Moisture effects on robustness of sorghum grain protein near‐infrared spectroscopy calibration

Abstract

BACKGROUND AND OBJECTIVES: A near‐infrared (NIR) spectroscopy method was developed for rapid and nondestructive evaluation of protein content of intact sorghum grains. Effect of grain sample moisture variation on the robustness of protein calibration was investigated. FINDINGS: An initial NIR protein calibration model with sorghum grains in 7.28%–11.57% moisture content range with coefficient of determination (R²) of 0.94 and standard error of cross‐validation (SECV) of 0.41%, predicted protein content of an external validation set of different varieties with R² = 0.90, root‐mean‐square error of prediction (RMSEP) = 0.42% and bias = −0.02%. However, when grains with a wider range of moisture content (7.50%–17.75%) were used for validation, prediction errors increased with R² = 0.72 RMSEP = 0.87% and bias = −0.32%. Inclusion of grains with a wider moisture range to the calibration set improved the performance of the calibration model with a R² = 0.83, RMSEP = 0.67% with a bias of −0.04%. CONCLUSIONS: Variation of moisture content in grains affected the performance of the NIR protein calibration model. Likewise, inclusion of moisture variation in the calibration sample set improved the robustness of the model. SIGNIFICANCE AND NOVELTY: In addition to the traits of interest, variation of other physical or chemical traits should also be considered for inclusion into the calibration sample set to improve the robustness of NIR calibration models. This is especially important when NIR spectroscopy methods are developed for evaluation of breeding populations as the future grain samples from numerous crosses may be substantially diverse.

Plant phenotyping relevance

ソルガム穀粒のタンパク質含量という植物形質をNIRで非破壊測定する校正法を開発し、水分変動に対する頑健性を検証・改善しており、フェノタイピング手法が中心である。

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