A Nondestructive Detection Method for the Muti-Quality Attributes of Oats Using Near-Infrared Spectroscopy

This study aimed to achieve a precise and non-destructive quantification of the amounts of total starch, protein, β-glucan, and fat in oats using near-infrared technology in conjunction with chemometrics methods. Eight preprocessing methods (SNV, MSC, Nor, DE, FD, SD, BC, SS) were employed to process the original spectra. Subsequently, the optimal PLS model was obtained by integrating feature wavelength selection algorithms (CARS, SPA, UVE, LAR). After SD-SPA, total starch reached its optimal state (<i>R</i>_p² = 0.768, <i>RMSEP</i> = 2.057). Protein achieved the best result after MSC-CARS (<i>R</i>_p² = 0.853, <i>RMSEP</i> = 1.142). β-glucan reached the optimal value after BC-SPA (<i>R</i>_p² = 0.759, <i>RMSEP</i> = 0.315). Fat achieved the optimal state after SS-SPA (<i>R</i>_p² = 0.903, <i>RMSEP</i> = 0.692). The research has shown the performance of the portable FT-NIR for a rapid and non-destructive quantification of nutritional components in oats, holding significant importance for quality control and quality assessment within the oat industry.