近红外仪器能量变化对模型的影响及OSC算法的应用
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摘要
近红外技术结合多元回归方法已经广泛应用于许多行业的物质相关成分含量的测定。近红外光谱不但包含着物质成分信息，而且这些信息完全掩盖在众多复杂背景当中(包括光散射、背景噪声以及背景漂移)。近红外光谱本身也容易受样品的形状、大小、颜色和仪器状态等参数的影响，其中仪器能量变化是一个重要影响参数。本文第一部分以玉米粉末样品，用漫反射法测定玉米粗蛋白含量，研究了近红外仪器能量变化对分析模型预测效果的影响。结果表明，随着仪器能量降低，模型相对标准偏差(RSD)有增大趋势，仪器相对能量从100％衰减到18％后，模型预测值RSD从2.5％增至4.72％。不同能量下的预测值与化学值相关直线的t检验(α=0.05)表明各相关直线均无显著性差异；模型预测值的方差分析(α=0.05)表明仪器能量变化并不会使未知样品预测值产生显著性差异。由于装样误差在模型整体误差中所占比重较大，所以严格控制装样在漫反射近红外分析中仍是一个值得注意的问题。
基于正交信号投影分解的正交信号校正(OSC)方法，是近年来在化学计量学领域中提出的一种全新数据预处理方法。OSC校正法在对原始数据进行滤波时，可确保滤除的信息与待测信息(浓度等)无关，因而优于传统的数据处理方法。本文第二部分考察了OSC校正算法应用于烟草粉末样品和液体样品模型的预测效果。结果表明在保证模型预测能力的同时，OSC校正法极强的滤波能力降低模型的复杂度，也即烟草烟碱模型主因子数从7减少为3，烟草总糖模型主因子数从6减少为3，四元混合体系液体样品中甲苯预测模型主因子数从4减少为3。OSC算法对于粉末状及其成分比较复杂的样品的分析表明其更具校正优势；而对于简单样品体系(如手工配制的纯化学品液体)的含量分析，其校正能力不明显。
The application of near-infrared (NIR) spectroscopy for estimating the various properties in samples has become widespread with the use of multivariate calibration. With NIR spectra, the analytical information is contained in small spectral variations and usually dominated by the features such as light scattering, background noise and baseline drift. NIR spectra are also affected by many factors frequently, such as material shape, size, color, and instrument status. The instrument's energy is a factor that will affect the spectra. In the first part of this thesis, the effect of NIR instrument's energy level on the model predictive power was studied with maize sample. 53 maize standard samples diffuse reflectance spectra were collected from 4000cm-1 ~10000cm-1 at 8 cm-1 resolution on Perkin-Elmer Spectrum One NTS near-infrared instrument at different energy level. 3 samples were scanned 10 times repeatedly at 100%, 76% and 34% energy level for energy variance analysis. Results show that relative standard deviation (RSD) of prediction value will become larger from 2.5% to 4.72% with energy decreasing form 100% to 18%. It is demonstrated that energy will not significantly affect predictive power by analysis of variance, because 3 samples F-value is 1.62, 3.02 and 2.23 that all less than critical value F0.05=3.35. At the same time, it is suggested that how to load samples is still an important issue in NIR diffuse reflectance analysis.
Orthogonal signal correction (OSC) is provided a novel spectral pre-processing method in recent years, which is based on the orthogonal projection. This pre-processing method not only removes noise from the spectrum, but also filters the irrelevant information from response matrix. In the second part of this thesis, the nicotine and total-sugar content of tobacco and
Benzene/Methylbenzene/Cyclohexane/CCl4 solution system was corrected by OSC combined with PLS. It is shown that regression models are fewer latent variables and more stable by using OSC method. The number of latent variables of nicotine model is reduced from 7 to 3; and the number of latent variables total-sugar is reduced from 6 to 3. At the same time, we also found that OSC is more excellent when it is applied on complex powder system than simple system.
基于正交信号投影分解的正交信号校正(OSC)方法，是近年来在化学计量学领域中提出的一种全新数据预处理方法。OSC校正法在对原始数据进行滤波时，可确保滤除的信息与待测信息(浓度等)无关，因而优于传统的数据处理方法。本文第二部分考察了OSC校正算法应用于烟草粉末样品和液体样品模型的预测效果。结果表明在保证模型预测能力的同时，OSC校正法极强的滤波能力降低模型的复杂度，也即烟草烟碱模型主因子数从7减少为3，烟草总糖模型主因子数从6减少为3，四元混合体系液体样品中甲苯预测模型主因子数从4减少为3。OSC算法对于粉末状及其成分比较复杂的样品的分析表明其更具校正优势；而对于简单样品体系(如手工配制的纯化学品液体)的含量分析，其校正能力不明显。
The application of near-infrared (NIR) spectroscopy for estimating the various properties in samples has become widespread with the use of multivariate calibration. With NIR spectra, the analytical information is contained in small spectral variations and usually dominated by the features such as light scattering, background noise and baseline drift. NIR spectra are also affected by many factors frequently, such as material shape, size, color, and instrument status. The instrument's energy is a factor that will affect the spectra. In the first part of this thesis, the effect of NIR instrument's energy level on the model predictive power was studied with maize sample. 53 maize standard samples diffuse reflectance spectra were collected from 4000cm-1 ~10000cm-1 at 8 cm-1 resolution on Perkin-Elmer Spectrum One NTS near-infrared instrument at different energy level. 3 samples were scanned 10 times repeatedly at 100%, 76% and 34% energy level for energy variance analysis. Results show that relative standard deviation (RSD) of prediction value will become larger from 2.5% to 4.72% with energy decreasing form 100% to 18%. It is demonstrated that energy will not significantly affect predictive power by analysis of variance, because 3 samples F-value is 1.62, 3.02 and 2.23 that all less than critical value F0.05=3.35. At the same time, it is suggested that how to load samples is still an important issue in NIR diffuse reflectance analysis.
Orthogonal signal correction (OSC) is provided a novel spectral pre-processing method in recent years, which is based on the orthogonal projection. This pre-processing method not only removes noise from the spectrum, but also filters the irrelevant information from response matrix. In the second part of this thesis, the nicotine and total-sugar content of tobacco and
Benzene/Methylbenzene/Cyclohexane/CCl4 solution system was corrected by OSC combined with PLS. It is shown that regression models are fewer latent variables and more stable by using OSC method. The number of latent variables of nicotine model is reduced from 7 to 3; and the number of latent variables total-sugar is reduced from 6 to 3. At the same time, we also found that OSC is more excellent when it is applied on complex powder system than simple system.
引文
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[27] 赵丽丽，赵龙莲，李军会，等．傅里叶变换近红外光谱仪扫描条件对数学模型预测精度的影响．光谱学与光谱分析，2004，24：41～44
[28] Andersson M., Josefson M., Langkilde F.W., et al. Monitoring of a film coating process for tablets using near infrared reflectance spectrometry. Journal of Pharmaceutical and Biomedical Analysis, 1999,20:27～37
[29] Savitzky A., Golay M.J.E.. Anal. Chem. 1993,65:3273～3289.
[30] 徐广通，袁洪福，陆婉珍．现代近红外光谱技术及应用进展．光谱学与光谱分析，2000，20：134～142
[31] Martens H., Naes T., Multivariate Calibration, Wiley, New York, 1989
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[33] Blanco M., Coello J., Itubbriaga H., et al. Effect of data methods in near-infrared diffuse reflectance spectroscopy for the delermination of the active compound in pharmaceutical preparation. Appl. Spectrosc, 1997,51:240～245
[34] 卢小泉，莫金垣．分析化学计量学中的新方法——小波分析．分析化学，1996，24：1100～1106
[35] Bin Chert, Xi-guang Fu, Dao-li Lu. Improvement of predicting precision of oil content in instant noodles by using wavelet transforms to treat near-infrared spectroscopy. Journal of Food Engineering. 2002,53:373～376
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[2] Büning-Pfaue H., Hartmann R., Harder J., et al. NIR-spectrometric analysis of food. Methodica development and achievable performance values. Fresenius J Anal Chem. 1998,360: 832～835
[3] 王建军，张冬青，张尧锋，等．近红外漫反射光谱法对粮油作物品质无损分析的研究．浙江农业学报，2002：14(2)：105～111
[4] 刘建学，吴守一，方如明．近红外光谱法快速检测大米蛋白质含量．农业机械学报，2001，(5)：68-70
[5] 刘国珍，陈祖刚，李丹，等．近红外光谱分析技术进展及其在烟草行业中的应用．烟草科技／烟草化学，2001，11(6)：15～17
[6] 王东丹，李天飞，吴玉萍，等．近红外光谱分析技术在烟草化学分析上的应用研究．云南大学学报(自然科学版)，2001，23(2)：135～137
[7] 赵龙莲，闵顺耕，严衍禄，等．傅立叶变换的近红外光谱法测定烟草中九种品质参数．光谱学与光谱分析，1998，18(4)：89～90
[8] Garrigues, Jose M, Perez-Ponce, et al. Fourier-transform infrared determination of nicotine in tobacco samples by transmittance measurements after leaching with CHCl_3. Analytica Chimica Acta, 1998,373:63～71
[9] Fan X.H., T. Asahara, Ohdan H., et al. Nondestructive and real-time evaluation of liver viability in brain dead donor for liver transplantation using near-infrared spectroscopy. Transpl Int 2000, 13 [Suppl 1]:272～277
[10] Valentina Quaresima, Marco Ferrari. Quantitation of muscle oxygenation by near-infrared spectroscopy methods. Eur J Appl Physiol, 2002,86:283～284
[11] J. Sastre Torao, S. H. C. van Hattum. Quantitative analysis of active compounds in pharmaceutical preparations by use of attenuated total-reflection Fourier transform mid-infrared spectrophotometry and the internal standard method. Fresenius J Anal Chem, 2001,371:532～535
[12] 陈斌，赵龙莲，李军会，等．近红外光谱法快速分析葛根中的有效成分．光谱学与光谱分析，2002，22(6)：976～979
[13] Andersson M., Josefson M., Langkilde F.W., et al. Monitoring of a film coating process for tablets using near infrared reflectance spectrometry. Journal of Pharmaceutical and Biomedical Analysis, 1999,20:27～37
[14] 褚小立，袁洪福，纪长青，等．近红外光谱快速测定重整汽油详细族组成．石油化工，2001，30(11)：866～870
[15] 李克忠，殷宗玲．用近红外光谱仪快速测定调和汽油中芳烃、烯烃的含量．分析测试技术与仪器，2002(8)：48～52
[16] Fischer D., Bayer T., Eichhorn K. J., et al. In-line process monitoring on polymer melts by
NIR-spectroscopy. Fresenius J Anal Chem. 1997,359:74～77
[17] 徐广通，杨玉蕊，陆婉珍，等．近红外光谱在线分析技术将优化乙烯生产工艺．化工进展，2001(1)：22～25
[18] Freitag H. Cammann K. Huth-Fehre T. Rapid identification of plastics from electronic devices with NIR-spectroscopy. Analytical Letters, 2000,33:1425～1431
[19] Garcia-Jares C. M., Médina B. Application of multivariate calibration to the simultaneous routine determination of ethanol, glycerol, fructose, glucose and total residual sugars in botrytized-grape sweet wines by means of near-infrared reflectance spectroscopy. Fresenius J Anal Chem, 1997,357:86-91
[20] Garcia-Alvarez M, Hermida M, Rodriguez-Otero JL, et al. Major components of honey analysis by near-infrared transflectance spectroscopy. Journal of Agricultural and Food Chemistry, 2000,48:5154-5158
[21] 赵锁劳，彭玉魁．我国黄土区土壤水分、有机质和总氮的近红外光谱分析．分析化学 2003，30，978-980
[22] 杨曙明，张瑜．应用近红外光谱分析技术快速分析饲料质量．分析测试学报，1999，18：83～87
[23] 张维军，江逆．近红外技术及其在预测饲料原料总氨基酸含量上的应用．中国饲料，2000(7)：28～29
[24] 严衍禄，景茂，张录达，等．傅立叶变换近红外漫反射光谱分析测量误差的研究．北京农业大学学报16卷增刊，1990：37～48
[25] 严衍禄，高文淑，张录达，等．傅立叶变换近红外漫反射光谱分析测定谷物粗蛋白质的规范方法．北京农业大学学报16卷增刊，1990：57～60
[26] 石师林，严衍禄，吕冬．傅立叶变换近红外漫反射光谱分析测定谷物粗蛋白质的规范方法．北京农业大学学报16卷增刊，1990：67～71
[27] 赵丽丽，赵龙莲，李军会，等．傅里叶变换近红外光谱仪扫描条件对数学模型预测精度的影响．光谱学与光谱分析，2004，24：41～44
[28] Andersson M., Josefson M., Langkilde F.W., et al. Monitoring of a film coating process for tablets using near infrared reflectance spectrometry. Journal of Pharmaceutical and Biomedical Analysis, 1999,20:27～37
[29] Savitzky A., Golay M.J.E.. Anal. Chem. 1993,65:3273～3289.
[30] 徐广通，袁洪福，陆婉珍．现代近红外光谱技术及应用进展．光谱学与光谱分析，2000，20：134～142
[31] Martens H., Naes T., Multivariate Calibration, Wiley, New York, 1989
[32] Geladi P., MacDougall D., Martens H.. Linearization and scatter-correction for near infrared reflectance spectra of meat. Appl. Spectrosc, 1985(3):491～500
[33] Blanco M., Coello J., Itubbriaga H., et al. Effect of data methods in near-infrared diffuse reflectance spectroscopy for the delermination of the active compound in pharmaceutical preparation. Appl. Spectrosc, 1997,51:240～245
[34] 卢小泉，莫金垣．分析化学计量学中的新方法——小波分析．分析化学，1996，24：1100～1106
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