## 3 Results and Discussion  
A descriptive statistical analysis of the values obtained as a result of the trials conducted on the tomato fruits used in this study can be found in Table 1.  
Table 2 displays the MLR model's results established to estimate the amount of brix in tomato fruit. According to the MLR model, while TKM, acidity, Vitamin C, L, a, b, C, h, K, Mg, Na, and P are significant, ash, pH, hardness, fructose, glucose, Ca, Mn, and lycopene \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Variables** & **Mean** & **Min** & **Max** & **Std,Dev** & **Variables** & **Mean** & **Min** & **Max** & **Std,Dev** \\ \hline Brix* & 4.32 & 2.90 & 6.82 & 0.76 & a & 24.78 & 16.99 & 31.78 & 3.07 \\ TKM & 5.72 & 4.14 & 9.15 & 0.84 & b & 23.30 & 18.55 & 31.97 & 3.01 \\ Ash & 0.50 & 0.30 & 0.75 & 0.09 & C & 34.06 & 25.27 & 45.02 & 3.92 \\ pH & 4.25 & 4.03 & 4.46 & 0.10 & h & 43.48 & 35.43 & 49.97 & 3.11 \\ Acidity & 0.38 & 0.27 & 0.54 & 0.06 & Manganese & 1.13 & 0.69 & 1.76 & 0.22 \\ Hardness & 1.07 & 0.55 & 1.70 & 0.26 & Sodium & 32.35 & 22.68 & 42.85 & 4.95 \\ Fructose & 1.24 & 0.51 & 2.18 & 0.29 & Calcium & 360.62 & 206.56 & 583.91 & 87.62 \\ Glucose & 1.10 & 0.48 & 2.05 & 0.31 & Potassium & 2305.29 & 1305.35 & 2936.53 & 365.29 \\ Lycopene & 51.32 & 20.08 & 105.23 & 15.72 & Magnesium & 115.41 & 78.32 & 171.63 & 23.58 \\ Vitamin C & 20.45 & 12.98 & 29.54 & 3.69 & Phosphor & 169.65 & 99.39 & 325.63 & 38.16 \\ L & 40.07 & 35.70 & 46.16 & 2.10 & & & & & \\ \hline \hline \end{tabular}  
* Number of observations: 294,
* Target Variable.  
\end{table} 
Table 1: Descriptive statistics of the variables 1. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variables** & **Std. ** & **Beta** & **Std. ** & _p Value_ \\ \hline TKM & 0.588 & 0.047 & 11.356 & 0.000 \\ Acidity & 0.267 & 0.592 & 6.238 & 0.000 \\ Vitamin C & 0.061 & 0.008 & \(-1.72\) & 0.087 \\ L & 0.213 & 0.017 & \(-4.637\) & 0.000 \\ a & 1.557 & 0.153 & 2.665 & 0.008 \\ b & 2.814 & 0.152 & 4.986 & 0.000 \\ C & 3.828 & 0.188 & \(-4.239\) & 0.000 \\ h & 0.585 & 0.062 & \(-2.3 are insignificant. All variables are significant at the 0.05 error level, except Vitamin C (significant at the 0.10 error level). According to the MLR result, the regression equation is given in Equation (5). \[ \begin{split}\widehat{brix}=2.816+0.588 TKM+0.267 Acidity-0.061 vitC-0.2 13L+1.557a+\\ 2.814b-3.828C-0.585-0.216K+0.116 Mg-0.199 Na+0.217P\end{split}  \tag{5}\]  
Table 3 displays the outcomes of the MLR and ANN models. According to the training data set results of the MLR model established to estimate the amount of brix in tomato fruit, the results can be calculated as MAE: 0.2349, RMSE: 0.3048, R\({}^{2}\): 0.8441, and MAPE: 5.5368, while, according to the ANN model, MAE: 0.0250, RMSE: 0.031, R\({}^{2}\): 0.9982, and MAPE: 0.5814. As seen in Table 3, the training and test data results of the MLR model are close to each other. A similar situation applies to the ANN model. Accordingly, there is no overlearning problem in MLR and ANN models. As seen in Table 1, the training and test data results of the MLR model are close to each other. A similar situation applies to the ANN model. Accordingly, there is no overlearning problem in MLR and ANN models. Based on the test data set findings, the MLR model predicts the amount of Brix with an average error of 0.2573, while the ANN model estimates it with an error of 0.0250. According to the RMSE result of the MLR model, the model estimates the brix amount with an RMSE error of 0.3343, while the ANN model estimates it with an RMSE error of 0.031. In the MLR model, the explained variation is approximately 78%, while, in the ANN model, it is 99.8%. The MAPE value of MLR is 5.8199, the predicted variable, namely, the brix amount, can be predicted with an error of approximately 5.8%, while the ANN model makes a prediction with an error of approximately 0.6%. However, in light of these outcomes, it is seen that the ANN model is more successful and makes better predictions than the MLR model in the training data set.