文献类型：期刊文献

英文题名：Construction of global and robust near-infrared calibration models based on hybrid calibration sets using Partial Least Squares (PLS) regression

作者：Ni, Lijun[1];Xiao, Lixia[1];Yao, Heming[2];Ge, Jiong[2];Zhang, Liguo[1];Luan, Shaorong[1]

机构：[1]East China Univ Sci & Technol, Coll Chem & Mol Engn, Shanghai 200237, Peoples R China;[2]Shanghai Tobacco Grp, Key Lab Cigarette Smoke, Shanghai, Peoples R China

年份：2019

卷号：52

期号：7

起止页码：1177

外文期刊名：ANALYTICAL LETTERS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000461872800011)】；

基金：The present work is supported by the Key Laboratory of the Cigarette Smoke Research of China National Tobacco Corporation, Technology Center of Shanghai Tobacco Group Corporation. The project name is "Establishment and Dynamic Maintaining of Global and Robust NIR Quantitative Calibration Models for Analyzing Tobacco" and project number is K2018-1-036.

语种：英文

外文关键词：Near-infrared spectroscopy; partial least squares (PLS) regression; global and robust models; hybrid calibration set

摘要：Near-infrared spectroscopy (NIR) models built on a particular instrument are often invalid on other instruments due to spectral inconsistencies between the instruments. In the present work, global and robust NIR calibration models were constructed by partial least square (PLS) regression based on hybrid calibration sets, which are composed of both primary and secondary spectra. Three datasets were used as case studies. The first consisted of 72 radix scutellaria samples measured on two NIR spectrometers with known baicalin content. The second was composed of 80 corn samples measured on two instruments with known moisture, oil, and protein concentrations. The third dataset included 279 primary samples of tobacco with known nicotine content and 78 secondary samples of tobacco with known nicotine concentrations. The effect of the number of secondary spectra in the hybrid calibration sets and the methods for selecting secondary spectra on the PLS model performance were investigated by comparing the results obtained from different calibration sets. This study shows that the global and robust calibration models accurately predicted both primary and secondary samples as long as the ratios of the number of primary spectra to the number of secondary spectra were less than 22. The models performance was not influenced by the selection method of the secondary spectra. The hybrid calibration sets included the primary spectral information and also the secondary spectra; information, rendering the constructed global and robust models applicable to both primary and secondary instruments.