## 3 Results and Discussions
### PLS Regression Models  
Using the PLS regression method, calibration and prediction models were developed for the various preprocessed spectra (Table 2). Among all of these calibration models, the S-G first-derivative preprocessed spectra-based model is better for MC, pH, and the smoothing preprocessed spectra-based model is better for SSC in intact tomatoes because of high correlation coefficient, minimal difference between RMSEC and RMSEP, and the minimal number of latent variables. PLS regression prediction results for MC, pH, and SSC are presented in the scatter plots shown in Figure 5. In all figures, the ordinate and abscissa axes represent the predicted and measured fitted values, respectively, of the corresponding parameters. The calibration correlation between the spectra and the MC of tomatoes was high, with \(r_{cal}\) from 0.81 to 0.88 and RMSEC from 0.44 to 0.54 (see Table 2). When the calibrated model was applied to the prediction set (35 samples), the results were applicable with \(r_{pred}=0.81\), RMSEP = 0.63% using the S-G first-derivative preprocessed spectra (see Figure 5a). This calibration model was better than that reported for MC in intact tomatoes using HSI and artificial neural networks in the range of 400-1000 nm, with a correlation coefficient (\(r_{pred}\)) of 0.773 [4]. For an online application, a smaller number of variables are important in order to develop a simple calibration model. In this study, the PLS model appeared to be acceptable since six factors (LVs) were used in the calibration model (see Table 2). In the case of pH, a good regression correlation coefficient was obtained in the calibration set, with \(r_{cal}\) from 0.32 to 0.76 and RMSEC from 0.06 to 0.09 respectively (see Table 2). When the model was used to predict the samples, the best results were found with \(r_{pred}=0.69\), RMSEP = 0.06 using S-G first derivative (see Figure 5b). The PLS model appeared to be acceptable due to the two factors (latent variables, LVs) used in the calibration model (see Table 2). The pH content of intact tomatoes in the prediction set ranges from 3.9 to 4.3; the lack of large data variation was considered to be influential on the regression results. Although another study of pH prediction in strawberry using HSI showed \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Items**} & \multicolumn{2}{c}{**MC (\%)**} & \multicolumn{2}{c}{**pH**} & \multicolumn{2}{c}{**SSC (\% Brix)**} \\ \cline{2-7}  & **Calibration** & **Prediction** & **Calibration** & **Prediction** & **Calibration** & **Prediction** \\ \hline No. of samples & 60 & 35 & 60 & 35 & 60 & 35 \\ Maximum value & 95.9 & 94.4 & 4.4 & 4.3 & 5.5 & 4.9 \\ Minimum value & 91.0 & 91.2 & 3.9 & 3.9 & 2.7 & 3.4 \\ Mean \(\pm\) SD & 93.6 \(\pm\) 0.94 & 93.4 \(\pm\) 0.92 & 4.1 \(\pm\) 0.1 & 4.1 \(\pm\) 0.08 & 3.9 \(\pm\) 0.47 & 3.9 \(\pm\) 0.44 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristics of calibration and prediction sample sets. Figure 4: Typical reflectance spectra of intact tomatoes. disparate findings, with standard error of prediction (SEP) values of 0.129, the models developed here for predicting pH displayed adequate predictive capacity for an online application [10]. For SSC in intact tomato measurements, the calibration correlation between the spectra and the SSC was as adequately high as 0.64-0.82, with the RMSEC ranging from 0.24% to 0.36% Brix (see Table 2). When the model was used to predict the samples, the prediction results were also desirable, with a correlation coefficient (\(r_{prad}\)) of 0.74 between the measured and the predicted values; the RMSEP was 0.33% Brix with smoothing preprocessed spectra (see Figure 5c). The PLS regression model appeared to be robust since only five factors (LVs) were used in the calibration model (see Table 2). Our results are consistent with the findings of [PERSON] et al. [35], who found a correlation coefficient (\(r\)) of 0.88 and RMSEP of 0.35% Brix for the prediction of SSC in pear using HSI with a spectral range of 930-2548 nm.