VALIDATION OF SOME SELECTED THIN LAYER DRYING MODELS OF CASSAVA CHIPS
Drying is a critical post-harvest process for preserving cassava as a staple food in many tropical countries. There is little or no enough data on the drying parameters and thin-layer drying models on this modified Force Convectional Solar Dryer (FCSD), and this gap hinder process optimization, reliable prediction of drying kinetics and large-scale application. The aim of this research was modification and validation of some selected thin layer drying models to improve the performance of the FCSD for quality product development of cassava chips. Drying process was observed for six months within dry season (January � March) and rainy season (July � September). Some selected thin-layer drying models were fit into the drying data of cassava chips using a nonlinear regression analysis programme. Three statistical criteria; R2, X2, and RMSE were used to evaluate the fitting performance of each drying model. The result show that the minimum drying time for the sample to reach safe moisture levels of (10 � 12%) was achieved at 14hrs from sample 3 in February, and march, followed by 16 hrs. in June and July from sample 3 as well as February and march from sample 2. The higher drying time was observed to be 20hrs in July, August and September from sample 1 and 18hrs from the 3 sample in some months within the dry and rainy season. It was observed that the smaller size cassava chips sample 3 dried at a much faster rate with no visible negative effect, followed by the moderate size sample 2 and the larger size sample 1. Page model was found to fit best the drying characteristics of three samples Viz; sample 2 and sample 3 in dry season while sample 3 in the rainy season. Other models are Hii model fitted best in dry and rainy season from sample 1. Also, Verma model fitted best in rainy season from sample 2. Hence, the best fitted model recommended for drying in this modified FCSD is page.
Authors : Ahmed, N.Y., Aliyu, B., Tashiwa, Y.I., Dash, S.K. and Rayaguru, K.
Category : Open Access Volume (Issue) : 11(1) Date Uploaded : 21st January 2026