Prediction of Total Soluble Solids Content Using Tomato Characteristics: Comparison Artificial Neural Network vs. Multiple Linear Regression

Tomatoes are among the world's most significant vegetables, both in terms of production and consumption. Harvesting takes place in tomato production when the important quality attribute of total soluble solids content reaches its maximum possible level. Tomato total soluble solids content (TSS) is among the most crucial attribute parameters for assessing tomato quality and for tomato commercialization. Determination of total soluble solids content by conventional measurement methods is both destructive and time-consuming. Therefore, the tomato processing industry needs a rapid identification method to measure total soluble solids content (TSS). In this study, we aimed to estimate how much soluble solids there are in beef tomato fruit by Artificial Neural Networks (ANN) and Multiple Linear Regression (MLR) methods. The models were assessed using the Coefficient of Determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) metrics. The training data set results of the MLR model established to estimate the amount of brix in tomato fruit, calculated as MAE: 0.2349, RMSE: 0.3048, R2: 0.8441, and MAPE: 5.5368, while, according to the ANN model, MAE: 0.0250, RMSE: 0.031, R2: 0.9982 and MAPE: 0.5814. According to the metric outcomes, the ANN-based model performed better in both the training and testing parts.