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首页> 外文期刊>Journal of near infrared spectroscopy >Non-invasive detection of protein content in corn distillers dried grains with solubles: method for selecting spectral variables to construct high-performance calibration model using near infrared reflectance spectroscopy
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Non-invasive detection of protein content in corn distillers dried grains with solubles: method for selecting spectral variables to construct high-performance calibration model using near infrared reflectance spectroscopy

机译：用可溶物无损检测玉米酒糟中的蛋白质含量：使用近红外反射光谱法选择光谱变量以构建高性能校准模型的方法

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摘要

Corn distillers dried grains with solubles (DDGS), a byproduct of the bioethanol industry, is commonly used as animal feed. This paper evaluates the use of backward variable selection partial least square (BVSPLS) and genetic algorithm (GA) methods to select the spectral variables of near infrared (NIR) reflectance spectroscopy and construct high-performance calibration models of protein content in corn DDGS. The BVSPLS analysis utilised 16% of the spectral variables. Compared to the full spectrum model, the model constructed from the variables selected by the BVSPLS analysis significantly improved the accuracy of the model fit and achieved a 19% decrease in the standard error of validation (SEP) and a 23% increase in the residual validation deviation (RPD). The GA analysis selected 8% of the total NIR spectral variables and the model constructed from these selected variables had a fitted accuracy comparable to that of the full spectrum model. The spectral variables selected by both the BVSPLS analysis and GA analysis significantly simplified the NIR calibration model and provided better correlation between the selected spectral variables and protein content of corn DDGS. These results also have important implications for the development of a rapid, non-invasive, online analysis system to detect protein content of corn DDGS in-situ.
机译：玉米蒸馏器干谷物和可溶物（DDGS）是生物乙醇工业的副产品，通常用作动物饲料。本文评估了使用后向变量选择偏最小二乘（BVSPLS）和遗传算法（GA）方法选择近红外（NIR）反射光谱的光谱变量，并构建了玉米DDGS中蛋白质含量的高性能校准模型。 BVSPLS分析使用了16％的光谱变量。与全光谱模型相比，通过BVSPLS分析选择的变量构建的模型显着提高了模型拟合的准确性，并且使标准验证（SEP）的标准误差降低了19％，剩余验证的标准误差提高了23％偏差（RPD）。 GA分析选择了总NIR光谱变量的8％，由这些选择的变量构建的模型具有与全光谱模型相当的拟合精度。通过BVSPLS分析和GA分析选择的光谱变量显着简化了NIR校准模型，并提供了所选光谱变量与玉米DDGS蛋白质含量之间的更好相关性。这些结果对于开发快速，非侵入性的在线分析系统以检测玉米DDGS蛋白质含量具有重要意义。

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