Application of mathematical and genetic algorithm-artificial neural network models in microwave drying of sprouted quinoa
Document Type : Original research
Authors
Department of Food Science and Technology, Faculty of Food Industry, Bu-Ali Sina University, Hamedan, Iran
Abstract
Quinoa is one of the pseudocereal grain that is rich in macro- and micronutrients. Sprouting is an effective process that improves the palatability, quality, nutritional value, and digestibility of quinoa seeds. In this research, the impact of microwave dryer power on the drying kinetics and moisture loss of sprouted quinoa was investigated. The sprouted quinoa seeds were dried as single layers at three different power levels (330, 440, and 550 W). The results showed that the drying time was decreased with increasing microwave power. Seven kinetic models were examined to simulate the experimental drying kinetics and the Page model showed the best performance. The effective moisture diffusivity coefficient (Deff) was calculated to be in the range of 1.43×10-10 m2/s to 2.93×10-10 m2/s, and increased significantly with increasing microwave power (p<0.05). The average rehydration ratio of dried sprouted quinoa changed from 251.04% to 290.10%, and increased with increasing microwave power. In addition, in this study a genetic algorithm-artificial neural network (GA-ANN) method was used for prediction of the moisture loss of sprouted quinoa. The optimal network containing four neurons in hidden layer was able to predict the moisture loss of sprouted quinoa with a coefficient of determination (r) of 0.996. The highest values of water loss, water diffusion, and rehydration rate were obtained when drying with a microwave power of 550 W. The results of this study can be useful in selecting optimal drying conditions for microwave drying of sprouted quinoa and as a basis for other sprouted crops.
Keywords
Main Subjects
Al-Qabba, M.M., El-Mowafy, M.A., Althwab, S.A., Alfheeaid, H.A.,
Aljutaily, T. & Barakat, H. (2020). Phenolic profile, antioxidant
activity, and ameliorating efficacy of Chenopodium quinoa
sprouts against ccl4-induced oxidative stress in rats. Nutrients
12(10), 2904. https://doi.org/10.3390/nu12102904
Alandia, G., Rodriguez, J.P., Jacobsen, S.E., Bazile, D. & Condori, B. (2020).
Global expansion of quinoa and challenges for the Andean region.
Global Food Security 26, 100429.
https://doi.org/10.1016/j.gfs.2020.100429
Arguello-Hernández, P., Samaniego, I., Leguizamo, A., Bernalte-García,
M.J. & Ayuso-Yuste, M.C. (2024). Nutritional and functional
properties of quinoa (Chenopodium quinoa Willd.) chimborazo
ecotype: insights into chemical composition. Agriculture 14(3),
396. https://doi.org/10.3390/agriculture14030396
Azimi-Nejadian, H. & Hoseini, S.S. (2019). Study the effect of microwave
power and slices thickness on drying characteristics of potato.
Heat and Mass Transfer 55(10), 2921-2930.
https://doi.org/10.1007/s00231-019-02633-x
Casalvara, R.F.A., Ferreira, B.M.R., Gonçalves, J.E., Yamaguchi, N.U.,
Bracht, A., Bracht, L., Comar, J.F., de Sá-Nakanishi, A.B., de
Souza, C.G.M., Castoldi, R., Corrêa, R.C.G. & Peralta, R.M.
(2024). Biotechnological, nutritional, and therapeutic applications
of quinoa (Chenopodium quinoa Willd.) and its by-products: a
review of the past five-year findings. Nutrients 16(6), 840.
https://doi.org/10.3390/nu16060840
Dil, E.A., Ghaedi, M., Asfaram, A., Mehrabi, F., Bazrafshan, A.A. & Ghaedi,
A.M. (2016). Trace determination of safranin O dye using
ultrasound assisted dispersive solid-phase micro extraction:
Artificial neural network-genetic algorithm and response surface
methodology. Ultrasonics Sonochemistry 33, 129-140.
https://doi.org/10.1016/j.ultsonch.2016.04.031
Garcia, M., Condori, B. & Castillo, C.D., (2015). Agroecological and
agronomic cultural practices of quinoa in south america, Quinoa:
Improvement and Sustainable Production, pp. 25-46.
Khodadadi, M. & Masoumi, A. (2025). Recent drying technologies used for
drying poultry litter (principles, advantages and disadvantages):
A comprehensive review. Poultry Science 104(2), 104677.
https://doi.org/10.1016/j.psj.2024.104677
Khodadadi, M., Masoumi, A. & Sadeghi, M. (2024). Drying, a practical
technology for reduction of poultry litter (environmental)
pollution: methods and their effects on important parameters.
Poultry Science 103(12), 104277.
https://doi.org/10.1016/j.psj.2024.104277
Khodadadi, M., Rahmati, M.H., Alizadeh, M.R. & Rezaei Asl, A. (2017).
Investigating the effect of air temperature and paddy final
moisture on the crack percent and conversion coefficient of
Iranian rice varieties in fluidized bed dryer. Journal of Food
Science and Technology (Iran) 13(60), 81-91.
Khodifad, B. & Dhamsaniya, N. (2020). Drying of food materials by
microwave energy-A review. International Journal of Current
Microbiology and Applied Sciences 9(5), 1950-1973.
https://doi.org/10.20546/ijcmas.2020.905.223
Le, L., Gong, X., An, Q., Xiang, D., Zou, L., Peng, L., Wu, X., Tan, M., Nie,
Table 5. The weight and bias data of the best model structure for estimation of moisture loss of sprouted quinoa during microwave drying.
Hidden
neurons
Bias Input neurons Output neuron
Treatment time (min) Microwave power (W) Moisture loss (%)
1 3.8122 3.3971 -0.1216 2.9799
2 1.1801 0.3532 0.8902 0.6760
3 1.4303 -0.7577 -0.6883 -0.8303
4 0.5927 1.8148 -0.3497 0.4299
Bias -2.6502
Vejdanivahid and Salehi JFBE 7(2): 44-50,2024
50
Z., Wu, Q., Zhao, G. & Wan, Y. (2021). Quinoa sprouts as
potential vegetable source: Nutrient composition and functional
contents of different quinoa sprout varieties. Food Chemistry 357,
129752. https://doi.org/10.1016/j.foodchem.2021.129752
Liu, X., Chen, X., Wu, W. & Peng, G. (2007). A neural network for predicting
moisture content of grain drying process using genetic algorithm.
Food Control 18(8), 928–933.
https://doi.org/10.1016/j.foodcont.2006.05.010
Martínez-Martínez, V., Gomez-Gil, J., Stombaugh, T.S., Montross, M.D. &
Aguiar, J.M. (2015). Moisture content prediction in the
switchgrass (Panicum virgatum) drying process using artificial
neural networks. Drying Technology 33(14), 1708-1719.
https://doi.org/10.1080/07373937.2015.1005228
Mirsafi, S.M., Sepaskhah, A.R. & Ahmadi, S.H. (2024). Quinoa growth and
yield, soil water dynamics, root growth, and water use indicators
in response to deficit irrigation and planting methods. Journal of
Agriculture and Food Research 15, 100970.
https://doi.org/10.1016/j.jafr.2024.100970
Najib, T., Heydari, M.M. & Meda, V. (2022). Combination of germination
and innovative microwave-assisted infrared drying of lentils:
effect of physicochemical properties of different varieties on
water uptake, germination, and drying kinetics. Applied Food
Research 2(1), 100040.
https://doi.org/10.1016/j.afres.2021.100040
Ng, C.Y. & Wang, M. (2021). The functional ingredients of quinoa
(Chenopodium quinoa) and physiological effects of consuming
quinoa: A review. Food Frontiers 2(3), 329-356.
https://doi.org/10.1002/fft2.109
Okon, O.G., (2021). The nutritional applications of quinoa seeds, in: Varma,
A. (Ed.), Biology and Biotechnology of Quinoa: Super Grain for
Food Security. Springer Singapore, Singapore, pp. 35-49.
Pulvento, C. & Bazile, D. (2023). Worldwide evaluations of quinoa—
biodiversity and food security under climate change pressures:
advances and perspectives. Plants 12(4), 868.
https://doi.org/10.3390/plants12040868
Salehi, F. (2020). Recent advances in the modeling and predicting quality
parameters of fruits and vegetables during postharvest storage: A
review. International Journal of Fruit Science 20(3), 506-520.
https://doi.org/10.1080/15538362.2019.1653810
Salehi, F. (2023). Effects of ultrasonic pretreatment and drying approaches
on the drying kinetics and rehydration of sprouted mung beans.
Legume Science 5(4), e211. https://doi.org/10.1002/leg3.211
Salehi, F., Goharpour, K. & Razavi Kamran, H. (2024). Effects of different
pretreatment techniques on the color indexes, drying
characteristics and rehydration ratio of eggplant slices. Results in
Engineering 21, 101690.
https://doi.org/10.1016/j.rineng.2023.101690
Salehi, F., Razavi Kamran, H. & Goharpour, K. (2023). Effects of ultrasound
time, xanthan gum, and sucrose levels on the osmosis dehydration
and appearance characteristics of grapefruit slices: process
optimization using response surface methodology. Ultrasonics
Sonochemistry 98, 106505.
https://doi.org/10.1016/j.ultsonch.2023.106505
Salehi, F. & Satorabi, M. (2021). Influence of infrared drying on drying
kinetics of apple slices coated with basil seed and xanthan gums.
International Journal of Fruit Science 21(1), 519-527.
https://doi.org/10.1080/15538362.2021.1908202
Samadzadeh, A., Zamani, G. & Fallahi, H.-R. (2020). Possibility of quinoa
production under South-Khorasan climatic condition as affected
by planting densities and sowing dates. Applied Field Crops
Research 33(1), 82-104.
https://doi.org/10.22092/aj.2020.125793.1392
Sedani, S.R., Pardeshi, I.L. & Dorkar, A.R. (2021). Study on the effect of
stepwise decreasing microwave power drying (SDMPD) of moth
bean sprouts on its quality. Legume Science 3(4), e84.
https://doi.org/10.1002/leg3.84
Soltanzadeh, A. & Aahmadpour Borazjani, M. (2022). Energy and economic
analysis of quinoa production in Iran: A case study in Iranshahr
Region. Agriculture, Environment & Society 2(2), 127-134.
https://doi.org/10.22034/aes.2022.349964.1040
Vejdanivahid, S. & Salehi, F. (2024). Application of the adaptive neuro-fuzzy
inference system to estimate mass transfer during convective
drying of microwave-treated quinoa sprouts. Innovative Food
Technologies 11(4), 356-372.
https://doi.org/10.22104/ift.2025.7407.2202
Xing, B., Teng, C., Sun, M., Zhang, Q., Zhou, B., Cui, H., Ren, G., Yang, X.
& Qin, P. (2021). Effect of germination treatment on the structural
and physicochemical properties of quinoa starch. Food
Hydrocolloids 115, 106604.
https://doi.org/10.1016/j.foodhyd.2021.106604
Yang, T., Zheng, X., Vidyarthi, S.K., Xiao, H., Yao, X., Li, Y., Zang, Y. &
Zhang, J. (2023). Artificial neural network modeling and genetic
algorithm multiobjective optimization of process of dryingassisted walnut breaking. Foods 12(9), 1897.
https://doi.org/10.3390/foods12091897
Aljutaily, T. & Barakat, H. (2020). Phenolic profile, antioxidant
activity, and ameliorating efficacy of Chenopodium quinoa
sprouts against ccl4-induced oxidative stress in rats. Nutrients
12(10), 2904. https://doi.org/10.3390/nu12102904
Alandia, G., Rodriguez, J.P., Jacobsen, S.E., Bazile, D. & Condori, B. (2020).
Global expansion of quinoa and challenges for the Andean region.
Global Food Security 26, 100429.
https://doi.org/10.1016/j.gfs.2020.100429
Arguello-Hernández, P., Samaniego, I., Leguizamo, A., Bernalte-García,
M.J. & Ayuso-Yuste, M.C. (2024). Nutritional and functional
properties of quinoa (Chenopodium quinoa Willd.) chimborazo
ecotype: insights into chemical composition. Agriculture 14(3),
396. https://doi.org/10.3390/agriculture14030396
Azimi-Nejadian, H. & Hoseini, S.S. (2019). Study the effect of microwave
power and slices thickness on drying characteristics of potato.
Heat and Mass Transfer 55(10), 2921-2930.
https://doi.org/10.1007/s00231-019-02633-x
Casalvara, R.F.A., Ferreira, B.M.R., Gonçalves, J.E., Yamaguchi, N.U.,
Bracht, A., Bracht, L., Comar, J.F., de Sá-Nakanishi, A.B., de
Souza, C.G.M., Castoldi, R., Corrêa, R.C.G. & Peralta, R.M.
(2024). Biotechnological, nutritional, and therapeutic applications
of quinoa (Chenopodium quinoa Willd.) and its by-products: a
review of the past five-year findings. Nutrients 16(6), 840.
https://doi.org/10.3390/nu16060840
Dil, E.A., Ghaedi, M., Asfaram, A., Mehrabi, F., Bazrafshan, A.A. & Ghaedi,
A.M. (2016). Trace determination of safranin O dye using
ultrasound assisted dispersive solid-phase micro extraction:
Artificial neural network-genetic algorithm and response surface
methodology. Ultrasonics Sonochemistry 33, 129-140.
https://doi.org/10.1016/j.ultsonch.2016.04.031
Garcia, M., Condori, B. & Castillo, C.D., (2015). Agroecological and
agronomic cultural practices of quinoa in south america, Quinoa:
Improvement and Sustainable Production, pp. 25-46.
Khodadadi, M. & Masoumi, A. (2025). Recent drying technologies used for
drying poultry litter (principles, advantages and disadvantages):
A comprehensive review. Poultry Science 104(2), 104677.
https://doi.org/10.1016/j.psj.2024.104677
Khodadadi, M., Masoumi, A. & Sadeghi, M. (2024). Drying, a practical
technology for reduction of poultry litter (environmental)
pollution: methods and their effects on important parameters.
Poultry Science 103(12), 104277.
https://doi.org/10.1016/j.psj.2024.104277
Khodadadi, M., Rahmati, M.H., Alizadeh, M.R. & Rezaei Asl, A. (2017).
Investigating the effect of air temperature and paddy final
moisture on the crack percent and conversion coefficient of
Iranian rice varieties in fluidized bed dryer. Journal of Food
Science and Technology (Iran) 13(60), 81-91.
Khodifad, B. & Dhamsaniya, N. (2020). Drying of food materials by
microwave energy-A review. International Journal of Current
Microbiology and Applied Sciences 9(5), 1950-1973.
https://doi.org/10.20546/ijcmas.2020.905.223
Le, L., Gong, X., An, Q., Xiang, D., Zou, L., Peng, L., Wu, X., Tan, M., Nie,
Table 5. The weight and bias data of the best model structure for estimation of moisture loss of sprouted quinoa during microwave drying.
Hidden
neurons
Bias Input neurons Output neuron
Treatment time (min) Microwave power (W) Moisture loss (%)
1 3.8122 3.3971 -0.1216 2.9799
2 1.1801 0.3532 0.8902 0.6760
3 1.4303 -0.7577 -0.6883 -0.8303
4 0.5927 1.8148 -0.3497 0.4299
Bias -2.6502
Vejdanivahid and Salehi JFBE 7(2): 44-50,2024
50
Z., Wu, Q., Zhao, G. & Wan, Y. (2021). Quinoa sprouts as
potential vegetable source: Nutrient composition and functional
contents of different quinoa sprout varieties. Food Chemistry 357,
129752. https://doi.org/10.1016/j.foodchem.2021.129752
Liu, X., Chen, X., Wu, W. & Peng, G. (2007). A neural network for predicting
moisture content of grain drying process using genetic algorithm.
Food Control 18(8), 928–933.
https://doi.org/10.1016/j.foodcont.2006.05.010
Martínez-Martínez, V., Gomez-Gil, J., Stombaugh, T.S., Montross, M.D. &
Aguiar, J.M. (2015). Moisture content prediction in the
switchgrass (Panicum virgatum) drying process using artificial
neural networks. Drying Technology 33(14), 1708-1719.
https://doi.org/10.1080/07373937.2015.1005228
Mirsafi, S.M., Sepaskhah, A.R. & Ahmadi, S.H. (2024). Quinoa growth and
yield, soil water dynamics, root growth, and water use indicators
in response to deficit irrigation and planting methods. Journal of
Agriculture and Food Research 15, 100970.
https://doi.org/10.1016/j.jafr.2024.100970
Najib, T., Heydari, M.M. & Meda, V. (2022). Combination of germination
and innovative microwave-assisted infrared drying of lentils:
effect of physicochemical properties of different varieties on
water uptake, germination, and drying kinetics. Applied Food
Research 2(1), 100040.
https://doi.org/10.1016/j.afres.2021.100040
Ng, C.Y. & Wang, M. (2021). The functional ingredients of quinoa
(Chenopodium quinoa) and physiological effects of consuming
quinoa: A review. Food Frontiers 2(3), 329-356.
https://doi.org/10.1002/fft2.109
Okon, O.G., (2021). The nutritional applications of quinoa seeds, in: Varma,
A. (Ed.), Biology and Biotechnology of Quinoa: Super Grain for
Food Security. Springer Singapore, Singapore, pp. 35-49.
Pulvento, C. & Bazile, D. (2023). Worldwide evaluations of quinoa—
biodiversity and food security under climate change pressures:
advances and perspectives. Plants 12(4), 868.
https://doi.org/10.3390/plants12040868
Salehi, F. (2020). Recent advances in the modeling and predicting quality
parameters of fruits and vegetables during postharvest storage: A
review. International Journal of Fruit Science 20(3), 506-520.
https://doi.org/10.1080/15538362.2019.1653810
Salehi, F. (2023). Effects of ultrasonic pretreatment and drying approaches
on the drying kinetics and rehydration of sprouted mung beans.
Legume Science 5(4), e211. https://doi.org/10.1002/leg3.211
Salehi, F., Goharpour, K. & Razavi Kamran, H. (2024). Effects of different
pretreatment techniques on the color indexes, drying
characteristics and rehydration ratio of eggplant slices. Results in
Engineering 21, 101690.
https://doi.org/10.1016/j.rineng.2023.101690
Salehi, F., Razavi Kamran, H. & Goharpour, K. (2023). Effects of ultrasound
time, xanthan gum, and sucrose levels on the osmosis dehydration
and appearance characteristics of grapefruit slices: process
optimization using response surface methodology. Ultrasonics
Sonochemistry 98, 106505.
https://doi.org/10.1016/j.ultsonch.2023.106505
Salehi, F. & Satorabi, M. (2021). Influence of infrared drying on drying
kinetics of apple slices coated with basil seed and xanthan gums.
International Journal of Fruit Science 21(1), 519-527.
https://doi.org/10.1080/15538362.2021.1908202
Samadzadeh, A., Zamani, G. & Fallahi, H.-R. (2020). Possibility of quinoa
production under South-Khorasan climatic condition as affected
by planting densities and sowing dates. Applied Field Crops
Research 33(1), 82-104.
https://doi.org/10.22092/aj.2020.125793.1392
Sedani, S.R., Pardeshi, I.L. & Dorkar, A.R. (2021). Study on the effect of
stepwise decreasing microwave power drying (SDMPD) of moth
bean sprouts on its quality. Legume Science 3(4), e84.
https://doi.org/10.1002/leg3.84
Soltanzadeh, A. & Aahmadpour Borazjani, M. (2022). Energy and economic
analysis of quinoa production in Iran: A case study in Iranshahr
Region. Agriculture, Environment & Society 2(2), 127-134.
https://doi.org/10.22034/aes.2022.349964.1040
Vejdanivahid, S. & Salehi, F. (2024). Application of the adaptive neuro-fuzzy
inference system to estimate mass transfer during convective
drying of microwave-treated quinoa sprouts. Innovative Food
Technologies 11(4), 356-372.
https://doi.org/10.22104/ift.2025.7407.2202
Xing, B., Teng, C., Sun, M., Zhang, Q., Zhou, B., Cui, H., Ren, G., Yang, X.
& Qin, P. (2021). Effect of germination treatment on the structural
and physicochemical properties of quinoa starch. Food
Hydrocolloids 115, 106604.
https://doi.org/10.1016/j.foodhyd.2021.106604
Yang, T., Zheng, X., Vidyarthi, S.K., Xiao, H., Yao, X., Li, Y., Zang, Y. &
Zhang, J. (2023). Artificial neural network modeling and genetic
algorithm multiobjective optimization of process of dryingassisted walnut breaking. Foods 12(9), 1897.
https://doi.org/10.3390/foods12091897
)