Artificial Neural Network and Mathematical Modeling to Estimate Losses in the Concentration of Bioactive Compounds in Different Tomato Varieties During Cooking

Tomato is a crop with high potential to be used in various food industry co-products, such as sauces. In addition to increasing the supply of differentiated products, processed foods have improved shelf life. However, as a consequence of thermal processing, there may be some important nutritional losses. In this context, the choice of suitable varieties for each type of processing based on the assessment of food losses is extremely important to both the processing industry and the consumer. Therefore, this work aimed to predict the percentage of concentration loss in tomatoes during cooking for sauce production using an artificial neural network (ANN). The prediction was made by analyzing the fresh fruit and comparing it to the cooked product. The study investigated bioactive compounds (vitamin C, ascorbic acid, phenolic compounds, flavonoids, carotenoids, anthocyanins, lycopene, and β-carotene), antioxidant activity (DPPH and FRAP), soluble solids, pH, titratable acidity, ratio, and total sugar. Nine commercial and non-commercial tomato varieties were evaluated. The artificial neural network used was the multilayer perceptron, and its results were compared with first-, second-, and third-degree polynomial regression techniques, evidencing its superiority. This superiority was confirmed by the higher correlation achieved using the ANN (R2 = 0.9025), outperforming the first-, second-, and third-degree regressions (R2 = 0.8817, 0.8819, and 0.8941, respectively). Furthermore, the ANN achieved a lower mean squared error (MSE = 0.000999) and strong validation performance, reinforcing its greater precision and reliability compared to traditional models.