Rapid and nondestructive prediction of total starch and amylose contents in single sorghum kernel (SSK) based on near infrared (NIR) spectroscopy.

Abstract

This study aimed to establish NIR spectroscopy models for fast predicting apparent amylose (AA) and total starch (TS) content in SSK. Reliable wet chemistry procedures for quantifying TS and AA in single sorghum kernel (SSK) were established, which achieved high accuracy with test errors below 1.0 %. The partial least squares (PLS) model with 2 latent variables (LVs) for AA prediction had coefficients of determination of 0.91 (R 2 cal ) and 0.85 (R 2 cv ), and root mean square errors (RMSE) of 1.90 % and 2.47 % for calibration (RMSEC) and cross-validation (RMSECV), respectively. It showed an R 2 pred of 0.83 and RMSE of 2.58 % for prediction (RMSEP) when validated with the independent validation set. The optimal SSK-TS NIR PLS calibration model was built from 187 calibration sorghum kernels with 10 LVs, which had a R 2 cal of 0.79, RMSEC of 2.76 % and RMSECV of 4.93 % and showed a R 2 pred of 0.72 and RMSEP of 3.19 % when applied to an independent validation set of 93 samples. Overall, this study successfully developed wet chemistry methods for measuring AA and TS contents in SSK and established NIR models for nondestructive prediction and sorting of sorghum kernels by their TS or AA content, serving as useful tools for sorghum breeding and application research.

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