文献类型：期刊文献

中文题名：基于SWCSS-CARS算法的综纤维素近红外分析模型传递

英文题名：Near infrared analysis model transfer of holocellulose based on SWCSS-CARS algorithm

作者：汪莹[1] 王红鸿[1] 熊智新[1] 黄浩冉[1] 胡云超[1] 刘智健[1] 梁龙[2]

第一作者：汪莹

机构：[1]南京林业大学轻工与食品学院,南京210037;[2]中国林业科学研究院林产化学工业研究所,南京210042

年份：2024

卷号：9

期号：3

起止页码：88-95

中文期刊名：林业工程学报

外文期刊名：Journal of Forestry Engineering

收录：CSTPCD;;Scopus;北大核心:【北大核心2023】；CSCD:【CSCD_E2023_2024】；

基金：中国林科院林业新技术所基本科研业务费专项资助(CAFYBB2019SY039)。

语种：中文

中文关键词：综纤维素含量;近红外光谱;稳定一致波长;波长优选;模型传递

外文关键词：holocellulose content;near infrared spectroscopy;stable consistent wavelength;wavelength optimization;model transfer

分类号：O657.63

摘要：纸浆材综纤维素的近红外快速无损检测是提高造纸工业智能制造水平的重要手段之一。然而,在实际应用中已有的近红外模型往往无法预测不同仪器测得的木材样品光谱,从而大大限制了该技术的广泛应用。为了降低重新建模和维护的成本,需要运用模型转移技术。笔者以实现纸浆材综纤维素含量近红外分析模型在3台棱光光谱仪器上的共享为目标,以在3台同型号的近红外光谱仪采集的纸浆材样本为研究对象,采用竞争性自适应重加权采样(CARS)波长优化算法,减少筛选稳定一致性波长(SWCSS)方法中无效波长的不利影响,以提高模型对2台从机测量样品的分析能力。建立基于SWCSS-CARS算法的偏最小二乘回归(PLSR)模型,并将其对从机样品的分析能力与单独的SWCSS和CARS的分析能力进行对比分析。结果表明,以综纤维素为基础的SWCSS-CARS方法选出的30个波长建立的主机模型对2台从机样品分析的RPD均大于4.6,Akaike信息准则(A IC)的值为67.68,远远小于模型传递前的3209.83和SWCSS算法的942.82,降低了光谱矩阵的维数,显著提高了模型传递效率。表明SWCSS-CARS算法能够有效去除SWCSS方法中的无效波长,实现了纸浆材综纤维素含量模型在3台同型号近红外光谱仪间的共享。
Near infrared spectroscopy is a non-destructive method that can be used to rapidly detect the content of holocellulose in pulpwood to improve the level of intelligent manufacturing in the paper industry.However,the existing near infrared models in practical applications are often unable to predict the spectra of wood samples measured by different instruments,which greatly limits the wide application of this technology.Model transfer is an important solution to the problem of inter-instrument differences in near infrared spectroscopy that makes calibration models difficult to generalize across multiple instruments.Additionally,model transfer reduces the cost of re-modeling and model maintenance.To realize the sharing of the near infrared analysis model of the content of holocellulose in pulpwood on three Lengguang S450 grating-scanning near infrared spectrometers,the pulpwood samples collected by these three same types of near infrared spectrometers were taken as the research objects,and the SWCSS-CARS combined algorithm was proposed.The competitive adaptive reweighted sampling(CARS)wavelength optimization algorithm was used to reduce the adverse effects of invalid wavelengths in the screening wavelengths with consistent and stable signals(SWCSS)method,to improve the analysis ability of the model for two target samples.In this study,the spectral data sets of 84 pulpwood wood flour samples measured by three Lengguang S450 grating-scanning near infrared spectrometers with same model and their corresponding synthesized cellulose content data sets were used.The Kennard-Stone method was used to divide all the samples into a calibration set of 56 samples and a prediction set of 28 samples,and the samples in the calibration and prediction sets of the master instrument and the target instruments corresponded to each other.A partial least squares regression(PLSR)model based on SWCSS-CARS algorithm was established,and its analytical ability for target samples was compared with that of SWCSS and CARS alone.The results showed that the relative standard deviation(RPD)of the master model established by the 30 wavelengths selected by the SWCSS-CARS method based on holocellulose for the analysis of two target samples was greater than 4.6,and the value of Akaike information criterion(A IC)was 67.68,which was much smaller than 3209.83 before the model transfer and 942.82 of the SWCSS algorithm.The dimension of the spectral matrix was reduced,and the model transfer efficiency was significantly improved.It was shown that the SWCSS-CARS algorithm can effectively remove the invalid wavelength in the SWCSS method,and successfully realize the sharing of the holocellulose content model of pulpwood among three same types of near infrared spectrometers.