## 5 Quality Evaluation of Potatoes and Sweet Potatoes with NIR-Hyperspectral Imaging Techniques  
The application of NIR-HSI for the quality evaluations of potatoes and sweet potatoes is summarized in Table 1. The water content and weight of potato tubers was assessed using the hyperspectral imaging technique and artificial neural network algorithms, where 934-997 nm was the wavelength range found to be selective for the absorption band in predicting the water content in the potato tuber [85]. Measurements of the dry matter of potato and sweet potato were conducted using hyperspectral imaging in conjunction with LWPLSR, PLSR, and MLR. Using the MLR model, a highly satisfactory prediction coefficient (R\({}^{2}\)P) of 0.96 was obtained [86]. A multispectral real-time system was developed to monitor the moisture content (MC) in dried potato and sweet potato products using near-infrared (NIR) and mid-infrared (MIR) hyperspectral techniques combined with chemometric algorithms. Multivariate models were created using partial least squares regression (PLSR), support vector machine regression (SVMR), locally weighted partial least square regression (LWPLSR), and a back propagation artificial neural network (BPANN) in the full spectral range of 900-10,372 cm\({}^{-1}\). The prediction (R\({}^{2}\)p) determination coefficients of 0.950 and 0.904, respectively, were obtained from the simplified SPA-LWPLSR and SPA-BPANN, respectively, indicating good model performances for the tuber MC prediction [87]. Additionally, the NIR hyperspectral technology was used to predict the starch content of sweet potato and potato [88]. The feasibility of hyperspectral imaging systems in monitoring the changes in the moisture (MC) and total anthocyanin (TA) contents of purple sweet potatoes [PSP] during convective hot air drying (CHD) and microwave drying (MD) was investigated [80]. The PLSR model was developed after spectra extraction to predict the TA and MC contents of the processed purple sweet potatoes. For the CHD, a determination coefficient in prediction (R\({}^{2}\)p) of 0.836 and 0.817 and a root mean square error (RMSEP) of 0.091 and 0.407 were reported for MC and TA, respectively. However, the R\({}^{2}\)p obtained for the MD was 0.831 and 0.766, with an RMSEP of 0.095 and 0.382 for MC and TA, respectively. The authors also established that HSI could be useful for visualizing the distribution of MC and TA during the drying process of the purple sweet potatoes. The authors observed a uniform distribution of MC and TA at the initial drying stage by CHD until after 45 min of drying, when high moisture loss was observed from the core of the sample. They reported that convective hot air drying has better distribution uniformity of the measured parameters than the microwave drying [80]. The starch contents of fresh-cut potatoes were analyzed with hyperspectral imaging techniques using Competitive Adaptive Reweighed Sampling (CARS) and the successive projection algorithm (SPA) to extract characteristic \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Trait** & **Product** & **Software** & **Equipment** & **Accuracy** & **Reference** \\ \hline \multirow{3}{*}{Adulteration} & \multirow{3}{*}{Cassava flour} & FMCIA- & CCD camera Xeva & \multirow{3}{*}{(R\({}^{2}\) = 0.98, SECV = 0.026)} & \multirow{3}{*}{[83]} \\  & & PLS & & 992—Xenics Infrared & \\  & & MATLAB-Mathworks & Solutions & & \\ \hline \multirow{3}{*}{Adulteration} & \multirow{3}{*}{Tapioca starch} & \multirow{3}{*}{PLSR} & Specim Fx17, Spectral & \multirow{3}{*}{The calibration set’s total} & \multirow{3}{*}{[84]} \\  & & & Imaging Ltd., Oulu, & & \\ \cline{1-1}  & & & Finland & & \\ \hline \hline \end{tabular} Note: PLS: partial least squares; FMCIA: first-derivative and mean-centering iteration algorithm; R\({}^{2}\); correlation coefficient; SECV: Standard error of cross-validation.  
\end{table} 
Table 2: Summary of NIR-HSI applications for quality analysis of cassava. wavelengths from the images.