复烤片烟常规化学成分的傅里叶变换近红外光谱法的模型转移
Model Transfer of Routine Chemical Components in Redried Lamina on Fourier Transform Near Infrared Spectroscopy
摘 要
为实现复烤片烟常规化学成分的模型在不同品牌傅里叶变换近红外仪器上的使用与共享，以贵州产区复烤片烟样品为研究对象，利用Kennard-Stone算法选择标准样品，将偏移量校正（BC）、截距斜率校正（SBC）和光谱空间转换（SST）等3种模型转移算法应用于不同品牌傅里叶变换近红外仪器的模型转移，并对3种模型转移算法的转移结果进行分析。结果表明：将复烤片烟常规化学成分的主机模型直接应用于从机预测时，主机和从机的预测值之间存在显著性差异；采用BC、SBC和SST可以实现不同品牌傅里叶变换近红外仪器的模型转移，其中SST转移结果最优。
傅里叶变换
复烤片烟
模型转移
near infrared spectroscopy
Fourier transform
redried lamina
model transfer
Abstract
In order to realize the use and share of model of routine chemical components in redried lamina on Fourier transform near infrared (NIR) instruments of different brands, the samples of redried lamina produced in Guizhou area were taken as the research object. The standard samples were selected by Kennard-Stone algorithm. Three model transfer algorithms, including the bias correction (BC), slope bias correction (SBC) and spectral space transformation (SST), were applied to model transfer on Fourier transform NIR instruments of different brands, and the transfer effects of the three model transfer algorithms were analyzed. The results showed that when the model of routine chemical components in redried lamina built from master instrument was directly applied to the slave instruments, there was significant difference in the predicted values between these instruments. The model transfer of Fourier transform NIR instruments of different brands could be realized by BC, SBC and SST. Among them, SST gave the best transfer results.
中图分类号 O657.33 DOI 10.11973/lhjy-hx201905001
所属栏目 试验与研究
基金项目 国家自然科学基金（21562014）；贵州中烟科技项目（GZZY/KJ/JS/2014-Y005-1）
收稿日期 2018/6/29
修改稿日期
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联系人作者彭黔荣(3435391@qq.com)
备注李阳阳,助理工程师,硕士,研究方向为烟草化学分析
引用该论文: LI Yangyang,PENG Qianrong,LIU Na,HU Yun,LI Jian,YANG Min,DENG Kui,ZHANG Wen. Model Transfer of Routine Chemical Components in Redried Lamina on Fourier Transform Near Infrared Spectroscopy[J]. Physical Testing and Chemical Analysis part B:Chemical Analysis, 2019, 55(5): 497～503
李阳阳,彭黔荣,刘娜,胡芸,李剑,杨敏,邓葵,张文. 复烤片烟常规化学成分的傅里叶变换近红外光谱法的模型转移[J]. 理化检验－化学分册, 2019, 55(5): 497～503
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参考文献
【1】ROGGO Y, CHALUS P, MAURER L, et al. A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies[J]. Journal of Pharmaceutical and Biomedical Analysis, 2007,44(3):683-700.
【2】TEYE E, HUANG X Y, AFOAKWAH N. Review on the potential use of near infrared spectroscopy (NIRS) for the measurement of chemical residues in food[J]. Food Science and Technology, 2015,46(2):211-230.
【3】郑健,朱立军,李军华,等.近红外光谱法测定卷烟纸中钠、钾、镁、钙和柠檬酸根的含量[J].理化检验-化学分册, 2015,51(8):1076-1079.
【4】DUAN J, HUANG Y, LI Z H, et al. Determination of 27 chemical constituents in Chinese southwest tobacco by FT-NIR spectroscopy[J]. Industrial Crops and Products, 2012,40:21-26.
【5】LONG Y C, DABROS T, HAMZA H. Analysis of solvent-diluted bitumen from oil sands froth treatment using NIR spectroscopy[J]. The Canadian Journal of Chemical Engineering, 2008,82(4):776-781.
【6】王家俊,刘巍.Karl Norris导数滤波器结合主成分分析转移NIR模型[J].烟草科技, 2007,40(3):31-35.
【7】蒋锦锋,李栋,赵明月,等.烟草主要化学成分的NIR定量模型传递[J].烟草科技, 2008,41(2):45-49.
【8】杨凯,刘鹏,王维妙,等.原烟在线近红外光谱模型转移研究[J].中国烟草学报, 2012,18(6):27-31.
【9】张进,蔡文生,邵学广.近红外光谱模型转移新算法[J].化学进展, 2017,29(8):902-910.
【10】DU W, CHEN Z P, ZHONG L J, et al. Maintaining the predictive abilities of multivariate calibration models by spectral space transformation[J]. Analytica Chimica Acta, 2011,690(1):64-70.
【11】COSTA R C, DE LIMA K M G. Prediction of parameters (soluble solid and pH) in intact plum using NIR spectroscopy and wavelength selection[J]. Journal of the Brazilian Chemical Society, 2013,24(8):1351-1356.
【12】YC/T 31-1996烟草及烟草制品试样的制备和水分测定烘箱法[S].
【13】YC/T 159-2002烟草及烟草制品水溶性糖的测定连续流动法[S].
【14】YC/T 468-2013烟草及烟草制品总植物碱的测定连续流动法[S].
【15】YC/T 161-2002烟草及烟草制品总氮的测定连续流动法[S].
【16】YC/T 217-2007烟草及烟草制品钾的测定连续流动法[S].
【17】YC/T 162-2002烟草及烟草制品氯的测定连续流动法[S].
【18】胡润文,夏俊芳.脐橙总糖近红外光谱模型传递研究[J].食品科学, 2012,33(3):28-32.
【19】LIANG C, YUAN H F, ZHAO Z, et al. A new multivariate calibration model transfer method of near-infrared spectral analysis[J]. Chemometrics and Intelligent Laboratory Systems, 2016,153:51-57.
【20】TOSCANO G, RINNAN Å, PIZZI A, et al. The use of near-infrared (NIR) spectroscopy and principal component analysis (PCA) to discriminate bark and wood of the most common species of the pellet sector[J]. Energy & Fuels, 2017,31(3):2814-2821.
【2】TEYE E, HUANG X Y, AFOAKWAH N. Review on the potential use of near infrared spectroscopy (NIRS) for the measurement of chemical residues in food[J]. Food Science and Technology, 2015,46(2):211-230.
【3】郑健,朱立军,李军华,等.近红外光谱法测定卷烟纸中钠、钾、镁、钙和柠檬酸根的含量[J].理化检验-化学分册, 2015,51(8):1076-1079.
【4】DUAN J, HUANG Y, LI Z H, et al. Determination of 27 chemical constituents in Chinese southwest tobacco by FT-NIR spectroscopy[J]. Industrial Crops and Products, 2012,40:21-26.
【5】LONG Y C, DABROS T, HAMZA H. Analysis of solvent-diluted bitumen from oil sands froth treatment using NIR spectroscopy[J]. The Canadian Journal of Chemical Engineering, 2008,82(4):776-781.
【6】王家俊,刘巍.Karl Norris导数滤波器结合主成分分析转移NIR模型[J].烟草科技, 2007,40(3):31-35.
【7】蒋锦锋,李栋,赵明月,等.烟草主要化学成分的NIR定量模型传递[J].烟草科技, 2008,41(2):45-49.
【8】杨凯,刘鹏,王维妙,等.原烟在线近红外光谱模型转移研究[J].中国烟草学报, 2012,18(6):27-31.
【9】张进,蔡文生,邵学广.近红外光谱模型转移新算法[J].化学进展, 2017,29(8):902-910.
【10】DU W, CHEN Z P, ZHONG L J, et al. Maintaining the predictive abilities of multivariate calibration models by spectral space transformation[J]. Analytica Chimica Acta, 2011,690(1):64-70.
【11】COSTA R C, DE LIMA K M G. Prediction of parameters (soluble solid and pH) in intact plum using NIR spectroscopy and wavelength selection[J]. Journal of the Brazilian Chemical Society, 2013,24(8):1351-1356.
【12】YC/T 31-1996烟草及烟草制品试样的制备和水分测定烘箱法[S].
【13】YC/T 159-2002烟草及烟草制品水溶性糖的测定连续流动法[S].
【14】YC/T 468-2013烟草及烟草制品总植物碱的测定连续流动法[S].
【15】YC/T 161-2002烟草及烟草制品总氮的测定连续流动法[S].
【16】YC/T 217-2007烟草及烟草制品钾的测定连续流动法[S].
【17】YC/T 162-2002烟草及烟草制品氯的测定连续流动法[S].
【18】胡润文,夏俊芳.脐橙总糖近红外光谱模型传递研究[J].食品科学, 2012,33(3):28-32.
【19】LIANG C, YUAN H F, ZHAO Z, et al. A new multivariate calibration model transfer method of near-infrared spectral analysis[J]. Chemometrics and Intelligent Laboratory Systems, 2016,153:51-57.
【20】TOSCANO G, RINNAN Å, PIZZI A, et al. The use of near-infrared (NIR) spectroscopy and principal component analysis (PCA) to discriminate bark and wood of the most common species of the pellet sector[J]. Energy & Fuels, 2017,31(3):2814-2821.
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