Rapid identification of milk samples by high and low frequency unfolded partial least squares discriminant analysis combined with near-infrared spectroscopy

被引：22

作者：

Bian, Xihui ^{[1
,2
]}

Zhang, Caixia ^{[1
,2
]}

Liu, Peng ^{[2
]}

Wei, Junfu ^{[2
]}

Tan, Xiaoyao ^{[1
]}

Lin, Ligang ^{[1
]}

Chang, Na ^{[2
]}

Guo, Yugao ^{[1
]}

机构：

[1] Tianjin Polytech Univ, State Key Lab Separat Membranes & Membrane Proc, Tianjin 300387, Peoples R China

[2] Tianjin Polytech Univ, Sch Environm & Chem Engn, Tianjin 300387, Peoples R China

来源：

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | 2017年 / 170卷

基金：

中国国家自然科学基金;

关键词：

Empirical mode decomposition; Partial least squares discriminant analysis; Near-infrared spectroscopy; Milk identification; Pattern recognition; EMPIRICAL MODE DECOMPOSITION; PERFORMANCE LIQUID-CHROMATOGRAPHY; MULTIVARIATE-ANALYSIS; PATTERN-RECOGNITION; MASS-SPECTROMETRY; ADULTERATED MILK; CHEMOMETRICS; REGRESSION; QUANTIFICATION; AUTHENTICATION;

D O I：

10.1016/j.chemolab.2017.09.004

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A high and low frequency unfolded partial least squares discriminant analysis (HLFUPLS-DA) for building a pattern recognition model of near-infrared (NIR) spectra is proposed to identify milk samples. In the approach, the spectra are decomposed into different frequency components by empirical mode decomposition (EMD) at first. Then the former high frequency components are summed as a high frequency matrix and vice versa. Thirdly, the high and low frequency matrices are extended to an extended matrix in the variable dimension. Finally, PLS-DA model is built between the extended matrix and the target vectors. Coupled with NIR spectroscopy, HLUPLS-DA is applied to identify milk samples of different qualities. Comparing with PLS-DA and other signal processing techniques combined with PLS-DA, the proposed method is proved to be a promising tool for spectral qualitative analysis of complex samples.

引用

页码：96 / 101

页数：6

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