<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</abstract>

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.

Prediction of Colour Characteristics of Microwave-Dried Tomato Slices Using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference Systems

Food Engineering Department, Faculty of Engineering and Technology

Contemporary Agriculture

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Variation in the colour of dried tomatoes is frequently a problem for both consumers and processors. This study investigated digital imaging and applied...

Prediction of Colour Characteristics of Microwave-Dried Tomato Slices Using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference Systems

Published Online: Nov 16, 2023

Page range: 107 - 121

Received: Oct 06, 2022

Accepted: Jul 06, 2023

DOI: https://doi.org/10.2478/contagri-2023-0014

Keywords
colour, microwave drying, tomatoes, ANN, ANFIS, modelling

© 2023 Jelili Babatunde Hussein et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Prediction of Colour Characteristics of Microwave-Dried Tomato Slices Using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference Systems

Published Online: Nov 16, 2023

Page range: 107 - 121

Received: Oct 06, 2022

Accepted: Jul 06, 2023

DOI: https://doi.org/10.2478/contagri-2023-0014

Keywordscolour, microwave drying, tomatoes, ANN, ANFIS, modelling

© 2023 Jelili Babatunde Hussein et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
colour, microwave drying, tomatoes, ANN, ANFIS, modelling