Determination of starch and amylose contents in various cereals using common model of near-infrared reflectance spectroscopy

Near-infrared reflectance spectroscopy (NIRS) was used to determine the total starch and amylose contents in various kinds of cereals namely wheat, waxy rice, non-waxy rice, millet, sorghum, waxy maize, buckwheat, barley, and hulless oat. The partial least-squares (PLS) analysis and principal component regression (PCR) were used to establish the calibration models. PLS model achieved a better effect than PCR at 1100 - 2500 nm, and the coefficient of determination (R-2) of the calibration and prediction sets were both higher than 0.9 after the best pre-treatment method, first derivative plus Savitzky-Golay. Additionally, the root mean square error (RMSE) was lower than 2.50, and the root mean square error of cross-validation (RMSECV) was less than 3.50 for starch. By comparing PLS models at different waveband regions, the optimal determination results for starch and amylose were obtained at 1923 - 1961 and 1724 - 1818 nm, respectively. NIRS was found to be a successful method to determine the starch and amylose contents in various cereals. (C) All Rights Reserved

Automated summary

By utilizing near-infrared reflectance spectroscopy, the total starch and amylose contents in various cereals were successfully determined, and efficient calibration models were established. The optimization of PLS models led to the best determination results within specific wavelength ranges.