文献类型：期刊文献

英文题名：Near infrared spectral calibration model transfer without standards by screening spectral points with scale invariant feature transform from master samples spectra

作者：Ni, Lijun[1];Chen, Haixia[1];Hong, Shijun[1];Zhang, Liguo[1];Luan, Shaorong[1]

机构：[1]East China Univ Sci & Technol, Coll Chem & Mol Engn, Shanghai 200237, Peoples R China

年份：2021

卷号：260

外文期刊名：SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY

收录：;EI(收录号：20212110405132);WOS:【SCI-EXPANDED(收录号:WOS:000670125400003)】；

语种：英文

外文关键词：Scale invariant feature transform; Spectral point screening; Near infrared spectral calibration model transfer; Baicalin

摘要：In order to realize calibration model transfer of near infrared (NIR) spectra without standards, scale invariant feature transform (SIFT) algorithm was applied to extract characteristic spectral points of NIR spectra in this study. Three sets of spectral points were selected by SIFT from the spectra of precision detection (SPD) of a radix scutellariae sample by continuously testing the sample three times. Aiming at obtaining high consistency of the three sets, the orthogonal table L9 (3(4)) was used to optimize the parameters of SIFT. Basing on the NIR spectra of several representative radix scutellariae samples, a series of spectral point sets were screened by SIFT with the optimized parameters. Three methods of further treating the spectral points sets to optimize the combination of the spectral points and provided three spectral point sets, which were recorded as U-i, U-u and U-ur, respectively. The partial least square (PLS) calibration models for predicting baicalin content of radix scutellariae were built on whole wavelengths, U-i, U-u and U-ur at different number of latent variables (nLVs), respectively. Compared with other PLS models, the models of SIFTur-PLS built on U-ur, which was obtained by taking union of the firstly selected spectral point sets, then eliminating the points with high deviance of SPD and those with high correlativity from the union, are most robust and always give lower or lowest prediction errors for both master and slave samples at many nLVs. It is a good way to filter stable, highly independent and characteristic spectral points to build robust PLS calibration models by combining SIFT algorithm with standard deviance analysis of SPD and correlative analysis. The models can be directly shared by the slave instrument, without needing transfer sets, and without requiring to correct the spectra of slave instruments or spectral calibration models. (C) 2021 Elsevier B.V. All rights reserved.