Application of adaptive neuro-fuzzy inference system to estimate mass transfer during convective drying of microwave-treated quinoa sprouts
Adaptive neuro-fuzzy inference system (ANFIS) is a new method for modeling and study of mass and heat transfer kinetics during food processing. This study examined the effects of microwave treatment time on the moisture loss rate, effective moisture diffusivity coefficient, and rehydration of quinoa...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Iranian Research Organization for Science and Technology (IROST)
2024-07-01
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| Series: | فناوریهای جدید در صنعت غذا |
| Subjects: | |
| Online Access: | https://jift.irost.ir/article_1513_6ec8eecb3ac837aa84494d1fea23e986.pdf |
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| Summary: | Adaptive neuro-fuzzy inference system (ANFIS) is a new method for modeling and study of mass and heat transfer kinetics during food processing. This study examined the effects of microwave treatment time on the moisture loss rate, effective moisture diffusivity coefficient, and rehydration of quinoa sprouts, and the mass transfer rate was modeled using kinetic models and ANFIS. To apply pretreatment, quinoa sprouts were placed in the microwave device for 0, 30, 60, and 90 s, and after leaving from the device, they were dried in a hot air dryer. The results of this study show that microwave pretreatment for 30 s increases the moisture removal rate, increases the effective moisture diffusivity coefficient, and reduces the drying time of fresh quinoa sprouts. With microwave pretreatment of quinoa sprouts for 30 s, it was observed that the effective moisture diffusivity coefficient increased significantly from 5.73×10-11 m2s-1 to 10.49×10-11 m2s-1 (p<0.05). Based on the results obtained from the section on investigating different kinetic models, the use of a logarithmic kinetic model is recommended to investigate the drying process of quinoa sprouts. With microwave pretreatment of quinoa sprouts for 30 s, it was observed that the rehydration of dried sprouts significantly increased from 196.27% to 253.86% (p<0.05). The overall structure of the ANFIS network in this study includes two inputs (microwave pretreatment time and heating time), 20 input membership functions, 10 rules in the middle layer, 10 output membership functions, and one output response (moisture loss of quinoa sprouts). The results of the ANFIS showed that using the optimal ANFIS structure, the moisture loss percentage of microwave-treated quinoa sprouts during convective drying can be predicted with high accuracy. In general, the appropriate condition for drying fresh quinoa sprouts is a 30 s microwave pretreatment followed by the use of a convection dryer. |
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| ISSN: | 2783-350X 2783-1760 |