Prediction of Colour Characteristics of Microwave-Dried Tomato Slices Using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference Systems
2023
Hussein Jelili Babatunde | Oke Moruf Olanrewaju | Agboola Fausat Fadeke | Sanusi Mayowa Saheed
Variation in the colour of dried tomatoes is frequently a problem for both consumers and processors. This study investigated digital imaging and applied soft-computational modelling using the Artificial Neural Network (ANN) and Adaptive Neuro-fuzzy Inference System (ANFIS) to evaluate the surface colour of microwave-dried tomato slices. The tomatoes were pretreated with water blanching, ascorbic acid, and sodium metabisulphite, then cut into slices of 4, 6, and 8 mm thickness. The slices were then dried in a microwave oven at power levels of 90, 180, and 360 W. The colour characteristics of the dried tomato slices (L*, a*, b*, colour change, browning index, hue, and chroma) were determined. The response variables were modelled and optimised using ANN and ANFIS. The efficiency and performance of the model were assessed using the coefficient of determination (R2), the root means square error (RMSE), and the mean absolute error (MAE). The results revealed the ranges of 36.70 – 48.83, 36.81 – 44.56, 31.03 – 40.34, 8.43 – 21.24, 11.78 – 39.82, 48.15 – 60.11, and 0.82 – 0.87 for the colour characteristics of L*, a*, b*, colour change, browning index, hue, and chroma, respectively. The outcomes showed that ANN and ANFIS models could make more accurate predictions. The predictive models were experimentally validated and agreed with the experimentally obtained values. However, the ANFIS model gave better performance, with higher values for R2 (1.000) and lower values for RMSE (0.02952) and MAE (0.02209). These findings will be helpful to processors and can be scaled up and adjusted for the bulk colour characteristics of microwave-dried tomatoes.
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