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Quality assessment and kinetics of dehydrated watermelon seeds: Part 1
Engineering in Agriculture, Environment and Food xxx (2017) 1e8
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Quality assessment and kinetics of dehydrated watermelon seeds: Part 1 Hossein Chaji a, Mahdi Hedayatizadeh b, * a
Agricultural Engineering Research Department, Khorasan Razavi Agricultural and Natural Resources Research and Education Center, AREEO, Mashhad, Iran b Faculty of Agriculture, University of Birjand, Birjand, Iran
a r t i c l e i n f o
a b s t r a c t
Article history: Received 17 September 2016 Received in revised form 20 January 2017 Accepted 24 January 2017 Available online xxx
Processing watermelon seeds has recently attained great attention in industry from two aspects of snack and high quality oil productions. Hence, in the first phase of our research, studying their drying kinetics, color quality in terms of L* a* b*, total color difference (DE), chroma and Hue angle and germination vigor of watermelon seeds were studied experimentally in detail with three drying air temperatures (40, 50 and 60 C) along with three levels of air velocity (0.5, 1 and 1:5 m s1 ). It was concluded that well-known Verma et al. thin layer drying model was greatly superior for prediction of watermelon seed drying kinetics in most cases while the effective moisture diffusivity of seeds was found ranging between 3.009 1010 and 6:805 1011 m2 s1 and the energy of activation was evaluated between 37.11 and 56:63 kJ mol1 . It was also inferred that air temperature level more profoundly affected the color and germination of the seeds than velocity while to maintain color quality and germination vigor high, the convectively drying temperatures should not exceed 50 C. © 2017 Asian Agricultural and Biological Engineering Association. Published by Elsevier B.V. All rights reserved.
Keywords: Watermelon Drying Germination Color quality
1. Introduction Preserving seeds of agricultural products against deterioration is a big problem in agriculture. Moreover, the conventional cheap open sun drying of seeds has also adversely affected the quality of seeds in terms of color and viability while insects' infestation and the hygienic - related problems have highlighted the great significance of utilizing a dryer and setting the drying conditions correctly for better gains. As good seed quality directly affects the success of crops and contributes significantly to enhance productivity levels, high-quality seed production is a challenge for the agricultural sector (Barrozo et al., 2014). Generally, the term thin layer drying refers to the grain drying process in which all grains are fully exposed to the drying air under constant drying conditions while all commercial flow dryers are designed on thin layer drying principles. Hence, thin layer is believed to be the best criteria for the simulation of food drying processes (Aghbashlo and SamimiAkhijahani, 2008). Internationality, numerous studies have sought
* Corresponding author. E-mail addresses: [email protected] (H. Chaji), [email protected] (M. Hedayatizadeh).
to model or describe the thin layer drying process of seeds. Mathematical modeling of thin layer drying of papaya seeds was considered for finding the appropriate drying conditions including three different temperatures each accompanied by three different velocities of air (Mocelin et al., 2014). Drying kinetics of seeds of three kinds of grape undertaking three different temperatures with a constant air velocity was also studied in detail and the corresponding effective moisture diffusivity and activation energy parameters were found. Moreover, the Lewis model as the excellent model for predicting all three grape seed was proposed (Roberts et al., 2008). Besides, the thin layer drying of melon seeds was studied through using five desorption isotherm and three thin layer drying models while it was found that the exponential model is adequate enough for predicting thin layer drying of melon seeds (Ajibola, 1989). A method for determination of temperature dependent moisture diffusivities of pumpkin seeds through natural and forced convection drying processes was also proposed. Therefore, the pumpkin seed particles were considered as of rectangular prisms with specified dimensions while execution of written computer program through the iterative technique led to specification of coefficient of diffusivity of the given seeds (Can, 2007). Many other studies are also performed for drying other
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Please cite this article in press as: Chaji, H., Hedayatizadeh, M., Quality assessment and kinetics of dehydrated watermelon seeds: Part 1, Engineering in Agriculture, Environment and Food (2017), http://dx.doi.org/10.1016/j.eaef.2017.01.006
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into the dryer. Nomenclature 2.2. Moisture content determination M Deff T MR L N Mt M0 Me D0 Ea Rg T V
Moisture content (db%) Effective moisture diffusivity (m2 s1 ) Time (s) Moisture ratio () Half thickness of the slab (watermelon seed) (m) Positive integer () Moisture content at any time (db%) Initial moisture content (db%) Equilibrium moisture content (db%) pre-exponential factor of the Arrhenius equation (m2 s1 ) Activation energy (J mol1 ) Universal gas constant (J mol1 K1 ) Absolute air temperature (K) Hot air velocity (m s1 )
agricultural seeds (wheat seed (Hemis et al., 2012), grape seed (Johann et al., 2016), barley grain (Markowski et al., 2007), canola seed (Hemis et al., 2015), hass avocado seed (Avhad and Marchetti, 2016), amaranth seed (Abalone et al., 2006)) among which watermelon hot air seed drying has been scarcely investigated except for an infrared drying study of watermelon seeds conducted by Doymaz (Doymaz, 2014) who applied different levels of infrared power to evenly and homogenously spread seeds on a pan. From the other side, based on the latest statistics released by Food and Agriculture Organization (FAO) watermelon-pertaining worldwide production could reach in excess of 182, 121, 406 metric tons by 2014 mainly produced by China, Iran, Turkey, Brazil, respectively while Iran's watermelon production share amounted to 3,947,057 metric tons (Food and Agriculture organization of United Nations statistics, 2016. FAOSTAT. http://www.fao.org/faostat/en/#home (accessed 12.07.2016)) whereas a major portion of watermelon production in Iran is only devoted to its seed production (Moradi et al., 2015). From the other side, watermelon has a water content of about 95% which makes it highly susceptible to deterioration (Falade et al., 2007) and poses the microbial spoilage threat to its seeds while dehydration as a conventional technique may seem useful for their preservation and extension of their shelf-life (Doymaz, 2014). Moreover, drying methods considerably affect the color, texture and porosity of the dehydrated materials (Chen et al., 2005). Based on the above mentioned points of significance and meeting the need for designing and fabrication of a high capacity industrial watermelon seed dryer in future (part 2), it was necessary to do the related experiments in a laboratory scale work and specify the crucial values for role-playing parameters. Hence, the effects of two parameters of drying air velocity and temperature on changes of color quality of seeds during drying, germination vigor of given seeds for possible planting and finally the drying kinetics of watermelon seeds were all investigated. 2. Materials and methods 2.1. Seed sample preparation Watermelons (Citrullus vulgaris variety) were obtained from a farm in Miyan Jolgeh district (Nishabur county, Razavi Khorasan Province, Iran). Their seeds were collected manually and washed to remove the remaining watermelon pomace and were blotted instantaneously with tissue paper to eliminate excess water on the surface. Afterwards, they were immediately put on trays and loaded
To determine the initial moisture content (M0) of watermelon seeds, 60 g of seeds (three samples of each 20 g) were weighed with an electronic balance of 0.01 g precision and laid inside an oven (D06062, Model 600, Memmert, Deutschland) at 105±1 C for 24 h. This step was replicated at least twice prior to beginning drying test. The average initial moisture content of samples was gained 49.93 (wb%). 2.3. Drying conditions and experimental set-up description To conduct the drying experiments, a laboratory-scale hot air dryer equipped with instrumentations was fabricated (Fig. 1). The dryer includes main components of air blower, electrical heaters, honeycomb, drying trays, dedicated balance and finally an automatic control unit. To supply air flowing through drying chamber a blower being rotated by a 2 hp 3-phase motor was used. This air velocity was measured at 9 points using an anemometer (LT lutron, LM-81AM, ± 0:1 m s1 , Lutron Electronic Enterprise Co., Ltd., Taipei, Taiwan) at the outlet and the mean value was reported as the velocity of air. Hence, the motor rotation speed was regulated to readily achieve the desired air velocities of 0.5, 1 and 1:5 m s1 . Meanwhile, to supply enough thermal energy for heating up the inlet drying air, three electrical elements (2 with 2500 W and 1 with 1500 W electrical power) were used. These electrical elements enabled regulation of air temperature through being continuously switched on and off by a temperature controller (Autonics TZ4ST14R, ±0.3%, Autonics Corporation, Gyeonggi-do, Korea) until the desired level of temperature (40, 50 and 60 C) was gained. The heated air passing through honeycomb would reach drying chamber which included three drying sieves each displaced 0.075 m. A digital balance (Kern 572-57, ±0.1 g accuracy, KERN & Sohn GmbH, Balingen, Germany) was used for measuring the sample weights. This balance was placed on top side of the drying chamber with stainless steel rods suspended from it inside the drying chamber hanging sieves which enabled recording the sample weight losses throughout the drying process at 10 min intervals without interruptions. To measure the relative humidity of hot drying air inside drying chamber a humidity meter (SAMWON ENG SU-503B, ±1%, Korea) was also used. The humidity and temperature sensors were all installed in drying chamber to monitor the ongoing changes. 2.4. Experimental procedure Prior to beginning the drying experiments, stabilized conditions, in terms of temperature and air velocity, were achieved inside the drying chamber. For each drying condition, about 180 g of watermelon seeds were spread uniformly in a single layer on three sieves (about 60 g/sieve) inside drying chamber. The desired levels of hot air temperature (40; 50 and 60 C) along with three air velocities (0:5; 1 and 1:5 m s1 ) were carefully provided to record the sample moisture changes while the relative humidity inside the chamber was also recorded throughout the tests. Drying continued until the mass difference between the two consecutive weighing was less than 0.05 g. 2.5. Theoretical analysis of experimental data 2.5.1. Effective moisture diffusivity Drying most agricultural products occurs in the falling rate period. Fick's second law of diffusion, as shown in Eq. (1), has been
Please cite this article in press as: Chaji, H., Hedayatizadeh, M., Quality assessment and kinetics of dehydrated watermelon seeds: Part 1, Engineering in Agriculture, Environment and Food (2017), http://dx.doi.org/10.1016/j.eaef.2017.01.006
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Fig. 1. Experimental drying set-up.
widely used to describe the drying process during the falling rate period for most biological materials (Roberts et al., 2008). Such an equation helps determine the effective moisture diffusivity.
vM ¼ V Deff ðVMÞ vt
(1)
where M, Deff and t are moisture content, effective moisture diffusivity representing the conductive term of all moisture transfer mechanisms and time, respectively. Considering some simplifying assumptions, assuming one-dimensional moisture movement volume change, constant temperature and diffusivity coefficients, and negligible external resistance, the analytical solution for the diffusion equation of an infinite slab (watermelon seed) is given as follows (Doymaz, 2014): ∞ Mt Me 8 X 1 MR ¼ ¼ 2 exp M0 Me p n¼0 ð2n þ 1Þ2
ð2n þ 1Þ2 p2 Deff ,t
!
4L2 (2)
where MR, Mt, M0, Me and L are the moisture ratio, moisture content at any time (kg water/kg dry solid), initial moisture content (kg water/kg dry solid), equilibrium moisture content of the sample (kg water/kg dry solid) and the half thickness of mentioned slab (a watermelon seed), respectively. The MR may be simplified to Mt=M since the value of equilibrium moisture content (Me) is 0 relatively small as compared with M0 and Mt (Sacilik et al., 2006; Sacilik, 2007; Goyal et al., 2007; Doymaz and Pala, 2003). Increases in n and drying time both cause the quantities of terms above one (n 2) drops drastically. Hence, retaining the first term of the above summation equation for more simplicity can be a good proximate of MR. To extract Deff , Eq. (2) is written in logarithmic form as follows:
lnðMRÞ ¼ ln
8
p2
p2 Deff ,t 4L2
(3)
Plotting lnðMRÞ versus time (t) leads to a straight line with . as its intercept and slope, respectively. ln 8 2 and p2 Deff p 4L2 Hence, in case of experimental data plotting, effective moisture
diffusivity (Deff ) can be calculated. 2.5.2. Energy of activation Temperature dependence of the effective moisture diffusivity has been shown to follow an Arrhenius relationship. Hence, it can be written as follows (Roberts et al., 2008; Akpinar et al., 2003; Babalis and Belessiotis, 2004):
Deff ¼ D0 exp
Ea Rg Tabs
(4)
where D0 is the pre-factor of the Arrhenius equation (m2 s1 ), Ea is the activation energy (kJ mol1 ), Rg is the universal gas constant (kJ mol1 K1 ), and Tabs is the absolute air temperature (K). The same as above, to find Ea the logarithmic form of Eq. (4) may help:
1 ln Deff ¼ lnðD0 Þ Ea Rg Tabs
(5)
Consequently, the activation energy is determined from the slope of the Arrhenius plot, lnðDeff Þ vs T1 abs . 2.5.3. Thin layer drying models fitting To model the drying kinetics of watermelon seeds, 12 broadly used models summarized in Table 1, were fitted to experimental data. The empirical drying constants for the thin layer drying models fitted were determined and the best prognosticating model was accepted based on the lowest and highest values for RMSE and coefficient of determination (R2 ), respectively. 2.5.4. Watermelon seed germination test One of the main reasons for drying watermelon seeds is also to keep them for future planting seasons. From this point of view, germination vigor of dried seeds which undertook different drying conditions became a critical issue. To investigate the effects of different levels of drying air temperature and velocity on germination vigor of watermelon seeds, a testing procedure based on ISTA (International Rules for Seed Testing, 2016) instructions was used. The germination results were based on a factorial statistical test including two parameters each with three different levels along with three replications (27 cases in total). SPSS (version 16) was used to carry out analysis of variance (ANOVA).
Please cite this article in press as: Chaji, H., Hedayatizadeh, M., Quality assessment and kinetics of dehydrated watermelon seeds: Part 1, Engineering in Agriculture, Environment and Food (2017), http://dx.doi.org/10.1016/j.eaef.2017.01.006
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Table 1 Mathematical models proposed by researchers. Name of Model
Descriptive equation
1) Newton 2) Page 3) Modified page I 4) Henderson and Pabis 5) Logarithmic 6) Two-term 7) Two-term exponential 8) Diffusion approach 9) Midilli et al. 10) Verma et al. 11) Silva
¼ expðktÞ ¼ expðktn Þ ¼ expðktÞn ¼ a:exppðktÞ ¼ a:expðktÞ þ c ¼ a:expðk0 tÞ þ b:expðk1 tÞ ¼ a:expðktÞ þ ð1 aÞ:expðkatÞ ¼ a:expðktÞ þ ð1 aÞ:expðkbtÞ ¼ a:expðktn Þ þ bt ¼ a:expðktÞ þ ð1 aÞ: expðgtÞ pffiffi MR ¼ expðat b t Þ MR ¼ a:expðktÞ þ b:expðgtÞ þ c:expðhtÞ MR MR MR MR MR MR MR MR MR MR
12) Modified Henderson and Pabis
2.5.5. Color change and kinetic The color of each food commodity is a decisive factor which profoundly affects the consumer acceptance of product (Aral and Bes¸e, 2016) while a huge amount of watermelon seeds produced are observed to be exposed to open-sun drying which has detrimental effects on its color quality. Such a discoloration causes a steep drop in real price of open sun-dried seeds. Moreover, the food material may be exposed to temperatures that have an adverse effect on quality and color (Maskan, 2001) while visual appearance of dried product has great influence on consumers’ acceptability which may lead to high sale rate of final product (Barreiro et al., 1997; Ibarz et al., 1999; Maskan, 2001). Watermelon seed case red color is a significant parameter which comes from Lycopene pigment (Falade et al., 2007). Heat, light, oxygen, and different food matrices are factors that have an effect on lycopene isomerization and autooxidation (Xianquan et al., 2005). Hence, through drying process of lycopene-containing products, great heed must be paid to preservation of such a valuable pigment and consequently, setting drying conditions in a way to retain the desired color of the end dried product is an important issue. Monitoring the color changes of watermelon seeds are expressed in terms of L* a* b*, representing lightness, color of green (a*) to red (a*) and color of blue (b*) to yellow (b*), respectively. Hence, for each drying condition (9 cases), two seeds were scanned at 60-min drying intervals from the start to the end of drying process. Scanning the samples took place in a specific chamber with opaque black inner walls and light tight. Then the images were transferred from RGB to Lab Mode and analyzed by ImageJ 1.42 software. Hence, L* a* b* values were determined. Having the color values mentioned above, the total color difference (DE) (Eq. (6)), chroma (Eq. (7)) and Hue angle (Eq. (8)) can be calculated to describe the color changes of watermelon seeds during drying:
DE ¼
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðL0 LÞ2 þ ða0 aÞ2 þ ðb0 bÞ2
(6)
where subscript “o” refers to the color reading of fresh watermelon seeds. The larger DE, the more color change from the reference (not-dried) case has been observed.
Chroma ¼
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi a2 þ b2
(7)
represents red hue, while angles of 90, 180, and 270 indicate yellow, green and blue hue, respectively). Moreover, through the present study kinetics of color changes is also covered to introduce the best fitted model which makes the prognostication of color indices possible. Hence, two commonly used models of first order and zero order have been used to evaluate the appearance of watermelon seeds as follows (Ibarz et al., 1999, Mohammadi et al., 2008):
Zero order : C C0 ¼ k0 t First order : C ¼ C0 expðk1 tÞ where C0 represents the initial value of color parameters (L*, a*, b*) at time zero, C is the value at time t, k0 the zero order kinetic constant, and k1 is the first order kinetic constant.
3. Results and discussion 3.1. Determination of effective moisture diffusivity To ascertain the effective moisture diffusivity pertaining to each drying condition, the slope of straight lines fitted to experimental data representing variations of LnðMRÞ vs. time of drying was found. The average watermelon seed thickness was measured 0.0018 (m) on average for 40 watermelon seeds. These graphs are provided in Fig. 2 showing different levels of air temperature with constant air velocity values. As seen, the fitted lines are approximately close to experimental data while for all three velocities of drying air, the uppermost line is pertinent to lowest temperature. The maximum and minimum values of effective moisture diffusivity were found 3:009 1010 m2 s1 and 11 2 1 6:805 10 m s relevant to drying conditions of V ¼ 1:5 m s1 ; T ¼ 60 C and V ¼ 0:5 m s1 ; T ¼ 40 C, respectively. The effective moisture diffusivity for watermelon seeds were also found within the general range of 1011 109 m2 s1 for food stuffs (Babalis and Belessiotis, 2004). To have the effective moisture diffusivity of watermelon seeds as a function of two decisive factors, hot air temperature and velocity, surface fitting was performed and the corresponding R2 is reported as following:
while chroma value indicates the degree of saturation of color and is proportional to the strength of the color and
T þ 4:262 1012 V2 þ 3:937 1013 V T
ba
=
Hue angle ¼ tan1
Deff ¼ 1:629 1010 þ 1:471 1011 V þ 7:331 1011
(8)
þ 1:101 1011 T2
Which expresses the color change (an angle of 0 or 360 Please cite this article in press as: Chaji, H., Hedayatizadeh, M., Quality assessment and kinetics of dehydrated watermelon seeds: Part 1, Engineering in Agriculture, Environment and Food (2017), http://dx.doi.org/10.1016/j.eaef.2017.01.006
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Fig. 2. Ln(MR) versus time for thin layer drying of watermelon seeds at 40, 50 and 60 C and Va ¼ 0:5; 1 and 1:5 ðm s1 Þ.
R2 ¼ 0:9711 and RMSE ¼ 2:119e 11
3.2. Determination of energy of activation The activation energy can be determined from the slope of Arrhenius plot LnðDeff Þ versus ððRÞ 1$T Þ while the intercept equals to g
abs
LnðD0 Þ (Eq. (5); Rg ¼ 8:3144598 J mol1 K1 ) and, in general, Ea lies in the domain of 12:7e110 kJ mol1 for drying of agricultural and food materials (Khanali et al., 2016). The given plot is presented in Fig. 3 and the corresponding energy of activation values along with their pertinent D0 are all brought in Table 2. To determine the trend of variations of activation energy for watermelon seeds versus changes in hot air velocity a curve was fitted which helps determine velocity of hot air corresponding to minimum value of Ea :
Ea ¼ 46:46 V2 108:7 V þ 99:38 Energy of activation as a significant parameter integrated closely with issue of drying process of agricultural materials depicting the required energy for initiation of moisture migration is observed to descend sharply with increase in air velocity up to 1:17 m s1 and to commence an ascending trend afterwards. Hence, velocity of air around 1 or 1:17 m s1 is recommended for achieving a higher rate of moisture diffusivity and consuming lesser energy for activation of moisture removal.
3.3. Modeling of drying behavior The best fitted models, ranking first to third, to experimental drying data of watermelon seeds are provided in Table 3. Based on highest magnitude of R2 along with lowest relevant RMSE, a decision is made to opt for the best thin layer drying models predicting the behavior of seeds throughout drying. Looking at Table 3, it seems Verma et al. model may fit the experimental data in most cases of drying conditions with acceptable accuracies. 3.4. Watermelon seed germination Based on statistical results, only air drying temperature showed significant differences (p < 0:01) on germination percentage (GP). The average of GP for the three temperatures of 40, 50 and 60 C were found 96.33%, 91.67% and 64.68%, respectively and the results of Duncan multiple range test (DMRT) revealed that they were in three different categories statistically. The results also showed that when the air drying temperature was 40, GP was tolerable (near to 96%), while with increasing temperature from 40 to 50 C, GP reduced a little (approximately 4%), while temperature increment to 60 C showed a big diminution in GP (almost 32%). Unlike temperature, for the three air drying velocities and their interaction with three air drying temperatures, no significant difference was statistically found effective on GPs. For displaying the relationship between GP and temperature, a quadratic polynomial is also provided here ðGP ¼ 0:117T2 þ 10:1T 120:7; R2 ¼ 0:98Þ. 3.5. Quality indices assessment 3.5.1. CIE color parameters It was observed that L had an ascending trend of variations in all drying cases demonstrating that the watermelon seeds went
Please cite this article in press as: Chaji, H., Hedayatizadeh, M., Quality assessment and kinetics of dehydrated watermelon seeds: Part 1, Engineering in Agriculture, Environment and Food (2017), http://dx.doi.org/10.1016/j.eaef.2017.01.006
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Fig. 3. LnðDeff Þ vs
1 Rg Tabs
related to different levels of air velocities.
Table 2 Energy of activation for three different hot air velocities. Velocity (m s1 ) Ea ðkJ mol D0 R
2
1
Þ
0.5
1
1.5
56.63
37.11
40.82
0.204538 0.9627
0.000163 (0.9996)
0.000722 0.9758
brighter undertaking temperature increase. The L corresponding to drying air temperatures of 40, 50 and 60 C were obtained 3.33, 4.23 and 8.11, respectively which showed that temperature increase from 50 to 60 C had a more significant effect on lightness of seeds in comparison to the same temperature increment from 40 to 50 C. On contrary to air temperature parameter, velocity of drying air only showed slight changes in DðL Þ. Hence, it maybe concluded that temperature increase has a deteriorating effect on color of watermelon seeds through increase of seeds' lightness. This lack of color and move to lightness is more probably due to Lycopene degradation (Shi et al., 1999). The high positive values of a index (21e26) pertaining to fresh seeds of watermelon fell between 17 and 24 after drying. Such a color change may be attributed to isomerization and autooxidation of the lycopene pigments (Shi et al., 1999). The observed decreases in Dða Þ for the temperatures of 40, 50 and 60 C also exhibited the substantial effect of air temperature increase from 50 to 60 C compared with the same temperature increment from 40 to 50 C on red color of the seeds. Moreover, the velocity of air didn't show such a great effect on Dða Þ. It is also worth mentioning that the reddish color of watermelon seeds is highly favored that fortunately is kept after drying. Dðb Þ also followed the same trends of variation as Dða Þ with temperature increase while the yellowness of the seed samples began to decrease. The maximum change observed for Dðb Þ was calculated 3.92 at temperature of 60 C. It was also seen that, on
contrary to the two other color indices, neither variation of temperature nor the velocity of drying air had a significant effect on Dðb Þ. 3.5.2. DE, chroma and Hue angle The total color difference (DE), being a combination of the L a b values, is an indicative of the variation of colors in foods during processing (Maskan, 2001; Kesbi et al., 2016). The experiments showed the values of 4.59, 6.36 and 10.21 at temperatures of 40, 50 and 60 C , respectively for DE which also reflect higher color changes at 60 C in comparison to other temperatures. The results also proved the deteriorating effects of temperatures above 50 C on color degradation of watermelon seed samples which certainly influences the customers’ acceptance. The initial values of chroma, expressing the saturation of color (Kesbi et al., 2016), fell within 29 and 36 for fresh seeds which also dropped during drying. It was observed that chroma decrease is attributed to both red and yellow color instabilities of samples equally. It was also observed that chroma reduction is further affected by air temperature rather than velocity and its maximum change (6.84) is observed at temperature of 60 C. Hue angle for the fresh seed samples was around 45 which is indicative of orange color of seeds rather than redness. The mean variation of this parameter in three temperatures of 40, 50 and 60 C were found 0.36, 0.03 and 2.82. Hence, the temperature rise brought about Hue angle to increase which is indicative of more red color loss in high temperatures. 3.5.3. Color fitting model To find out how the color of food materials changes as a function of drying time, usually, two well-known models of zero and first orders were fitted to experimental results for L a b. It was found that only first order model could acceptability predict the magnitudes of color indices at different combinations of velocity and temperature. The values for coefficients are summarized in Table 4.
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Table 3 Model fitting and their pertinent constants. Condition T
C
Model name
Coefficients
V m s1
60
1.5
50
1.5
40
1.5
60
1
50
1
40
1
60
0.5
50
0.5
40
0.5
Verma et al. Two-term MIdilli et al. Verma et al. Two-term page Two-term Verma et al. Diffusion Midilli et al. Two-term exponential page Logarithmic Two-term exponential page Two-term Diffusion Verma et al. Verma et al. Diffusion Logarithmic Midilli et al. Diffusion Verma et al. Verma et al. Diffusion Henderson and Pabis
a
b
c
g
k1
k2
K
n
0.85 0.84 1 0.57 0.43 e 0.70 0.70 0.3 0.98 1.67 e 0.98 0.02 e 0.38 0.4 0.4 0.11 0.9 0.9 1.00 0.5 0.5 0.95 0.05 0.96
e 0.15 0.0001 e 0.56 e 0.29 e 0.13 0.0004 e e e e e 0.63 0.14 e e 0.15 e 0.0001 0.31 e e 0.05 e
e e e e e e e e e e e e 0.015 e e e e e e e 0.05 e e e e e e
0.006 e e 0.031 e e e 0.04 e e e e e e e e e 0.004 0.019 e e e e 0.006 0.75 e e
e 0.02 e e 0.03 e 0.005 e e e e e e e e 0.028 e e e e e e e e e e e
e 0.006 e e 0.006 e 0.04 e e e e e e e e 0.004 e e e e e e e e e e e
0.022 e 0.02 0.006 e 0.04 e 0.005 0.04 0.003 0.017 0.007 0.009 0.43 0.01 e 0.03 0.03 0.003 0.02 0.02 0.018 0.019 0.019 0.003 0.07 0.003
e e 0.95 e e 0.73 e e e 1.34 e 1.13 e e 0.96 e e e e e e 0.88 e e e e e
R2
RMSE
0.9998 0.9998 0.9997 0.9994 0.9993 0.9982 0.9998 0.9998 0.9998 0.9987 0.9950 0.9950 0.9995 0.9995 0.9994 0.9996 0.9995 0.9995 0.9997 0.9996 0.9996 0.9993 0.9990 0.9990 0.9984 0.9983 0.9973
0.003 0.004 0.004 0.006 0.006 0.01 0.002 0.003 0.004 0.011 0.020 0.021 0.005 0.006 0.006 0.004 0.005 0.004 0.004 0.004 0.005 0.007 0.007 0.008 0.009 0.009 0.012
Table 4 The values of k1 104 in first order model. 0:5 m s1
1 m s1
k1
40 C 50 C 60 C
1:5 m s1
k1
k1
L
a
b
L
a
b
L
a
b
1.74 4.90 8.50
2.02 5.92 14.38
2.55 7.26 8.41
2.02 4.96 9.84
2.34 4.21 16.77
2.76 10.1 10.26
2.23 4.13 11.24
2.53 6.87 16.74
2.96 4.88 10.36
Considering the color coefficients brought in Table 4, it is observed that for all color indices, the absolute values of their coefficients all follow an increasing trend for all the three velocities of drying air along with temperature increase while it must be taking into account that plus (þ) and the minus () signs indicate the formation and degradation of quality parameters, respectively. Hence, the trends of color index changes had an exponential form for all the three indices throughout the drying time. 4. Conclusion Through the present study: ✓ Drying of watermelon seeds was experimentally investigated and the drying kinetics of seeds including energy of activation and moisture diffusivity parameters were determined; ✓ Verma et al. model gave better fitting representation of experimental data in most of drying conditions; ✓ Air drying temperature had much more significant effect on both germination vigor of seeds than the velocity; ✓ The recommended temperature for convectively drying of watermelon seeds in terms of both germination and color quality was found 50 C; ✓ The first-order model could acceptably prognosticate the trend of color changes and color indices for all drying conditions.
✓ Generally, the air velocity of 1 m s1 along with temperature of 50 C is recommended for gaining better results with watermelon seeds drying. References n, A., Cassinera, A., Lara, M.A., 2006. Thin layer drying of amaranth Abalone, R., Gasto seeds. Biosyst. Eng. 93 (2), 179e188. Aghbashlo, M., Samimi-Akhijahani, H., 2008. Influence of drying conditions on the effective moisture diffusivity, energy of activation and energy consumption during the thin-layer drying of berberis fruit (Berberidaceae). Energy Convers. Manag. 49 (10), 2865e2871. Ajibola, O.O., 1989. Thin-layer drying of melon seed. J. Food Eng. 9 (4), 305e320. Akpinar, E., Midilli, A., Bicer, Y., 2003. Single layer drying behaviour of potato slices in a convective cyclone dryer and mathematical modeling. Energy Convers. Manag. 44 (10), 1689e1705. Aral, S., Bes¸e, A.V., 2016. Convective drying of hawthorn fruit (Crataegus spp.): effect of experimental parameters on drying kinetics, color, shrinkage, and rehydration capacity. Food Chem. 210, 577e584. Avhad, M.R., Marchetti, J.M., 2016. Mathematical modelling of the drying kinetics of Hass avocado seeds. Ind. Crop. Prod. 91, 76e87. Babalis, S.J., Belessiotis, V.G., 2004. Influence of the drying conditions on the drying constants and moisture diffusivity during the thin-layer drying of figs. J. food Eng. 65 (3), 449e458. Barreiro, J.A., Milano, M., Sandoval, A.J., 1997. Kinetics of colour change of double concentrated tomato paste during thermal treatment. J. Food Eng. 33 (3), 359e371. Barrozo, M.A.S., Mujumdar, A., Freire, J.T., 2014. Air-drying of seeds: a review. Dry. Technol. 32 (10), 1127e1141. Can, A., 2007. An analytical method for determining the temperature dependent moisture diffusivities of pumpkin seeds during drying process. Appl. Therm.
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Eng. 27 (2), 682e687. Chen, H.H., Hernandez, C.E., Huang, T.C., 2005. A study of the drying effect on lemon slices using a closed-type solar dryer. Sol. Energy 78 (1), 97e103. Doymaz, I., Pala, M., 2003. The thin-layer drying characteristics of corn. J. Food Eng. 60 (2), 125e130. Doymaz, I., 2014. Experimental study and mathematical modeling of thin-layer infrared drying of watermelon seeds. J. Food Process. Preserv. 38 (3), 1377e1384. Falade, K.O., Igbeka, J.C., Ayanwuyi, F.A., 2007. Kinetics of mass transfer, and colour changes during osmotic dehydration of watermelon. J. Food Eng. 80 (3), 979e985. Food and Agriculture organization of United Nations statistics, 2016. FAOSTAT. http://www.fao.org/faostat/en/#home (Accessed 12 July 2016). Goyal, R.K., Kingsly, A.R.P., Manikantan, M.R., Ilyas, S.M., 2007. Mathematical modelling of thin layer drying kinetics of plum in a tunnel dryer. J. Food Eng. 79 (1), 176e180. Hemis, M., Choudhary, R., Watson, D.G., 2012. A coupled mathematical model for simultaneous microwave and convective drying of wheat seeds. Biosyst. Eng. 112 (3), 202e209. py, Y., Raghavan, V.G., 2015. Experiments and Hemis, M., Choudhary, R., Garie modelling of the microwave assisted convective drying of canola seeds. Biosyst. Eng. 139, 121e127. Ibarz, A., Pagan, J., Garza, S., 1999. Kinetic models for colour changes in pear puree during heating at relatively high temperatures. J. Food Eng. 39 (4), 415e422. International Rules for Seed Testing, 2016. http://www.seedtest.org/en/home.html (Accessed 12 July 2016). Johann, G., de Menezes, M.L., Pereira, N.C., da Silva, E.A., 2016. Comparing models to Neumann and Dirichlet conditions in grape seed drying. Appl. Therm. Eng. 93, 865e871. Kesbi, O.M., Sadeghi, M., Mireei, S.A., 2016. Quality assessment and modeling of
microwave-convective drying of lemon slices. Eng. Agric. Environ. Food 9 (3), 216e223. Khanali, M., Banisharif, A., Rafiee, S., 2016. Modeling of moisture diffusivity, activation energy and energy consumption in fluidized bed drying of rough rice. Heat Mass Transf. 1e9. ska, M., Białobrzewski, I., 2007. Drying Markowski, M., Sobieski, W., Konopka, I., Tan characteristics of barley grain dried in a spouted-bed and combined IRconvection dryers. Dry. Technol. 25 (10), 1621e1632. Maskan, M., 2001. Kinetics of colour change of kiwifruits during hot air and microwave drying. J. food Eng. 48 (2), 169e175. Mocelin, B., Oliveira Jr., D.L., Chielle, D.P., Tanabe, E.H., Bertuol, D.A., Schwaab, M., Meili, L., 2014. Mathematical modeling of thin layer drying of papaya seeds in a tunnel dryer using particle swarm optimization method. Part. Sci. Technol. 32 (2), 123e130. Mohammadi, A., Rafiee, S., Emam-Djomeh, Z., Keyhani, A., 2008. Kinetic models for colour changes in kiwifruit slices during hot air drying. World J. Agric. Sci. 4 (3), 376e383. Moradi, R., Moghaddam, P.R., Mansoori, H., 2015. Energy use and economical analysis of seedy watermelon production for different irrigation systems in Iran. Energy Rep. 1, 36e42. Roberts, J.S., Kidd, D.R., Padilla-Zakour, O., 2008. Drying kinetics of grape seeds. J. Food Eng. 89 (4), 460e465. Sacilik, K., Elicin, A.K., Unal, G., 2006. Drying kinetics of Üryani plum in a convective hot-air dryer. J. Food Eng. 76 (3), 362e368. Sacilik, K., 2007. Effect of drying methods on thin-layer drying characteristics of hull-less seed pumpkin (Cucurbita pepo L.). J. food Eng. 79 (1), 23e30. Shi, J., Le Maguer, M., Kakuda, Y., Liptay, A., Niekamp, F., 1999. Lycopene degradation and isomerization in tomato dehydration. Food Res. Int. 32 (1), 15e21. Xianquan, S., Shi, J., Kakuda, Y., Yueming, J., 2005. Stability of lycopene during food processing and storage. J. Med. food 8 (4), 413e422.
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Quality assessment and kinetics of dehydrated watermelon seeds: Part 1 Hossein Chaji a, Mahdi Hedayatizadeh b, * a
Agricultural Engineering Research Department, Khorasan Razavi Agricultural and Natural Resources Research and Education Center, AREEO, Mashhad, Iran b Faculty of Agriculture, University of Birjand, Birjand, Iran
a r t i c l e i n f o
a b s t r a c t
Article history: Received 17 September 2016 Received in revised form 20 January 2017 Accepted 24 January 2017 Available online xxx
Processing watermelon seeds has recently attained great attention in industry from two aspects of snack and high quality oil productions. Hence, in the first phase of our research, studying their drying kinetics, color quality in terms of L* a* b*, total color difference (DE), chroma and Hue angle and germination vigor of watermelon seeds were studied experimentally in detail with three drying air temperatures (40, 50 and 60 C) along with three levels of air velocity (0.5, 1 and 1:5 m s1 ). It was concluded that well-known Verma et al. thin layer drying model was greatly superior for prediction of watermelon seed drying kinetics in most cases while the effective moisture diffusivity of seeds was found ranging between 3.009 1010 and 6:805 1011 m2 s1 and the energy of activation was evaluated between 37.11 and 56:63 kJ mol1 . It was also inferred that air temperature level more profoundly affected the color and germination of the seeds than velocity while to maintain color quality and germination vigor high, the convectively drying temperatures should not exceed 50 C. © 2017 Asian Agricultural and Biological Engineering Association. Published by Elsevier B.V. All rights reserved.
Keywords: Watermelon Drying Germination Color quality
1. Introduction Preserving seeds of agricultural products against deterioration is a big problem in agriculture. Moreover, the conventional cheap open sun drying of seeds has also adversely affected the quality of seeds in terms of color and viability while insects' infestation and the hygienic - related problems have highlighted the great significance of utilizing a dryer and setting the drying conditions correctly for better gains. As good seed quality directly affects the success of crops and contributes significantly to enhance productivity levels, high-quality seed production is a challenge for the agricultural sector (Barrozo et al., 2014). Generally, the term thin layer drying refers to the grain drying process in which all grains are fully exposed to the drying air under constant drying conditions while all commercial flow dryers are designed on thin layer drying principles. Hence, thin layer is believed to be the best criteria for the simulation of food drying processes (Aghbashlo and SamimiAkhijahani, 2008). Internationality, numerous studies have sought
* Corresponding author. E-mail addresses: [email protected] (H. Chaji), [email protected] (M. Hedayatizadeh).
to model or describe the thin layer drying process of seeds. Mathematical modeling of thin layer drying of papaya seeds was considered for finding the appropriate drying conditions including three different temperatures each accompanied by three different velocities of air (Mocelin et al., 2014). Drying kinetics of seeds of three kinds of grape undertaking three different temperatures with a constant air velocity was also studied in detail and the corresponding effective moisture diffusivity and activation energy parameters were found. Moreover, the Lewis model as the excellent model for predicting all three grape seed was proposed (Roberts et al., 2008). Besides, the thin layer drying of melon seeds was studied through using five desorption isotherm and three thin layer drying models while it was found that the exponential model is adequate enough for predicting thin layer drying of melon seeds (Ajibola, 1989). A method for determination of temperature dependent moisture diffusivities of pumpkin seeds through natural and forced convection drying processes was also proposed. Therefore, the pumpkin seed particles were considered as of rectangular prisms with specified dimensions while execution of written computer program through the iterative technique led to specification of coefficient of diffusivity of the given seeds (Can, 2007). Many other studies are also performed for drying other
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into the dryer. Nomenclature 2.2. Moisture content determination M Deff T MR L N Mt M0 Me D0 Ea Rg T V
Moisture content (db%) Effective moisture diffusivity (m2 s1 ) Time (s) Moisture ratio () Half thickness of the slab (watermelon seed) (m) Positive integer () Moisture content at any time (db%) Initial moisture content (db%) Equilibrium moisture content (db%) pre-exponential factor of the Arrhenius equation (m2 s1 ) Activation energy (J mol1 ) Universal gas constant (J mol1 K1 ) Absolute air temperature (K) Hot air velocity (m s1 )
agricultural seeds (wheat seed (Hemis et al., 2012), grape seed (Johann et al., 2016), barley grain (Markowski et al., 2007), canola seed (Hemis et al., 2015), hass avocado seed (Avhad and Marchetti, 2016), amaranth seed (Abalone et al., 2006)) among which watermelon hot air seed drying has been scarcely investigated except for an infrared drying study of watermelon seeds conducted by Doymaz (Doymaz, 2014) who applied different levels of infrared power to evenly and homogenously spread seeds on a pan. From the other side, based on the latest statistics released by Food and Agriculture Organization (FAO) watermelon-pertaining worldwide production could reach in excess of 182, 121, 406 metric tons by 2014 mainly produced by China, Iran, Turkey, Brazil, respectively while Iran's watermelon production share amounted to 3,947,057 metric tons (Food and Agriculture organization of United Nations statistics, 2016. FAOSTAT. http://www.fao.org/faostat/en/#home (accessed 12.07.2016)) whereas a major portion of watermelon production in Iran is only devoted to its seed production (Moradi et al., 2015). From the other side, watermelon has a water content of about 95% which makes it highly susceptible to deterioration (Falade et al., 2007) and poses the microbial spoilage threat to its seeds while dehydration as a conventional technique may seem useful for their preservation and extension of their shelf-life (Doymaz, 2014). Moreover, drying methods considerably affect the color, texture and porosity of the dehydrated materials (Chen et al., 2005). Based on the above mentioned points of significance and meeting the need for designing and fabrication of a high capacity industrial watermelon seed dryer in future (part 2), it was necessary to do the related experiments in a laboratory scale work and specify the crucial values for role-playing parameters. Hence, the effects of two parameters of drying air velocity and temperature on changes of color quality of seeds during drying, germination vigor of given seeds for possible planting and finally the drying kinetics of watermelon seeds were all investigated. 2. Materials and methods 2.1. Seed sample preparation Watermelons (Citrullus vulgaris variety) were obtained from a farm in Miyan Jolgeh district (Nishabur county, Razavi Khorasan Province, Iran). Their seeds were collected manually and washed to remove the remaining watermelon pomace and were blotted instantaneously with tissue paper to eliminate excess water on the surface. Afterwards, they were immediately put on trays and loaded
To determine the initial moisture content (M0) of watermelon seeds, 60 g of seeds (three samples of each 20 g) were weighed with an electronic balance of 0.01 g precision and laid inside an oven (D06062, Model 600, Memmert, Deutschland) at 105±1 C for 24 h. This step was replicated at least twice prior to beginning drying test. The average initial moisture content of samples was gained 49.93 (wb%). 2.3. Drying conditions and experimental set-up description To conduct the drying experiments, a laboratory-scale hot air dryer equipped with instrumentations was fabricated (Fig. 1). The dryer includes main components of air blower, electrical heaters, honeycomb, drying trays, dedicated balance and finally an automatic control unit. To supply air flowing through drying chamber a blower being rotated by a 2 hp 3-phase motor was used. This air velocity was measured at 9 points using an anemometer (LT lutron, LM-81AM, ± 0:1 m s1 , Lutron Electronic Enterprise Co., Ltd., Taipei, Taiwan) at the outlet and the mean value was reported as the velocity of air. Hence, the motor rotation speed was regulated to readily achieve the desired air velocities of 0.5, 1 and 1:5 m s1 . Meanwhile, to supply enough thermal energy for heating up the inlet drying air, three electrical elements (2 with 2500 W and 1 with 1500 W electrical power) were used. These electrical elements enabled regulation of air temperature through being continuously switched on and off by a temperature controller (Autonics TZ4ST14R, ±0.3%, Autonics Corporation, Gyeonggi-do, Korea) until the desired level of temperature (40, 50 and 60 C) was gained. The heated air passing through honeycomb would reach drying chamber which included three drying sieves each displaced 0.075 m. A digital balance (Kern 572-57, ±0.1 g accuracy, KERN & Sohn GmbH, Balingen, Germany) was used for measuring the sample weights. This balance was placed on top side of the drying chamber with stainless steel rods suspended from it inside the drying chamber hanging sieves which enabled recording the sample weight losses throughout the drying process at 10 min intervals without interruptions. To measure the relative humidity of hot drying air inside drying chamber a humidity meter (SAMWON ENG SU-503B, ±1%, Korea) was also used. The humidity and temperature sensors were all installed in drying chamber to monitor the ongoing changes. 2.4. Experimental procedure Prior to beginning the drying experiments, stabilized conditions, in terms of temperature and air velocity, were achieved inside the drying chamber. For each drying condition, about 180 g of watermelon seeds were spread uniformly in a single layer on three sieves (about 60 g/sieve) inside drying chamber. The desired levels of hot air temperature (40; 50 and 60 C) along with three air velocities (0:5; 1 and 1:5 m s1 ) were carefully provided to record the sample moisture changes while the relative humidity inside the chamber was also recorded throughout the tests. Drying continued until the mass difference between the two consecutive weighing was less than 0.05 g. 2.5. Theoretical analysis of experimental data 2.5.1. Effective moisture diffusivity Drying most agricultural products occurs in the falling rate period. Fick's second law of diffusion, as shown in Eq. (1), has been
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Fig. 1. Experimental drying set-up.
widely used to describe the drying process during the falling rate period for most biological materials (Roberts et al., 2008). Such an equation helps determine the effective moisture diffusivity.
vM ¼ V Deff ðVMÞ vt
(1)
where M, Deff and t are moisture content, effective moisture diffusivity representing the conductive term of all moisture transfer mechanisms and time, respectively. Considering some simplifying assumptions, assuming one-dimensional moisture movement volume change, constant temperature and diffusivity coefficients, and negligible external resistance, the analytical solution for the diffusion equation of an infinite slab (watermelon seed) is given as follows (Doymaz, 2014): ∞ Mt Me 8 X 1 MR ¼ ¼ 2 exp M0 Me p n¼0 ð2n þ 1Þ2
ð2n þ 1Þ2 p2 Deff ,t
!
4L2 (2)
where MR, Mt, M0, Me and L are the moisture ratio, moisture content at any time (kg water/kg dry solid), initial moisture content (kg water/kg dry solid), equilibrium moisture content of the sample (kg water/kg dry solid) and the half thickness of mentioned slab (a watermelon seed), respectively. The MR may be simplified to Mt=M since the value of equilibrium moisture content (Me) is 0 relatively small as compared with M0 and Mt (Sacilik et al., 2006; Sacilik, 2007; Goyal et al., 2007; Doymaz and Pala, 2003). Increases in n and drying time both cause the quantities of terms above one (n 2) drops drastically. Hence, retaining the first term of the above summation equation for more simplicity can be a good proximate of MR. To extract Deff , Eq. (2) is written in logarithmic form as follows:
lnðMRÞ ¼ ln
8
p2
p2 Deff ,t 4L2
(3)
Plotting lnðMRÞ versus time (t) leads to a straight line with . as its intercept and slope, respectively. ln 8 2 and p2 Deff p 4L2 Hence, in case of experimental data plotting, effective moisture
diffusivity (Deff ) can be calculated. 2.5.2. Energy of activation Temperature dependence of the effective moisture diffusivity has been shown to follow an Arrhenius relationship. Hence, it can be written as follows (Roberts et al., 2008; Akpinar et al., 2003; Babalis and Belessiotis, 2004):
Deff ¼ D0 exp
Ea Rg Tabs
(4)
where D0 is the pre-factor of the Arrhenius equation (m2 s1 ), Ea is the activation energy (kJ mol1 ), Rg is the universal gas constant (kJ mol1 K1 ), and Tabs is the absolute air temperature (K). The same as above, to find Ea the logarithmic form of Eq. (4) may help:
1 ln Deff ¼ lnðD0 Þ Ea Rg Tabs
(5)
Consequently, the activation energy is determined from the slope of the Arrhenius plot, lnðDeff Þ vs T1 abs . 2.5.3. Thin layer drying models fitting To model the drying kinetics of watermelon seeds, 12 broadly used models summarized in Table 1, were fitted to experimental data. The empirical drying constants for the thin layer drying models fitted were determined and the best prognosticating model was accepted based on the lowest and highest values for RMSE and coefficient of determination (R2 ), respectively. 2.5.4. Watermelon seed germination test One of the main reasons for drying watermelon seeds is also to keep them for future planting seasons. From this point of view, germination vigor of dried seeds which undertook different drying conditions became a critical issue. To investigate the effects of different levels of drying air temperature and velocity on germination vigor of watermelon seeds, a testing procedure based on ISTA (International Rules for Seed Testing, 2016) instructions was used. The germination results were based on a factorial statistical test including two parameters each with three different levels along with three replications (27 cases in total). SPSS (version 16) was used to carry out analysis of variance (ANOVA).
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Table 1 Mathematical models proposed by researchers. Name of Model
Descriptive equation
1) Newton 2) Page 3) Modified page I 4) Henderson and Pabis 5) Logarithmic 6) Two-term 7) Two-term exponential 8) Diffusion approach 9) Midilli et al. 10) Verma et al. 11) Silva
¼ expðktÞ ¼ expðktn Þ ¼ expðktÞn ¼ a:exppðktÞ ¼ a:expðktÞ þ c ¼ a:expðk0 tÞ þ b:expðk1 tÞ ¼ a:expðktÞ þ ð1 aÞ:expðkatÞ ¼ a:expðktÞ þ ð1 aÞ:expðkbtÞ ¼ a:expðktn Þ þ bt ¼ a:expðktÞ þ ð1 aÞ: expðgtÞ pffiffi MR ¼ expðat b t Þ MR ¼ a:expðktÞ þ b:expðgtÞ þ c:expðhtÞ MR MR MR MR MR MR MR MR MR MR
12) Modified Henderson and Pabis
2.5.5. Color change and kinetic The color of each food commodity is a decisive factor which profoundly affects the consumer acceptance of product (Aral and Bes¸e, 2016) while a huge amount of watermelon seeds produced are observed to be exposed to open-sun drying which has detrimental effects on its color quality. Such a discoloration causes a steep drop in real price of open sun-dried seeds. Moreover, the food material may be exposed to temperatures that have an adverse effect on quality and color (Maskan, 2001) while visual appearance of dried product has great influence on consumers’ acceptability which may lead to high sale rate of final product (Barreiro et al., 1997; Ibarz et al., 1999; Maskan, 2001). Watermelon seed case red color is a significant parameter which comes from Lycopene pigment (Falade et al., 2007). Heat, light, oxygen, and different food matrices are factors that have an effect on lycopene isomerization and autooxidation (Xianquan et al., 2005). Hence, through drying process of lycopene-containing products, great heed must be paid to preservation of such a valuable pigment and consequently, setting drying conditions in a way to retain the desired color of the end dried product is an important issue. Monitoring the color changes of watermelon seeds are expressed in terms of L* a* b*, representing lightness, color of green (a*) to red (a*) and color of blue (b*) to yellow (b*), respectively. Hence, for each drying condition (9 cases), two seeds were scanned at 60-min drying intervals from the start to the end of drying process. Scanning the samples took place in a specific chamber with opaque black inner walls and light tight. Then the images were transferred from RGB to Lab Mode and analyzed by ImageJ 1.42 software. Hence, L* a* b* values were determined. Having the color values mentioned above, the total color difference (DE) (Eq. (6)), chroma (Eq. (7)) and Hue angle (Eq. (8)) can be calculated to describe the color changes of watermelon seeds during drying:
DE ¼
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðL0 LÞ2 þ ða0 aÞ2 þ ðb0 bÞ2
(6)
where subscript “o” refers to the color reading of fresh watermelon seeds. The larger DE, the more color change from the reference (not-dried) case has been observed.
Chroma ¼
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi a2 þ b2
(7)
represents red hue, while angles of 90, 180, and 270 indicate yellow, green and blue hue, respectively). Moreover, through the present study kinetics of color changes is also covered to introduce the best fitted model which makes the prognostication of color indices possible. Hence, two commonly used models of first order and zero order have been used to evaluate the appearance of watermelon seeds as follows (Ibarz et al., 1999, Mohammadi et al., 2008):
Zero order : C C0 ¼ k0 t First order : C ¼ C0 expðk1 tÞ where C0 represents the initial value of color parameters (L*, a*, b*) at time zero, C is the value at time t, k0 the zero order kinetic constant, and k1 is the first order kinetic constant.
3. Results and discussion 3.1. Determination of effective moisture diffusivity To ascertain the effective moisture diffusivity pertaining to each drying condition, the slope of straight lines fitted to experimental data representing variations of LnðMRÞ vs. time of drying was found. The average watermelon seed thickness was measured 0.0018 (m) on average for 40 watermelon seeds. These graphs are provided in Fig. 2 showing different levels of air temperature with constant air velocity values. As seen, the fitted lines are approximately close to experimental data while for all three velocities of drying air, the uppermost line is pertinent to lowest temperature. The maximum and minimum values of effective moisture diffusivity were found 3:009 1010 m2 s1 and 11 2 1 6:805 10 m s relevant to drying conditions of V ¼ 1:5 m s1 ; T ¼ 60 C and V ¼ 0:5 m s1 ; T ¼ 40 C, respectively. The effective moisture diffusivity for watermelon seeds were also found within the general range of 1011 109 m2 s1 for food stuffs (Babalis and Belessiotis, 2004). To have the effective moisture diffusivity of watermelon seeds as a function of two decisive factors, hot air temperature and velocity, surface fitting was performed and the corresponding R2 is reported as following:
while chroma value indicates the degree of saturation of color and is proportional to the strength of the color and
T þ 4:262 1012 V2 þ 3:937 1013 V T
ba
=
Hue angle ¼ tan1
Deff ¼ 1:629 1010 þ 1:471 1011 V þ 7:331 1011
(8)
þ 1:101 1011 T2
Which expresses the color change (an angle of 0 or 360 Please cite this article in press as: Chaji, H., Hedayatizadeh, M., Quality assessment and kinetics of dehydrated watermelon seeds: Part 1, Engineering in Agriculture, Environment and Food (2017), http://dx.doi.org/10.1016/j.eaef.2017.01.006
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Fig. 2. Ln(MR) versus time for thin layer drying of watermelon seeds at 40, 50 and 60 C and Va ¼ 0:5; 1 and 1:5 ðm s1 Þ.
R2 ¼ 0:9711 and RMSE ¼ 2:119e 11
3.2. Determination of energy of activation The activation energy can be determined from the slope of Arrhenius plot LnðDeff Þ versus ððRÞ 1$T Þ while the intercept equals to g
abs
LnðD0 Þ (Eq. (5); Rg ¼ 8:3144598 J mol1 K1 ) and, in general, Ea lies in the domain of 12:7e110 kJ mol1 for drying of agricultural and food materials (Khanali et al., 2016). The given plot is presented in Fig. 3 and the corresponding energy of activation values along with their pertinent D0 are all brought in Table 2. To determine the trend of variations of activation energy for watermelon seeds versus changes in hot air velocity a curve was fitted which helps determine velocity of hot air corresponding to minimum value of Ea :
Ea ¼ 46:46 V2 108:7 V þ 99:38 Energy of activation as a significant parameter integrated closely with issue of drying process of agricultural materials depicting the required energy for initiation of moisture migration is observed to descend sharply with increase in air velocity up to 1:17 m s1 and to commence an ascending trend afterwards. Hence, velocity of air around 1 or 1:17 m s1 is recommended for achieving a higher rate of moisture diffusivity and consuming lesser energy for activation of moisture removal.
3.3. Modeling of drying behavior The best fitted models, ranking first to third, to experimental drying data of watermelon seeds are provided in Table 3. Based on highest magnitude of R2 along with lowest relevant RMSE, a decision is made to opt for the best thin layer drying models predicting the behavior of seeds throughout drying. Looking at Table 3, it seems Verma et al. model may fit the experimental data in most cases of drying conditions with acceptable accuracies. 3.4. Watermelon seed germination Based on statistical results, only air drying temperature showed significant differences (p < 0:01) on germination percentage (GP). The average of GP for the three temperatures of 40, 50 and 60 C were found 96.33%, 91.67% and 64.68%, respectively and the results of Duncan multiple range test (DMRT) revealed that they were in three different categories statistically. The results also showed that when the air drying temperature was 40, GP was tolerable (near to 96%), while with increasing temperature from 40 to 50 C, GP reduced a little (approximately 4%), while temperature increment to 60 C showed a big diminution in GP (almost 32%). Unlike temperature, for the three air drying velocities and their interaction with three air drying temperatures, no significant difference was statistically found effective on GPs. For displaying the relationship between GP and temperature, a quadratic polynomial is also provided here ðGP ¼ 0:117T2 þ 10:1T 120:7; R2 ¼ 0:98Þ. 3.5. Quality indices assessment 3.5.1. CIE color parameters It was observed that L had an ascending trend of variations in all drying cases demonstrating that the watermelon seeds went
Please cite this article in press as: Chaji, H., Hedayatizadeh, M., Quality assessment and kinetics of dehydrated watermelon seeds: Part 1, Engineering in Agriculture, Environment and Food (2017), http://dx.doi.org/10.1016/j.eaef.2017.01.006
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H. Chaji, M. Hedayatizadeh / Engineering in Agriculture, Environment and Food xxx (2017) 1e8
Fig. 3. LnðDeff Þ vs
1 Rg Tabs
related to different levels of air velocities.
Table 2 Energy of activation for three different hot air velocities. Velocity (m s1 ) Ea ðkJ mol D0 R
2
1
Þ
0.5
1
1.5
56.63
37.11
40.82
0.204538 0.9627
0.000163 (0.9996)
0.000722 0.9758
brighter undertaking temperature increase. The L corresponding to drying air temperatures of 40, 50 and 60 C were obtained 3.33, 4.23 and 8.11, respectively which showed that temperature increase from 50 to 60 C had a more significant effect on lightness of seeds in comparison to the same temperature increment from 40 to 50 C. On contrary to air temperature parameter, velocity of drying air only showed slight changes in DðL Þ. Hence, it maybe concluded that temperature increase has a deteriorating effect on color of watermelon seeds through increase of seeds' lightness. This lack of color and move to lightness is more probably due to Lycopene degradation (Shi et al., 1999). The high positive values of a index (21e26) pertaining to fresh seeds of watermelon fell between 17 and 24 after drying. Such a color change may be attributed to isomerization and autooxidation of the lycopene pigments (Shi et al., 1999). The observed decreases in Dða Þ for the temperatures of 40, 50 and 60 C also exhibited the substantial effect of air temperature increase from 50 to 60 C compared with the same temperature increment from 40 to 50 C on red color of the seeds. Moreover, the velocity of air didn't show such a great effect on Dða Þ. It is also worth mentioning that the reddish color of watermelon seeds is highly favored that fortunately is kept after drying. Dðb Þ also followed the same trends of variation as Dða Þ with temperature increase while the yellowness of the seed samples began to decrease. The maximum change observed for Dðb Þ was calculated 3.92 at temperature of 60 C. It was also seen that, on
contrary to the two other color indices, neither variation of temperature nor the velocity of drying air had a significant effect on Dðb Þ. 3.5.2. DE, chroma and Hue angle The total color difference (DE), being a combination of the L a b values, is an indicative of the variation of colors in foods during processing (Maskan, 2001; Kesbi et al., 2016). The experiments showed the values of 4.59, 6.36 and 10.21 at temperatures of 40, 50 and 60 C , respectively for DE which also reflect higher color changes at 60 C in comparison to other temperatures. The results also proved the deteriorating effects of temperatures above 50 C on color degradation of watermelon seed samples which certainly influences the customers’ acceptance. The initial values of chroma, expressing the saturation of color (Kesbi et al., 2016), fell within 29 and 36 for fresh seeds which also dropped during drying. It was observed that chroma decrease is attributed to both red and yellow color instabilities of samples equally. It was also observed that chroma reduction is further affected by air temperature rather than velocity and its maximum change (6.84) is observed at temperature of 60 C. Hue angle for the fresh seed samples was around 45 which is indicative of orange color of seeds rather than redness. The mean variation of this parameter in three temperatures of 40, 50 and 60 C were found 0.36, 0.03 and 2.82. Hence, the temperature rise brought about Hue angle to increase which is indicative of more red color loss in high temperatures. 3.5.3. Color fitting model To find out how the color of food materials changes as a function of drying time, usually, two well-known models of zero and first orders were fitted to experimental results for L a b. It was found that only first order model could acceptability predict the magnitudes of color indices at different combinations of velocity and temperature. The values for coefficients are summarized in Table 4.
Please cite this article in press as: Chaji, H., Hedayatizadeh, M., Quality assessment and kinetics of dehydrated watermelon seeds: Part 1, Engineering in Agriculture, Environment and Food (2017), http://dx.doi.org/10.1016/j.eaef.2017.01.006
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7
Table 3 Model fitting and their pertinent constants. Condition T
C
Model name
Coefficients
V m s1
60
1.5
50
1.5
40
1.5
60
1
50
1
40
1
60
0.5
50
0.5
40
0.5
Verma et al. Two-term MIdilli et al. Verma et al. Two-term page Two-term Verma et al. Diffusion Midilli et al. Two-term exponential page Logarithmic Two-term exponential page Two-term Diffusion Verma et al. Verma et al. Diffusion Logarithmic Midilli et al. Diffusion Verma et al. Verma et al. Diffusion Henderson and Pabis
a
b
c
g
k1
k2
K
n
0.85 0.84 1 0.57 0.43 e 0.70 0.70 0.3 0.98 1.67 e 0.98 0.02 e 0.38 0.4 0.4 0.11 0.9 0.9 1.00 0.5 0.5 0.95 0.05 0.96
e 0.15 0.0001 e 0.56 e 0.29 e 0.13 0.0004 e e e e e 0.63 0.14 e e 0.15 e 0.0001 0.31 e e 0.05 e
e e e e e e e e e e e e 0.015 e e e e e e e 0.05 e e e e e e
0.006 e e 0.031 e e e 0.04 e e e e e e e e e 0.004 0.019 e e e e 0.006 0.75 e e
e 0.02 e e 0.03 e 0.005 e e e e e e e e 0.028 e e e e e e e e e e e
e 0.006 e e 0.006 e 0.04 e e e e e e e e 0.004 e e e e e e e e e e e
0.022 e 0.02 0.006 e 0.04 e 0.005 0.04 0.003 0.017 0.007 0.009 0.43 0.01 e 0.03 0.03 0.003 0.02 0.02 0.018 0.019 0.019 0.003 0.07 0.003
e e 0.95 e e 0.73 e e e 1.34 e 1.13 e e 0.96 e e e e e e 0.88 e e e e e
R2
RMSE
0.9998 0.9998 0.9997 0.9994 0.9993 0.9982 0.9998 0.9998 0.9998 0.9987 0.9950 0.9950 0.9995 0.9995 0.9994 0.9996 0.9995 0.9995 0.9997 0.9996 0.9996 0.9993 0.9990 0.9990 0.9984 0.9983 0.9973
0.003 0.004 0.004 0.006 0.006 0.01 0.002 0.003 0.004 0.011 0.020 0.021 0.005 0.006 0.006 0.004 0.005 0.004 0.004 0.004 0.005 0.007 0.007 0.008 0.009 0.009 0.012
Table 4 The values of k1 104 in first order model. 0:5 m s1
1 m s1
k1
40 C 50 C 60 C
1:5 m s1
k1
k1
L
a
b
L
a
b
L
a
b
1.74 4.90 8.50
2.02 5.92 14.38
2.55 7.26 8.41
2.02 4.96 9.84
2.34 4.21 16.77
2.76 10.1 10.26
2.23 4.13 11.24
2.53 6.87 16.74
2.96 4.88 10.36
Considering the color coefficients brought in Table 4, it is observed that for all color indices, the absolute values of their coefficients all follow an increasing trend for all the three velocities of drying air along with temperature increase while it must be taking into account that plus (þ) and the minus () signs indicate the formation and degradation of quality parameters, respectively. Hence, the trends of color index changes had an exponential form for all the three indices throughout the drying time. 4. Conclusion Through the present study: ✓ Drying of watermelon seeds was experimentally investigated and the drying kinetics of seeds including energy of activation and moisture diffusivity parameters were determined; ✓ Verma et al. model gave better fitting representation of experimental data in most of drying conditions; ✓ Air drying temperature had much more significant effect on both germination vigor of seeds than the velocity; ✓ The recommended temperature for convectively drying of watermelon seeds in terms of both germination and color quality was found 50 C; ✓ The first-order model could acceptably prognosticate the trend of color changes and color indices for all drying conditions.
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Please cite this article in press as: Chaji, H., Hedayatizadeh, M., Quality assessment and kinetics of dehydrated watermelon seeds: Part 1, Engineering in Agriculture, Environment and Food (2017), http://dx.doi.org/10.1016/j.eaef.2017.01.006