Modelling some quality attributes of a convective Hot-Air dried tomato slices using ANN and ANFIS techniques
Adekanmi Olusegun Abioye, Jelili Babatunde Hussein, Moruf Olanrewaju Oke, Islamiyat Folashade Bolarinwa
Abstract
The study investigated how different processing combinations affect the quality of tomatoes dried in a convective hot-air dryer. The Taguchi technique was used to plan the experiments. Three pretreatment methods were used: water blanching (WBP), ascorbic acid (AAP), and sodium metabisulphite (SMP). The slice thickness was changed from 4 to 6 mm, and the air temperature was changed from 40 to 60°C. Standardised protocols were followed to assess the quality attributes, percentage shrinkage, rehydration ratio, as well as the levels of lycopene, β-carotene, and ascorbic acid in the dried tomatoes. The artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) models were trained using the data. At the best conditions of SMP, 6 mm slice thickness and 40ᵒC air temperature, the quality attributes were; 90.89%, 4.22, 10.74 mg/100g, 9.14 mg/100g, and 25.14 mg/100g, respectively. The findings demonstrate that ANN and ANFIS models provide a more accurate prediction. The ANFIS model, on the other hand, has proven to be more effective, with a greater coefficient of determination (≥ 0.9988) and lower root mean square error (≤ 0.02076) and mean absolute error (≤ 0.01623). The predictive models were experimentally verified to be accurate when compared to experimental results.