A practical approach for near infrared spectral quantitative analysis of complex samples using partial least squares modeling |
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Authors: | ZhiChao Liu Xiang Ma YaDong Wen Yi Wang WenSheng Cai and XueGuang Shao |
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Institution: | (1) Research Center for Analytical Sciences, College of Chemistry, Nankai University, Tianjin, 300071, China;(2) Hongta Group, R&D Center, Yuxi, 653100, China |
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Abstract: | The number of latent variables (LVs) or the factor number is a key parameter in PLS modeling to obtain a correct prediction.
Although lots of work have been done on this issue, it is still a difficult task to determine a suitable LV number in practical
uses. A method named independent factor diagnostics (IFD) is proposed for investigation of the contribution of each LV to
the predicted results on the basis of discussion about the determination of LV number in PLS modeling for near infrared (NIR)
spectra of complex samples. The NIR spectra of three data sets of complex samples, including a public data set and two tobacco
lamina ones, are investigated. It is shown that several high order LVs constitute main contributions to the predicted results,
albeit the contribution of the low order LVs should not be neglected in the PLS models. Therefore, in practical uses of PLS
for analysis of complex samples, it may be better to use a slightly large LV number for NIR spectral analysis of complex samples.
Supported by the National Natural Science Foundation of China (Grant Nos. 20775036 & 20835002) |
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Keywords: | number of latent variables partial least squares (PLS) regression near-infrared (NIR) spectroscopy tobacco lamina cross validation (CV) |
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