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.

中文翻译：

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使用单核 NIR 光谱对珍珠小米 [Pennisetum glaucum （L.） R. Br.] 成分进行无损表征


作为一种具有高营养特性的无麸质谷物，珍珠粟 [Pennisetum glaucum （L.） R. Br.] 越来越被认为是一种具有丰富谷物营养价值的旱地弹性替代粮食作物。本文探讨了单核近红外 （SKNIR） 光谱与多变量分析相结合在快速无损评估珍珠粟粒蛋白质、水分、脂肪、纤维和灰分含量方面的潜力。在堪萨斯州立大学、海斯农业研究中心 （ARCH） 的旱地和灌溉条件下，对连续两年（2021 年和 2022 年）收获的样品进行了评估，并使用 SKNIR 和常规实验室方法进行了分析。使用偏最小二乘回归开发模型校准。结果显示，模型的性能令人满意，蛋白质、水分、脂肪、纤维和灰分含量的标准误差交叉验证分别为 1.04%、0.17%、0.39%、0.21% 和 0.16%。研究结果表明，SKNIR 可以成为高效筛选高通量珍珠粟成分的潜在工具，这将帮助育种者和谷物加工商优化谷物特性并提高谷物品质和产品。