Least-squares support vector machines and near infrared spectroscopy for quantification of common adulterants in powdered milk
Least-squares support vector machines and near infrared spectroscopy for quantification of common adulterants in powdered milk
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DOI：
10.1016/j.aca.2006.07.008
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发表时间：
2006-10-02
影响因子：
6.2
通讯作者：
Poppi, Ronei Jesus
中科院分区：
文献类型：
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作者：
Borin, Alessandra;Ferrao, Marco Flores;Poppi, Ronei Jesus
本文提出了使用最小二乘支持向量机（LS-SVM）作为一种替代的多元校正方法，同时定量的一些常见的掺假物（淀粉，乳清或蔗糖）在奶粉样品中发现，使用近红外光谱与直接测量漫反射。由于三种掺杂物的光谱差异，当所有掺杂物组在同一数据集中时，存在非线性行为，使得难以使用线性方法，例如偏最小二乘回归（PLSR）。使用LS-SVM建立了优秀的模型，具有较低的预测误差和相对于PLSR的上级性能。这些结果表明，它可以建立强大的模型来量化一些常见的掺假奶粉使用近红外光谱和最小二乘支持向量机作为一个非线性多变量校正程序。(c)2006 Elsevier B. V.保留所有权利。
This paper proposes the use of the least-squares support vector machine (LS-SVM) as an alternative multivariate calibration method for the simultaneous quantification of some common adulterants (starch, whey or sucrose) found in powdered milk samples, using near-infrared spectroscopy with direct measurements by diffuse reflectance. Due to the spectral differences of the three adulterants a nonlinear behavior is present when all groups of adulterants are in the same data set, making the use of linear methods such as partial least squares regression (PLSR) difficult. Excellent models were built using LS-SVM, with low prediction errors and superior performance in relation to PLSR. These results show it possible to built robust models to quantify some common adulterants in powdered milk using near-infrared spectroscopy and LS-SVM as a nonlinear multivariate calibration procedure. (c) 2006 Elsevier B.V. All rights reserved.