration of sinapic acid from 10 to 100 mg/L. This result is coherent with the results of [START_REF] Cañadas | Sustainable Recovery of High Added-Value Vanilla Compounds from Wastewater Using Green Solvents[END_REF] on different p-hydroxycinnamic acids using CPME and 2-MeTHF as solvents.

According to As a result of these considerations, a selection of only four solvents was made for the extraction of sinapic acid from mustard bran hydrolysate utilizing HFMC. These solvents encompass two volatile options (MIBK, CPME) and two non-volatile choices (octyl acetate and octanol).

Study of sinapic acid extraction in HFMC

In order to understand the LLE of sinapic acid assisted by HFMC, the kinetics of extraction were established for the four solvents selected, and the extraction efficiency obtained were compared to those of the simple LLE. Despite the non-dispersion of the phases into each other with the membrane contactor, some diffusion may occur due to their mutual solubility. Therefore, the solubility of the mutual phases was also measured.

Extraction kinetics

The four solvents selected are MIBK, CPME, octanol and octyl acetate. The extraction kinetics of sinapic acid using the HFMC from a feed phase consisting of the hydrolysate are presented in Figure 44. whereas it takes 30 min with octanol. However, in the case of octyl acetate, the kinetic was followed up to 160 min, although equilibrium was challenging to achieve (Figure S2 in supplementary material).

This means that the extraction is carried out at a low speed. This phenomenon could be attributed to the higher viscosity of octyl acetate (11 mPa•s at 25 °C) compared to other solvents, such as octanol (7.3 mPa•s at 25 °C). Therefore, octyl acetate appears to be a solvent of lower interest as regards its reliability for comparing extraction yields.

Comparison of the extraction efficiency of the solvents between HFMC and simple LLE

After obtaining the extraction kinetics of sinapic acid by the four solvents selected, the extraction efficiencies obtained with the implementation in the HFMC is compared to the one obtained with a simple liquid-liquid extraction. Extraction kinetics can also be used to determine the mass transfer coefficients, which indicate the speed of extraction in the membrane contactor. These results are presented Table 5.

Different superscript letters indicate a significant difference between groups (p < 0.05) (lowercase letter in column and capital letter in line).

Table 5 shows that there are no significant differences between the simple LLE and the LLE assisted by HFMC with regards to the extraction efficiency, except for MIBK. However, the difference being low for an extraction efficiency, it will be neglected. Thus, the use of membrane contactor has no effect on the extraction efficiency of sinapic acid compared to a simple LLE. However, the use of a HFMC avoids mixing of the phases.

According to Table 5, the highest mass transfer coefficients were obtained with the volatile solvents, MIBK and CPME (25 ± 1 and 15,3 ± 0,5 10 -6 m/s, respectively), whereas the lowest were given with the non-volatile solvents, octanol and octyl acetate (4.1 ± 0,2 x 10 -6 and 4.5 ± 0,5 x 10 -6 m/s, respectively).

Therefore, the extraction with volatile solvents is 4 to 6 times faster compared to non-volatile ones. It seems that the diffusion of sinapic acid in the stationary phase of the solvent located in the pore of the membrane appears more limited with non-volatile solvents. The lower diffusion could be due to steric hindrance linked to the long chain length of the non-volatile solvents. Concerning the mass transfer coefficients obtained with MIBK and CPME, they are consistent with the same orders of magnitude as those found by Sciubba et al. in 2009 [25] in a HFMC, dealing with the extraction of vanillin by butyl acetate.

Besides, the extraction efficiency with octanol (93.1 ± 0.7 %) is higher than with CPME (87.0 ± 2.0 %), whereas its mass transfer coefficient is lower. Therefore, the speed of extraction does not seem to be related to the extraction efficiency. It means than an increase of the feed flow rate to reduce the extraction time [START_REF] Agrahari | Removal of Benzoic Acid from Water by Reactive Extraction Using Hollow Fiber Membrane Contactor: Experiment and Modeling[END_REF] can be expected to not reduce the extraction efficiency.

Mutual phase solubilities

The mutual solubilities of the phases were measured after HFMC-assisted LLE using mustard bran hydrolysate and the four selected solvents. Indeed, such solubilities are useful information to determine the loss of each phase in the other phase by diffusion, to prevent phase emulsion by the drop of interfacial tension, or to design liquid-liquid extractors [START_REF] Stephenson | Mutual solubility of water and aliphatic alcohols[END_REF]. The results are presented in [START_REF] Stephenson | Mutual solubility of water and aliphatic alcohols[END_REF][START_REF] Watanabe | Cyclopentyl Methyl Ether as a New and Alternative Process Solvent[END_REF][START_REF] Hiromitsu | Solubility of 4-methyl-2pentanone in aqueous phase of various salt concentrations[END_REF][START_REF] Stephenson | Mutual binary solubilities: water-alcohols and water-esters[END_REF][START_REF] Stephenson | Mutual solubilities: water-ketones, water-ethers, and water-gasoline-alcohols[END_REF] Table 6 shows that the solubility of water is important in octanol (3.62 ± 0.12 % v/v) and in a lesser extend in MIBK (1.41 ± 0.07 % v/v), whereas its solubility is limited in CPME and octyl acetate (0.58 ± 0.03 % and 0.22 ± 0.08 % v/v, respectively).

In addition, the solubilities of the non-volatile solvents (octanol and octyl acetate) in water are negligible (0.16 ± 0.03 % and < 10 -3 % v/v, respectively). However, the solubilities of the volatile solvent (MIBK and CPME) in water are significant and thus must be considered (2.6 ± 0.5 % and 1.07 ± 0.05 % v/v, respectively). All of the measured solubilities are in accordance with the literature.

According to these results, it appears that the mutual solubility of MIBK and water is not negligible. It should be considered when high volumes are processed in terms of solvent loss and contamination of the solvent and aqueous phases, having cost and environmental consequences.

After stating the basis of the extraction of sinapic acid with the HFMC, and showing the proof of concept with different solvents, some operating conditions were studied to find the optimal mass of sinapic acid that can be recovered in the solvents.

Improvement of the quantity of sinapic acid extracted by LLE assisted by HFMC

The improvement of quantity of sinapic acid extracted in the solvent was carried out using HFMC by studying the effect of the initial concentration of sinapic acid in the feed phase and the effect of the feedto-solvent ratio. In addition to the calculation of the extraction efficiency, the speed of extraction with the mass transfer coefficient, and the loss of solvent in the aqueous feed phase due to the solubility of solvent in water, the purity of sinapic acid was also investigated. The purity can be easily calculated after evaporation of the volatile solvents. However, for non-volatile solvents, this implies a more complex desextraction step such as alkaline stripping or diluent-swing [START_REF] Chemarin | Recovery of 3-hydroxypropionic acid from organic phases after reactive extraction with amines in an alcohol-type solvent[END_REF]. Therefore, only the two volatile solvents (MIBK and CPME) were selected for this study.

Effect of the initial concentration in the feed phase

According to part 3.1.2. , there is little impact of the initial concentration of sinapic acid in a model solution on the extraction efficiency. In this part, the study deals with the effect of the initial concentration of sinapic acid contained in different batches of mustard bran hydrolysate. The initial concentrations of sinapic acid in the hydrolysates are 43 ± 2, 87 ± 1 and 274 ± 22 mg/L, with corresponding dry matter contents of 0.5%, 1% and 5% (w/w), respectively. The extraction kinetics of sinapic acid in the HFMC by MIBK are presented in Figure 5. In addition, the mass transfer coefficients obtained with the different initial concentrations are equivalent (p > 0.05) with a value of 27 ± 3 x 10 -6 m/s. Therefore, the speed of extraction is not dependent of the initial concentration of sinapic acid in the hydrolysate. As a consequence, its initial concentration should be as high as possible to recover the highest concentration in the solvent. In the present study, the maximal initial concentration in the hydrolysate was limited to 274 ± 22 mg/L due to the process used to produce the hydrolysate.

Effect of the feed-to-solvent ratio

The interest to increase the feed-to-solvent ratio lies in the potential increase of the mass of sinapic acid 4:1, 10:1 (v/v) respectively. Thus, the extraction durations appear longer with higher feed-to-solvent ratios. To have a more precise comparison, the mass transfer coefficients were calculated according to the kinetics. It was found that the highest mass transfer coefficients with MIBK and CPME are at a feedto-solvent ratio of 1:1 (v/v) (27.3 ± 0,4 x 10 -6 and 19 ± 3 x 10 -6 m/s, respectively), whereas it is low at 10:1 (v/v) (19 x 10 -6 and 11 ± 2 x 10 -6 m/s, respectively). Therefore, the increase of the feed-to-solvent ratio tends to decrease the mass transfer coefficient, either using the hydrolysate or the model buffered solution.

In addition, in the case of MIBK for a feed-to-solvent ratio of 2:1 (v/v), the mass transfer coefficient is found higher with the model buffered solution (26 x 10 -6 m/s) than with the hydrolysate (21 x 10 -6 m/s). This is also observed with CPME at 4:1 (v/v) (16 ± 1 x 10 -6 and 12 ± 1 x 10 -6 m/s respectively). Therefore, the lower mass transfer coefficient observed with the hydrolysate might be explained by a higher resistance from the aqueous boundary layer compared to the model buffered solution, represented in the increase of the feed-to-solvent ratio between 1:1 to 10:1 (v/v) (slopes of -2.94 and -5.21, respectively). Thus, the decrease in extraction efficiency with CPME is twice as sensitive as MIBK.

However, some degradation of sinapic acid can occur during the extraction. Therefore, the recovery yield (Figure8(b)) is also a relevant parameter to consider as it represents the effective mass of sinapic acid obtained in the solvent with respect to the initial mass in the aqueous feed phase. While the extraction efficiency is equal to the recovery yield in the case of CPME, this is not the case for MIBK.

Indeed, the recovery yield is lower for all the ratios, showing a loss up to 20 points between the extraction efficiency and the recovery yield, represented by ordinates at the origin. However, the slope remains equivalent (-2.73), leading to an equivalent effect of the feed-to-solvent ratio. Further investigation showed that sinapic acid is oxidized with MIBK into thomasidioic acid (Figure S1 in supplementary material), accounting for the loss of sinapic acid.

The antagonistic effect of the feed-to-solvent ratio on the recovery yield and on the mass of sinapic acid recovered in the solvent is presented in Figure 9. The integration of these two effects is represented by the recovery capacity, also presented. Figure 99 shows that an increase of the feed-to-solvent ratio implies a linear reduction of the recovery yield (Figure 99(a)), and a power law increase of the mass of sinapic acid recovered in the solvent accordingly (Figure 99(b)). This antagonistic effect can be integrated and represented by the recovery capacity. It corresponds to the mass of sinapic acid that could be obtained for a same initial volume of solvent used for the LLE assisted by HFMC. This could be calculated by Eq. 8 using the parameters of the linear regression of the predicted recovery yield in function of the feed-to-solvent ratio (Table 7)

and the initial mass of sinapic acid in the aqueous feed phase. The models obtained showed Figure 8(c) seem parabolic. In the case of CPME, a maximum recovery capacity is reached with a feed-to-solvent ratio of 8:1 (v/v), corresponding to 0.9 gram of sinapic acid recovered per liter of CPME implemented. This result is obtained in the following conditions: for an initial concentration of sinapic acid in the feed phase set at 230 mg/L and a closed-loop extraction with one extraction cycle. In the case of MIBK though, the maximum recovery capacity is not reached with the maximum feed-to-solvent ratio tested (10:1 v/v). The corresponding recovery capacity (1.2 g/L) is still higher than the one obtained with CPME, and as from the feed-to-solvent ratio of 5:1 (v/v).

Therefore, MIBK seems to be a better solvent than CPME regarding the recovery capacity. In order to In addition to the recovery capacity, the loss of solvent by diffusion in the aqueous feed phase should also be taken into account. After measuring the mutual phase solubilities, the loss of solvents in the hydrolysate can be calculated in function of the feed-to-solvent ratio. This is presented in Figure 10. Figure 10 shows that the loss of the solvents in the hydrolysate increases linearly between the feed-tosolvent ratio 1:1 to 10:1 (v/v) with MIBK and CPME (slopes of 2.41 and 1.05 respectively). Thus, the loss of solvent is 2.5 times higher with MIBK than with CPME. This is due to the higher solubility of MIBK in water (2.6 ± 0.5 % v/v) than CPME in water (1.07 ± 0.05 %). Therefore, an increase of the feed-to-solvent ratio increases the volume of hydrolysate in which the solvent can solubilize, leading to higher losses of solvent. The losses of solvents represent 11% and 24% (v/v) at a phase ratio of 10:1 (v/v) for CPME and MIBK, respectively. The difficulties to recover the solvents lost in the hydrolysate may have a significant impact on the costs of the process. Therefore, the choice in selecting the best solvent between MIBK and CPME would require a techno-economic evaluation and a life cycle analysis.

The purity of sinapic acid could be calculated after evaporation of the solvents. The purities obtained were 29.4 ± 0.4 % (w/w) with CPME and 33 ± 8 % (w/w) with MIBK. Therefore, the use of CPME or MIBK resulted in equivalent purities (p > 0.05). It is important to mention that further investigations in the case of CPME showed that benzoic acid and sorbic acid, coming from the enzymatic cocktail implemented during the production of the mustard bran hydrolysate, were the main impurities (36.1 ± 0.6 % and 12.8 ± 0.4 % w/w, respectively). Finally, the sinapic acid purity in the initial hydrolysate dry

Conclusion

In this study, different solvents from the class of alcohols, esters, ethers and ketone were found efficient to extracted more than 80% (w/w) of sinapic acid from an aqueous phase for pH < 5. They were efficient for model aqueous solution of sinapic acid and mustard bran hydrolysate.

The implementation of a HFMC was found operational for the extraction of sinapic acid from the hydrolysate with either volatile and non-volatile solvents. The initial concentration of sinapic acid in the feed phase was found not limiting the extraction efficiency or speed, thus allowing its concentration in the solvent.

The volatile solvents tested with HFMC were proportionally lost in the aqueous feed phase by the increase of the feed-to-solvent ratio, leading to increasing cost of the process. Also, the recovery yield of sinapic acid decreases with the increase of the feed-to-solvent ratio. Therefore, there is an optimal condition in order to obtain the highest recovery capacity of sinapic acid in the solvent. It was found at 8:1 (v/v) with CPME and above 10:1 (v/v) with MIBK.

The use of the HFMC effectively circumvented phase emulsions and facilitated a 70-fold increase in sinapic acid concentration within the solvent in contrast to its initial content within the hydrolysate.

Consequently, LLE assisted by HFMC emerges as a straightforward and scalable technological approach for generating commercially viable bio-based sinapic acid from Brassicaceae by-products.

The purity of the obtained extracts is influenced to a greater extent by the impurities inherent in the commercial enzymatic cocktails and additional substances found in mustard bran. These substances arise from the mustard manufacturing process. In order to improve the extract's purity, further efforts are required to address these factors.

Figure 2 :

 2 Figure 2: Experimental setup of the pilot for membrane based solvent extraction

6 .

 6 It represents the mass of sinapic acid obtained in the organic phase compared to the initial mass in the aqueous feed phase. The difference with extraction efficiency could indicate a potential reduction in sinapic acid content throughout the extraction process, possibly attributable to degradation.

5 .

 5 Extraction efficiency remains relatively modest (< 15%) up to a pH of 7 across the majority of tested solvents, with the exception of ethyl acetate, MIBK, and 2-MeTHF, which exhibit slightly higher yields (15% -25%). However, it becomes clear that, to achieve an extraction efficiency surpassing 80%, the chosen pH must be maintained at values lower than 5. The significant extraction efficiency at pH 4 is noteworthy, given the natural pH of around 4.2 in the mustard bran hydrolysate. Therefore, in the rest of the study, the focus is on pH 4.

Figure 3 :

 3 Figure 3: Main form of sinapic acid as a function of pH 3.1.2. Effect of the concentration of sinapic acid in the aqueous phase

Figure 4 :

 4 Figure 4: Extraction kinetic of sinapic acid in the HFMC with different solvents

Figure 4

 4 Figure 4 shows that the concentration of sinapic acid drop sharply in function of the extraction time with all the solvent tested, meaning that the extraction of sinapic acid in the HFMC from a mustard bran hydrolysate is effective. The extraction equilibrium is reached after 8 min with MIBK and CPME,

Figure 5 :

 5 Figure 5: Evolution of sinapic acid extraction with MIBK vs the initial concentration of sinapic acid in the feed phase pH 4.1 -4.3, 25 °C, feed in the lumen side of the fibers, feed-to-solvent ratio: 1:1 (v/v).

Figure 4

 4 Figure4shows equivalent extraction kinetics whatever the initial concentration of sinapic acid in the hydrolysates. The extraction efficiency at equilibrium is 95.2 ± 1.0 %, and is not significantly different from that obtained with the simple LLE (part 3.1.2. ) (p > 0.05). This shows that the initial concentration of sinapic acid in the hydrolysate does not impact the extraction efficiency over the concentration range studied (43 -274 mg/L).

Figure 6 :

 6 Figure 6: Extraction kinetics of sinapic acid with MIBK (a) or CPME (b) vs the feed-to-solvent ratio pH 4.1, 25 °C, 𝐶 0 (𝑎𝑞) = 230 ± 36 mg/L, feed in the lumen side of the fibers, using a mustard bran hydrolysate or a buffered model solution of sinapic acid.

Figure 5Figure 6

 6 Figure 5Figure 6 shows that the extraction equilibrium is delayed as the feed-to-solvent ratio increases. Indeed, for the MIBK, the extraction equilibrium is reached at 5, 15, 15-20 and > 40 min for the feedto-solvent ratios of 1:1, 2:1, 4:1, 10:1 (v/v) respectively. The same phenomenon is observed for the CPME; the extraction equilibrium is reached at 6, 20-30 and 30 min for the feed-to-solvent ratios of 1:1,

Figure 7 Figure 7 :Figure 8 :

 778 Figure 7.

  88 (a) shows that the extraction efficiencies with MIBK and CPME decrease linearly with

Figure 9 :

 9 Figure 9 : Effect of the feed-to-solvent ratio on (a) the recovery yield, (b) the mass of sinapic acid recovered in the solvent, (c) the recovery capacity, when extracting sinapic acid from a hydrolysate with the membrane contactor pH 4, 25 °C, 𝐶 0 (𝑎𝑞) = 230 ± 36 mg/L, feed in the lumen side of the fibers, 𝑉 0 (𝑜𝑟𝑔𝑎) =0.5 L.

  capacity of the solvents, further study should focus on the intensification of the enrichment of sinapic acid in the initial hydrolysate, and on the development of technics for solvent recycling.

Figure 10 :

 10 Figure 10 : Effect of the feed-to-solvent ratio on the loss of solvent when extracting sinapic acid from a hydrolysate with the membrane contactor pH 4, 25 °C, 𝐶 0 (𝑎𝑞) = 230 ± 36 mg/L, feed in the lumen side of the fibers.

  4 ± 0.1 % (w/w). Therefore, the use of the HFMC allowed the concentration of the sinapic acid by 70 in the solvent phase.

Table 1 :

 1 Solvents used to extract sinapic acid

	Solvent	Chemical nature Carbon chain length	Boiling point (° C) *	Purity Supplier
	Butanol	alcohol	C4	118	> 99%	Fischer
	Octanol	alcohol	C8	196	99%	AlfaAesar
	Dodecanol	alcohol	C12	258	> 99%	TCI
	Ethyl acetate	ester	C2	77	99.9%	VWR
	Octyl acetate	ester	C8	211	> 98%	TCI
	Lauryl acetate	ester	C12	265	≥ 98%	Merck
	2-MeTHF	cyclic ether	C5	80	> 98%	TCI
	CPME	ether	C5	106	≥ 99%	Merck
	MIBK	ketone	C6	117	100%	VWR

2-MeTHF: 2-methyl tetrahydrofuran, CPME: cyclopentyl methyl ether, MIBK: methyl isobutyl ketone.

*According to PubChem website datasheet

[28]

Table 2 :

 2 Characteristics of the hollow fiber

	France). The setup comprised a HFMC, specifically the 2.5x8 X50 Liqui-Cel™ module equipped with
	hydrophobic polypropylene fibers from 3M (Minnesota, United States). The module characteristics are
	detailed in Table 2 and the experimental setup of the pilot is presented Figure 2.

2.4. Membrane-based solvent extraction

2.4.1. Description of the equipment

The extractions were carried out on a membrane contactor pilot developed by Seprosys (La Rochelle,

membrane contactor Module material Polypropylene Fiber effective length 146 mm Fiber material

		Polypropylene	Wall thickness	40 µm
	Pore size	0.03 µm	Shell internal diameter 58.4 mm
	Porosity	40%	Shell internal length	20.3 mm
	Inner fiber diameter	220 µm	Number of fibers	~ 9800
	Outer fiber diameter 300 µm	Effective surface area	1.4 m 2

Table 3 :

 3 Comparison of extraction efficiency (%) of sinapic acid for different solvents vs. pH

	Solvents	pH 3	pH 4	pH 5	pH 6	pH 7
	2-MeTHF	100.0 ± < 0.0 a	92.4 ± 0.5 b	39.6 ± 4.5 a	24.4 ± 0.9 b 19.3 ± 4.8 a
	MIBK	-	98.5 ± 0.1 a	-	43.1 ± 4.8 a	-
	CPME	94.7 ± 0.6 e	87.1 ± 1.9 c, d 18.9 ± 1.0 c, d	8.2 ± 0.5 c	3.7 ± 0.1 b
	Ethyl acetate	96.0 ± 0.1 d	89.5 ± 0.1 b,c	26.3 ± 0.5 b	25.0 ± 4.2 b 15.8 ± 1.2 a
	Octyl acetate	89.1 ± 0.5 f	86.2 ± 0.7 d	17.3 ± 0.5 d	10.9 ± 0.4 c	4.1 ± 1.4 b
	Lauryl acetate	80.4 ± 0.4 g	79.0 ± 1.1 e	28.5 ± 0.3 b	13.9 ± 4.4 c	-
	Butanol	97.0 ± 0.3 c	86.8 ± 0.2 d	16.1 ± 0.4 d	4.0 ± 2.5 c	1.2 ± 0.3 b
	Octanol	98.0 ± 0.3 b	88.1 ± 0.1 c,d 20.2 ± 0.6 c, d	6.7 ± 1.3 c	7.3 ± 1.2 b
	Dodecanol	93.8 ± 0.1 e	80.6 ± 0.3 e	23.2 ± 2.1 c	3.6 ± 2.3 c	3.7 ± 2.2

b

Different superscript letters (in column) indicate a significant difference between the extraction efficiency of solvents (p < 0.05) for each pH. Specifically, "a" designates the highest extraction efficiency at each pH, with "b," "c," and so forth, meaning significant decreasing levels of extraction efficiency. 𝐶 0 (𝑎𝑞) = 100 mg/L, n ≥ 2. The missing results for MIBK and lauryl acetate are due to the lower final pH observed which biased the results. This may be due to the undesired acidification of the un-buffered media by these solvents.

Table 4 :

 4 Comparison of extraction efficiency (%) of sinapic acid from a model solution or a hydrolysate vs. initial concentration at pH 4

	Concentration of sinapic acid mg/L

Table 4

 4

	These criteria encompass the following:
	1-Short Carbon Chain Lengths: Solvents characterized by shortened carbon chains exhibit elevated
	water solubility, which could potentially impede extraction due to a reduction in interfacial tension
	between the aqueous and organic phases. Consequently, the exclusion of butanol and ethyl acetate
	was considered appropriate.

, the extraction efficiencies of sinapic acid from the model solution (50 mg/L) and the hydrolysate (55 mg/L) are equivalent with ethyl acetate. However, when working with the hydrolysate, the extraction efficiency is comparatively lower by a margin of 5 points with 2-MeTHF, CPME, butanol, and octanol; by 10 points with dodecanol and octyl acetate; and even up to 17 points with lauryl acetate. Consequently, the hydrolysate appears to exert a diminishing effect on extraction efficiency in comparison to a binary solution containing sinapic acid and water. Considering practical implications, it is advisable to prioritize solvents exhibiting extraction efficiencies that closely approach or surpass the 80% threshold.

Given the demonstrated efficiency of all tested solvents for sinapic acid extraction from the hydrolysate, specific criteria were established to narrow down the solvent selection for employment with HFMC.

2-Long Carbon Chain Lengths: Solvents with protracted carbon chain lengths exhibit comparatively diminished extraction efficiency. Their heightened viscosity and melting points may also introduce

Table 5 :

 5 Comparison of the a simple LLE and LLE assisted by HFMC

		Simple LLE	LLE assisted by HFMC
	Solvents	Extraction efficiency	Extraction efficiency	Mass transfer coefficient
		(%)	(%)	(10 -6 m/s)
	MIBK	96.2 ± 0,2 a A	95.1 ± 0.1 a B	25 ± 1 a
	CPME	85.5 ± 0.1 c A	87 ± 2 b,c A	15.3 ± 0.5 b
	Octanol	92.1 ± 0.3 b A	93.1 ± 0.7 a,b A	4.1 ± 0.2 c
	Octyl acetate	77.8 ± 0.5 d A	80.2 ± 2.7 c A (at 20 min)	4.5 ± 0.5 c

Table 6Erreur ! Source du renvoi introuvable..Table 6 :

 6Erreur6 Mutual phase solubility of water and solvent

Measured solubility of water (% v/v) Literature data "solubility of water" (% v/v) Measured solubility in water (% v/v) Literature data "solubility in water" (% v/v)

	Octanol	3.62 ± 0.12	3.72	0.16 ± 0.03	0.06
	Octyl acetate	0.22 ± 0.08	0.37	< 10 -3	0.04
	CPME	0.58 ± 0.03	0.26	1.07 ± 0.05	1.28
	MIBK	1.41 ± 0.07	2.01	2.6 ± 0.5	1.5 -2.4
	Aqueous phase: mustard bran hydrolysate, pH 4.3, 25 °C. References for literature data :

Table 7 :

 7 Parameters of the linear regression of the recovery yield in function of the feed-to-solvent ratio with MIBK and CPME

		slope	y-intercept	R²
	MIBK	-2.73	80.39	0.83
	CPME	-5.36	91.62	0.98

Acknowledgements

This work was supported by Extractis (Amiens, France). The Region Grand Est, the Conseil Départemental de la Marne and the Grand Reims are thanked for their financial support, as well as Charbonneaux-Brabant for providing the mustard bran. We express our gratitude to Dr. Marwen Moussa and Prof. Violaine Athès from Université Paris-Saclay for their valuable assistance during this work and in the implementation of the membrane contactor set-up.


Côme Oosterhof

Nicolas Gasnier

Santiago Peña Luque

Yannick Tanguy

EXTRACTION OF SMALL DAM RESERVOIRS USING A COMBINATION OF DIGITAL TERRAIN MODEL AND WATER MASK DERIVED FROM SATELLITE IMAGES

Dam reservoirs account for only a small fraction of the global freshwater but they have major importance for human activities, providing water for agriculture, domestic, recreational, and industrial use, especially in a context of water scarcity induced by climate change [START_REF] Bezner Kerr | Food, Fibre, and Other Ecosystem Products[END_REF]. They also have major impacts on the hydro-and ecosystems [START_REF] Morden | Small artificial impoundments have big implications for hydrology and freshwater biodiversity[END_REF]. While they are subject to numerous studies across multiple disciplinary fields [START_REF] Kellner | The controversial debate on the role of water reservoirs in reducing water scarcity[END_REF][START_REF] Habets | The cumulative impacts of small reservoirs on hydrology: A review[END_REF], no exhaustive database of existing dam reservoirs is available, and the available data are limited to the largest reservoirs. Thus, there is a need for a method to automatically extract dam reservoirs from widely available data such as satellite images. This task is different from the broader water surface extraction task [START_REF] Maurício | Automatic water detection from multidimensional hierarchical clustering for sentinel-2 images and a comparison with level 2a processors[END_REF][START_REF] Li | Water body classification from high-resolution optical remote sensing imagery: Achievements and perspectives[END_REF] as the goal is also to distinguish dam reservoirs from other waterbodies. Some approaches have been proposed to achieve it, for example by Van Soesbergen et al. [START_REF] Arnout Van Soesbergen | Dam reservoir extraction from remote sensing imagery using tailored metric learning strategies[END_REF]. However, this extraction task using only single images can be very difficult for very small waterbodies (below 1 hectare). To improve the results, we propose a new method that combines a water mask and a digital elevation model (DEM). The water mask can be extracted from any very high-resolution satellite image: optical or SAR. The DEM can be derived from SAR interferometric data (TanDEM-X) or optical stereo images (future CO3D mission [START_REF] Lebègue | Co3d, a worldwide one onemeter accuracy dem for 2025[END_REF]).

The idea behind our method is the fact that dams are built to prevent water from flowing in the direction of the downward terrain slope, as illustrated in Figure 1 (right). Where there is a dam, the terrain's upward slope (right red arrow) is oriented in the same direction as the inward-pointing normal vector to the waterbody contour (right blue arrow). Contrariwise, where there is no dam, the upward slope is always oriented in the opposite direction as the in normal vector to the contour (i.e. the banks are higher than the water), which prevents the water from flowing out of the waterbody. The dot product between the inward-pointing normal vector to the water contour and the gradient of the elevation (DEM) is positive where there is a dam and negative where there is no dam. We use the gradient of the smoothed water mask (i.e. the binary mask convoluted with a Gaussian kernel) as a surrogate for the inward-pointing normal vector and the gradient of the smoothed DEM as a surrogate for the upward slope direction. We call the dot product between these two gradients the Gradients Dot Product (GDP). It can be computed in every pixel using the method presented below and is positive in dams and negative elsewhere. This is illustrated in Figure 3 where the values of the pixels of these images are plotted along a line A-B that contains a dam (as in Figure 2). It can be noted that the GDP is computed using the gradient of the DEM in the neighborhood of the waterbody, but not in the waterbody itself. Identifying a waterbody corresponding to a dam reservoir using its GDP image is a straightforward task as the dam area is positive while the non-dam contours of the waterbody are negative (see examples in Figure 5). This task can be done using a CNN classifier. With adequate training, such a classifier is relatively robust to errors in the GDP caused by errors in the DEM and the binary water mask. This also allows for distinguishing usual dam reservoirs from other artificial reservoirs that are completely surrounded by a levee (e.g. reservoirs called bassines in French). The proposed method consists of 5 steps, as presented in Figure 2: 1. The binary water mask is computed using a water extraction method. In our tests, we used a Random Forest approach based on radiometric indices followed by a morphological regularization.

2. The water mask is cropped around the considered waterbody.

3. The DEM is cropped according to the same footprint 4. Both cropped images are processed and combined to obtain a Gradient Dot Product (GDP) image of the same size. The two components of each gradient are computed using a convolution of the Sobel kernels with the water mask image smoothed beforehand by convolution with a Gaussian kernel. The gradient dot product image can then be computed by summing the pixel-wise multiplications of the gradients components x and y images. A post-processing is applied to set the pixels inside the initial water mask to zero, as the DEM is not known inside the waterbody.

5. This GDP image is given to a CNN classifier which classifies between dam reservoir and other waterbodies. We chose a ResNet [START_REF] He | Deep residual learning for image recognition[END_REF] network that has been pretrained using the ImageNet dataset. As labeled data are scarce, we give the RGB color representation of the GDP image (blue to red color scale) as input to the classifier after normalization, rotation, and re-sampling steps. This RGB representation is closer to the natural images of ImageNet, which limits the amount of training required.

We tested our method using Pléiades optical images (70cm resolution 50cm pixels) and the RGE Alti DEM. The training and validation dataset used to train the classifier consists in manually-labelled data from the Haute-Garonne department in France. It contains 817 samples: 409 for the "dam reservoir" class and 408 for other water bodies. The test dataset contains 134 dam reservoirs and 65 other water bodies from the Tarn department. We compared our method to a classification method using another ResNet-based CNN classifier that we trained and tested using the binary water mask and the DEM as distinct inputs. We compared the accuracy obtained with the train dataset for three area ranges: between 0.1 and 0.2 hectare, between 0.2 and 0.7 hectare, and greater than 0.7 hectare. We obtained an accuracy of 0.58, 0.75, and 0.89 respectively while for the separated mask/DEM classifier it was 0.54, 0.60, and 0.73. This shows a significant improvement for the GDP-based method compared to the separate binary mask and DEM.

The experiments show that our approach based on the Gradients Dot Product (GDP) yields encouraging results. Future work should consider applying the method to other sources of binary mask and DEM as well as other geographic areas. This method could also be adapted to extract other kinds of waterbodies, for example, "bassines" agricultural reservoirs. The proposed Gradient Dot Product could also be used as an input to detect the accurate location of the dam within a given reservoir, for example, for the Stockwater method [START_REF] Santiago Peña Luque | Stockwater -advances in reservoir stock monitoring from space[END_REF]

Fig. 1 .

 1 Fig. 1. Sectional view of a reservoir showing a dam contour (right) and a non-dam contour (left).Where the dam is located, the upward slope direction (red arrows) is in the same direction as the inward direction for the water mask (blue arrows). Here the proposed GDP image is therefore positive. At the non-dam water bank (right) they are in opposite directions and the GDP is negative.

Fig. 2 .

 2 Fig. 2. Example of a dam reservoir. The contour of the water mask is displayed on a the corresponding Pléiades optical image (left). A dam can be seen in the image. The dashed line AB corresponds to the section along which the values of the pixels are plotted in Figure 3 and to the section of the cross-sectional view Figure 1 (from left to right). The right Figure shows the corresponding GDP image, with the negative values in blue and the positive values in red.

Fig. 3 .

 3 Fig. 3. Pixel values of the images along the A-B line defined Figure 3, for the corresponding mask and DEM. (a) for the mask raster (dashed line) and smoothed mask values (plain line). (b) DEM elevation (dashed red line) and smoothed DEM elevation (plain red line) (c) gradients values (projected on the A-B axis) of the smoothed mask (red) and of the smoothed DEM (red). (d) shows the GDP before (dotted line) and after (plain line) the post-processing. As two-dimensional kernels are used for smoothing and derivation, the values for one profile cannot be inferred from the other one-dimensional profiles.

Fig. 4 .

 4 Fig. 4. General principle of the propose method. The numbers correspond to the steps presented below. In the GDP (Gradient Dot Product) image, the negative values are displayed in blue, and the positive values (that correspond to the location of the dam) are in red.

Fig. 5 .

 5 Fig. 5. Examples of GDP images for different waterbodies : typical dam reservoir (a,d), typical non-dam waterbodies (b,e), and raised basins (c,f). The negative values are displayed in blue, and the positive values (that correspond to the location of the dam) are in red.