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Open AccessArticle

PLS Subspace-Based Calibration Transfer for Near-Infrared Spectroscopy Quantitative Analysis

1
School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
2
College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
*
Author to whom correspondence should be addressed.
Academic Editors: Christian Huck and Krzysztof B. Bec
Molecules 2019, 24(7), 1289; https://doi.org/10.3390/molecules24071289
Received: 15 March 2019 / Revised: 27 March 2019 / Accepted: 28 March 2019 / Published: 2 April 2019
In order to enable the calibration model to be effectively transferred among multiple instruments and correct the differences between the spectra measured by different instruments, a new feature transfer model based on partial least squares regression (PLS) subspace (PLSCT) is proposed in this paper. Firstly, the PLS model of the master instrument is built, meanwhile a PLS subspace is constructed by the feature vectors. Then the master spectra and the slave spectra are projected into the PLS subspace, and the features of the spectra are also extracted at the same time. In the subspace, the pseudo predicted feature of the slave spectra is transferred by the ordinary least squares method so that it matches the predicted feature of the master spectra. Finally, a feature transfer relationship model is constructed through the feature transfer of the PLS subspace. This PLS-based subspace transfer provides an efficient method for performing calibration transfer with only a small number of standard samples. The performance of the PLSCT was compared and assessed with slope and bias correction (SBC), piecewise direct standardization (PDS), calibration transfer method based on canonical correlation analysis (CCACT), generalized least squares (GLSW), multiplicative signal correction (MSC) methods in three real datasets, statistically tested by the Wilcoxon signed rank test. The obtained experimental results indicate that PLSCT method based on the PLS subspace is more stable and can acquire more accurate prediction results. View Full-Text
Keywords: calibration transfer; NIR spectroscopy; PLS; quantitative analysis model calibration transfer; NIR spectroscopy; PLS; quantitative analysis model
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MDPI and ACS Style

Zhao, Y.; Yu, J.; Shan, P.; Zhao, Z.; Jiang, X.; Gao, S. PLS Subspace-Based Calibration Transfer for Near-Infrared Spectroscopy Quantitative Analysis. Molecules 2019, 24, 1289.

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