文献类型：期刊文献

英文题名：Transferring near infrared spectral calibration models without standards via multistep wavelength selection

作者：Ni, Lijun[1];Zhang, Zhange[1];Zhang, Liguo[1];Luan, Shaorong[1]

机构：[1]East China Univ Sci & Technol, Coll Chem & Mol Engn, 130 Melong Rd, Shanghai 200237, Peoples R China

年份：2023

卷号：31

期号：4

起止页码：171

外文期刊名：JOURNAL OF NEAR INFRARED SPECTROSCOPY

收录：;EI(收录号：20233014441186);WOS:【SCI-EXPANDED(收录号:WOS:001032376400001)】；

语种：英文

外文关键词：Scale invariant feature transform; multistep wavelength selection; near infrared spectral calibration model transfer; standard deviation of samples spectra; correlation analysis

摘要：Two case studies were conducted to verify calibration model transfer methods without standards by multi-step wavelength selection, using 3-7 near infrared spectrometers to predict ingredients in corn and total plant alkaloids (TPA) in tobacco leaves. Based on the characteristic wavelengths of Uc, which are selected using the scale-invariant feature transform (SIFT), this study advances two multistep wavelength selection methods by selecting wavelengths with high independence and a high standard deviation of the sample spectra (SDSS). The first method, SIFT-SDSS-CORX, selects important characteristic wavelengths Uc-i from Uc whose SDSS is greater than a threshold SDSScrita. Subsequently, rx, the correlation coefficient matrix between spectral signals of Uc-i, is calculated, and only one wavelength is retained from those whose correlation coefficients exceed a threshold, rx(crit.)(a) The wavelength set Uc-i-rx, which is finally screened, is important and independent. In the second method, SIFT-CORX-SDSS, Uc-rx is first selected from Uc by retaining only one wavelength from those whose correlation coefficients between spectral signals of Uc exceed a threshold, rx(crit)(b). Subsequently, the wavelengths Uc-rx-i with SDSS exceeding a threshold SDSScritb are selected from Uc-rx. Near infrared spectroscopy calibration models for predicting protein and oil in corn and TPA in tobacco leaves were built using partial least squares regression (PLS) based on different wavelength sets of Uc, Uc-i, Uc-i-rx, Uc-rx, and Uc-rx-i, respectively. The latent variables used in the PLS models were determined by an accumulative contribution ratio over 99.9%. The results indicate that the PLS models built on Uc-i-rx and Uc-rx-i are effective on both primary and secondary units for corn and tobacco samples. This study utilises a three-step wavelength selection method to select highly independent, important, and characteristic spectral variables, thereby enhancing the robustness, simplicity, and interpretability of NIR) calibration models and facilitating their transfer to secondary units without standards.