C Hernandez-Aguilar

A Dominguez-Pacheco

C Valderrama-Bravo

A Cruz-Orea

E Martínez Ortiz

Photoacoustic spectroscopy in the characterization of bread with turmeric addition

Keywords: bread, wheat flour, turmeric, photoacoustic spectroscopy, antioxidant activity. 1

In this research, we to apply photoacoustic spectroscopy and evaluate bread added with turmeric (Curcuma longa Linnaeus) at different concentrations in relation to wheat flour weight (0, 1.25, 2.5, 5 and 10%). The amplitude of the photoacoustic signal was related to measures of antioxidant activity and evaluate the sanitary quality and also, the texture, color and sensory acceptability of the breads. It is shown that: i) The photoacoustic signal produced by bread significantly increases with the turmeric concentration, such that it raises more than 100% for a concentration of 10% at the wavelengths of 428 and 515nm. ii) The Bread antioxidant activity is directly proportional to the level of photoacoustic signal at 515 nm and its value increases from 73.21% to 91.73%, when the concentration increases from 0 to 10%. iii) The sanitary quality of bread added with 2.5 % of turmeric is relatively higher than the ones obtained for other lower concentrations. No fungal colonies were observed in bread mixed with 2.5%, 5% and 10%, after six days of storage. iv) The addition of turmeric powder for bread making can slow down their textural changes (hardness, elasticity, cohesiveness, resilience and chewiness) during storage. v) Bread at 2.5 and 5% of turmeric concentration showed the yellowest color, while the one with a 10% concentration exhibited the most reddish color.

Finally, through Principal component analysis it was possible to demonstrate that a concentration of either 5 or 10% of turmeric added to wheat flour could be used to improve its sanitary quality and antioxidant activity.

However, one of the major challenges facing the food industry is the development of techniques to obtain healthier and safer food products (Velázquez, 2011;[START_REF] Nemes | Screening the experimental domain for the microwave-assisted extraction of secoisolariciresinol diglucoside from flaxseed prior to optimization procedures[END_REF]2011), because the quality of food has an impact on the health of the population and their quality of life (Hernández-Aguilar et al., 2019a) as well as in consumer acceptance. In this sense, there have been a number of comprehensive reviews on technologies for both process control in the food industry [START_REF] Kondakci | Recent applications of advanced control techniques in food industry[END_REF]Neethirajan and Jayas, 2017) and food evaluation [START_REF] Lembe | NIR spectroscopy applications for internal and external quality analysis of citrus fruit-a review[END_REF][START_REF] Qu | Applications of near-infrared spectroscopy in food safety evaluation and control: A review of recent research advances[END_REF][START_REF] Ravikanth | Extraction of spectral information from hyperspectral data and application of hyperspectral imaging for food and agricultural products[END_REF][START_REF] Vadivambal | Applications of thermal imaging in agriculture and food industry-A Review[END_REF][START_REF] Karoui | Fluorescence spectroscopy measurement for quality assessment of food systemsa review[END_REF]. Finding, there are several conventional methods for their evaluation, despite their effectiveness, some of them are laborious, destructive, expensive, time-consuming, require calibration and use of chemicals [START_REF] Su | Non-destructive and rapid evaluation of staple foods quality by using spectroscopic techniques: a review[END_REF]. In this sense, various authors have proposed other alternatives methods, e.g. analysis of spectral and thermal images, color measurement, as well as the use of spectroscopic techniques. Among the spectroscopic techniques that have been applied for food analysis, are Raman, Nuclear magnetic resonance, Fluorescence, Near infrared, visible / infrared and UV / VIS, etc. [START_REF] Pojić | Near infrared spectroscopy-advanced analytical tool in wheat breeding, trade, and processing[END_REF][START_REF] Karoui | Fluorescence spectroscopy measurement for quality assessment of food systemsa review[END_REF][START_REF] Pankaj | A review of novel physical and chemical decontamination technologies for aflatoxin in food[END_REF]. These techniques have been successfully used in the food area. Using them for the determination of pesticide contamination, determination of fumonisins, microorganisms, level of deterioration, identification and differentiation of grains, analysis of phenolic compounds in food, etc. [START_REF] Sowoidnich | Application of diode-laser Raman spectroscopy for in situ investigation of meat spoilage[END_REF][START_REF] Lee | Determination and prediction of fumonisin contamination in maize by surface-enhanced Raman spectroscopy (SERS)[END_REF][START_REF] Pojić | Near infrared spectroscopy-advanced analytical tool in wheat breeding, trade, and processing[END_REF][START_REF] Foca | Minimisation of instrumental noise in the acquisition of FT-NIR spectra of bread wheat using experimental design and signal processing techniques[END_REF][START_REF] Lu | Application of mid-infrared and Raman spectroscopy to the study of bacteria[END_REF].

In the case of UV / VIS spectroscopy have been used for determination of phenolic content and antioxidant activity. [START_REF] Muhammad | Improvement of antioxidant activity and physical stability of chocolate beverage using colloidal cinnamon nanoparticles[END_REF] used UVvisible spectrophotometry to measure the absorbance at 760 nm to estimate the total phenolic content of the cocoa powder, the colloidal cinnamon nanoparticles and the chocolate beverage, applying the Folin -Ciocalteu method. Regard, antioxidant activity, various method has been reported, among them, DPPH savenging assay that is the widely used one [START_REF] De | A new, rapid online-HPTLC method for evaluation of DPPH reduction with ascorbic acid[END_REF]Yamaguchi et al., 1998;[START_REF] Krishnaswamy | Optimization of microwave-assisted extraction of phenolic antioxidants from grape seeds (Vitis vinifera)[END_REF]. This method has been applied to determine the antioxidant activity of Grape (Vitus vinifera), lemon (Citrus limon), pomegranate (Punica granatum), orange (Citrus sinensis), hemp (C. sativa) seed, seaweeds, Golden oyster mushroom (Pleurotus cornucopiae), samples of extrudates and raw flours for corn and yams, among much other food [START_REF] Paari | Evaluation of irradiation and heat treatment on antioxidant properties of fruit peel extracts and its potential application during preservation of goat fish Parupenaeus indicus[END_REF][START_REF] Krishnaswamy | Optimization of microwave-assisted extraction of phenolic antioxidants from grape seeds (Vitis vinifera)[END_REF][START_REF] Teh | The use of microwave and pulsed electric field as a pretreatment step in ultrasonic extraction of polyphenols from defatted hemp seed cake (Cannabis sativa) using response surface methodology[END_REF][START_REF] Devi | Bioprotective properties of seaweeds: in vitro evaluation of antioxidant activity and antimicrobial activity against food borne bacteria in relation to polyphenolic content[END_REF][START_REF] Jo | The effects of subcritical water treatment on antioxidant activity of golden oyster mushroom[END_REF]. The measure of antioxidant activity has been obtained from obtaining optical absorption measurements of the extracts of samples obtained around 520 nm (517 and 515 nm). Although has also been obtained at optical absorption measurement of 330 nm [START_REF] De | A new, rapid online-HPTLC method for evaluation of DPPH reduction with ascorbic acid[END_REF]. Chemical and reagents are required to carry out these measurements [START_REF] Sellami | Drying sage (Salvia officinalis L.) plants and its effects on content, chemical composition, and radical scavenging activity of the essential oil[END_REF]; methods that could avoid the use of these have been reported in the literature, such as is the case of Photoacoustic spectroscopy (PAS) technique.

Photoacoustic spectroscopy (PAS) is considered as a "green" technology [START_REF] He | Spectroscopy: the best way toward green analytical chemistry?[END_REF] and could hence be a convenient option to be incorporated in the food industry for diverse specific applications (Hernández-Aguilar et al., 2019a). PAS technique allows probing the quality of food by means of its color [START_REF] Bicanic | Sensing the heat of tomato products red: the new approach to the objective assessment of their color[END_REF], which is closely related to the quality and flavor of food [START_REF] Kaur | The Review on Spectrophotometric Determination of Synthetic Food Dyes and Lakes[END_REF]. This was demonstrated by various authors through the optical characterizing of food products, such as marine algae, coffee, milk, cheese, yogurt, tortillas, maize, ketchup, pastas, chocolates, vegetables, etc. [START_REF] Rosencwaig | in-layer, chromatography and photoacoustic spectrometry[END_REF][START_REF] Reis | Water soluble components in coffee beans: photoacoustic assessment[END_REF][START_REF] Martel | Photoacoustic analysis of some milk products in ultraviolet and visible light[END_REF][START_REF] Bicanic | On the photoacoustic, photothermal and colorimetric quantification of carotenoids and other phytonutrients in some foods: a review[END_REF][START_REF] Dóka | Exploiting direct and indirect methods for the estimation of the total carotenoid concentration in dried pastas[END_REF][START_REF] Lee | Quality characteristics of bread added on chia seed powder[END_REF]Hernández-Aguilar et al., 2011 ;2012). Likewise, few researches of PAS applied to the study of bread products have been found. Hernández- Aguilar et al. (2019b) investigated the relation between optical absorption spectra obtained by PAS and secondary metabolites on bread of wheat flour added with lentil sprouts. Taking into account that the world population is becoming more aware of what it consumes for disease prevention and improved quality of life, the interest for nutraceutical food has been significantly increasing due to its direct relationship with human health [START_REF] Šporin | Quality characteristics of wheat flour dough and bread containing grape pomace flour[END_REF][START_REF] Serpa-Guerra | The nanotech potential of turmeric (Curcuma longa L.) in food technology: A review[END_REF].

In our study we thought it could be interesting to characterize by photoacoustic spectroscopy (non-conventional method), wheat bread added to different concentrations of turmeric, relating the level of the photoacoustic signal at the wavelength of 515 nm with the value of the antioxidant capacity of the bread measured at the same wavelength by a conventional method (DPPH Method). Complementing the study, other properties of the turmeric bread were evaluated as texture, color, sensory acceptance, and sanitary quality. There are no reports in the literature to apply PAS and evaluate bread added with curcuma and related to measures of antioxidant activity and evaluate the sanitary quality of the product as it is done in the present study.

MATERIALS AND METHODS

Elaboration of bread

Turmeric powder (TP) with certification kosher as natural nutritional supplement was acquired in a local supermarket of the city of Mexico. Dough was prepared at concentrations of TP of 0, 1.25, 2.5, 5 and 10% in relation to wheat flour weight (600 g). Five types of bread formulations were elaborated with common ingredients of egg (average weight 48g), salt (1.25 g), sugar (4g), olive oil (70 g), and yeast (11 g). Turmeric powder was incorporated 0, 7.5, 15, 30 and 60 g, in 600, 592.5, 585, 570 and 540 g wheat flour, respectively. The concentrations of Turmeric used in this research were chosen according to that reported by other authors in the literature [START_REF] Jung | Investigation of Factors on the Sensory Characteristics of Milk Bread with Tumeric Powder (Curcuma longa L.) Using Fractional Factorial Design Method[END_REF][START_REF] Jeon | Quality characteristics of bread added with turmeric powder[END_REF] and also considering not to exceed 10g of it to avoid toxic effects of consumption or adverse effects by oral ingestion that have been reported by other authors [START_REF] Vincent | Natural chemopreventive therapy: Curcumin[END_REF]. The final formulations five expressed by percentage are showed in Table (1). The ingredients were mixed for 5 min, in a blender with a spiral hook. After its fermentation in 300 g warm water (30 °C), the yeast was incorporated into the mixture, which was then kneaded for 10 min and placed in a mold covered with a moistened cotton fabric during 30 min. Next, the dough was again kneaded for 10 min and put in a greased mold for bread, where it increased its volume during 25 min. Afterwards, each type of the doughs was placed in different molds and simultaneously baked in a pre-heated electric oven (Black+Decker) at 180° C, for 50 min. After baking, the bread was allowed to cool down to room temperature during 24 h and subsequently cut it into slices (1.5 cm thick) through an electric knife (Hamilton beach type EK08, 121 V, 11 Hz).

Photoacoustic Spectroscopy experimental setup

The experimental setup of PAS consists of a Xenon lamp used as the light excitation source, a monochromator applied to obtain a monochromatic light beam, and a mechanical chopper employed to modulate the light beam at the fixed frequency f = 17 Hz ( f at which the electret microphone used has the best response), as shown in Fig. 1.

The modulated beam was focused onto an optical fiber to guide it to the photoacoustic (PA) cell, whose signal was then detected by an electret microphone (photoacoustic sensor) connected to the cell through a fine channel (1 mm diameter). The PA signal was amplified by means of a lock-in amplifier (EG&G, Mod. 5210) before being recorded in a personal computer, as a function of beam wavelength. Experiments were performed for wavelengths between 300 nm to 700 nm (Range where it is possible to observe the absorbent bands of the compounds contained in the bread) and by placing the samples (0, 1.25, 2.5, 5 and 10% of turmeric concentration) into the PA cell with a diameter of 6 mm. The samples of each type of bread were cut into circular slides with a diameter of 5 mm, such that they do not obstruct the microphone sensor. The operating principle is based on the photoacoustic effect discovered by A. G. Bell in 1880 and modeled by [START_REF] Rosencwaig | in-layer, chromatography and photoacoustic spectrometry[END_REF]Gersho (1970) (Hernández.Aguilar et al., 2019a).

Which is based on the absorption capacity of each material when a modulated beam of light falls on it. The light absorbed by the sample is transformed into a modulated heat emitted by it within a hermetically sealed FA cell. As air is present inside the FA cell, an adiabatic process occurs that causes pressure changes inside the FA cell. The pressure changes are captured by an electret microphone and this is what generates a photoacoustic signal value.

Antioxidant activity (DPPH method)

Chemicals

The 2,2-diphenyl-1-picrylhydrazil (DPPH) and 5,7, were purchased from Sigma Aldrich (St. Louis, MO, USA). All others chemicals of analytical grade were purchased from Merck Company (Darmstadt, Germany).

The extraction of compounds in the bread samples was carried out based on the methodology proposed by [START_REF] Ranjbar | Effect of iron-enrichment on the antioxidant properties of wheat flour and bread[END_REF] and [START_REF] Ragaee | Effects of fiber addition on antioxidant capacity and nutritional quality of wheat bread[END_REF]. The dry flour sample was mixed for 5 minutes with a methanol-acetonewater solution (7:7:6; v/v/v) to obtain a concentration extract 33g/L. Then the solution was centrifuged for 5000 rpm for 10 min. The supernatant was removed, and the pH adjusted with 4N HCl. Subsequently, the antioxidant capacity was determined by the DPPH method, following the methodology of [START_REF] Yu | Comparison of antioxidant properties of refined and whole wheat flour and bread[END_REF]. A solution of DPPH methanol (4 mol / L) was mixed with 0.1 mL of the extract. To evaluate the antioxidative activity of specific compounds, the latter can react with a stable radical, 2,2-Diphenyil-picrylhydrazyl (DPPH) in a methanol solution.

The reduction of DPPH is followed by monitoring the decrease in its absorbance at a characteristic wavelength during the reaction; in its radical form, DPPH absorbs at 515 nm [START_REF] Brand-Williams | Use of a free radical method to evaluate antioxidant activity[END_REF]. Then, Absorbance was measured at 515 nm by UV-vis spectrophotometer (VE-5100UV) and the results were reported as mol equivalent of Trolox per gram of dry sample.

Sanitary quality

Sanitary quality test adapted according to [START_REF] Neergaard | Seed Pathology[END_REF] and Pérez et al. (2015) was established under a randomized complete block design with four repetitions for each bread sample. We randomly selected 4 slices of each bread type (0, 1.25, 2.5, 5 and 10% of turmeric concentration) and subsequently place them in a hermetically sealed container of polystyrene properly sterilized. The incubated samples were then placed in a closed room at an average temperature of 25 °C. The observation and quantification of the existing fungal colonies were performed at 3 and 6 days of incubation. Incubation Elapsed, observations were performed by a stereoscopic microscope to record the development of fungal colonies in the breads. Their gender was determined, according to the type of mycelium and spores, and proceeded to quantify the numbers of colonies in the breads contaminated containing each type of fungus. The gender of fungus was identified according to taxonomic keys [START_REF] Ravimannan | Study on fungi associated with spoilage of bread[END_REF].

Texture Profile Analysis (TPA)

The texture of the bread samples (turmeric and control) was evaluated by using a Texture Analyzer Brookfield Model CT3 25 K USA with a load cell of 25 kg. From each slice the bread samples were cut the crust and the crumb of the central part was used, to which cubes of 3 x 3 x 1.5 cm were marked to obtain regular geometric bodies and, 5 repetitions of each type of bread were cut with the electric knife (Hamilton beach type EK08, 121 V, 11 Hz). The samples were packed in polyethylene bags, placed separately to avoid damage to the texture of the bread, and were allowed to stabilize 10 minutes before measurement. The analysis was performed for two 20% compression cycles by means of a TA General Probe Kit with a TA25/1000 with a cylinder of diameter and length of 50.8 and 20 mm, respectively: operating at a speed of 1.7 mm/s. The obtained parameters were the hardness, elasticity, resilience, cohesiveness, and chewiness. The TPA analysis with four replicas at 24 ± 1°C was performed 18 and 90 hours after the bread elaboration.

Color analysis

The color of the bread samples was determined on a Minolta CR 300 colorimeter. The color parameters corresponding to the uniform color space CIELAB (a, b y L*) were directly obtained by the equipment. L* indicates brightness (100 = White y 0 = Black), "a", indicates greenish-reddish [negative (green) to red (positive)] and "b", indicates blueish-yellowish [negative (blue) to yellow (positive)]. The equipment was calibrated by using a reference white L*= 96.65, a= -0.03 and b= 1.86.

Sensory acceptability

The sensory acceptability test was carried by using of nine points hedonic scales method (where 1 = Dislike Extremely, 2 = Dislike Very Much, 3 = Dislike Moderately, 4 = Dislike Slightly, 5 = Neither Like nor Dislike, 6 = Like Slightly, 7 = Like Moderately, 8 = Like Very Much and 9 = Like Extremely) according to Laureati et al. (2012). In this study, were selected consumers (15) of bread, between 20 and 50 years old (11 woman and 4 men), reside in the city and the state of Mexico.

Each consumer was given an evaluation instrument (questionnaire) and received a set of five complete pieces of bread with thickness of 1.5 cm, placed inside a plastic bag (low-density polyethylene) hermetically sealed. The bread was made seven hours before being placed in the bags, from each mass according to the elaboration process, 23 pieces were obtained. In addition to the questionnaire, water to rinse the mouth and coffee grains to neutralize odors was given to each participant. The questionnaire applied was divided in three sections: 1) consumer general data, 2) Bread score on 10 selected attributes: color, porosity, sponginess, hardness, cohesiveness, aroma, chewiness, flavor and healthy preferences and attributes in general, and 3) direct questions related to their purchase decision on the proposed breads and their healthy preferences, as well as their health status of them and / or relatives mainly (Hernández-Aguilar et al., 2019b).

Statistical analysis and Principal Component Analysis (PCA)

Variance analysis

Bread texture and color measurements were carried out under an experimental design with three repetitions of each type of bread (0, 1.25, 2.5, 5, and 10%). The mean difference between breads was determined using analysis of variance (ANOVA), followed by a Tukey test at 95% confidence level using Minitab® version 17 statistical software (Minitab Inc., State College PA, USA).

PCA

The principal component analysis was applied to the experimental data obtained for the variables evaluated in wheat bread elaborated with (a) 0, (b) 1.25%, (c) 2.5%, (d) 5% and (e) 10% of turmeric concentration. The analysis was performed using the R Project software version 0.10-47, with R Commander and factoMiner. The data matrix used was formed from the measurements of the photoacoustic signals (515 and 475 nm), absorbance (A515 nm), fungal colonies (on days 3 and 6), color parameters ((a, b and L *), antioxidant activity (DPPH) and texture variables (hardness, elasticity, resilience, cohesiveness, and chewiness). Finally, the linear equation of the correlation and the intensity of linear correlation (r) between the variables: photoacoustic signal (normalized) at 515nm and DPPH was determined (Table 4). The r was quantified through Pearson's linear correlation coefficient. It was calculated from the quotient of the covariance between the standard deviations of the data obtained from the variables [START_REF] Vila | Correlación lineal y análisis de regresión[END_REF]. The normalized signal was obtained by the quotient of the amplitudes of the photoacoustic signals at each wavelength with respect to the initial value of the signal at the beginning of the photoacoustic spectrum.

Statistical analysis of photoacoustic spectra using kernel estimator

Statistical analysis of the data was performed using the probability density function (PDF), which specifies how the probabilities are distributed over the values adopted by the random variable [START_REF] Soong | Fundamentals of probability and statistics for engineers[END_REF]. To calculate the PDF, among other non-parametric methods are used: kernel estimators. They are used to estimate a continuous and uniform distribution from a finite set of observed points [START_REF] Rojas-Lima | Statistical Analysis of Photopyroelectric Signals using Histogram and Kernel Density Estimation for differentiation of Maize Seeds[END_REF].

The kernel density estimate method is based on each data point X 1 , X 2 ,. . . , X n of a sample of size n from a random variable with a unknown probability density function density fx (x), is replaced by a specified function K (•) called the kernel, centered at each data point Xi and with a scaling parameter h called smoothing constant. The kernel functions are added together and the resulting function is scaled to have a smooth curve called the density estimate [START_REF] Rojas-Lima | Statistical Analysis of Photopyroelectric Signals using Histogram and Kernel Density Estimation for differentiation of Maize Seeds[END_REF]. One of the most used kernels is the Gaussian kernel. The equations for the calculation of Density estimate of f x (x) and Gaussian kernel are possible to observe in Table 2 and the data processing was carried out using MatLab R2020a software.

RESULTS AND DISCUSSION

Photoacoustic Spectroscopy and antioxidant activity

Figures 2a and2b show the photoacoustic spectra and their densities for the wheat bread elaborated with 0, 1.25, 2.5, 5 and 10% of TP. Note that the amplitude of the photoacoustic signal (PA-s) is quite sensitive to the TP concentration and the wavelength (  ). For the absorption band (250-300 nm), the photoacoustic spectra of different samples exhibit similar behaviors and values. This maximum absorption band could be related to the presence of flavonoids [START_REF] Amarowicz | Antioxidant activity of phenolic fractions of lentil (Lens culinaris)[END_REF] and proteins contained in wheat [START_REF] Martin | Aeration during bread dough mixing: II. A population balance model of aeration[END_REF]. Note also that a second band of maximum absorption only appears for turmeric breads (i.e., in pure wheat bread it does not occur) and its bandwidth increases with the TP concentration. The photoacoustic signal of the widest bandwidth (350-550 nm) for bread with 10% TP, tends to increase for 350-450 nm and decrease within 450-550 nm. This increase is maximum at the wavelength of 428 nm, which is a point of interest, since it corresponds to the maximum absorption peak of curcuminoids [START_REF] Hegge | Investigation of curcumin-cyclodextrin inclusion complexation in aqueous solutions containing various alcoholic co-solvents and alginates using an UV-VIS titration method[END_REF], such as curcumin, the polyphenol responsible of the yellow color of turmeric [START_REF] Akram | Curcuma longa and curcumin: a review article[END_REF]. It was found that in this absorbent point ( = 428 nm) there are significant statistical differences when comparing the PA-signal obtained in amplitude of the different breads elaborated in this investigation (Table 2, column 3). It is possible observe that the value of the photoacoustic signal increases with the increase of turmeric powder to the bread. This means that the photoacoustic signal is a function of the added turmeric for making bread. For this latter wavelength, the amplitude of the PA-signal increases from 0.05 mV (control-bread made without turmeric) to 0.181 mV (bread added with 10% TP), which represents an increase of more than 100%. The spectrum of bread without turmeric does not show the absorption band corresponding to the characteristic yellow pigment of turmeric bread. On the other hand, the PA-signal amplitude also increases (from 0.02 to 0.08 mV) with the TP concentration at 515 nm, which is the wavelength where the antioxidant activity is measured [START_REF] Ranjbar | Effect of iron-enrichment on the antioxidant properties of wheat flour and bread[END_REF]. Although this latter increase is smaller than the one at 428 nm, the increase of the PA-signal amplitude thus indicates the improvement of antioxidant activity, as shown in Fig. 3a.

Table 3 shows significant statistical differences in antioxidant activity between breads with and without TP (0, 1.25, 2.5, 5 and 10%). The different letters in the columns indicates that the values are statistically different ( ≤ 0.05).

The lowest value of antioxidant activity (DPPH (μmol equivalent of Trolox/g) was found in the control bread (20.24) and the highest one in the turmeric bread at 10% (35.25), which represents an increase by 74%.

The results (Table 3, column 5) indicate that there is a significant difference between antioxidant potential and turmeric powder concentrations added to wheat flour in the bread making process, which is consistent with the data reported by [START_REF] Sikkhamondhol | Quality of bread with added turmeric (Curcuma longa L.): powder, essential oil and extracted residues[END_REF] and [START_REF] Lim | Quality and antioxidant properties of bread containing turmeric (Curcuma longa L.) cultivated in South Kore[END_REF], when determining that the total level of antioxidant with DPPH is higher with the addition of turmeric powder in bread. In the food industry, antioxidants are used as bio-ingredients to prevent oxidative deterioration of fats and oils [START_REF] Nanditha | Antioxidants in bakery products: a review[END_REF]. Recent research on species has suggested that its active components as antioxidants can be used as natural bio-ingredients [START_REF] Gottardi | Beneficial effects of spices in food preservation and safety[END_REF]) in bakery products. Curcumin which is the main active component of turmeric is one of the most widely used species as natural antioxidants and is used as a medicinal herb for its medicinal properties, as an antifungal, antitumor and anti-inflammatory [START_REF] Tuba | Antioxidant and radical scavenging properties of curcumin[END_REF].

The probability distribution function estimated by the Gaussian kernel of the photoacoustic spectra shows multimodal behaviors, i.e., the data obtained from the photoacoustic signals come from different data populations, related to the different components of bread (Fig 2 .b). It is possible observe that wheat bread B 0 (0% TP) exhibits a range with maximum values between 0-0.05 mV, it is found the maximum value around of 0.03 mV of the amplitude of the photoacoustic signal (photoacoustic signal data), which indicates that in this region the values occur with a relatively frequency high. In the range of values of the amplitude of the photoacoustic signal (0-0.05), note that in the pan B 0 they are within the wavelength range of 400-800 nm (Fig. 2a). The highest values of the photoacoustic spectrum (0.05-0.25 mV) (Fig- 2a), have a lower frequency of occurrence of the data (Fig. 2b) and are in the wavelength range of 250 -400 nm (Fig. 2a). Similarly, for bread B 1 (1.25% TP), it is possible to observe that the maximum occurs for 0.03 mV, although this value occurs with less frequency than bread B 0 (without TP), it would correspond to the range of 525-800 nm (Fig. 2a). However, in this density function (B 1 -1.25% TP) a second region appears (0.10-0.15 mV) with a maximum value close to 0.12 mV, when observing Fig. 2a, it corresponds to the wavelength range of 325-475 nm. In the curve corresponding to the density function of turmeric bread B 2 (2.5%), the first maximum coincides with that found in bread B 1 (1.25%) with a value of the photoacoustic signal of 0.03mV and have a similar frequency of occurrence. The second region formed (0.106-0.156 mV) presents a maximum peak around 0.143 mV, these values located in Fig. 2a within the wavelength range of 325-475 nm. The increase in the value of the maximum point of the amplitude of the photoacoustic signal (photoacoustic signal data) in this region of the kernel for B 2 , is related to an increase in yellow pigment, with respect to bread B 1 (1.25%). For both regions of curve de kernel de bread B 1 and B 2 the wavelength range of 325-475 nm is associated (Fig. 2a).

According to the literature, curcuminoids are found in this wavelength range, as has been shown by several authors such as [START_REF] Silva-Buzanello | Validation of an Ultraviolet-visible (UV-Vis) technique for the quantitative determination of curcumin in poly (l-lactic acid) nanoparticles[END_REF] who obtained the UV-VIS spectrum of pure curcumin, where an absorption band was found in the range of 300-500 nm. Finally, the first and second maxima of the density functions of turmeric breads at 5 and 10% of TP occur at 0.05 and 0.14 mV, respectively. In general, the density function for the spectrum of bread B 0 (without turmeric) exhibits two populations, one with high and the other with low occurrence.

For breads (B 1 , B 2 , B 3 and B 4 ), in the range of values of approximately 0.10-0.17 mV (Fig. 2b), they belong to the wavelength range of 300-525 nm, which is associated to the presence of turmeric in the bread, since in wheat bread (without turmeric) this absorption band is not present. Photoacoustic spectra of turmeric powder from various sources have been found with an absorbent band around the range of 300-550 nm (Hernández-Aguilar et al., 2015b;Alvarado et al., 2018). Therefore, the kernel curves of bread with and without turmeric showed different pattern of behavior.

Sanitary quality

Figure 3b shows that when the amount of turmeric increases, the sanitary quality of bread is improved; such that there are no fungal colonies developed for breads with turmeric concentration of 2.5, 5, and 10%. The fungi found in the bread slices were: Penicillium sp. (growing colonies in green color with dense conidia), Rhizopus sp. (white cottony mycelia, with black dots) and Aspergillus sp. (yellow or yellowish green colonies), being the most abundant the fungus Penicillium.

This fact is consistent with the antifungal and fungistatic properties of turmeric due to the curcuminoids (Apisariyakul et al., 1995, Abbasi and[START_REF] References Abbasi | Biological evaluation of Turmeric (Curcuma longa)[END_REF]. These curcuminoids, including curcumin [START_REF] González-Albadalejo | Curcumin and curcuminoids: Chemistry, structural studies and biological properties[END_REF], possess various biological activities, including the suppression of the proliferation of a wide variety of tumor cells from the skin, lung, colon, stomach and breast [START_REF] Khalil | Curcumin antifungal and antioxidant activities are increased in the presence of ascorbic acid[END_REF]; as well as antidiabetic and antimicrobial properties making it one of the most studied natural remedies with high medicinal value [START_REF] Kunnumakkara | Curcumin, the golden nutraceutical: multitargeting for multiple chronic diseases[END_REF]. So, the addition of turmeric to staple food as bread makes it a functional product with pro-health extensive properties.

Texture Profile Analysis

Table 4 shows the results of the texture profile analysis, indicating significant difference ( ≤ 0.05) between the evaluated variables (hardness, elasticity, cohesiveness, resilience and chewiness) of the different types of bread on days 0 and 3 of evaluation. Hardness results, corresponding to the maximum force of the first product compression cycle, indicate higher hardness in bread with 10% turmeric powder, with an increase of 31% from day 0 and day 3. This increase is even higher (48%) with respect to the control bread (no turmeric content). The hardness of the turmeric bread at 5% increased from day 0 to day 3 by 15%, while no differences were observed for turmeric breads with lower concentrations (1.25% and 2.5%). The bread elasticity on day 0 representing the material ability to recover its form after being deformed, took its highest (6.01 ± 0.28) and lowest (4.4 ± 0.35) values for bread with 2.5% and 0% of turmeric, respectively. The resilience, a textural parameter used to describe the elastic properties of samples [START_REF] Peng | Characterization of textural, rheological, thermal, microstructural, and water mobility in wheat flour dough and bread affected by trehalose[END_REF], did not exhibited significant differences (p > 0.05) between days 0 and 3 for turmeric bread at 10%.

In relation to the cohesiveness, which is the limit at which a material can deform during the second compression cycle, it took its highest values for the control and 2.5% turmeric breads, on day 0. This represents 20% more cohesiveness than the one for bread with 10% TP, which exhibited the lowest cohesiveness (0.38 ± 0.01). The chewiness that is the energy required to chew bread produced at different turmeric concentrations, showed no significant difference between days 0 and 3 for turmeric breads at 2.5% and 10%. For breads with 1.25% and 5% of turmeric concentration, the chewiness decreased on day 3, while that of control bread increased by 70% from day 0 to 3. This is consistent with relatively high increase of its hardness. [START_REF] He | Changes in bread firmness and moisture during long-term storage[END_REF] reported that the firmness of the bread increases with the loss of moisture from the crust. So, the addition of TP for bread making could slow their changes during storage. Bread making is a complex process that involves various unit operations such as: mixing, fermentation and baking, in which there is water evaporation, volume increase, starch gelatinization, protein denaturation and crust formation [START_REF] Manu | Influence of size distribution of proteins, thiol and disulfide content in whole wheat flour on rheological and chapati texture of Indian wheat varieties[END_REF]. On the other hand, turmeric is a yellow phenolic pigment and numerous studies have reported its pharmacological activity; including, effective antioxidant activity that can eliminate superoxide, hydrogen peroxide and nitric oxide radicals [START_REF] Akter | Antioxidant activity of different species and varieties of turmeric (Curcuma spp): Isolation of active compounds[END_REF]2018[START_REF] Ho | Quality and antioxidant properties of bread containing turmeric (Curcuma longa L.) cultivated in South Korea[END_REF]. Phenolic antioxidants can form complexes with proteins by means of hydrogen links between the hydroxyl groups of the phenols and the carbonyl group of the peptide residue of proteins [START_REF] Almajano | Changes in the antioxidant properties of protein solutions in the presence of epigallocatechin gallate[END_REF]. Therefore, among the components of the dough such as wheat proteins, fiber, fat, sugars, and phenolic antioxidants; the addition of turmeric alters the properties of the dough and the texture attributes of bread, as shown in Table 2.

Color analysis

The results of the color analysis of the bread samples at the different evaluation days are shown in Table 4, which indicates significant differences ( 05 . 0  p ) between the different types of bread and evaluation days (0 and 3) .

The highest value of "b" color is for bread with 2.5% turmeric (51.72); while the yellowest color (b = 49.9) of bread at 5% did not show significant differences ( 05 . 0  p ) . This fact indicates that the bread with 2.5% concentration of turmeric exhibits a high sanitary quality, since no colonies of fungi were observed after six days of storage. In this time, the bread with 10% concentration of turmeric underwent a decrease of its "b" value to b=41.33 and a significant increase of its "a" value to a=5.78, which indicates the bread tendency to a redder color, because of the relatively high "a" value of the turmeric powder. On the other hand, the value of L* decreased as the turmeric concentration increased, such that the bread samples on day 0 (L*=75. 85, 70.09, 71.06, 63.58, 56.71), were clearer than on day 3 (L*=63. 95, 63.32, 60.76, 51.33, 46,48), for the different concentrations of turmeric (0, 1.25, 2.5, 5 and 10%). In this regard, [START_REF] Ho | Quality and antioxidant properties of bread containing turmeric (Curcuma longa L.) cultivated in South Korea[END_REF] reported that the color change of bread is related to the turmeric pigments and phenolic compounds, which have oxidation reactions. So, when changing bread color, it would be necessary to evaluate the consumer preferences. So, despite of its enhanced sanitary quality, the elaboration of functional bread with 10% of turmeric concentration would perhaps no longer be attractive for the consumer. This is the reason why we recommend the production of bread with 2.5% of turmeric concentration.

Sensory acceptability

The results of the ANOVA for the 10 variables evaluated in the bread acceptability test, significant statistical differences (P <0.05) were obtained in seven of them: color, porosity, sponginess, aroma, flavor, healthy option, and general attributes (Table 5). It can be seen, in the color atribute, bread added to the 1.25% concentration of turmeric powder (B 2 ) was preferred, followed by bread B 3 (2.5% TP), and B 4 (5% TP). The least accepted bread in the color attribute was B5 (10% concentration of turmeric powder).

Sensory acceptability for the attributes porosity, sponginess, aroma, and flavor attributes was evaluated in a higher score for wheat bread without curcuma powder (B 1 , 0% of TP), with a score of 7.63, 7.0 and 6.45 and 5.90, for each one of these attributes, respectively. It should be noted that as the addition in the concentration of turmeric powder, increased the preference of these attributes in the bread decreased. Bread with a 10% concentration of turmeric (B 5 ) being the least accepted bread with a score of 6.36, 5.18, 4.72 and 4.09, for each of the attributes mentioned. It is important to note that the attribute most affected in acceptance due to the addition of turmeric was the taste of the bread, with a decrease in acceptance of around 30% when compared to control bread (0% addition of turmeric). In the evaluation of bread as a healthy option, consumer acceptance increased as the concentration of turmeric powder added to bread increased.

Choosing the bread with the best healthy characteristics, the one with the highest concentration of turmeric (B 5 ).

Increasing its acceptance by more than 100% with respect to control wheat bread (0% of TP). It should be noted that all consumers participating in the evaluation or family members are diabetic and / or hypertensive. In this way, they are interested in the consumption of healthy products, such as bread added with turmeric. In relation to the attributes in general, the most accepted breads were B 3 (2.5% of TP) and B 2 (1.25). It was found that the least accepted breads by consumers are B 1 (0%) and B 5 (10% of TP). It is important to note that the most influential attributes in consumer preferences are that the bread is a healthy option, followed by taste, smell and color. The evaluated population mentions their interest in health care in these times of pandemic, being a definitive aspect for the acquisition of a new type of bread; is that can be healthy. Another important aspect to clarify is that consumers have purchasing power and, at least one member of the family has an education at the University level.

Despite the various benefits of turmeric, it is well known that curcumin, i.e. the active element of curcuma, has poor absorption and rapid metabolism [START_REF] Stohs | Highly Bioavailable Forms of Curcumin and Promising Avenues for Curcumin-Based Research and Application: A Review[END_REF]. To overcome this, there is evidence from numerous studies that to increase bioavailability, formulations should be prepared, for example, with pepper or phospholipids [START_REF] Kesarwani | Bioavailability enhancers of herbal origin: An overview[END_REF]Patil et al. 2013). Thus, in this present investigation Olive oil is used in bread making to improve the bioavailability of Turmeric by adding it to wheat flour in the bread making process.

In the maximum concentration of turmeric (10%), 60 g of Turmeric powder is added to 540 g of wheat flour.

Twenty three slices of bread are generated from the dough that is formed. A maximum common person could eat three slices on average, which would imply consuming less than 10 g of turmeric allowed consumption and in this way there would be no risk of toxicity.

Principal component analysis

Figure 4 shows the plot of the PCA, it is possible to observe that 4 groups of data were formed, where only group 4 showed two types of bread with similarities, which corresponds to the types of wheat bread elaborated with 5 and 10% of turmeric concentration (TC). Note that the similarities of these two types of bread (5 and 10% of TC), are due to a similar behavior of the variables: PA428nm, PA515nm, chewiness, DPPH, hardness and health options (A).

The other three cluster formed (1-bread (0% of TC), 2-bread (1.25%) and 3-bread (2.5%)) had no similarities in behavior of variables. It is relevant to highlight that, according to the PCA (quadrant 3), turmeric-free bread (0%) is characterized by the existence of fungal colonies for the days evaluated (3 and 6). Conversely, for the breads of cluster four (5 and 10% of TC), showed opposite behavior (There were no fungal colonies). Furthermore, it is possible to observe in the circle of correlations (Figure 4) that the variables DPPH and amplitude of the photoacoustic signal (at 515 nm) are highly correlated, since they form an angle of less than 90 ° between them. On the other hand, they have a negative correlation with the resilience variables and the color component L *.

They also have a negative correlation with the variables that represent the number of fungal colonies (fungi day 1 and 2).

It is important to highlight that the sensory acceptability of the breads is better in the breads without turmeric and with turmeric at 1.25% (characteristics with less added amount of turmeric), which was found with the lower antioxidant activity and sanitary quality of the bread. The consumer evaluation in the sensory test, in the attribute of bread as a healthy option, coincides with the characteristics of a higher concentration of added turmeric and therefore, higher values in antioxidant activity, levels of photoacoustic signals in the wavelengths of 428 and 515 nm (PA428nm and PA515nm), etc. Finally, the PA signal (normalized) at 515 nm was also related with its DPPH by linear equation of the correlation and the intensity of linear correlation (r). Obtaining the equation: y = ax+b; where a =17.683168, b = 14.01203; and R 2 = 0.86841 (see Figure 5).

In Fig. 5 In Fig. 4a, it is possible to observe that when the antioxidant activity increases, the photoacoustic signal is increased. In this way, there is a relationship between these measures. What could lay the foundation for future research, where a correlation between the photoacoustic signal and antioxidant activity is established, with a possible opportunity to apply to have a quantitative measure of that antioxidant activity.

, it is possible to observe that when the antioxidant activity increases, the photoacoustic signal is increased. In this way, there is a relationship between these measures. What could lay the foundation for future research, where a correlation between the photoacoustic signal and antioxidant activity is established, with a possible opportunity to apply to have a quantitative measure of that antioxidant activity.

The main idea lies in relating the photoacoustic signal of five types of bread (averaged from 4 repetitions) with the value of the antioxidant activity, obtained using a conventional technique. Then, we were interested in knowing the correlation between the photoacoustic signal at 515 nm, and the antioxidant activity obtained at that same wavelength. Photoacoustic spectroscopy is not often used, especially for testing bread products. There are no reports in the literature to apply photoacoustic spectroscopy and evaluate bread added with curcuma and related to measures of antioxidant activity. Through these results, it could open the possibility that future work will be carried out considering the development of devices under the photoacoustic principle, using a near light source or at 515 nm that can measure the antioxidant activity of bakery products. Future studies should focus on two perspectives 1) Assemble portable equipment that uses a laser light source or LED diodes, at a wavelength of 515 nm or close. 2) Carry out a study using a larger quantity of samples to specify, depending on the photoacoustic signal obtained, which value of antioxidant activity corresponds to. In this way, demonstrate the effectiveness of photoacoustic spectroscopy to determine antioxidant activity in turmeric bread. So, it is possible said that future work could be directed to employ portable photoacoustic instrumentation to determine antioxidant activity

CONCLUSIONS

In the present research project, it has been found:

(i)
Existing changes in characteristics of hardness, elasticity, cohesiveness, resilience and chewiness, color, DPPH, PA signals (515 nm and 428 nm) and sanitary quality could be due to the existing compounds in the absorption band (325-500 nm) that presented differences in the photoacoustic spectra obtained from the bread without turmeric (control) and the breads at different concentrations of turmeric (1.25, 2.5, 5 and 10% ). The addition of turmeric powder for bread making can slow down their textural changes (hardness, elasticity, cohesiveness, resilience and chewiness). On the other hand, it increased the antioxidant properties beneficial to health and to the sanitary quality of bread, but consumer acceptability (flavor, color and aroma) decreases with increasing curcuma concentration.

(ii)

The photoacoustic signal amplitude produced by bread significantly increases with the turmeric concentration, such that it raises more than 100% for a concentration of 10% at the wavelengths of 428 and 515 nm. There was a positive correlation between the amplitude level of the photoacoustic signal obtained by photoacoustic spectroscopy (unconventional method) and the antioxidant activity of bread obtained by conventional method (DPPH).

(iii)

The Bread antioxidant activity is directly proportional to turmeric powder concentration and the amplitude of the photoacoustic signal at 515 nm, being the maximum increase (74%) at the concentration of 10%. At a 10% concentration of turmeric, three slices of bread contain approximately 8g of turmeric.

(iv)

The probability distribution function estimated by the Gaussian kernel of the photoacoustic spectra shows multimodal behaviors and we have observed that bread with different concentrations of turmeric showed different pattern of behavior.

(v)

The sanitary quality increase with the turmeric concentration. No fungal colonies were observed in bread mixed with 2.5%, 5% and 10%, after six days of storage, with possible fungistatic effect.

(vi) Bread at 2.5 and 5% of turmeric concentration showed the yellowest color. Additionally, through Principal component analysis it was possible to demonstrate the strong positive correlation between DPPH and the PA signal (515 nm). As well as a negative correlation of both variables with respect to the number of fungal colonies in bread. It is shown that bread to 5 and 10% of TC have a similar behavior in relation to these variables. In this way, a concentration of either 5 or 10% of turmeric added to wheat flour could be used to improve its sanitary quality and antioxidant activity.

ACKNOWLEDGEMENTS:

The authors would like to thank the "Instituto Politécnico Nacional", through the Complying with ethical standards.

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Ethical approval:

The manuscript has not been sent to more than one journal to be considered.

The work submitted is original and has not been published elsewhere in any form or language (partial or complete).

This study has not been divided into several parts and has not been sent to several magazines or to a magazine over time.

The results will be presented clearly, honestly and without fabrication, falsification or inappropriate manipulation of data (including image-based manipulation).

It complies with the specific rules of the discipline to acquire, select and process data.

No data, texts or theories of others are presented as if they were the author's own. And the appropriate awards must be given to other works

  Mean ± standard deviation values followed of different letter in each row and column between days 1 and 3, are significantly different (ρ < 0.05). L* indicates brightness (100 = White y 0 = Black), "a", indicates greenish-reddish [negative (green) to red (positive)] and "b", indicates blueish-yellowish [negative (blue) to yellow (positive)]. CB: Control bread (without addition of turmeric), TB: Turmeric bread

Figure captions Figure 1 .

 captions1 Figure captions

Figure 2 .

 2 Figure 1. Scheme of PAS experimental setup used in our experiments.Figure 2. Characterization of turmeric bread with different TP concentrations (0, 1.25, 2.5, 5 and 10%): a) Spectra of the photoacoustic signals and their b) density functions. Figure 3. Photoacoustic signal as a function of the concentration of turmeric bread (0, 1.25, 2.5, 5 and 10%) and a) DPPH and b) Number of fungal colonies formed on bread after six days.Figure 4. Principal component analysis (Dim 1 (68.57%) x Dim 2 (20.70%)) of the variables evaluated in the bread samples at 0, 1.25, 2.5, 5 and 10%. Figure 5. Correlation between the normalized PA signal of the sample with its DPPH (mol equivalent of Trolox /g).

Figure 4 .

 4 Figure 1. Scheme of PAS experimental setup used in our experiments.Figure 2. Characterization of turmeric bread with different TP concentrations (0, 1.25, 2.5, 5 and 10%): a) Spectra of the photoacoustic signals and their b) density functions. Figure 3. Photoacoustic signal as a function of the concentration of turmeric bread (0, 1.25, 2.5, 5 and 10%) and a) DPPH and b) Number of fungal colonies formed on bread after six days.Figure 4. Principal component analysis (Dim 1 (68.57%) x Dim 2 (20.70%)) of the variables evaluated in the bread samples at 0, 1.25, 2.5, 5 and 10%. Figure 5. Correlation between the normalized PA signal of the sample with its DPPH (mol equivalent of Trolox /g).

  Figure 1.

  Figure 2.

  Figure 4.

  IPN for the support to develop the experiments of the present study. We also thank Ing. Esther Ayala for her technical support at the Physics Department, CINVESTAV-IPN.

CONACYT, COFAA, EDI and projects SIP scholarships. The support Research and Postgraduate Secretariat (Project SIP 20196252 and SIP 20201809), and the Photothermal Techniques Laboratory of Physics Department, CINVESTAV-

Table captions Table 1 .

 captions1 Turmeric bread formulations

Table 2 .

 2 Equations used to calculate density estimate, linear correlation, and correlation coefficient

Table 3 .

 3 Photoacoustic signal levels at 428 and 515 nm, as well as antioxidant activity and effects on the sanitary quality of bread Table4. Texture profile and color analysis of bread samples with different concentrations of turmeric.

Table 5 .

 5 Mean consumer scores for sensory acceptability of 5 breads (0, 1.25, 2.5, 5 and 10% of turmeric powder).

	Table 1.
	Ingredients	CB	TB-1.25	TB-2.5	TB-5.0	TB-10
		(%)	(%)	(%)	(%)	(%)
	Wheat flour	81.72	80.7	79.68	77.63	73.55
	Olive oil	9.53	9.53	9.53	9.53	9.53
	Egg	6.54	6.54	6.54	6.54	6.54

CB: Control bread (without addition of turmeric), TB: Turmeric bread

Table 3 .

 3

	PA-
	s:
	Phot
	oac
	oust
	ic
	sign
	al;
	d:
	day

turmeric at wheat bread Digital image of the bread PA-s 515 nm PA -s 428 nm DPPH (μmol equivalent of Trolox/g) Digital image of bread on the sixth day

					No. of	No. of
					Colonies	colonies
					3 d	6 d
	(a) 0.00% (White)	0.02c	0.05d	20.24a	2	16
	(b) 1.25%	0.05b	0.132c	20.37 a	1	1
	(c) 2.50%	0.06b	0.161b	27.66 b	0	0
	(d) 5.00%	0.082a	0.163b	29.39b	0	0
	(e) 10.00 %	0.083a	0.181a	35.25c	0	0

Table 4 .

 4

	Treatment	CB		TB 1.25%	TB 2.5%	TB 5%	TB 10%
		Day 0	Day 3	Day 0	Day 3	Day 0	Day 3	Day 0	Day 3	Day 0	Day 3
	Hardness	14.13 f ± 1.47	20.93 cd ± 0.59	16.42 ef ± 0.47	16.58 ef ± 0.62	17.33 ef ± 2.20	15.2 f ± 0.53	18.89 de ± 0.64	21.81 c ± 0.31	24.98 b ±1.20	32.67 a ± 1.44
	Elasticity (mm)	4.4 d ± 0.35	5.49 abc ± 0.02	4.81 bcd ± 0.37	4.63 cd ± 0.01	6.01 a ± 0.28	5.45 abc ± 0.26	5.78 ab ± 0.41	4.29 cd ± 0.24	4.92 abcd ± 0.08	4.42 d ± 0.25
	Cohesiviness	0.5 bc ± 0.02	0.51 ab ± 0.02	0.44 de ± 0.01	0.55 a ± 0.01	5.45 abc ± 0.26	0.49 bcd ± 0.02	0.46 cd ± 0.01	0.48 bcd ± 0.01	0.41 ef ± .01	0.38 f ± 0.01
	Resilience	0.25 b ± 0.02	0.23 bcd ± 0.008	0.2 de ± 0.01	0.3 a ± 0.04	0.22 cde ± 0.02	0.17 f ± 0.01	0.19 e ± 0.01	0.24 bc ± 0.008	0.16 f ± 0.01	0.16 f ± 0004
	Chewiness (mJ)	33.3 c ± 2.86	56.63 a ± 2.07	33.13 c ± 0.75	38.27 c ± 1.25	52.23 ab ± 3.01	33.87 c ± 2.36	47.37 b ± 3.56	37.6 c ± 1.78	57.23 a ± 2.51	54.83 a ± 2.07
	a	-0.85 d ± 0.05	-1.23 d ± 0.12	-6.18 i ± 0.09	-5.58 h ± 0.07	-4.11 g ± 0.05	-1.62 f ± 0.14	1.25 d ± 0.01	4.29 c ± 0.23	5.78 b ± 0.05	8.3 a ± 0.22
	b	17.09 f ± 0.34	18.10 f ± 0.94	47.42 bc ± 0.38	46.71 c ± 1.46	51.72 a ± 0.06	50.54 a ± 0.71	49.89 ab ± 0.07	45.59 c ± 1.49	41.33 d ± 0.07	37.7 e ± 1.90
	L*	75.85 a ± 0.50	63.95 c ± 1.46	70.09 b ± 0.49	63.32 cd ± 2.14	71.06 b ± 0.11	60.76 d ± 0.43	63.58 c ± 0.03	51.33 f ± 0.90	56.71 e ± 0.11	46.48 g ± 0.72

Table 5

 5 ColorPorosity Sponginess Hardness Cohesiveness Aroma Chewiness Flavor

	Healthy	General
	option	attributes


Martin Quinson
email: martin.quinson@loria.fr

Esial Uhp

Flavien Vernier
email: flavien.vernier@univ-savoie.fr

Byte-Range Asynchronous Locking in Distributed Settings

This paper investigate a mutual exclusion algorithm on distributed systems. We introduce a new algorithm based on the Naimi-Trehel algorithm, taking advantage of the distributed approach of Naimi-Trehel while allowing to request partial locks. Such ranged locks offer a semantic close to POSIX file locking, where threads lock some parts of the shared file. We evaluate our algorithm by comparing its performance with to the original Naimi-Trehel algorithm and to a centralized mutual exclusion algorithm. The considered performance metric is the average time to obtain a lock.

Introduction

In multi-processes settings, shared resources must be protected against concurrent modifications to ensure data consistency. This mutual exclusion problem has been well studied in shared-memory environments. However, most solutions are not applicable to distributed settings since they rely on shared variables.

In distributed settings, mutual exclusion has to be provided by protocols relying on message exchanges. Several algorithms have been proposed in the literature to serialize access to a shared resource. They can be sorted in two groups: permission-based (e.g. Ricart-Agrawala [START_REF] Ricart | An optimal algorithm for mutual exclusion in computer networks[END_REF]) and token-based (e.g. Raymond [START_REF] Raymond | A tree-based algorithm for distributed mutual exclusion[END_REF], Naimi-Trehel [START_REF] Naimi | A log(n) ditributed mutual exclusion algorithm based on path reversal[END_REF]). The principle of the first group of algorithms is that a node can enter the critical section (CS) only after receiving the permission of the other nodes. The drawback of this approach is the high communication overhead. In the algorithms of the second group, the right to enter the CS is granted by the possession of a token, which is unique in the system and passed over the nodes. Several algorithms of this group exhibit a O(log N ) message complexity using tree-based approaches [START_REF] Naimi | A log(n) ditributed mutual exclusion algorithm based on path reversal[END_REF], [START_REF] Raymond | A tree-based algorithm for distributed mutual exclusion[END_REF].

We extend the Naimi-Trehel algorithm [START_REF] Naimi | A log(n) ditributed mutual exclusion algorithm based on path reversal[END_REF] in two ways. First, we assume that the protected resource is an array of elements and allows locks on subranges. This semantic is close to the POSIX file locking, where threads lock some consecutive parts of the file. This synchronization schema is useful for high performance computing [START_REF] Gustedt | parXXL: A Fine Grained Development Environment on Coarse Grained Architectures[END_REF].

The other extension we propose is inspired from [START_REF] Gustedt | Data HandOver: Reconciling message passing and shared memory[END_REF] and consists in asynchronous locks. Applications can declare locks without immediately trying to acquire them, and continue to proceed while the locking facility acquires the needed grants from other nodes. Such asynchronous locks are not part of the POSIX standard, but they are useful in a distributed setting, where transferring the data associated to the lock over the network can be time-consuming. Asynchronous locking thus eases the overlapping of communication and computation.

To our knowledge, it is the first time that the Naimi-Trehel algorithm is extended to offer asynchronous ranged locks. [START_REF] Wagner | Token-based read/writelocks for distributed mutual exclusion[END_REF] extends this algorithm for Read/Write-locks (provided that the duration of the critical sections is constant). [START_REF] Sopena | A faulttolerant token-based mutual exclusion algorithm using a dynamic tree[END_REF] provides solution for fault tolerance to the Naimi-Trehel algorithm, extending the work of [START_REF] Naimi | How to detect a failure and regenerate the token in the log(n) ditributed algorithm for mutual exclusion[END_REF], [START_REF] Mueller | Fault tolerance for token-based synchronization protocols[END_REF]. [START_REF] Arantes | Distributed mutual exclusion algorithms for grid applications: A hierarchical approach[END_REF] proposes an extension for hierarchical heterogeneous systems, at the price of introducing possible process starvation. [START_REF] Mueller | Prioritized token-based mutual exclusion for distributed systems[END_REF] introduces an extension allowing different priorities for nodes. It should be possible to extend the algorithms presented here for read/write locks, fault-tolerance, heterogeneity and request priority, but this is beyond our scope.

Naimi-Trehel is based on a distributed waiting queue along which the token circulates, and on a distributed tree structure which root is the queue tail. We present an extension to this algorithm for ranged locks. The new algorithm splits the queue in partial queues when partial locks are requested. Within each queue, only the head node can be granted the lock, and the concurrency appears between the several sub-queues.

We assume that nodes handle messages in FIFO order, and that the system is failure free.

The rest of this paper is organized as follows: §2 presents the semantic of asynchronous ranged locks. §3 presents the Naimi-Trehel algorithm. Our extension is then presented in §4 and evaluated in §5.

Asynchronous Ranged Locking

The locking overlay requires that messages are handled at any time. Since the application may naturally The data are received along with the lock to allow their modification. Indeed, the data used by the high performance computing applications we target can be quite large, and we have to take their size into account.

This interface can trivially be used for synchronous locking: unlock remains unchanged while lock simply chains lockCreate and doLock.

The locking semantic is that locks only apply on a part of the resource designed by the range. The resource is considered as a sequential array, and it is possible to serve several locks at the same time provided that their range do not overlap. When the ranges overlap, requests are served in a FIFO mode.

Naimi-Trehel Algorithm

Algorithm 2 presents an asynchronous version of the base Naimi-Trehel algorithm for distributed mutual exclusion (detailed in [START_REF] Naimi | A log(n) ditributed mutual exclusion algorithm based on path reversal[END_REF]). Only regular locks are considered; range locking will be introduced latter.

In this algorithm, nodes are granted to enter the critical section if they have the token. Unsatisfied requests are stored in a distributed waiting queue, which is simply-listed: each node only knows its direct successor (i.e. the node to which it should pass the token afterwards). This is the next variable of each node. A node Instead, nodes are arranged in a distributed tree for which the root is the node placed at the tail of the queue (tree invariant). If A is the child of B in that tree, then A considers B as the queue's tail. It will send any lock requests to B. They are forwarded along the tree edges until the real tail of the queue is reached. This provides a decentralized knowledge of the tail position. The father of node A is owner from A's point of view (even if it is only the probable owner).

The main idea of the Naimi-Trehel algorithm is that the tree gets updated on the fly during the request propagation. When A forwards a request on B's behalf, it means that B enters the queue. It is thus the new tail of the queue, so A sets B as its new owner.

Initially, no node requests the token, one node has the token and is considered as the owner by all nodes.

Split Waiting Queues (SWQ) algorithm

The SWQ algorithm can be seen as the Naimi-Trehel algorithm in which the token that represents a global resource can be split to lock the resource partially. Each sub-token represents a range of the global resource. These sub-tokens are managed like the original Naimi-Trehel token. According to the SWQ algorithm, a subtoken can be split if a sub-part of its range is requested. Respectively two continuous sub-tokens can be merged to give only one sub-token. In the following, the terms of tokens and sub-tokens are interchangeable.

We now detail our modifications to the Naimi-Trehel algorithm. The owners and nexts variables are changed from scalar values to lists. For example, nexts indicates the peers to which I should pass the lock after use. This list may contain several elements if several peers have requested for separate subranges. owned and request are changed from boolean to a list of ranges for which the condition holds. Two new variables are introduced: requestReceived lists the parts of the requested token we already received while waitingRequest lists the received requests that conflict with my own request.

The messages exchanged between servant to request the token contain the requester id, the requested range (noted request) and the range already found.

As in the original algorithm, if a node does not know the owner of tokens, it is itself the owner.

The SWQ Algorithm details

To keep this presentation simple, we assume that a given node only requests one continuous range at a given time, and release its token before the next request.

In the SWQ Algorithm, a request is proceeded sequentially -the first part of the request is looked for, then the subsequent part is looked for, and so onsuch as not to generate a dead lock. Oppositely to the classical Naimi-Trehel algorithm, if a node requests a range, it doest not immediately become the owner of this range. It only does so when its request message running the network finds a part of the request. This mechanism avoids some dead-lock or loop configurations.

The SWQ algorithm can be decomposed into three steps: the initial request, the reception and forwarding requests, and the exchange of tokens. These steps are achieved by the functions given in Algorithm 3.

The algorithm starts when a node asks a part of the token. It checks if is the owner of the first part When a node receives a request (see requestTo-kenCB), it checks if it is the owner of the searched part of this request. If it finds a part and this part is not in conflict with its own request, it updates the already found range of the request message and it sends the information about the found part to the original requester. If it is not the original requester, it updates its nexts and owners lists. In all cases, if the request is not satisfied, it forwards this request to the supposed owner of the searched part of the request. A particular case can appear if this node is the original requester. In this case the node does not send the information about the found part, it directly updates its requestReceived list if it has the token and does not update its owners list. If the found part is in conflict with its own request, the request message is stored into the waitingRequest list and will be proceeded when the local node is the owner of its own request.

The other actions performed by the algorithm -the management of information messages and tokens exchange -are less complex. When a node receives the information about a found part of its request, it updates its owners list. It becomes the new owner of this part. If the nexts list of a node is not empty, it sends each token of this list to the requester if it has the token and does not use it, and it updates its owned and nexts lists if necessary. When a node receives a token, it updates its owned list and its requestReceived list.

Experimentations

The experiments were run on the SimGrid simulator [START_REF] Casanova | SimGrid: a Generic Framework for Large-Scale Distributed Experiments[END_REF]. We compare the Naimi-Trehel algorithm, Centralized range token management and the SWQ algorithm. In each experiment, every node requests 25 locks.

We study the impact of the following parameters: the time each lock is kept by nodes (denoted α), the fraction of the resource locked (denoted range), the number of nodes and the size of the resource.

Table 1 presents the timing of the studied algorithms for representative values α and range when the resource size is fixed to 64kb (8192 double values). Since the Naimi-Trehel algorithm does not consider ranges, its timing are the same for the different values of range.

The first result is that the Naimi-Trehel algorithm leads to smaller waiting times than the centralized implementation when the full range is requested. For example, when α = 1 for 162 nodes, locks are obtained in 284s on average with NT vs. 396s with centralized. This result is not new, but is clearly highlighted here.

Conversely, when only parts of the resource are locked, NT leads to bigger waiting time than centralized. For example, when α = 1 for 162 nodes, locks are still obtained in 284s on average with NT while centralized now achieves 33.3s waiting times. This clearly demonstrates the potential benefit of partial locking, since it shows that the whole benefit of distributed locking from NT is nullified by the optimization made possible by partial locks.

When comparing SWQ to NT, one can remark that it is always slower than the classical Naimi-Trehel algorithm on locks requesting the full resource. This is because of the guards we added to manage the split tokens. Thus, although the global behavior of these two algorithms is the same with global requests, the SWQ algorithm is slowed down by its split token management ability. The benefit of SWQ becomes clear when considering partial locks. For example when α = 1 for 162 nodes, NT achieves 284s waiting times for partial The comparison of SWQ to centralized is not very flattering for our algorithm: in the presented experiments, centralized is always faster than SWQ. Table 2 compares the timings of centralized and SWQ when increasing the resource size. Former experiments were done with a resource of 64kb, we now use 640kb and 6.25Mb resources (respectively 81,960 and 819,600 double values). The range is kept to 1 16 here. These results show the huge impact that the resource size have on the waiting times. When size=640kb, centralized still outperform SWQ, but not as much as before: For α = 1 for 162 nodes, centralized is more than three time faster than SWQ for 64kb, and "only" 30% faster for 640kb. When size=6.25Mb, SWQ is three time faster than centralized.

This can be explained by the fact that the data is transfered to the requesting applications along with the lock. Centralized thus struggles with the amount of data exchanged by its central point. So, for larger resources, the cost of our distributed design is smaller than the gain of its lack of central bottleneck.

These results highlight the benefits of SWQ for partial locking of large resources.

Conclusion

This paper introduces a new distributed mutual exclusion algorithm presenting two main specificities: it allows to partially lock the resource and it manages the resource and the locks without any central point. The proposed algorithm should reveal useful in context of grid computing, where distributed processors handle large data sets, making centralized management of the mutual exclusion penalizing or even impossible. This is the case for systems with geographically distributed clusters. Experimental results show that in such contexts, our decentralized algorithm reduces the latency to acquire a critical section and thus improves the overall performance of parallel iterative algorithms.

Moreover the proposed algorithm allows asynchronous locks. Applications can declare locks without immediately trying to acquire them, and continue to proceed while the locking facility acquires the needed grants from other nodes. Such asynchronous locks are not directly part of the POSIX standard, but are useful in a distributed setting, where transferring the data associated to the lock over the network can be timeconsuming. This semantic extension allows the data to be sent in advance to the requester.

We foresee two directions of future work: First, we plan to compare the presented simulation results to live deployments of our program. Then, we would like to relax some constraints that we introduced for the management of conflicts between split queues so that our performance becomes closer to the Naimi-Trehel ones for full resource locking.

Algorithm 1 :

 1 Application Interface.

	Local Data: servant (peer): Locking facility process
	Function lockCreate(R ∈ resources,
	range ∈ P (r)) → lockID
	send (servant,"lockCreate", R, range)
	Function lockTest(lockID) → bool
	return rpc (servant, "lockTest")
	Procedure doLock(lockID → data)
	send (servant, "lockMe", lockID)
	waitMsg (expeditor=servant, type="proceed")
	Procedure unlock(lockID, data)
	send (servant, "unlock", lockID, data)
	block when waiting for a lock or using it, the locking
	facility cannot be part of the application itself and
	should be placed in separate thread or process. We thus
	separate the application using the locking facility from
	the servant providing this facility.
	Algorithm 1 presents the servant interface.
	Since locks are asynchronous, their creation (with
	lockCreate) is not blocking. The request sent
	to the servant contains the resource's name and the
	subrange to lock. The application can use lockTest
	to test whether trying to acquire the lock (with
	doLock) would block. Locks can be canceled with
	unlock, even if they were not yet acquired.

Algorithm 2 :

 2 Naimi-Trehel algorithm.

	Local Data: owner (peer)
	next (peer)
	request (bool)
	token (bool)
	appliBlocked (bool)
	appli (peer)
	Callback lockCreateCB(range, mode)
	request ← true
	if (owner = self ) then
	send (owner, "forwardRequest", self)
	owner ← self
	Callback forwardRequestCB(requester:
	peer)
	if (token) then next ← requester
	else send (owner,"forwardRequest",requester)
	owner ← requester
	releaseToken()
	Callback GiveTokenCB()

token ← true unblockAppli(); releaseToken() Callback lockMeCB() appliBlocked ← true ; unblockAppli() Callback testLockCB() return token ∧ request Callback unlockCB() request ← false ; releaseToken() Procedure releaseToken() if (token ∧ ¬request ∧ next = ∅) then send (next, "GiveToken") token ← false ; next ← ∅ Procedure unblockAppli() if (appliBlocked ∧ token ∧ request) then send (appli, "proceed") appliBlocked ← false requesting the lock should contact the node currently at the queue's tail to enter the queue. Keeping the tail location information globally up-to-date would imply a broadcast (i.e. O(n) messages per lock).

Algorithm 3 :

 3 Split Waiting Queue. Callback exchangeTokenCB(token: token t) add token into owned add token into requestReceived of its request (see requestToken in Algorithm 3). If it finds this part, it updates the already found range of the request, and if it owns this token, it updates its requestReceived list. Let us recall that a node can be the owner of a token that it has not received yet. If it is not the owner of all its requested range it sends a request message to the owner of the part that follows the already found part.

	Local Data: nexts (tokensList)
	owners (tokensList)
	owned (tokenList)
	request (token t)
	requestReceived (tokensList)
	waitingRequest (requestsList)
	Procedure requestToken()
	found ← Search First Part of request
	if (found!= null ∧ found ∈ owned) then
	add found into requestReceived
	if (found!= request) then
	nextOwner ← Search Next Owner of found
	requestMsg ← new message (request,
	found, mySelf)
	send (nextOwner, "requestToken",
	requestMsg )
	Callback requestTokenCB(requestMsg:
	request t)
	found )
	else if (found ∈ owned) then
	add found into requestReceived
	if (requestMsg.found != requestMsg.request)
	then
	nextOwner ← Search Next Owner of
	requestMsg.found
	send (nextOwner, "requestToken",
	requestMsg )
	if (mySelf != requestMsg.requester) then
	update owners with found
	else
	add requestMsg into waitingRequest
	Callback foundPartCB(token: token t)
	remove token from owners Procedure sendTokens()
	foreach element in nexts do
	if element.token ∈ owned ∧ do not use
	element.token then
	send (element.next, "exchangeToken",
	element.token)
	remove element.token from nexts
	remove element.token from owned

found ← Search Next Part(requestMsg) if !conflict() ∨ requestIsSatified() then if (found!= NULL) then update(requestMsg, found) if (mySelf != requestMsg.requester) then add found into nexts send (requestMsg.requester, "foundPart",

Table 1 .

 1 Average timings in seconds (size=64kb).

	α	range	algo	2	12	# of nodes 22 42	82	162
	0.25	1 16	NT central 0.2 0.8 SWQ 0.3 NT 0.8	13.1 23.4 44.7 82.6 1.6 3.6 7.6 16.3 3 5 11 32.6 13.1 23.4 44.7 82.6	165 33.5 117 165
		full	central	1	19.1 37.8 70.2	138	277
			SWQ	0.9	26.6 72.1	228	745	2807
	1	1 16	NT central 0.2 1.4 SWQ 0.3 NT 1.4	21.2 1.5 3.1 21.2	39 3.3 5.1 39	75.1 7.2 12.3 30.3 143 15.7 75.1 143	284 33.3 116 284
		full	central 1.6	27.6 53.8	101	199	396
			SWQ	1.4	26.9 72.1	228	747	2810
	5	1 16	NT central 0.6 4.5 SWQ 0.6 NT 4.5	64.8 5.1 6.2 64.8	122 9.7 13.1 31.3 60.9 237 463 18.9 37.2 122 237 463	922 73.1 153 922
		full	central 5.7	71.7	137	264	520	1035
			SWQ	4.5	64.8	122	268	787	2860

Table 2 .

 2 Impact of size on timings range = 1 locks while SWQ achieves 116s waiting times in the same settings.