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Weighted SPXYE (WSPXYE) and Its Application to Transfer Set Selection in Near Infrared Spectra |
ZHENG Kai-yi1, FENG Tao1, ZHANG Wen1, HUANG Xiao-wei1, LI Zhi-hua1, ZHANG Di1, SHI Ji-yong1, Yoshinori Marunaka2, ZOU Xiao-bo1* |
1. Key Laboratory of Modern Agriculture Equipment and Technology, School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
2. Department of Molecular Cell Physiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto 602-8566, Japan |
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Abstract Selecting samples in the transfer set is also important in calibration transfer. The purpose of selecting samples in the transfer set is selecting standard samples of both primary and secondary spectra with the same concentrations. After that, the transfer model between primary and secondary spectra can be generated. Finally, the prediction set of secondary spectra can be corrected by transfer model and estimated by the model generated by primary spectra. The commonly used sample selection methods include Kennard-Stone (KS), SPXY and SPXYE methods. Based on the features of those methods, a new sample selection method called weighted SPXYE (WSPXYE) was proposed and applied in transfer set selection. The WSPXYE defines the distance between each paired samples in advance, which is composed of the normalized distances between spectra (dxs), concentration (dys) and errors (des). The weighted sum of the former three distances can set as the WSPXYE distance: dwspxye=αdxs+βdys+(1-α-β)des. After obtaining dwspxye, the samples with large values of dwspxye, can be selected as transfer set. WSPXYE is the generalization on KS, SPXY and SPXYE methods, while KS, SPXY and SPXYE methods are special cases of WSPXYE with the weights of α and β set as 1 and 0; 0.5 and 0.5 and 0.333 and 0.333, respectively. Two calibration transfer methods, including direct standardization (DS) and canonical correlation analysis combined with informative component extraction (CCA-ICE) has been applied to testing the transfer set selected by WSPXYE. Results showed that WSPXYE could choose proper weights to select good transfer samples to achieve low errors in both validation and prediction sets.
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Received: 2020-01-23
Accepted: 2020-05-02
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Corresponding Authors:
ZOU Xiao-bo
E-mail: zou_xiaobo@ujs.edu.cn
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