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Issue 20, 2011
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Wavelet unfolded partial least squares for near-infrared spectral quantitative analysis of blood and tobacco powder samples

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Abstract

Continuous wavelet transform (CWT) has been shown to be a high-performance signal processing technique in multivariate calibration. However, the signal processed by CWT with a specific wavelet may account for only a part of the information. To effectively utilize more abundant information contained in analytical signals, a method, named as wavelet unfolded partial least squares (WUPLS), was proposed. In the approach, the measured dataset is firstly extended by CWT with different wavelets, and then partial least squares (PLS) is employed to develop the quantitative model between the extended dataset and the target values. In order to select the representative wavelets, principal component analysis (PCA) is used to investigate the distribution of the signals obtained by CWT with different wavelets. The performance of the method was tested with blood and tobacco powder samples. Compared with the results obtained by PLS methods, the WUPLS method combined with signal processing techniques is proven to be a promising tool for improving the near-infrared (NIR) spectral analysis of complex samples.

Graphical abstract: Wavelet unfolded partial least squares for near-infrared spectral quantitative analysis of blood and tobacco powder samples

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Publication details

The article was received on 19 Mar 2011, accepted on 19 Jul 2011 and first published on 26 Aug 2011


Article type: Paper
DOI: 10.1039/C1AN15222J
Citation: Analyst, 2011,136, 4217-4221
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    Wavelet unfolded partial least squares for near-infrared spectral quantitative analysis of blood and tobacco powder samples

    M. Zhang, W. Cai and X. Shao, Analyst, 2011, 136, 4217
    DOI: 10.1039/C1AN15222J

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