文献类型：期刊文献

英文题名：Application of SWSRA-DS algorithm in improving the model transfer for near infrared analysis of pulpwood holocellulose

作者：Wang, Honghong[1] Hu, Yunchao[1] Liu, Zhijian[1] Wang, Ying[1] Huang, Haoran[1] Xiong, Zhixin[1] Liang, Long[2]

第一作者：Wang, Honghong

通信作者：Xiong, ZX[1]

机构：[1]Nanjing Forestry Univ, Coll Light Ind & Food Engn, Nanjing 210037, Peoples R China;[2]Chinese Acad Forestry, Inst Chem Ind Forest Prod, Nanjing 210042, Peoples R China

年份：2023

卷号：135

外文期刊名：INFRARED PHYSICS & TECHNOLOGY

收录：;EI(收录号：20234615067792);Scopus(收录号：2-s2.0-85176436105);WOS:【SCI-EXPANDED(收录号:WOS:001112354600001)】；

基金：This work was funded by the Fundamental Research Funds of Research Institute of Forest New Technology, CAF (CAFYBB2019SY039) .

语种：英文

外文关键词：Spectrum ratio analysis; Direct Standardization optimization; Holocellulose content; Near infrared spectroscopy; Model transfer

摘要：The SWSRA-DS combined algorithm is proposed with the goal of sharing the near infrared analysis model of the holocellulose content of pulpwood on three different types of spectroscopic instruments. That is, the screening wavelengths based on spectrum ratio analysis (SWSRA) algorithm is used to select the wavelengths with good stability and consistency. These important wavelength variables, which are insensitive to the measured sample parameters, can reduce the differences in sample information response by different instruments or measurement conditions. Then the systematic errors that still existed after the SWSRA method calibration are further calibrated using the Direct Standardization (DS) method on the basis of these wavelengths. This combined algorithm can improve the generalizability of the master model, reduce the spectrum matrix dimension, and make the model transfer more stabilized and simple. The results show that the SWSRA-DS combined algorithm is able to reduce the RMSEP of the master model to predict the holocellulose content of samples measured on the target 1 and target 2 instruments from 2.01% and 9.45% to 0.96% and 1.08%, respectively. The SWSRA-DS algorithm result is compared with the calibration results of SWSRA and DS alone and the commonly used PDS and S/B model transfer algorithms to transfer performance is significantly improved, which provides a new idea for the sharing of NIR analysis models among different types of spectroscopic instruments.