Non-destructive characterization of pearl millet [Pennisetum glaucum (L.) R. Br.] composition using single-kernel NIR spectroscopy

Abstract

As a gluten-free cereal with high nutritional properties, pearl millet [Pennisetum glaucum (L.) R. Br.] has been increasingly regarded as an alternative dryland resilient food crop with enriched grain nutritional value. This paper explores the potential of single-kernel near-infrared (SKNIR) spectroscopy combined with multivariate analysis for rapid and non-destructive evaluation of protein, moisture, fat, fiber, and ash contents of pearl millet grains. Samples harvested from two consecutive years (2021 and 2022) were evaluated under dryland and irrigated conditions in Kansas State University, Agricultural Research Center, Hays (ARCH), KS and were analyzed using SKNIR and conventional laboratory methods. Model calibrations were developed using partial least squares regression. Results showed satisfactory performance of models with standard errors cross-validation of 1.04%, 0.17%, 0.39%, 0.21%, and 0.16%, respectively, for protein, moisture, fat, fiber, and ash content. The findings suggest that SKNIR can be a potential tool for high-throughput pearl millet composition screening efficiently, which will assist breeders and grain processors to optimize grain properties and enhance the grain quality and products.

Plant phenotyping relevance

単粒NIR分光とPLS回帰による穀粒成分の非破壊・高スループット推定モデルを開発・検証しており、植物器官（穀粒）の形質取得が中心である。

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