## 4 NIR-Hyperspectral Imaging Spectroscopy for Yam and Cassava Food Quality  
NIR spectroscopy has recently moved from traditional spectroscopy to coupling with other technologies, including NIR-microscopy, NIR-MIR Spectroscopy, and NIR-Hyperspectral imaging spectroscopy for the quality assessment of root and tuber crops. Along with increased spectra quality from the millions of spectral data points acquired at each wavelength, NIR-HSI also gives information on the spatial distribution of the target product's chemical components. Numerous root and tuber crops, particularly potatoes and sweet potatoes, have been reported to use NIR-HSI for their food quality assessment [26; 75; 76; 77; 78; 79; 80; 81; 82]. [PERSON] et al. [18] mentioned in their review paper that only one work characterizing cassava by applying NIR-HIS, that of [PERSON] and [PERSON] [83], had been reported at the time of their research. The authors employed the HSI method to identify the adulteration of cassava flour in Irish organic wheat flour (OWF). Between 900 and 1700 nm, hyperspectral images were taken using OWF samples that had different levels of percentage adulterations. For quantitative analysis, PLSR and principal component regression (PCR) were used, and feature wavelengths were chosen using the first derivative and mean centering iteration procedure using the loading plots of PCA (FMCIA). Wavelengths were further decreased following the corresponding feature using the model regression coefficients (RC). The RC-FMCIA-PLSR model produced the best admixture detection outcome for OWF mixed with cassava flour, with R\({}^{2}\)P = 0.973 and RMSEP = 0.036. [PERSON] et al. [36] later reported on another use of NIR-HSI for identifying adulterations of cassava flour in tapioca starch. This investigation added limestone powder to tapioca starch at intervals of 0.5% across a range of 0-100% (wt/wt) to create adulterated tapioca starch. A calibration set of samples (N = 141) and a prediction set of samples (N = 61) were used in the study. All samples were scanned with the NIR-HSI equipment at a wavelength of 935-1720 nm. The model's prediction accuracy was perfect, with a correlation coefficient (R) of 0.99 and a root mean square error of prediction (RMSEP) of 2.47%. Using prediction model visualization techniques, the study demonstrated the potential of NIR-HSI as a quick way for identifying the levels of adulteration in tapioca starch. The application of NIR-HSI for dolomite adulteration in tapioca starch was also evaluated in a different study by the same researchers, who added dolomite in concentrations ranging from 0.5 to 100% (wt/wt). Using NIR-HSI at 935 to 1720 nm, 400 samples of pure and contaminated tapioca starch were scanned. These samples were separated into a calibration set (N = 300) and a validation set (N = 100). For preprocessing, [PERSON]'s first derivative differentiation was utilized to create the ideal environment for the classification model. The model's classification of pure and contaminated tapioca was assessed to be 100% accurate [84]. Although there are many practical applications using NIR-HSI for other root and tuber crops, especially potato and sweet potatoes (Table 1), only a few studies were reported on using NIR-HSI for quality characterization of cassava and yam tubers. A standard operating procedure (SOP) for monitoring water distribution in fresh yam using HSI was reported in the framework of the RTBfoods project. However, this SOP only described the use of HSI to detect the longitudinal distribution of water in fresh roots and tubers using multivariate analysis [85]. Therefore, further SOPs must be developed to investigate the cross-sectional parts of the root and tuber crop for physical and chemical characteristics.