期刊
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
卷 258, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.saa.2021.119870
关键词
Sample selection; NIR spectroscopy; PLS regression; Spectra similarity; Salvia miltiorrhiza analysis
类别
资金
- Shandong Province Major Science and Technology Innovation Project [2018CXGC1405, 2018CXGC1411]
- Fundamental Research Fund of Shandong University [2018JC017]
- National Science and Technology Major Project Major New Drug Creation [2018ZX09201010]
- National Key R&D Plan Key Technology and Demonstration Research on Advanced Manufacturing of Traditional Chinese Medicine Oral Preparations [2019YFC1711203]
- Shandong Provincial Natural Fund Project [ZR2020MH409]
- Development Project of Qinghai Provincial Key Laboratory [2020-ZJ-512T05]
- [1310019007]
This study proposed a method to construct calibration and validation sets by selecting samples maximally similar to the test samples based on spectra data, which is more suitable and specific for unknown test samples, improving measurement accuracy and predictive performance.
As is known to all, the construction of calibration and validation sets is of great importance for how to select representative samples into subsets so that the calibration model can be built, evaluated and predicted effectively for model development. In this study, a method was proposed for the calibration and validation sets constructed by selecting samples maximally similar to the test samples based on the spectra data. Both the Euclidean distance and Mahalanobis distance were attempted to estimate the spectra similarity. The method to select samples for calibration is more suitable and specific to unknown test samples in practical applications, thus improving the measurement accuracy. In addition, the optimization of calibration set size was carried out to avoid the influence of unnecessary samples. Two data sets of Salvia miltiorrhiza (S. miltiorrhiza) and corn by near infrared spectroscopy (NIR) were used to test the performance of the proposed method compared with two typical sample-selection algorithms, Kennard Stone (KS) and sample set partitioning based on joint x-y distances (SPXY). The experimental results indicated that the proposed method could select a more targeted set of samples for the unknown test samples and had the superior predictive performance to the KS and SPXY methods. (c) 2021 Elsevier B.V. All rights reserved.
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