# Rapid Estimation of Moisture Content in Unpeeled Potato Tubers Using Hyperspectral Imaging
## 3 Results and Discussion
It can also be observed that there is not much difference between the performance of Model I with no wavelength selection and with \(\beta\)-coefficient wavelength selection. The selection of critical wavelengths was then carried out to see if this enhanced the models' prediction accuracy. Using the \(\beta\)-coefficient variable selection approach, the number of wavelengths in the PLSR models effectively decreased to 79 spectral bands, i.e., the wavelengths from 732.53 to 969.63 nm were considered, as shown in Figure 5. In this case, the performance of only one model (i.e., 1D-SG preprocessing) was acceptable (RPD \(\geq\) 1.4) when considering both no wavelength selection and \(\beta\)-coefficient wavelength selection. Moreover, the 1D-SG preprocessing methodology performance for the reduced wavelength range was superior to the PLSR models utilizing the full wavelength range (R\({}^{2}\) = 0.66, RMSE = 4.31, and RPD \(\geq\) 1.71). However, the selection of PLSR wavelengths through the \(\beta\)-coefficient method did not improve the accuracy of this model (R\({}^{2}\) = 0.64, RMSE = 4.40, and RPD = 1.67) as shown in Figure 6. Nevertheless, Table 1 shows that both models showed better performance using the reduced wavelength range compared to the models utilizing all wavelengths. The VIP-based wavelength selection performance \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{\begin{tabular}{c} **Preprocessing** \\ **Method** \\ \end{tabular} } & \multirow{2}{*}{**LV**} & \multirow{2}{*}{\begin{tabular}{c} **Wavelength** \\ **(A/B/C)** \\ \end{tabular} } & \multicolumn{2}{c}{\
\begin{tabular}{c} **No Wavelength** \\ **Selection** \\ \end{tabular} } & \multicolumn{2}{c}{
\begin{tabular}{c} **VIP Based Wavelength** \\ **Selection** \\ \end{tabular} } & \multicolumn{2}{c}{\(\beta\)**-Coefficient**} \\  & & & & & & & & & & & **Wavelength Selection** \\ \hline \multirow{2}{*}{I} & NO & 2 & 79/17/13 & 0.07 & 1.04 & 7.11 & 0.07 & 1.03 & 7.12 & 0.07 & 1.03 & 7.12 \\ \hline II & 1D-SG & 12 & 79/28/27 & 0.66 & 1.71 & 4.31 & 0.58 & 1.55 & 4.77 & 0.64 & 1.67 & 4.40 \\ \hline III & 2D-SG & 1 & 79/46/46 & 0.10 & 1.05 & 7.00 & 0.10 & 1.05 & 7.01 & 0.10 & 1.05 & 7.01 \\ \hline IV & SNV & 2 & 79/14/14 & 0.22 & 1.13 & 6.53 & 0.22 & 1.13 & 6.50 & 0.22 & 1.13 & 6.51 \\ \hline V & MSC & 2 & 79/16/18 & 0.18 & 1.10 & 6.69 & 0.18 & 1.10 & 6.67 & 0.18 & 1.10 & 6.67 \\ \hline VI & MF5 & 2 & 79/17/14 & 0.07 & 1.04 & 7.11 & 0.07 & 1.03 & 7.12 & 0.07 & 1.03 & 7.12 \\ \hline VII & MF9 & 2 & 79/18/15 & 0.07 & 1.04 & 7.11 & 0.07 & 1.03 & 7.13 & 0.07 & 1.03 & 7.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The PLSR results when considering wavelengths between 732.53 and 969.63 nm (a total of 79 spectral bands). Figure 4: Measured vs. predicted moisture content in PLSR regression. was comparatively low in both the cases when we considered full wavelength and when considering wavelengths between 732.53 and 969.63 nm. Based on Figures 5 and 6, it can be seen that model II with optimum wavelength (732.53-969.63 nm) selection performed well compared to model I with full wavelength selection. [PERSON] et al. [61] applied the combination of computer vision and backscattering imaging for predicting the moisture content and color changes of sweet potato. The RGB parameters and backscattering parameters were used in the study. It is observed that the calibration (R\({}^{2}\) = 0.66 and RMSEC = 0.19) and validation (R\({}^{2}\) = 0.63 and RMSEC = 0.19) model based on RGB parameters for predicting moisture content shows equal performance with our model II (choosing optimum wavelength). In fact, our model II performed well when compared with the calibration (R\({}^{2}\) = 0.59 and RMSEC = 0.20) and validation (R\({}^{2}\) = 0.54 and RMSEC = 0.22) model based on backscattering parameters for predicting moisture content. This study aimed to determine the optimum wavelength range and preprocessing methods to predict moisture content in intact, unpeeled potatoes.