Okonkwo, Clinton E. and Olaniran, Abiola F. and Adeyi, Abiola J. and Adeyi, Oladayo and Ojediran, John O. and Erinle, Oluwakemi C. and Mary, Iranloye Y. and Taiwo, Abiola E. (2022) Neural network and adaptive neuro-fuzzy inference system modeling of the hot air-drying process of orange-fleshed sweet potato. Journal of Food Processing and Preservation, 46 (3).
Full text not available from this repository.Abstract
The primary objective of this study is to determine the hot air drying characteristics and nutritional quality of orange-fleshed sweet potato (OFSP) in a convective dryer. Three temperatures (323.15, 333.15, and 343.15 K) and fan speed levels (0.5, 0.9, and 1.3 m/s) were used. A rehydration study of dried OFSP was also carried out. Modeling and prediction of experimental moisture data were done using artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) models. The result showed that the drying rate and rehydration ratio were significantly (p < .05) affected by drying temperature and fan speed levels. The effective diffusivity (Deff) of the samples ranged from 2.5 × 10–9 to 4.25 × 10–9 m2/s, and it was found to increase with temperature and fan speed. Protein and fat content appeared to be strongly influenced by drying processing variables, whereas other properties appeared to be insignificant. ANFIS showed better modeling ability than ANNs in predicting the experimental moisture data of OFSP with R2 and RMSE values of .99786 and 0.0225 respectively. In conclusion, the findings from this research will be useful in product optimization and process monitoring of hot air drying of OFSP, in establishing its drying temperature and fan speed.
| Item Type: | Article |
|---|---|
| Subjects: | A General Works > AC Collections. Series. Collected works Q Science > Q Science (General) Q Science > QR Microbiology |
| Depositing User: | DR ABIOLA OLANIRAN |
| Date Deposited: | 15 Jan 2024 08:14 |
| Last Modified: | 15 Jan 2024 08:14 |
| URI: | https://eprints.lmu.edu.ng/id/eprint/5043 |
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