SOFT-COMPUTING ESTIMATION OF PRE-TREATED MANGO (MANGIFERA INDICA) KERNEL MOISTURE CONTENT IN A TRAY DRYER

Abstract

This study presents Adaptive Neuro-fuzzy Inference System (ANFIS) technique for the estimation of pre-treated mango kernel moisture content in a tray dryer. The blanching and drying experiments were conducted at different drying air temperatures (45 -750C), drying time (0 - 420 minutes), blanching temperatures (40 - 1000C) and blanching time (3 - 9 minutes). The experimental input and output (moisture ratio) data were used to architect different ANFIS structures at different epoch numbers, input and output Membership Functions (MFs). The best structure for the estimation was achieved using trap input MF, constant output MF and 1500 epoch number. The Root Mean Squared Error (RMSE) and correlation coefficient R2 showed 0.0097 and 0.958 respectively for the ANFIS structure; furthermore, K value that compares ratio of the best ANFIS checking and training error is 0.87. The results of this investigation show the capability of ANFIS for the estimation of pre-treated mango kernel moisture content in a tray dryer.