## 3 Results and Discussions
### PLS Regression Models
By a 95% confidence paired \(t\)-test, there were no significant differences between the experimental values of MC, pH, and soluble solid content (SSC) and those predicted by HSI. These results demonstrate that a calibration model for prediction of internal qualities of intact tomato using HSI has been successfully developed and validated. In the above PLS regression results, individual wavelength contributions by MC, pH, and SSC contents in tomatoes was not considered in the prediction results. This was because the PLS regression method first applied linear transform to the entire individual wavelength data [34]. As a result, it was \begin{table}
\begin{tabular}{l l c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**Preprocessing Method**} & \multirow{2}{*}{**No. of LVs**} & \multicolumn{2}{c}{**Calibration**} & \multicolumn{2}{c}{**Prediction**} \\ \cline{3-6}  & & & \(r_{cal}\) & **RMSEC** & \(r_{prad}\) & **RMSEP** \\ \hline \multirow{6}{*}{MC} & Raw & 7 & 0.81 & 0.44 & 0.79 & 0.56 \\  & Smoothing (moving average) a  & 8 & 0.87 & 0.45 & 0.79 & 0.65 \\  & Max normalization & 7 & 0.88 & 0.44 & 0.71 & 5.74 \\  & Mean normalization & 6 & 0.84 & 0.51 & 0.76 & 0.64 \\  & Range normalization & 7 & 0.86 & 0.48 & 0.77 & 0.62 \\  & S–G first derivatives b  & 6 & 0.85 & 0.50 & 0.81 & 0.63 * \\  & S–G second derivatives b  & 9 & 0.88 & 0.45 & 0.71 & 0.69 \\  & MSC c  & 5 & 0.83 & 0.52 & 0.74 & 0.66 \\  & SNV d  & 5 & 0.81 & 0.54 & 0.73 & 0.67 \\ \hline \multirow{6}{*}{pH} & Raw & 3 & 0.72 & 0.06 & 0.65 & 0.06 \\  & Smoothing (moving average) a  & 3 & 0.75 & 0.06 & 0.71 & 0.06 \\  & Max normalization & 2 & 0.33 & 0.09 & 0.50 & 0.08 \\  & Mean normalization & 2 & 0.32 & 0.09 & 0.50 & 0.08 \\  & Range normalization & 2 & 0.32 & 0.09 & 0.49 & 0.08 \\  & S–G first derivatives b  & 2 & 0.75 & 0.06 & 0.69 & 0.06 * \\  & S–G second derivatives b  & 2 & 0.76 & 0.06 & 0.67 & 0.06 \\  & MSC c  & 2 & 0.32 & 0.09 & 0.37 & 0.08 \\  & SNV d  & 2 & 0.32 & 0.09 & 0.37 & 0.08 \\ \hline \multirow{6}{*}{SSC} & Raw & 7 & 0.82 & 0.24 & 0.63 & 0.33 \\  & Smoothing (moving average) a  & 5 & 0.80 & 0.28 & 0.74 & 0.33 * \\  & Max normalization & 6 & 0.80 & 0.28 & 0.53 & 0.41 \\  & Mean normalization & 6 & 0.80 & 0.28 & 0.55 & 0.40 \\  & Range normalization & 6 & 0.79 & 0.29 & 0.54 & 0.39 \\  & S– first derivatives b  & 7 & 0.78 & 0.30 & 0.65 & 0.36 \\  & S–G second derivatives b  & 6 & 0.64 & 0.36 & 0.50 & 0.39 \\  & MSC c  & 5 & 0.78 & 0.29 & 0.55 & 0.40 \\  & SNV d  & 5 & 0.77 & 0.30 & 0.47 & 0.42 \\ \hline \hline \end{tabular} Notes. a Number of segments: 13, b Number of segments: 13 and polynomial order: 2; c Multiple Scatter Correction; d Standard Normal Variate; RMSEC, and RMSEP are the root mean square error of calibration and prediction respectively, unit: % for MC, a.u. for pH and % Brix for SSC; \(r_{cal}\) and \(r_{prad}\) are the correlation coefficient of calibration and prediction respectively; S–G: Savitzky–Golay; LVs: Latent variables,* Best model.  
\end{table} 
Table 2: Partial least squares regression results for the prediction of moisture content (MC), pH, and soluble solid content (SSC) for intact tomatoes with different preprocessed spectra. difficult to determine how individual wavelengths were directly related to the MC, pH, and SSC contents in tomatoes to be predicted. However, it would be helpful to examine how MC, pH, and SSC in tomatoes were simply related to individual wavelengths so that a better understanding of their correlated spectra might be achieved. Figure 5: Prediction results of the established PLS regression models for chemical contents in intact tomatoes (**a**) moisture content (**b**) pH and (**c**) soluble solid content (SSC).