MOJ ISSN: 2381182X MOJFPT
Food Processing & Technology
Research Article
Volume 2 Issue 4  2016
Characterization, Drying and Sorption Isotherms of Tunisian Tomato Experimental and Mathematical Investigation
Bilel Hadrich^{1}*, Imen Kharrat^{1} and Nabil Kechaou^{2}
^{1}Biological Engineering Department, National School of Engineers of Sfax, University of Sfax, Tunisia
^{2}MFAGPE Laboratory, National School of Engineers of Sfax, University of Sfax, Tunisia
Received: April 29, 2016  Published: June 20, 2016
*Corresponding author:
Bilel Hadrich, Biological Engineering Department, National School of Engineers of Sfax, University of Sfax, BP ‘1173’, 3038, Sfax, Tunisia, Email:
Citation:
Hadrich B, Kharrat I, Kechaou N (2016) Characterization, Drying and Sorption Isotherms of Tunisian Tomato Experimental and Mathematical
Investigation. MOJ Food process Technol 2(4): 00044. DOI:
10.15406/mojfpt.2016.02.00044
Abstract
The composition of the Tunisian Tomato (TT) reflects a very important nutritional value. In fact, the TT is rich in fiber (42 g/100g d.m.), protein (16.994 g/100g d.m.) and also potassium (245 mg/100 g d.m.) which is an essential micronutrient for humans thanks to its role in reducing the risk of hypertension. This renders the TT one of the major sources of this important mineral nutrient. An experimental determination of TT’s sorption isotherms is carried out at 45 and 55°C using the method of saturated salt solutions. The sorption curves are then approximated by twelve empirical models. The Peleg model yields the best fitting for both temperatures and for des/adsorption phenomenon. Drying of TT slices is carried out at the same two temperatures: 45 and 55 °C and two air velocities: 0.5 and 1.6 m/s. The convective drying characteristics of TT slices are established in a pilot dryer to assess the effect of drying air conditions on drying kinetics. Also, twelve different drying models are tested and compared according to their standard error and correlation coefficients to fit TT drying curves. The Logarithmic model shows the best fitting for all drying curves on all tested conditions.
Keywords: Tomato; Drying kinetics; Sorption isotherms; Empirical models
Introduction
Drying activity of tomato is prominently at the branch of the tomato processing. Tunisia is a big producer of tomato with 1100 000 tons in 2013, it is ranked 18th globally and 6th at MIAPT countries (Mediterranean International Association of the Processing Tomato) [1]. Dried tomato consumption is very low in Tunisia because it has been abandoned in favor of the canned though it is in the eating habits of Tunisians. Dried tomato production is mainly oriented towards export. Indeed, a remarkable evolution of tomato production shows an increasing request for the export of the dried tomatoes. However this activity must be better supervised and structured at the levels of drying techniques, the hygiene, the storage conditions and the quality, in order to continue the positive evolution and to conquer new markets. The optimization of the drying operation must meet two essential requirements which are restricted consumption of energy and preservation of the biological quality of the dried product [24]. We study in this work the sorption isotherms and the drying kinetics of tomato in order to contribute to the understanding of tomatoes drying.
Materials and Methods
All analyzes were performed in triplicate and results are expressed as mean ± standard deviation.
Moisture content and water activity
The dry weight was determined by drying the sample at a temperature of 105°C until obtaining constant weight [5]. The moisture content (kg/kg dry matter) is calculated by the following formula:
$X=\frac{m{m}_{s}}{{m}_{s}}$
with m: mass of the wet sample (kg) before drying, ms: mass of dry sample (kg) after drying.
The dry matter content (g/100 g fresh weight) was determined by following formula:
$\text{drymattercontent}=\frac{{m}_{s}}{m}\times 100$
The determination of water activity was established by NOVASINA apparatus (AW Sprint TH500) at 25°C.
Protein
Total nitrogen was determined by the Kjeldahl method. Protein was calculated using the general factor (6.25) [6].
Ash and mineral compositions
Ash was determined by the incineration of the sample in a muffle furnace at 550°C for 3 h [5]. The residue was dissolved in HNO ^{3} and the mineral constituents (Ca, Mg, Na, K, Zn, Cu and Fe) were analyzed, using an atomic absorption spectrophotometer (Analytikjena AAS Zeenit 700) [5].
Lycopene
The determination of the lycopene content was performed according to the method developed by [7].
Ascorbic acid
The extraction of vitamin C (ascorbic acid) was performed with 20 ml of metaphosphoric acid (HPO^{3})^{n} (10%) from 20 g of crushed tomatoes. After centrifugation (5000 rpm, 15 min), the obtained pellet was washed again with 20 ml of the metaphosphoric acid solution, the supernatant was recovered and adjusted with distilled water to 50 ml [8]. The dosage of vitamin C was performed according to the titration method by 2,6dichlorophenolindophenol (D.C.P.I.P) [9].
Fat content
Fat content was determined by the Soxhlet method, using petroleum ether as a solvent at 40 °C [5].
Dietary fiber
Total, soluble and insoluble dietary fiber content was determined according to the enzymatic–gravimetric method [10].
Sorption isotherms
The adsorption and desorption isotherms of tomato were determined at 45 and 55°C by using the static gravimetric method. Ten saturated salt solutions were prepared by dissolving an appropriate quantity of salt in distilled water [2]. Each salt solution was prepared in a glass jar of 1 liter with an insulating lid. Every glass jar was a quarter filled with a saturated salt solution. The jars were placed in an oven of a fixed temperature for 24h to be stabilized. Each jar provided a fixed relative humidity (RH) corresponding to a fixed water activity (a^{w}) for each temperature and salt concentration (Table 1). The mass transfer between the product and the surrounding ambience in the jar was assured by moisture vapor diffusion. Equilibrium was considered to be reached when the change in samples weight did not exceed 0.0001 g.
Experimental drying
The study of the convective drying kinetics of tomato was carried out using the pilot dryer presented in Figure 1. The used experimental apparatus is a closed loop. The air flow running in the test section is horizontal to the product surface. First, the tomatoes were washed with distilled water and then carefully cut into four quarters. After the setting rate of air flow, the controller starts to heat up to the setting temperature. The air flows into the dryer with the fan; it passes the electrical resistors where it is heated. Once the experimental conditions are stabilized, the sample to dry is placed on the support plate of a balance in the horizontal test section (Figure 1) to establish the drying operation. The drying kinetics were determined from the measurements of the weight variation of the samples over time. These measurements are made using a precision electronic balance digital display. The balance is linked to a computer, and equipped with a data output for the acquisition of the mass over time. The obtained raw results were treated using a Matlab program carried out in this work to eliminate any disturbance caused by air ventilation. This program is founded using the polynomial sliding protocol for 5 by 5 points. The Matlab program calculates also the drying rate. Then the reduced moisture content evolution
${X}_{r}=\left(X{X}_{eq}\right)/\left({X}_{0}{X}_{eq}\right)$
was presented versus time and the drying rate as a function of moisture content variation; with
${X}_{eq}$
: equilibrium moisture content calculated by desorption modelling;
${X}_{0}$
: initial moisture content. Experiments were investigated on two levels of air temperature (45 and 55°C) and two levels of air velocity (0.5 and 1.6 m/s).
Sorption isotherms and drying curves fitting
The fitting of sorption isotherms and drying curves allows describing the experimental curves with empirical and semiempirical models and identifying many useful parameters. Twelve models were tested for fitting of sorption isotherms (Table 2) and twelve models for drying curves fitting (Table 3). Those are the most used models for fitting data of sorption isotherms and drying curves.
Model parameters are determined by minimizing the difference between the experimental and calculated data. The mathematical treatment boils down to the use of nonlinear regression by the software Curve Expert 1.4. The ability of these equations to analyze experimental data is verified by two statistical terms: standard error (SE) and correlation coefficient (r). Indeed, the greatest values of r and the smallest values â€‹â€‹of SE are the criteria that justify the choice of the most suitable model for the description of experimental data.
Results and Discussion
Chemical composition
Table 4 summarizes the chemical composition of fresh tomatoes. The water activity refers to the availability of water in organic products. A value close to unity (0.860) is associated with the formation of a liquid water film on the surface of the pores whose dimensions are greater than or of the order of micrometer. The water retained by capillary forces explains the nonstorability to the state [33,34]. We note also that the tomato is rich in water: about 95% of the fresh matter which explains that the food is not very rich in calories (1820 kcal/100 g) [35]. Analysis of fresh tomatoes (Table 4) shows also that these are rich in soluble fiber and insoluble fiber. Indeed, total fibers have about the contents of (42 g/100g d.m.). Fresh tomato is low in fat and cholesterolfree, and has average protein content about 17 g/100g d.m. This has been well noted by [35]. A grade of ascorbic acid content (1.616 g/100 g d.m.) shows the richness of the tomatoes in vitamin C.
Table 5 shows the mineral composition of fresh tomatoes. The tomato contains many minerals like most fruits and vegetables; it brings about a lot of potassium (245 mg/100 g d.m.) which is an essential micronutrient for humans and it can reduce the risk of hypertension which made her a significant source of this important mineral [35]. The obtained chemical and mineral compositions of fresh tomatoes are in the same order of obtained values by an anterior work for tomatoes byproduct [36].
Salt 
NaOH 
LiCl 
MgCl_{2} 
K_{2}CO_{3} 
NaBr 
SrCl_{2} 
NaNO_{3} 
NaCl 
KCl 
BaCl_{2} 
45°C 
0.0622 
0.1112 
0.3112 
0.4157 
0.5195 
0.61 
0.6999 
0.7493 
0.8174 
0.89 
55°C 
0.053 
0.1097 
0.2993 
0.4008 
0.5015 
0.5465 
0.6815 
0.7477 
0.8070 
0.8776 
Table 1: Water activities of used saturated salt solutions at 45 and 55°C [2].
Model 
Equation 
GAB [11] 
${X}_{eq}=\frac{{X}_{m}\cdot A\cdot B\cdot {a}_{w}}{\left(1B\cdot {a}_{w}\right)\cdot \left(1B\cdot {a}_{w}+A\cdot B\cdot {a}_{w}\right)}$

BET [12] 
${X}_{eq}=\frac{{X}_{m}\cdot A\cdot {a}_{w}}{\left(1{a}_{w}\right)\cdot \left(1+\left(A1\right)\cdot {a}_{w}\right)}$

Modified BET [13] 
${X}_{eq}=\frac{A}{1B\cdot {a}_{w}}$

Halsey [14] 
${X}_{eq}={\left(\frac{A}{\mathrm{ln}\left({a}_{w}\right)}\right)}^{1/B}$

Oswin [15] 
${X}_{eq}=A\cdot {\left(\frac{{a}_{w}}{1{a}_{w}}\right)}^{B}$

White and Eiring [16] 
${X}_{eq}=\frac{1}{A+B\cdot {a}_{w}}$

Adam & Shove [17] 
${X}_{eq}=A+B\cdot {a}_{w}+C\cdot {a}_{w}{}^{2}+D\cdot {a}_{w}{}^{3}$

Iglesias & Chirife [18] 
${X}_{eq}=A+B\cdot \left(\frac{{a}_{w}}{1{a}_{w}}\right)$

Caurie [16] 
${X}_{eq}=\mathrm{exp}\left(A+B\cdot {a}_{w}\right)$

Smith [19] 
${X}_{eq}=AB\cdot \mathrm{ln}\left(1{a}_{w}\right)$

Chung & Pfost [20] 
${X}_{eq}=\frac{1}{A}\cdot \mathrm{ln}\left(\frac{T\cdot \mathrm{ln}({a}_{w})}{B}\right)$

Peleg [21] 
${X}_{eq}=\frac{1}{A}\cdot \mathrm{ln}\left(\frac{T\cdot \mathrm{ln}({a}_{w})}{B}\right)$

Table 2: Tested models for sorption isotherms fitting.
Model 
Equation 
Newton [22] 
${X}_{r}=\mathrm{exp}(a\cdot t)$

Henderson & Pabis [23] 
${X}_{r}=a\cdot \mathrm{exp}(b\cdot t)$

Modified Henderson & Pabis [24] 
${X}_{r}=a\cdot \mathrm{exp}(b\cdot t)+c\cdot \mathrm{exp}(d\cdot t)+e\cdot \mathrm{exp}(f\cdot t)$

Logarithmic [25] 
${X}_{r}=a\cdot \mathrm{exp}(b\cdot t)+c$

Two terms [26] 
${X}_{r}=a\cdot \mathrm{exp}(b\cdot t)+c\cdot \mathrm{exp}(d\cdot t)$

Two termsexponential [27] 
${X}_{r}=a\cdot \mathrm{exp}(b\cdot t)+\left(1a\right)\cdot \mathrm{exp}(b\cdot a\cdot t)$

Diffusional approach [25] 
${X}_{r}=a\cdot \mathrm{exp}(b\cdot t)+\left(1a\right)\cdot \mathrm{exp}(b\cdot c\cdot t)$

Verma et al. [28] 
${X}_{r}=a\cdot \mathrm{exp}(b\cdot t)+\left(1a\right)\cdot \mathrm{exp}(c\cdot t)$

Page [29] 
${X}_{r}=\mathrm{exp}\left(a\cdot {t}^{b}\right)$

Modified Page [30] 
${X}_{r}=\mathrm{exp}\left({\left(a\cdot t\right)}^{b}\right)$

MidilliKucuk [31] 
${X}_{r}=a\cdot \mathrm{exp}(b\cdot {t}^{c})+d\cdot t$

Wang and singh [32] 
${X}_{r}=1+a\cdot t+b\cdot {t}^{2}$

Table 3: Tested models for drying curves fitting.
Compound 
Content 
a_{w} 
0.860 ± 0.003 
Moisture content (%) 
94.336 ± 0.010 
Protein 
16.994 ± 1.166 
Lycopene 
0.086 ± 0.005 
Ascorbic acid 
1.575 ± 0.059 
Fat 
1.123 ± 1.156 
Insoluble fiber 
31.22 ± 0.003 
Soluble fiber 
10.12 ± 0.003 
Ash 
8.691 ± 0.744 
Table 4: Chemical composition of fresh tomatoes (g/100g d.m.)
Mineral 
Content 
Sodium 
5.18 10^{2} ± 0.007 
Potassium 
245 ± 0.006 
Magnesium 
5.07 10^{2} ± 0.004 
Calcium 
9.037± 0.455 
Iron 
2.85 10^{2} ± 0.004 
Zinc 
3.98 10^{3} ± 0.002 
Manganese 
7.51 10^{3} ± 0.004 
Table 5: Mineral composition of fresh tomatoes (mg/100 g d.m.).
Sorption isotherms and modelling results
Figure 2 shows the variation of equilibrium moisture content (X^{eq}) of tomato slices depending on a^{w} for the adsorption and desorption isotherms for 45°C. As seen, the sorption isotherms in tomatoes have a sigmoid shape of type II according to the BET classification [12], as for most food. This type of isotherm is characterized by an asymptotic behavior in the field of high a^{w} in which water behaves like pure water. By wearing the adsorption isotherms and desorption of water from tomatoes on the same graph (Figure 2), we find that the adsorption curve does not overlap with the desorption one. This not even minimal coincidence is called hysteresis phenomenon. Most often, the isotherm of adsorption is lower than that of the dehydration. Same behaviors are observed for 55°C and all values of equilibrium moisture content for adsorption and desorption for the same a^{w} value, are lower than obtained for 45°C because of temperature effect (results not shown). The same form of desorption isotherm was obtained previously by [37] for other temperatures.
Tables 6 shows a comparison between models used for modelling the adsorption and desorption isotherms for 45°C and 55°C. All models show a very important quality (r > 0.89 and SE < 0.13). Peleg model has the highest correlation coefficient (0.997 < r < 1) and the lowest standard error (0.006 < ES < 0.119) in the case of both adsorption and desorption isotherms for 45 and 55°C. Table 7 shows identified parameters of Peleg model for desorption and adsorption isotherms. Figure 2 shows also fitting results using Peleg model versus experimental one. In this work Peleg model is chosen for modelling the sorption isotherm for tomato at 45 and 55°C.
Drying curves and kinetics
The initial moisture content (X^{0}) of tomatoes varies from 16.655 to 17.349 kg/kg d.b. and was decreased to a final moisture content ranging from 0.625 to 0.048 kg/kg d.b. The curves of the reduced moisture content as a function of time (Xr = f(t)) and the drying rate versus moisture content at the tested temperatures and air velocities are shown in Figure 3. The shape of the drying kinetics of tomato is similar for all tested temperatures and velocities. The moisture content of the product decreases rapidly in the first drying time, then more and more slowly in long time. Indeed, at the start of drying, the moving mechanism of water through the walls capillarity pores of the solid to the surface is obtained in an accelerated manner reflecting a rapid decrease in the water activity of the product. At the end of the drying process, product temperature increase at the center and the decrease of its moisture content lead to a new state of equilibrium in the product; a balance wherein the enthalpy difference between air temperature and product is reduced and solid water activity is equal to the relative humidity of the air. In this equilibrium, the water migrates therefore more hardly in the product and the internal transfer of matter becomes a limiting phenomenon. Other phenomenon are added and make the transfer difficult in the material such as the solute concentration, the growing phenomenon of crusting and the product surface hardening, reflecting a possible resistance of the cell walls [3]. According to Figure 3, the comparison of the curves shows that the increasing of air temperature from 45 to 55°C reduced the drying time. This fact is due to the increase of the osmotic pressure of the water in the product. Temperature has a greater effect on the tomato moisture content than the velocity of drying. The temperature has a higher influence at 55°C. We can therefore conclude that there is a negative correlation between temperature and drying time. These results are in agreement with others obtained for other temperatures [37,38] and other food products such as carrot, apple, beet pulp etc… [39,40]. The comparison of kinetics obtained at the same temperature and three velocities shows that the drying is faster for the higher velocity [41]. However, for a constant drying temperature 55°C, increasing the drying rate from 0.5 to 1.6m/s is a waste of energy since the increase of drying velocity has no effect on the moisture content as a function of time. Drying kinetics of tomato did not show a constant rate period. Therefore, the drying of tomato took place during the falling rate period.
The evolution of the reduced water content (X^{r}) versus time (t) for different temperatures and different velocities were fitted by several models proposed in the literature. Only models of Newton, Henderson and Pabis, Modified Henderson and Pabis, Logarithmic, Two terms and Wang show a best fitting for all curves. The relative standard error (SE) for all models is between 0.006 and 0.061 and the relative correlation coefficient r shows values ranging from 0.977 to 1 (Table 8). The better model shown best fitting for all conditions is Logarithmic model. It is characterized by the minimum SE (0.006 ≤ SE ≤ 0.017) and maximum r (0.998 ≤ r ≤ 1). Logarithmic model is then selected to describe the drying curves of tomato for temperature ranging from 45 to 55°C and velocity from 0.5 to 1.6 m/s. Figure 3 shows also the fitting quality of logarithmic model at two temperatures and two velocities. Table 9 shows the optimum logarithmic parameters for all conditions.
Figure 2: Fitted with Pelge model () and exprimental adsorption (solid motif) and desorption (empty motif) isotherms at 45°C.
Figure 3: Drying kinetics at 4555°C and 0.51.6 m/s. (a): Experimental reduced moisture content as function of drying time (points) and Logarithmic model (); (b): drying rate as function of moisture content during drying.
Model 
Type of sorption 
45°C 
55°C 
r 
SE 
r 
SE 
GAB 
Desorption 
0.9948 
0.0316 
0.9994 
0.00808 
Adsorption 
0.89676 
0.13948 
0.99598 
0.01617 
BET 
Desorption 
0.95823 
0.08354 
0.99742 
0.01578 
Adsorption 
0.96283 
0.08027 
0.99433 
0.01809 
Modified BET 
Desorption 
0.9766 
0.06287 
0.98991 
0.03117 
Adsorption 
0.9803 
0.0587 
0.98056 
0.0334 
Halsey 
Desorption 
0.9901 
0.04102 
0.99958 
0.00631 
Adsorption 
0.99024 
0.041 
0.99445 
0.0179 
Oswin 
Desorption 
0.97843 
0.0604 
0.99735 
0.01602 
Adsorption 
0.97837 
0.06145 
0.99638 
0.01446 
White and Eiring 
Desorption 
0.9766 
0.06287 
0.98991 
0.03117 
Adsorption 
0.9803 
0.0587 
0.98056 
0.0334 
Adam and Shove 
Desorption 
0.99522 
0.03236 
0.99355 
0.02828 
Adsorption 
0.99205 
0.0424 
0.99903 
0.00851 
Iglesias and Chirife 
Desorption 
0.96884 
0.07241 
0.99763 
0.01512 
Adsorption 
0.97423 
0.06703 
0.9849 
0.02947 
Caurie 
Desorption 
0.95413 
0.08752 
0.9875 
0.03451 
Adsorption 
0.957 
0.08622 
0.9973 
0.01249 
Smith 
Desorption 
0.96716 
0.07391 
0.96575 
0.05681 
Adsorption 
0.9663 
0.0765 
0.98687 
0.02749 
Chung and Pfost 
Desorption 
0.9388 
0.10625 
0.89875 
0.10182 
Adsorption 
0.92574 
0.11914 
0.90141 
0.07867 
Peleg 
Desorption 
0.99785 
0.02162 
0.99936 
0.00886 
Adsorption 
0.99666 
0.0275 
0.99949 
0.00621 
Table 6: Statistical parameters of all tested models for sorption isotherms at 45 and 55°C.
Temperature 
Type of sorption 
A 
B 
C 
D 
45°C 
Desorption 
0.408 
0.296 
1.578 
7.91 
Adsorption 
0.373 
0.266 
1.566 
7.328 
55°C 
Desorption 
0.1067 
0.3502 
0.7183 
4.288 
Adsorption 
0.2117 
0.6691 
1.377 
7.739 
Table 7: Optimum Peleg model parameters for desorption and adsorption isotherms.

T = 45°C
v = 0.5 m/s 
T = 55°C
v = 0.5 m/s 
T = 45°C
v = 1.6 m/s 
T = 55°C
v = 1.6 m/s 
Model 
r 
SE 
r 
SE 
r 
SE 
r 
SE 
Newton 
0.999 
0.010 
0.999 
0.011 
0.997 
0.023 
1.000 
0.009 
Henderson and Pabis 
0.999 
0.011 
0.999 
0.011 
0.997 
0.023 
1.000 
0.009 
Modified Henderson and Pabis 
0.999 
0.011 
0.999 
0.011 
0.997 
0.026 
1.000 
0.010 
Logarithmic 
1.000 
0.007 
1.000 
0.006 
0.998 
0.017 
1.000 
0.009 
Two terms 
0.999 
0.011 
0.999 
0.011 
0.997 
0.025 
1.000 
0.009 
Wang 
0.991 
0.037 
0.993 
0.033 
0.993 
0.034 
0.977 
0.061 








Table 8: Statistical parameters of all tested models for drying curves.

T = 45°C ; v = 0.5 m/s 
T = 55°C ; v = 0.5 m/s 
T = 45°C ; v = 1.6 m/s 
T = 55°C ; v = 1.6 m/s 
a 
1.014 
1.019 
1.046 
0.999 
b 
0.057 
0.074 
0.095 
0.188 
c 
0.030 
0.037 
0.072 
0.003 
Table 9: Optimum Logarithmic model parameters for all tested conditions.
Conclusion
Desorption and adsorption isotherms of tomato were established using gravimetric method. The results show a sigmoid shape of type II according to the BET classification. Peleg model shows the best fit for sorption isotherms of tomato at 45 and 55°C. The optimum parameters of Peleg model are determined. Drying kinetics of tomato showed the absence of a constant rate period. Therefore, the drying of tomato took place during the falling rate period. The drying temperature has a greater effect on the tomato moisture content than the velocity. Logarithmic model shows the best fit for the drying curves obtained at different conditions (T = 45 and 55°C; v = 0.5 and 1.6 m/s). The optimum parameters of Logarithmic model are also presented.
References
 http://faostat3.fao.org
 Dumoulin E, Bimbenet JJ, Bonazzi C, Daudin JD, Mabonzo E, et al. (2004) Activité de l’eau, teneur en eau des produits alimentaires: isothermes de sorption de l’eau. Industrie Alimentaires et Agricoles, (Cahier Scientifique), p. 819.
 Bonazzi C, Bimbenet JJ (2008) Séchage des Produits AlimentairesMatériels et applications. Techniques de l’Ingénieur Traité Agroalimentaire p. 117.
 Multon JL, Bizot H, Martin G (1991) Mesure de l’eau adsorbée dans les aliments: teneur en eau, activité de l’eau, sorption. Techniques d’analyse et contrôle dans les IAA, (2nd edn), Paris, p. 163.
 Association of Official Analytical Chemists (1997) Official methods of analysis (16th edn), Washington, DC, USA.
 Afnor (1970) Directives générales pour le dosage de l’azote avec minéralisations selon la méthode. In: Kjeldahl & Afnor (Eds.), Paris.
 Fish WW, PerkinsVeazie P, Collins JK (2002) A quantitative assay for lycopene that utilizes reduced volumes of organic solvents. J Food Compos Anal 15(3): 309317.
 Association of Official Analytical Chemists (2007) Method 967.21: vitamin C (ascorbic acid) in vitamin preparations and juices. In: Horwitz W & Latimer W (Eds.), Official methods of analysis. p. 2223.
 Association of Official Analytical Chemists (1984) Method 967.21Method IFU n° 17: vitamin C (ascorbic acid) in vitamin preparations and juices. ISO 65576552.
 Lee SC, Prosky L, De Vries J (1992) Determination of total, soluble, and insoluble dietary fiber in foods: Enzymaticgravimetric method, MESTRIS buffer: Collaborative study. J AOAC Int 75: 395416.
 Van Der Berg C, Bruin S (1981) Water activity and its estimation in food systems. In: LB Rockland GF, Stewart (Eds.), Theoretical aspects, in Water Activity: influence on food quality. Academic Pres, New York, USA, p. 161.
 Brunauer S, Emmett PH, Tell E (1938) Adsorption of gases in multimolecular layers. J Am Chem Soc 60(2): 309319.
 Jaafar F, Michaíowski S (1990) Modified BET equation for sorption/desorption isotherms. Drying Technol 8(4): 811827.
 Halsey G (1948) Physical adsorption on nonuniform surfaces. J Chem Phys 16(10): 931.
 Oswin CR (1946) The kinetics of package life. III. The isotherms. J Chem Technol Biotechnol 65: 419–421.
 Castillo MD, Martínez EJ, González HHL, Pacin AM, Resnik SL (2003) Study of mathematical models applied to sorption isotherms of Argentinean black bean varieties. J Food Eng 60: 343348.
 Chirifie J, Iglesias HA (1978) Equations for fitting water sorption isotherms of foods: Part I. A review. Int J Food Sci Technol 13(3): 159174.
 Iglisias H, Chirife J (1976) BET monolayer values in dehydrated food. Comparison with BET theory. LebensmittelWissenschaft und Technologie 9(b): 123129.
 Smith SE (1947) Sorption of wheat vapour by high polymers. J Am Chem Soc 69: 646651.
 Chung DS, Pfost HB (1967) Adsorption and desorption of water vapour by cereal grains and their products. Part II. Transaction of the ASEA 10(4): 549551.
 Peleg M (1993) Assessment of a semiempirical four parameter general model for sigmoid moisture sorption isotherms. Journal of Food Process Engineering 16: 2137.
 Lewis WK (1921) The rate of drying of solid materials. Ind Eng Chem 13(5): 427432.
 Zhang Q, Litchfield JB (1991) An optimization of intermittent corn drying in a laboratory scale thin layer dryer. Drying Tech 9(2): 383395.
 Karathanos VT (1999) Determination of water content of dried fruits by drying kinetics. J of Food Eng 39(4): 337344.
 Yaldiz O, Ertekin C (2001) Thin layer solar drying of some vegetables. Drying Tech 19(34): 583597.
 Henderson SM (1974) Progress in developing the thin layer drying equation. Transactions of ASAE 17(6): 11671168.
 SharafEdeen YL, Hamady MY, Blaisdell JL (1979) Mathematical simulation of fully exposed ear corn and its components. Trans ASAE (Am Soc Agric Eng) N°796523, p. 20.
 Verma LR, Bucklin RA, Endan JB, Wratten FT (1985) Effects of drying air parameters on rice drying models. Trans ASAE (Am Soc Agric Eng) 28(1): 296301.
 Page GE (1949) Factors influencing the maximum rates of air drying shelled corn in thin layer. Unpublished Maters Thesis, Prude University, Lafayette, Indiana, USA.
 White GM, Ross IJ, Ponelert R (1981) Fully exposed drying of popcorn. Trans ASAE (Am Soc Agric Eng) 24(2): 466–468
 Midilli A, Kucuk H, Yapar ZA (2002) New model for singlelayer drying. Drying Tech 20(7): 15031513.
 Wang CY, Singh RP (1978) A single layer drying equation for rough rice. ASAE, Paper n° 3001.
 Jomaa W (1991) Thèse de Doctorat: Séchage de Matériaux Fortement Déformables. Université de Bordeaux, France.
 Madiouli J (2010) Thèse de Doctorat: Mesure de Retrait au Cours du Processus Thermique. Ecole Nationale d’Ingénieur de Monastir.
 Céline Ch (2010) Stabilité de microconstituants de la tomate (composés phénoliques, caroténoides, vitamines C et E) au cours des procédés de transformation : études en systèmes modèles, mise au point d’un modèle stoechiocinétique et validation pour l’étape unitaire de préparation de sauce tomate. Université d’Avignon. France.
 Azabou S, Abid Y, Sebii H, Felfoul I, Gargouri A, et al. (2016) Potential of the solidstate fermentation of tomato by products by Fusarium solani pisi for enzymatic extraction of lycopene. LWT  Food sci technol 68: 280287.
 Belghith A, Azzouz S, ElCafsi A (2016) Desorption isotherms and mathematical modeling of thin layer drying kinetics of tomato. Heat Mass Transfer 52(3): 407419.
 Demiray E, Tulek Y, Yilmaz Y (2013) Degradation kinetics of lycopene, ßcarotene and ascorbic acid in tomatoes during hot air drying. LWT  Food sci technol 50(1): 172176.
 Boudhrioua N, Michon C, Cuvelier G, Bonazzi C (2002) Influence of ripeness and air temperature on changes in banana texture during drying. J Food Eng 55(2): 115121.
 Timoumi S, Mihoubi D, Zagrouba F (2003) Study of the food quality changes during drying process by two heating modes: Convection & infrared, In 4th European Congress of Chemical Engineering, Espagne.
 Boughali S, Benmoussa H, Bouchekima B, Mennouche D, Bouguettaia H, et al. (2009) Crop drying by indirect active hybrid solar  Electrical dryer in the eastern Algerian Septentrional Sahara. Sol Energ 83(12): 22232232.

