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Chemical Industry & Chemical Engineering Quarterly

Available on line at Association of the Chemical Engineers of Serbia AChE www.ache.org.rs/CICEQ

Chem. Ind. Chem. Eng. Q. 25 (3) 207−215 (2019) CI&CEQ

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VALI RASOOLI SHARABIANI1

EBRAHIM TAGHINEZHAD2

1Faculty of Agriculture and Natural Resources, University of

Mohaghegh Ardabili, Ardabil, Iran 2Moghan College of Agriculture

and Natural Resources, University of Mohaghegh Ardabili, Ardabil,

Iran

SCIENTIFIC PAPER

UDC 66.047:633.18:51

QUANTIFYING OF THE RELATIONSHIP BETWEEN NOVEL INTERMITTENT DRYING VARIABLES AND SOME QUALITY PROPERTIES OF PARBOILED RICE USING RESPONSE SURFACE METHODOLOGY

Article Highlights • The first models for predicting HRY, hardness and color value using RSM were

established • The effects of various drying conditions on quality properties of parboiled rice were

significant • RSM technique has the capability of predicting quality features nondestructively • Optimized parameters of the best quality properties were determined using RSM Abstract

In this research, the effects of intermittent drying variables on some quality properties of parboiled rice were investigated, then a mathematical model was applied to predict the value of quality features non-destructively using res-ponse surface methodology (RSM). The intermittent drying variables consisted of hot air temperature (40, 50 and 60 °C), radiation intensity (0.21, 0.31 and 0.41 W/cm2) and microwave power (100, 200 and 300 W). The intermittent drying was performed at two stages with a tempering time between drying steps using a hybrid drying of hot air–infrared radiation and microwave drying at the first stage and second stage, respectively. According to RSM results, the effect of drying variables on the quality properties of parboiled rice were signi-ficant (p < 0.01). Also, the best mathematical model for prediction of quality properties of samples was the quadratic equation (R2 = 0.96-0.98). The HRY (61.8-73.2%), hardness (118.63-215.27 N) and color value (17.28-19.22) inc-reased, while the lightness (64.17-59.51) decreased during drying. RSM can be able to predict the optimization parameters for the best quality properties (i.e., HRY = 72.42%, lightness = 59.47, color value = 19.25 and hardness = 213.91 N) based on temperature of 60 °C, radiation of 0.41 W/cm2 and power of 300 W.

Keywords: intermittent drying, paddy, parboiled rice, quality, response surface methodology.

Rice (Oryza sativa L.) is one of the main staple foods for more than half of the world’s population [1]. The Fajr variety is the most popular rice for con-sumption and export in Iran, though it has low milling

Correspondence: E. Taghinezhad, Moghan College of Agriculture and Natural Resources, University of Mohaghegh Ardabili, P.O. Box 56199-11367, Ardabil, Iran. E-mail: [email protected] Paper received: 13 August, 2017 Paper revised: 16 August, 2018 Paper accepted: 5 December, 2018

https://doi.org/10.2298/CICEQ170813033S

efficiency [2]. So, the parboiling process had been used for the solving of this problem. The parboiling process is a hydrothermal treatment of the paddy before milling that it includes three basic steps: soak-ing, steaming and drying [3].

Several drying methods had been applied to dry the parboiled rice such as superheated-steam, vacuum, hot air, sun and fluidized bed drying [4]. Hot air drying is an easy and frequent method for drying food mat-erials. However, some limitations of the hot air dryer are long drying time and low energy efficiency [5]. While infrared and microwave drying ensure rapid

V.R. SHARABIANI, E. TAGHINEZHAD: QUANTIFYING OF THE RELATIONSHIP… Chem. Ind. Chem. Eng. Q. 25 (3) 207−215 (2019)

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and efficient distribution of heat in the material as a result, advantages of this method are high heat efficiency, low energy consumption and short drying time [6-8].

The application of hybrid drying of infrared radi-ation and hot air provides a synergistic effect, so it is considered to be more efficient as compared to radi-ation or hot air heating alone [9-11]. Hybrid dryers had been extensively used for drying of different pro-ducts, including onions [12], dog-roses [13], carrots and potatoes [10], apple slices [14], a whole longan fruit [9], and a paddy [15]. Also, hybrid hot air–micro-wave drying had been applied to different products such as apples [16], grapes [17], Thompson seedless grapes [8], peeled longans [18], and mango slices [19], which have advantages of hot air and microwave drying separately. So, the hybrid dryer is one of the research topics in the drying field of food science.

Intermittent drying is a periodic drying technique and has high advantages as compared to continuous drying, because of a tempering period that decreases the moisture gradients thereby increasing the drying rate [20,21]. Recently, intermittent drying has been widely applied in paddy drying industries. Intermittent drying was recommended by other researchers for drying of rough rice [20,22-25].

Optimization techniques are often applied to obtain the best conditions for the drying of products. RSM has been used to optimize the process and it has been widely applied for different processes of drying in the food industry using many research results [26-32]. RSM is a useful method for modeling and prediction of response variables based on inde-pendent factors. This method is preferred because of its simplicity and high efficiency.

To our knowledge, no researchers have rep-orted the modeling and optimization of intermittent drying variables for parboiled rice using RSM. The objectives of this research were to measure the effect of a novel method of intermittent drying on drying time and some quality properties of parboiled rice. Also, RSM was applied to optimize the main parameters of hybrid intermittent HIM drying (hot air-infrared + mic-rowave (HIM)) for parboiled paddy. The research was performed to find the best mathematical model for prediction of some quality attributes based on drying variables. The results of the study will provide the proper conditions for parboiled rice drying and related equipment designing.

MATERIALS AND METHODS

Materials

Paddy samples (Fajr variety) were purchased from a Rice Research Center of Mazandaran, Iran. The samples were stored in separate plastic bags at 5±1 °C in a refrigerator [33]. In this state, the moisture content and amylose value of samples were 11±1 (w.b.) and 22.9%, respectively [34]. Moisture content of paddy samples was measured using an oven at 130 °C for 24 h in triplicate [35].

Parboiling process

Parboiling process involves three stages: soak-ing, steaming and drying. A) Soaking: samples were soaked in a well-stirred water bath with a temperature of 65±0.5 °C for 180 min [36]. Researchers had rep-orted that parboiled paddy will have the best quality at a soaking temperature of 65 °C and steaming time of 4 min for the Fajr variety [37]. B) Steaming: after soaking, the paddy samples were drained and left to cool to ambient temperature for 2 h [38]. The sample (1 kg) was steamed using an instrument that was manufactured by other researchers [38]. The samples were placed on a pot (the pot contained 10 L of boil-ing water (96 °C)) using a metal mesh. The paddy was steamed for 4 min at 96 °C [37,39-41]. C) Drying: after steaming, the paddy samples were transferred to an experimental intermittent dryer.

Drying method

Drying experiments were carried out in two stages. a) At the first stage, a hybrid drying of hot air-infrared (hot air temperatures (40, 50 and 60 °C), radiation intensity (0.21, 0.31 and 0.41 W/cm2 and airflow velocity (1 m/s)) for the reduction of moisture content from 35 to 23%, (w.b.) was used. b) After tempering, microwave drying was applied at the sec-ond stage (100, 200 and 300 W) for decreasing of sample moisture content from 23 to 13% (w.b.). The tempering duration was eight times longer than drying time at the ambient temperature [42]. Other res-earchers suggested that thw microwave dryer should be used at the last stage or at low moisture content for finish drying [43-46]. Also, microwaves that are applied during later stages of hot air-infrared drying penetrate easily and are absorbed selectively in wet layers [47]. In order to determine the radiation inten-sity, the distance between the emitter and sample was changed. Distance between the infrared lamp and samples were 30, 20 and 10 cm for 0.21, 0.31 and 0.41 W/cm2, respectively. In each drying expe-riment, 200 g of samples were put on a 20 cm by 20 cm surface. Each drying condition was conducted in


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triplicate. During drying experiments, the mean ranges of ambient temperature variation and relative humidity were 28±2 °C and 25±3%, respectively. Measuring of temperature, velocity, humidity and weight of samples were performed using a thermo-meter (Lutron, Taiwan), anemometer (Anemometer, Lutron-YK, Taiwan), humidity meter (Testo 650, 05366501, Germany) and digital balance (AND, model EK600i, Japan, ±0.01 g).

Head rice yield (HRY)

After drying, the husking of the parboiled paddy was performed by a hulling instrument (Satake Ltd, Tokyo, Japan). Then, the samples were polished using a polisher (Satake Ltd, Tokyo, Japan) during 90 s [48]. Broken and whole grains were divided by a lab-oratory rice grader (FQS-13X20, Sensewealth, China). Head rice yield (HRY) value was obtained by dividing the amount of the whole kernel to the paddy [49].

Lightness and color value

The lightness and color of samples was mea-sured by a colorimeter (Model 4510, Reston Com-pany, USA). Before measurement, the instrument was calibrated with white and black plates provided by the manufacturer. The results were expressed in terms of L*, a*, b* values; L* represents brightness from black to white, positive and negative a* values

represent redness/greenness and b* value represents yellowness/blueness. Ten replications were per-formed for each treatment and the average of the measured color values were used for reporting. The color value of parboiled rice was computed using Eq. (1) [50]:

Color value = ( ) ( )+2 2a b (1)

Hardness

A material testing machine (H50 K-S, Hounsfield, England) was used to measure the hardness of par-boiled rice. The samples were put on a flat plate and pressed by a flat probe of 12 mm diameter and a 500 N load cell fixed parallel to the base, at a cross-head speed of 1 mm/min. Each experiment was replicated ten times and the average value was reported [2,51].

Experimental design and statistical analysis

The effect of independent variables (factors) was investigated on response variables using RSM. The central composite design (CCD) experimental data was used for model fitting. It could fit the best polynomial equation. The data analyses were per-formed using Design Expert software versions 7.0.0 (2007, Stat-Ease company, USA). The design inc-luded 17 experiments and was adopted by five rep-lications of the center point, as shown in Table 1.

Table 1. The experimental data based central composite design; HRY = head rice yield. Triplicate runs were performed all design point and average recorded. The experimental runs were randomized

Run order Std. order Drying temperature

(°C) Radiation

intensity (W/cm2)Microwave power

(W) HRY (%) Lightness Color value Hardness (N)

1 20 50 0.31 200 65.21 60.92 18.72 157.50

2 11 50 0.21 200 63.82 62.62 18.23 145.17

3 15 50 0.31 200 64.89 60.98 18.91 150.90

4 5 40 0.21 300 63.77 63 17.95 144.30

5 2 60 0.21 100 63.89 62.56 18.26 169.73

6 8 60 0.41 300 73.21 59.51 19.22 215.27

7 4 60 0.41 100 68.24 60.42 18.81 189.80

8 14 50 0.31 300 66.68 60.6 18.87 173.50

9 12 50 0.41 200 69.76 60.79 18.76 164.77

10 16 50 0.31 200 65.43 60.9 18.62 154.20

11 6 60 0.21 300 65.01 61.01 18.90 193.50

12 9 40 0.31 200 65.43 61.53 18.48 146.43

13 10 60 0.31 200 66.70 60.34 19.02 185.57

14 1 40 0.21 100 61.80 64.17 17.28 118.63

15 7 40 0.41 300 70.13 61.05 18.77 163.73

16 19 50 0.31 200 65.52 60.89 18.64 154.40

17 13 50 0.31 100 64.31 60.98 18.49 142.97

18 17 50 0.31 200 65.70 60.91 18.72 152.65

19 3 40 0.41 100 68.00 62.04 18.27 140.47

20 18 50 0.31 200 65.89 60 18.63 153.93


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Mathematical models between the independent variables (drying temperature (°C), radiation intensity (W/cm2) and microwave power (W)) and dependent variables (HRY, lightness, color value and hardness) for parboiled rice were appraised by means of multiple linear regression analysis [52] of the fol-lowing equation:

=−

= = = +

= + +

+ + +

0 112

1 1 1

ni ii

n n nii i i j i ji i j i

Y b b y

b y b y y e (2)

where b0, bi, bii, bij are regression constant coef-ficients, and yi and yj are the independent variables. Also, parameters Y, n and e are the dependent vari-ables, number of independent variables and the ran-dom error term, respectively.

The relationships between the responses were evaluated by means of adjusted R2, predicted R2, cor-relation coefficients of determination (R2) and coef-ficient of variation (C.V.) [53]. A good model will have a high predicted R2. The data were subjected to ana-lysis of variance (ANOVA). The significant terms in the model were found by ANOVA. The significance was analyzed with a confidence level above 95% (p < 0.05).

RESULTS AND DISCUSSION

Experimental design and model development

The regression equations for the response variables and p value, R2, Adj R2, Pred R2 and C.V. (%) values are given in Table 2. R2 value should be at least 0.8 for a good fit of a regression model [54]. R2 of all models ranged between 0.96 and 0.98. All models were significant (p < 0.01) and there was no significant lack of fit in any of the response variables, validating the treatment [55]. Also, the high R2 values revealed that the regression model fits the data well. Thus, the models could be used to predict the amount of HRY (%), lightness, color value and hardness (N).

Head rice yield (HRY)

One of the most important quality parameters that was studied, with respect to the economic value of parboiled rice, was the HRY. Figure 1 shows the

HRY (%) as a function of drying temperature (line-arly), radiation intensity (quadratic) and microwave power (linearly). According to Figure 1, the increasing of drying temperature (from 40 to 60 °C), radiation intensity (from 0.21 to 0.41 W/cm2) and microwave power (from 100 to 300 W) caused an increase of HRY. Reports related to the effect of the different drying conditions on the HRY have also been pre-sented by other researchers [57]. They reported that the HRY of long-grain SP 1 parboiled rice was rel-atively high when drying temperature increased. Also, HRY values were between 61.8-73.21%. The highest and lowest HRY were obtained for the samples that had been dried at treatments 60 °C - 0.41 W/cm2 – 300 W and 40 °C - 0.21 W/cm2 – 100 W, respectively. These findings are similar to results reported by other researchers [56]. They reported the HRY values in the range of 60-80% for parboiled rice (KDML 105 paddy). As shown in Table 2, the drying variables (tempera-ture, radiation intensity and microwave power) have significant influence (p < 0.01) on the extent of par-boiled rice HRY. Also, the best model for prediction of HRY was the quadratic model (R2 = 97% and adjusted R2 = 96%). Similar observations were rep-orted by other researchers [38,58]. Therefore, it seems established that the increasing of drying vari-ables at the range we investigated may lead to an increase in the degree of starch gelatinization (DSG), because high temperature and radiation causes the heat radiation to penetrate into the rice grain kernel and lead into greater DSG [57]. In other words, suit-able conditions of drying lead to gelatinization and stronger structure by diffusing into the inter-granular space of starch, thus facilitating the milling process which increased the HRY of parboiled rice [59]. So, the increase of HRY could also be related to facil-itating the separation of the gelatinized kernels from the husk following the drying. As a result, the milling becomes easier following this separation of the husk from the kernel [60].

Lightness

The lightness value ranges from 59.51 to 64.17, lightness values were relatively similar to those rep-orted by others [41]. The darkest rice was observed

Table 2. Anova table and regression equation of response variables; A, B and C are coded factors for drying temperature (°C), radiation intensity (W/cm2) and microwave power, respectively

Response variable Equation P value R2 value Adj R2 Pred R2 C.V. (%)

HRY (%) 65.58 + 0.79A + 3.11B + 1.25C + 0.50BC + 1.19B2 < 0.0001 0.97 0.96 0.91 0.76

Lightness 60.81 - 0.80A - 0.95B - 0.50C +0.91B2 < 0.0001 0.95 0.93 0.92 0.47

Color value 18.71 + 0.35A + 0.32B + 0.26C – 0.12 AB – 0.27B2 < 0.0001 0.96 0.95 0.92 0.51

Hardness (N) 155 + 24.03A + 10.27B + 12.87C + 11.74A2 < 0.0001 0.98 0.98 0.97 1.76


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Figure 1. Effect of drying temperature, radiation intensity and microwave power on head rice yield.

when rice was treated at 60 °C drying temperature, 0.41 W/cm2 radiation intensity and 300 W microwave power. Figure 2 represents response surface curves

for lightness. The quadratic effects of independent vari-ables revealed that lightness decreased with the sev-erity of the drying process. The R2 and adjusted R2

Figure 2. Effect of drying temperature, radiation intensity and microwave power on lightness of parboiled rice.


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observed were 95 and 93%, respectively (Table 2). Figure 2 shows that, with the increase of drying tem-perature, radiation intensity and microwave power, lightness decreased continuously and significantly (p < 0.01). These findings are in agreement with other researches [61-63]. Consequently, based on results, it can be concluded that rice should not be treated under severe drying treatment. Higher temperature application could affect the lightness of rice that leads to poor quality of rice and less demand in the market [64].

Color value

Color value of parboiled rice is one of the quality indicators that is related to the value on the market [64]. As Table 2 presented, drying variables had significant (p < 0.01) effects on the color values of parboiled rice. The quadratic model was the most appropriate model to describe the relationship between the drying conditions and color values. The R2 was 96%, the adjusted R2 was found to be 95% and all p-value coefficients were significant (p < 0.01). The influence of the various drying variables and their products on the color value of parboiled rice is shown in Figure 3. The color value of parboiled rice varied between 17.28-19.22. The highest and lowest color values were related to drying variables of 60 °C – 0.41 W/cm2 – 300 W and 40 °C – 0.21 W/cm2- 100 W,

respectively. Similar results have been presented with investigation of the effect of the different parboiling conditions on the color value by other researchers [36,56,65]. Increasing of color value at high tempera-tures (radiation intensity or microwave power) can be related to the increasing of the browning rate during drying [66]. Also, many researchers evaluated the color of parboiled rice [2,61,63]. Finally, color changes during parboiling resulted in non-enzymatic browning (Maillard reactions) and the drying variables influ-enced the intensity of color. In other words, the color parameters indicated that during parboiling, red and yellow bran pigments diffused from the bran into the endosperm. Bran pigments diffused into the endo-sperm contribute to the parboiled rice color [67].

Hardness

Figure 4 shows the effect of the drying variables and their products on the hardness of parboiled rice. A quadratic relationship was suggested between independent and response variables (hardness, R2 = = 0.98, Adj. R2 = 0.98, Pred. R2 = 0.97, C.V.= 1.76% (Table 2)). The hardness values of parboiled rice inc-reased with the increasing of drying variables (quad-ratic, linearly and linearly for drying temperature, radiation intensity and microwave power, respect-ively). The increase in hardness was severe at higher drying variables. Increasing the drying temperature

Figure 3. Effect of drying temperature, radiation intensity and microwave power on color value of parboiled rice.


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from 40 to 60 °C, the radiation intensity from 0.21 to 0.41 W/cm2 and the microwave power from 100 to 300 W caused an increase in hardness from 118.63 to 215.27 N. Similar observations were made by other researchers [58]. They reported that hardness of the parboiled rice increased with an increase in drying temperature where a higher degree of gelatinization occurred. So, the most likely explanation for the inc-rease in hardness during drying is that more grains are gelatinized, leading to increased fracture resistance.

Optimization

The optimal values of the independent variables selected for the response variables were obtained by solving of the regression equation (Table 2) using the Design-Expert software. Mathematically, a target must first be determined based on the response variables for optimization of the process. In this study, the optimization was performed according to the fol-lowing targets: HRY, lightness, color value and hardness:

a) Percentage of head rice yield must be maxi-mum;

b) The value of lightness must be minimum; c) The color value must be maximum; d) The hardness must be maximum. The optimal condition for this step was esti-

mated as: drying temperature = 60 °C, radiation int-

ensity = 0.41 W/cm2 and microwave power = 300 W. The predicted HRY, lightness, color value and hard-ness under the above conditions were 72.42%, 59.47, 19.25 and 213.91 N, respectively (with desirability = = 97%). Furthermore, the optimal conditions were applied to three independent replicates to validate the predicted model. The results of this work have shown that response surface methodology could be used to optimize the HRY, lightness, color value and hard-ness in the parboiling process using drying tempera-ture, radiation intensity and microwave power as independent variables.

CONCLUSION

In this study, the effect of drying variables (dry-ing temperature, radiation intensity and microwave power) on some quality properties of parboiled rice samples was studied under intermittent drying. The quality parameters were significantly (p < 0.01) influ-enced by means of drying variables. The value of HRY, lightness, color value and hardness for par-boiled rice during drying were obtained in the range of 61.80 to 73.21%, 59.51 to 64.17, 17.28 to 19.22 and 118.63 to 215.27 N, respectively. The obtained results indicated that the amount of HRY, color value and hardness increased by increasing of drying vari-ables, while the amount of lightness decreased. The

Figure 4. Effect of drying temperature, radiation intensity and microwave power variables on hardness of parboiled rice.


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RSM was applied to predict the some quality pro-perties of parboiled rice. Also, it can be able in opti-mizing the drying variables. The quadratic model (R2 0.95-0.98) was the most appropriate model to des-cribe the relationship between the drying variables and HRY, lightness, color values and hardness of parboiled rice. The best drying variables were: drying temperature = 60 °C, radiation intensity = 0.41 W/cm2 and microwave power = 300 W. The offered results in this study can be applied for the drying of parboiled rice and the designing of related equipment.

Acknowledgment

We acknowledge the financial support of the University of Mohaghegh Ardabili for this research project.

REFERENCES

[1] R.J.B. Heinemann, P.L. Fagundes, E.A. Pinto, M.V.C. Penteado, U.M. Lanfer-Marquez, J. Food Compos. Anal. 18 (2005) 287-296

[2] E. Taghinezhad, M.H. Khoshtaghaza, S. Minaei, A. Latifi, Int. J. Food Eng. 11 (2015) 547-556

[3] N. Behroozi-Khazaei, A. Nasirahmadi, J. Food Sci. Technol. (2017) 1-8

[4] T. Swasdisevi, W. Sriariyakula, W. Tia, S. Soponronnarit, J. Food Eng. 96 (2010) 455-462

[5] T. Orikasa, S. Koide, S. Okamoto, T. Imaizumi, Y. Muramatsu, J.-I. Takeda, T. Shiina, A. Tagawa, J. Food Eng. 125 (2014) 51-58

[6] I. Das, S.K. Das, S. Bal, J. Food Eng. 62 (2004) 9-14

[7] B. Wu, H. Ma, W. Qu, B. Wang, X. Zhang, P. Wang, J. Wang, G.G. Atungulu, Z. Pan, J. Food Process Eng. 37 (2014) 111-121

[8] A.S. Kassem, A.Z. Shokr, A.R. El-Mahdy, A.M. Abouka-rima, E.Y. Hamed, J. Saudi Soc. Agric. Sci. 10 (2011) 33- –40

[9] P. Nuthong, A. Achariyaviriya, K. Namsanguan, S. Achariyaviriya, J. Food Eng. 102 (2011) 233-239

[10] H.U. Hebbar, K.H. Vishwanathan, M.N. Ramesh, J. Food Eng. 65 (2004) 557-563

[11] J. Nejadi, A.M. Nikbakht, J. Food Process Eng. (2016) Doi: 10.1111/jfpe.12373

[12] D.G. Praveen Kumar, H.U. Hebbar, M.N. Ramesh, LWT-Food Sci. Technol. 39 (2006) 700-705

[13] A. Motevali, R. Tabatabaee Koloor, J. Cleaner Prod. 154 (2017) 445-461

[14] H.S. El-Mesery, G. Mwithiga, J. Food Sci. Technol. 52 (2015) 2721-2730

[15] D. Zare, H. Naderi, M. Ranjbaran, Drying Technol. 33 (2015) 570-582

[16] T.V. Gamage, P. Sanguansri, P. Swiergona, M. Eelkema, P. Wyatt, P. Leach, D.L.J. Alexander, K. Knoerzer, Innov. Food Sci. Emerg. Technol. 29 (2015) 261–270

[17] R. Dehbooreh, M. Esmaiili, Iran. Food Sci. Technol. Res. J. 5 (2009) 108-122

[18] J. Varith, P. Dijkanarukkul, A. Achariyaviriya, S. Achariy-aviriya, J. Food Eng. 81 (2007) 459-468

[19] Y.-Y. Pu, D.-W. Sun, Biosys. Eng. 156 (2017) 108-119

[20] A. Cihan, K. Kahveci, O. Hacıhafızoğlu, J. Food Eng. 79 (2007) 293-298

[21] S.J. Kowalski, A. Pawłowski, Drying Technol. 28 (2010) 1023-1031

[22] A. Cihan, M.C. Ece, J. Food Eng. 49 (2001) 327-331

[23] A. Ghasemi, M. Sadeghi, S.A. Mireei, J. Crop Prod. Process. 5 (2016) 51-62

[24] A. Ghasemi, M. Sadeghi, S.A. Mireei, Drying Technol. (2017), Doi: 10.1080/07373937.07372017.01303777

[25] M. Assar, M. Golmohammadi, M. Rajabi-Hamaneh, M.N. Hassankiadeh, Chem. Eng. Commun. 203 (2016) 1242- –1250

[26] K.W. Kong, A. Ismail, C.P. Tan, N.F. Rajab, LWT - Food Sci. Technol. 43 (2010) 729-735

[27] K. Muzaffar, B.V. Dinkarrao, P. Kumar, F. Yildiz, Cogent Food Agric. 2 (2016) 1127583

[28] C. Baltacioglu, Ital. J. Food Sci. (2015) 50-56

[29] D.F. Cortés-Rojas, C.R.F. Souza, W.P. Oliveira, Chem. Eng. Res. Des. 93 (2015) 366-376

[30] A.D. Moghaddam, M. Pero, G.R. Askari, J. Food Sci. Technol. 54 (2017) 174-184

[31] H.T. Vu, C.J. Scarlett, Q.V. Vuong, Drying Technol. (2016) 1-11

[32] D.D. Tengse, B. Priya, P.A.R. Kumar, J. Food Meas. Charact. 11 (2017) 85-92

[33] J. Buggenhout, K. Brijs, J.A. Delcour, Cereal Chem. J. 91 (2014) 554-559

[34] N. Tabkhkar, B. Rabiei, A. Sabouri, Aust. J. Crop Sci. 6 (2012) 980-985

[35] AOAC, Association of Official Analytical Chemists Arlington, VA, 1995

[36] E. Taghinezhad, M.H. Khoshtaghaza, S. Minaei, T. Suzuki, T. Brenner, Rice Sci. 23 (2016) 339-344

[37] E. Taghinezhad, M.H. Khoshtaghaza, S. Minaei, A. Latifi, Int. J. Food Eng. 2015, pp. 547

[38] E. Taghinezhad, T. Brenner, J. Food Process Eng. 40 (2017), Doi: 10.1111/jfpe.12483

[39] D. Spencer, Stone. Webster. Eng. Corp. Boston, MA, 1983

[40] N. Kar, R.K. Jain, P.P. Srivastav, J. Food Eng. 39 (1999) 17-22

[41] M.R. Islam, P. Roy, N. Shimizu, T. Kimura, Food Sci. Technol. Res. 8 (2002) 106-112

[42] J. Aquerreta, A. Iguaz, C. Arroqui, P. Virseda, J. Food Eng. 80 (2007) 611- 618

[43] D.G. Prabhanjan, H.S. Ramaswamy, G.S.V. Raghavan, J. Food Eng. 25 (1995) 283-293

[44] A.E. Kostaropoulos, G.D. Saravacos, J. Food Sci. 60 (1995) 344-347

[45] T. Funebo, T. Ohlsson, J. Food Eng. 38 (1998) 353-367


215

[46] M. Maskan, J. Food Eng. 44 (2000) 71-78

[47] G. Sumnu, E. Turabi, M. Oztop, LWT-Food Sci. Technol. 38 (2005) 549-553

[48] E. Taghinezhad, M.H. Khoshtaghaza, T. Suzuki, S. Minaei, T. Brenner, J. Food Process Eng., A 39 (2016) 442-452

[49] A. Nasirahmadi, B. Emadi, M.H. Abbaspour-Fard, H. Aghagolzade, Rice Sci. 21 (2014) 116-122

[50] L. Lamberts, E. De Bie, G.E. Vandeputte, W.S. Veraver-beke, V. Derycke, W. De Man, J.A. Delcour, Food Chem. 100 (2007) 1496-1503

[51] M.A.K. Miah, A. Haque, M.P. Douglass, B. Clarke, Int. J. Food Sci. Tech. 37 (2002) 539-545

[52] A.I. Khuri, J.A. Cornell, Response Surfaces, Marcel Dekker, New York, 1987

[53] R.H. Myers, D.C. Montgomery, C.M. Anderson-Cook, Response Surface Methodology: Process and Product Optimization Using Designed Experiments, 4th ed., Wiley Inc., Canada, 2016, p. 686

[54] N. Danbaba, I. Nkama, M.H. Badau, M.N. Ukwungwu, A.T. Maji, M.E. Abo, H. Hauwawu, K.I. Fati, A.O. Oko, Int. J. Agric. Forestry 4 (2014) 154-165

[55] M. Vázquez, R. Delgado, A.J. Castro, Starch 61 (2009) 601-609

[56] K. Sareepuang, S. Siriamornpun, L. Wiset, N. Meeso, World J. Agric. Sci. 4 (2008) 409-415

[57] S. Tirawanichakul, O. Bualuang, Y. Tirawanichakul, Songklanakarin, J. Sci. Technol. 34 (2012) 557-568

[58] O. Bualuang, Y. Tirawanichakul, S. Tirawanichakul, J. Food Process. Preserv. 37 (2013) 1119-1132

[59] M.R. Islam, N. Shimizu, T. Kimura, J. Food Eng. 63 (2004) 433-439

[60] A.J. Ayamdoo, B. Demuyakor, W. Dogbe, R. Owusu, M.A. Ofosu, Am. J. Food Technol., A 8 (2013) 31-42

[61] G. Elbert, M.P. Tolaba, C. Suárez, J. Food Eng. 47 (2001) 37-41

[62] S. Bhattacharya, J. Food Eng. 29 (1996) 99-106

[63] B. Lv, B. Li, S. Chen, J. Chen, B. Zhu, J. Cereal Sci. 50 (2009) 262-265

[64] S. Parnsakhorn, A. Noomhorm, Agric. Eng., Int., Vol. X.2008, Manuscript FP 08 009

[65] M.A.K. Miah, A. Haque, M.P. Douglass, B. Clarke, Int. J. Food Sci. Tech., A 37 (2002) 527-537

[66] M. Ahmadi Ghavidelan, R. Amiri Chayjan, Food Meas. (2016), Doi: 10.1007/s11694-11016-19414-11690

[67] L. Lamberts, K.M.R. Brijs, N. Verhelst, J.A. Delcour, J. Agric. Food Chem. 54 (2006) 9924-9929.

VALI RASOOLI SHARABIANI1 EBRAHIM TAGHINEZHAD2

1Faculty of Agriculture and Natural Resources, University of Mohaghegh

Ardabili, Ardabil, Iran 2Moghan College of Agriculture and

Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran

NAUČNI RAD

KVANTIFIKACIJA ODNOSA IZMEĐU NOVIH USLOVA PREKIDNOG SUŠENJA I NEKIH POKAZATELJA KVALITETA PRETKUVANOG PIRINČA PRIMENOM METODOLOGIJE POVRŠINE ODZIV

U radu su istraživani efekti uslova prekidnog sušenja na neke pokazatelje kvaliteta pret-kuvanog pirinča, a zatim je primenjen matematički model za predviđanje vrednosti karakteristika kvaliteta bez razaranja metodom površinske reakcije (RSM). Procesni uslovu prekidnog sušenja su bili: temperatura toplog vazduha (40, 50 i 60 °C), intenzitet zračenja (0,21, 0,31 i 0,41 W/cm2) i snaga mikrotalasne (100, 200 i 300 W). Prekidno sušenje je izvedeno u dve faze sa vremenskim intervalomom odležavanja između faza sušenja korišćenjem hibridnog sušenja toplim vazduhom - infracrveno i mikrotalasno u prvoj i drugoj fazi. Prema rezultatima RSM, uticaj procesnih uslova sušenja na pokaza-telje kvaliteta pretkuvanog pirinča bio je značajan (p < 0,01). Takođe, najbolji matema-tički model za predviđanje pokazatelje kvaliteta uzoraka je kvadratna jednačina (R2 0,96-0,98). HRI (61,8-73.2%), tvrdoća (118,63-215,27 N) i vrednost boje (17,28-19,22) su porasli, dok je osetljivost (64,17-59,51) smanjena tokom sušenja. Optimalni uslovi koji obezbeđuju najbolje pokazatelje kvaliteta (tj. HRI = 72,42%, lakoća = 59,47, vred-nost boje = 19,25 i tvrdoća = 213,91 N) su: temperatura 60 °C, intenzitet zračenja 0.41 W/cm2 i snaga 300 W.

Ključne reči: prekidno sušenje, pirinač, pretkuvani pirinač, kvalitet, metodologija površine odziva.

vali rasooli sharabiani quantifying of the … › ciceq › 2019 › no3 › ciceq_vol25_...

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