Treating NIR data with orthogonal discrete wavelet transform: Predicting concentrations of a multi-component system through a small-scale calibration set |
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Authors: | Chen-Bo Cai |
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Institution: | State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, PR China |
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Abstract: | Through randomly arranging samples of a calibration set, treating their NIR spectra with orthogonal discrete wavelet transform, and selecting suitable variables in terms of correlation coefficient test (r-test), it is possible to extract features of each component in a multi-component system respectively and partial least squares (PLS) models based on these features are capable of predicting the concentration of every component. What is perhaps more important, with the proposed strategy, the predictive ability of the model is at least not impaired while the size of the calibration set can be obviously reduced. Therefore, it provides a more economical, rapid, as well as convenient approach of NIR quantitative analysis for multi-component system. In addition, all important factors and parameters related to the proposed strategy are discussed in detail. |
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Keywords: | Near-infrared spectroscopy (NIR) Discrete wavelet transform (DWT) Correlation coefficient test (r-test) Random experimental design Partial least squares (PLS) Multi-component Small-scale calibration set |
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