Fuzzy-logic modelling for quality prediction of smoked Tilapia (<i>Oreochromis niloticus</i>) fish

Abstract

Statistical modelling of hot smoke processing of pre-treated Tilapia (Oreochromis niloticus) fish was reportedly inaccurate, making it difficult to design, predict, and reproduce the finished product’s quality; hence, accurate modelling of this process is a gap in study. This study filled this gap and extended the literature by investigating the accuracy of artificial intelligent based model for the same process. Fuzzy inference system (FIS) model was developed using the already presented dataset in the literature from where inaccurate statistical models were reportedly derived. The dataset is on the effect of smoke temperature (80, 90 and 100OC) and smoke time (2.00, 2.50 and 3.00 h) on the gross energy value (GEV) (Kcal/g) and the overall acceptability (OA) properties of brined pre-solar dried and brined non-dried Tilapia (Oreochromis niloticus) fish. The efficiency of FIS membership function types (pimf, trimf and gbellmf) on the accuracy of the developed FIS model was also investigated. Coefficient of determination, root mean square error, individual percentage error and model accuracy were used to discern the model accuracy. Results showed that FIS had a modelling accuracy (?2 value) between 0.9873 and 0.9999 as against 0.1072 and 0.5800 reported for the statistical model. The results suggested that FIS model outperformed the statistical model of Tilapia (Oreochromis niloticus) smoke processing and it is recommended for process/product design, control, and standardization.