Fuzzy-logic modelling for quality prediction of smoked Tilapia (Oreochromis niloticus) 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 (𝑅2 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.

Determining Tilapia Quality Using the Fuzzy Logic

“…Some previous studies, namely by (Adeyi et al, 2021) have shown that fuzzy methods can be used to evaluate fish quality with more accurate results than traditional classification methods (Tutuhatunewa, 2021). However, further research is still needed to evaluate the effectiveness of fuzzy methods in measuring overall fish quality Parenreng et al, 2022).…”

Experimental Study on the Thin – Layer Drying Behaviour of Goat Bone for Plant Meals

“…Controlling the drying process in food or agricultural products is imperative to prevent undesirable outcomes and the loss of micronutrients. The initial step in processing animal bones, including their conversion into bone paste and other derivatives, invariably involves drying [3]. Notably, drying is an energy-intensive operation, with prolonged periods escalating the energy requirements for producing the dry product [4,5,6,7,8].…”

“…While the primary aim of drying is to reduce moisture content to an acceptable level, it also entails the destruction of harmful microbes, crucial for enhancing the bones' shelf life [9]. The resulting bone paste serves as a versatile raw material for a variety of products, including bone paste cookies, high-calcium rice noodles, meatballs, and meat stuffing [3]. Additionally, consistent drying is essential to prevent case-hardening, wherein external drying traps moisture inside, and fostering mold growth [10].…”