Effective measurement of starch and dry matter content in fresh cassava tubers using interactance Vis/NIR spectra

The quality of cassava in this study was indexed according to the starch content (SC) and dry matter content (DM). The NIR spectroscopy had the potential to measure changes in SC and DM of field-grown cassava. However, due to the non-homogenous nature of cassava root and its unpredictable shape, measuring it using NIR spectroscopy presents a challenge. Therefore, the model developed from different scanning method (horizontal and vertical lines) and surface (original surface, removed brownish outer layer, and fresh surface) were compared. To demonstrate that the model performance could improve by using a few significant wavelengths, its accuracy was compared using either the full wavelength or individual wavelengths selected by bootstrapping soft shrinkage analysis (BOSS) and competitive adaptive reweighted sampling (CARS) methods. Finally, the impact of physical properties (sample sections, peel thickness, varieties, and ages) on model error were determined. The prediction accuracy was found to depend on the scanning method. Since the model developed using horizontal spectra obviously obtained highest accuracy. The difference in surface had no effect on accuracy based on the horizontal spectra. Therefore, original surface scanning was chosen due to its non-destructive performance. The prediction model using the CARS-PLSR method was considered appropriate for predicting SC and BOSS-PLSR for predicting DM, providing R2cal, roal, SEP, and ratio of prediction to standard deviation (RPD) of 0.69, 0.66, 3.18%, and 1.81, respectively; and 0.63, 0.54, 4.15%, and 1.50, respectively. In addition, the sample section, variety and peel thickness of the cassava root were not effected in the error of prediction.