Original scientific paper                                                                                                                                                      DOI: 10.17508/CJFST.2021.13.2.07

Modelling and optimization of the drying process and the quality parameters of dried osmo-pretreated onions (Allium cepa)

https://orcid.org/0000-0002-8979-805XKehinde Peter Alabi1 *, A. M. Olaniyan2, M.O. Sunmonu3

1Department of Food and Agricultural Engineering, Faculty of Engineering and Technology, Kwara State University, Malete, P.M.B. 1530, Ilorin, Nigeria
2Department of Agricultural and Bioresources Engineering, Faculty of Engineering, Federal University, Oye-Ekiti, Nigeria
3Department of Food Engineering, Faculty of Engineering and Technology, University of Ilorin, Nigeria

Article history:
Received: November 13, 2020
Accepted: March 8, 2021
Modelling and optimization represent an important aspect of drying in food processing, providing a fast and convenient means for quality prediction. The research focuses on modelling and optimization of process parameters such as drying rate, water loss, solid gain, vitamin C, manganese, and iron of dried osmo-pretreated onion slices. Least square regression analysis in the Math-lab computer software was used to model and optimise the process parameters., Six (6) mathematical models were developed for each output from the regression analysis that was carried out. The criteria for adjudging these models were the values of their adjusted coefficient of multiple determinations, prediction error sum of squares (also called deleted residual), R2 for prediction, coefficient of variation CV, and the Dubin-Watson test for autocorrelation. The models were checked for adequacy using these criteria, and those found to be adequate were selected from among the other possible combinations. Hence, the best-optimized obtained results from the models are 27.50 g/h, 1.61 g/g, 0.15 g/g, 77.52 mg/100 g, 2.79 mg/1000 g, and 2.19 mg/1000 g for drying rate, water loss, solid gain, vitamin C, manganese, and iron, respectively.
quality prediction