An adaptive strategy for selecting representative calibration samples in the continuous wavelet domain for near-infrared spectral analysis
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Sample selection is often used to improve the cost-effectiveness of near-infrared (NIR) spectral analysis. When raw NIR spectra are used, however, it is not easy to select appropriate samples, because of background interference and noise. In this paper, a novel adaptive strategy based on selection of representative NIR spectra in the continuous wavelet transform (CWT) domain is described. After pretreatment with the CWT, an extension of the Kennard–Stone (EKS) algorithm was used to adaptively select the most representative NIR spectra, which were then submitted to expensive chemical measurement and multivariate calibration. With the samples selected, a PLS model was finally built for prediction. It is of great interest to find that selection of representative samples in the CWT domain, rather than raw spectra, not only effectively eliminates background interference and noise but also further reduces the number of samples required for a good calibration, resulting in a high-quality regression model that is similar to the model obtained by use of all the samples. The results indicate that the proposed method can effectively enhance the cost-effectiveness of NIR spectral analysis. The strategy proposed here can also be applied to different analytical data for multivariate calibration.
KeywordsSample selection Near-infrared spectroscopy Continuous wavelet transform Extension of the Kennard–Stone (EKS) algorithm
This study is supported by National Natural Science Foundation (Nos 20325517 and 20575031), the Ph.D. Programs Foundation of the Ministry of Education (MOE) of China (No. 20050055001), and the Teaching and Research Award Program for Outstanding Young Teachers (TRAPOYT) in High Education Institutions of the MOE of China.
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