近红外光谱技术应用于中草药口服液在线质量控制的化学计量学建模

doi:10.16085/j.issn.1000-6613.2017-1413

化工进展 ›› 2018, Vol. 37 ›› Issue (05): 1923-1932.DOI: 10.16085/j.issn.1000-6613.2017-1413

近红外光谱技术应用于中草药口服液在线质量控制的化学计量学建模

李晶晶¹, 周昭露¹, 黄生权², 鲁亮², 黄延盛², 田淑华³, FALOLA Akinola A³, 李璇³, 李杰¹, 舒逸聃¹, 王学重¹

1 华南理工大学化学与化工学院, 广东广州 510640;
2 无限极(中国)有限公司, 广东广州 510623;
3 晶格码(青岛)智能科技有限公司, 山东青岛 266109

收稿日期:2017-07-10 修回日期:2017-11-20 出版日期:2018-05-05 发布日期:2018-05-05
通讯作者: 王学重,教授,博士生导师,研究方向为制药工程和过程控制;黄生权,教授级高级工程师,研究方向为食品工程和制药过程控制。
作者简介:李晶晶(1992-),女,硕士研究生,从事近红外建模技术研究。E-mail:l.jingjing03@mail.scut.edu.cn。

Development of chemometric models based on near infrared spectroscopy for on-line quality control of a Chinese herbal liquid tonic

LI Jingjing¹, ZHOU Zhaolu¹, HUANG Shengquan², LU Liang², HUANG Yansheng², TIAN Shuhua³, FALOLA Akinola A³, LI Xuan³, LI Jie¹, SHU Yidan¹, WANG Xuezhong¹

1 School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou 510640, Guangdong, China;
2 Infinitus(China), Guangzhou 510623, Guangdong, China;
3 Pharmavision(Qingdao) Intelligent Technology Ltd., Qingdao 266109, Shandong, China

Received:2017-07-10 Revised:2017-11-20 Online:2018-05-05 Published:2018-05-05

摘要/Abstract

摘要： 以一种中草药口服液中多糖含量、可溶性固形物含量及pH为对象，利用近红外光谱技术对其进行实时在线检测，重点研究了化学计量学建模方法。建模过程采用了一种系统工程的方法，综合考虑了光谱预处理、特征波段选择、数据选择和分组、近红外原始光谱和化学分析参考值的误差分析，并把工具箱集成在一起。还考察了光谱采集的重复性、采样点取样条件，并对实验室测定的参考值进行了重复性及测量误差的考察。通过对比最终选择的是偏最小二乘法（PLS）结合遗传算法自动选波段。除此之外，以模型的交叉验证均方根误差（RMSECV）、预测均方根误差（RMSEP）、质量指标化学参考值与预测值间的相对误差为指标筛选最佳建模参数。建模结果显示，多糖模型预测值与化学参考值间的相对误差可控制在10%内，可溶性固形物误差在5%内，pH误差在4%内。生产使用表明，所建立的模型可有效应用于在线、快速对质量指标做出准确的预测。

关键词: 近红外光谱, 偏最小二乘, 中草药, 质量控制, 变量选择

Abstract: Chemometric model development methods were investigated for the application of the near infrared spectroscopy (NIR)to on-line real-time monitoring of the content of polysaccharides,the content of soluble solids as well as pH of a liquid tonic. A system engineering approach was employed in building chemometric models via integration of spectra preprocessing,feature wave number selection,grouping and data selection for training and validation,as well as examination of the raw NIR measurements and laboratory analytical reference values. The final selected methods for data pre-processing,feature wave number selection and model building were the first order derivative,genetic algorithm,and PLS. The model performance was evaluated by RMSEC,RMSEV,and RMSEP through cross validation and assessed using test data as well as real data from the industrial process. It was shown that the relative error between the predicted value and the reference value could be controlled within 10% for the content of polysaccharide. The relative errors for predicting soluble solid and pH were controlled within 5% and 4%,respectively. The models were successfully used in commercial applications for timely estimation of the product quality related properties.

Key words: near infrared spectroscopy, partial least squares, Chinese herbal medicine, quality control, variable selection

中图分类号:

TQ460.6⁺3

李晶晶, 周昭露, 黄生权, 鲁亮, 黄延盛, 田淑华, FALOLA Akinola A, 李璇, 李杰, 舒逸聃, 王学重. 近红外光谱技术应用于中草药口服液在线质量控制的化学计量学建模[J]. 化工进展, 2018, 37(05): 1923-1932.

LI Jingjing, ZHOU Zhaolu, HUANG Shengquan, LU Liang, HUANG Yansheng, TIAN Shuhua, FALOLA Akinola A, LI Xuan, LI Jie, SHU Yidan, WANG Xuezhong. Development of chemometric models based on near infrared spectroscopy for on-line quality control of a Chinese herbal liquid tonic[J]. Chemical Industry and Engineering Progress, 2018, 37(05): 1923-1932.

参考文献

[1] 周昭露,李杰,黄生权,等. 近红外光谱技术在中药质量控制应用中的化学计量学建模:综述和展望[J]. 化工进展,2016,35(6):1627-1645. ZHOU Z L,LI J,HUANG S Q,et al. Development of chemometric modelling in the application of NIR to the quality control of Chinese herbal medicine:literature review and future perspectives[J]. Chemical Industry and Engineering Progress,2016,35(6):1627-1645.
[2] JAMROGIEWICZ M. Application of the near-infrared spectroscopy in the pharmaceutical technology[J]. Journal of Pharmaceutical & Biomedical Analysis,2012,66(66):1-10.
[3] WANG X Z. Data mining and knowledge discovery for process monitoring and control[M]. London:Springer,1999.
[4] 李杰,李传坤,王春利,等. 基于优化动态PLS的常压塔重整料终馏点软测量研究[J]. 计算机与应用化学,2016,33(3):27-29. LI J,LI C K,WANG C L,et al. The final boiling point soft-sensing of reforming feedstock on crude column based on optimized dynamic PLS[J]. Computers and Applied Chemistry,2016,33(3):27-29.
[5] KAWANO S,FUJIWARA T,IWAMOTO M. Nondestructive determination of sugar content in satsuma mandarin using near infrared (NIR)transmittance[J]. Engei Gakkai Zasshi,1993,62(2):465-470.
[6] KAWANO S,WATANABE H,IWAMOTO M. Determination of sugar content in intact peaches by near infrared spectroscopy with fiber optics in interactance mode[J]. Engei Gakkai Zasshi,1992,61(2):445-451.
[7] 何勇,李晓丽,邵咏妮. 基于主成分分析和神经网络的近红外光谱苹果品种鉴别方法研究[J]. 光谱学与光谱分析,2006,26(5):850-853. HE Y,LI X L,SHAO Y N. Discrimination of varieties of apple using near infrared spectra based on principal component analysis and artificial neural network model[J]. Spectroscopy and Spectral Analysis,2006,26(5):850-853.
[8] WU Z,XU E,WANG F,et al. Rapid determination of process variables of Chinese rice wine using FT-NIR spectroscopy and efficient wavelengths selection methods[J]. Food Analytical Methods,2015,8(6):1456-1467.
[9] MA C Y,WANG X Z. Simultaneous characterization of multiple properties of solid and liquid phases in crystallization processes using NIR[J]. Particuology,2011,9(6):589-597.
[10] LI R F,WANG X Z,ABEBE S B. Monitoring batch cooling crystallization using NIR:development of calibration models using genetic algorithm and PLS[J]. Particle & Particle Systems Characterization,2008,25(4):314-327.
[11] ABEBE S,WANG X Z,LI R,et al. The information content in nir spectral data for slurries of organic crystals[J]. Powder Technology,2008,179(3):176-183.
[12] 战皓,柳梦婷,方婧,等. 近红外分析技术在中药鉴定和含量测定中的应用研究进展[J]. 中国实验方剂学杂志,2015,21(12):231-234. ZHAN H,LIU M T,FANG J,et al. Application of near infrared analytical technique in identification and content determination of traditional Chinese medicine[J]. Chinese Journal of Experimental Traditional Medical Formulae,2015,21(12):231-234.
[13] 吴功煌,史新元,乔延江. 近红外模式识别技术在中药质量控制中的应用研究进展[J]. 世界科学技术(中医药现代化),2010,12(2):265-270. WU G H,SHI X Y,QIAO Y J. The application and development of pattern recognition by near-infrared spectroscopy in quality control of traditional Chinese medicine[J]. World Science and Technology(Modernization of Traditional Chinese Medicine and Materia Medica),2010,12(2):265-270.
[14] 刘冰,刘振尧,朱乾华,等. 近红外光谱法同时测定黄芪精口服液中黄芪多糖和黄芪甲苷的含量[J]. 分析科学学报,2011,27(2):195-198. LIU B,LIU Z Y,ZHU Q H,et al. Simultaneous determination of astragalus polysacharin and astragalosideⅣ in Huangqijing oral liquid by near infrared spectroscopy[J]. Journal of Analytical Science,2011,27(2):195-198.
[15] GALV O R K H,ARAUJO M C U,JOS G E,et al. A method for calibration and validation subset partitioning[J]. Talanta,2005,67(4):736.
[16] ZHANG C H,YUN Y H,FAN W,et al. Rapid analysis of polysaccharides contents in glycyrrhiza by near infrared spectroscopy and chemometrics[J]. International Journal of Biological Macromolecules,2015,79:983.
[17] 刘爽悦,李文龙,瞿海斌,等. 基于近红外光谱的丹红注射液提取过程质量在线检测方法研究[J]. 中国中药杂志,2013,38(11):1657-1662. LIU S Y,LI W L,ZHAI H B,et al. On-line monitoring of extraction process of danhong injection based on near-infrared spectroscopy[J]. China Journal of Chinese Materia Medica,2013,38(11):1657-1662.
[18] CHEN J,WANG X Z. A new approach to near-infrared spectral data analysis using independent component analysis[J]. Journal of Chemical Information & Computer Sciences,2001,41(4):992-1001.
[19] ZHAO C,GAO F. Spectra calibration modeling and statistical analysis for cumulative quality interpretation and prediction[J]. AIChE Journal,2012,58(2):466-479.
[20] ZHAO C,GAO F,WANG F. Spectra data analysis and calibration modeling method using spectra subspace separation and multiblock independent component regression strategy[J]. AIChE Journal,2011,57(5):1202-1215.
[21] 保罗·戈培林. 化学计量学实用指南[M]. 北京:科学出版社,2012. GEMPERLINE Paul. Practical guide to chemometrics[M]. Beijing:Science Press,2012.
[22] ISAKSSON T,NAES T. The Effect of multiplicative scatter correction (MSC)and linearity improvement in NIR spectroscopy[J]. Applied Spectroscopy,1988,42(7):1273-1284.
[23] ZHAO N,WU Z,CHENG Y,et al. MDL and RMSEP assessment of spectral pretreatments by adding different noises in calibration/validation datasets[J]. Spectrochimica Acta Part A:Molecular & Biomolecular Spectroscopy,2016,163:20.
[24] JONES G,WILLETT P,GLEN R C,et al. Development and validation of a genetic algorithm for flexible docking[J]. Journal of Molecular Biology,1997,267(3):727-748.
[25] HAUPT R L,HAUPT S E. Practical genetic algorithms[M]. 2nd ed. New York:Wiley,2004.
[26] NORGAARD L,SAUDLAND A,WAGNER J,et al. Interval partial least-squares regression(iPLS):a comparative chemometric study with an example from near-infrared spectroscopy[J]. Applied Spectroscopy,2000,54(3):413-419.
[27] JIANG J H,BERRY R J,SIESLER H W,et al. Wavelength interval selection in multicomponent spectral analysis by moving window partial least-squares regression with applications to mid-infrared and near-infrared spectroscopic data[J]. Analytical Chemistry,2002,74(14):3555-3565.
[28] KASEMSUMRAN S,DU Y P,MURAYAMA K,et al. Near-infrared spectroscopic determination of human serum albumin,γ-globulin,and glucose in a control serum solution with searching combination moving window partial least squares[J]. Analytica Chimica Acta,2004,512(2):223-230.
[29] LI H D,XU Q S,LIANG Y Z. Random frog:An efficient reversible jump Markov Chain Monte Carlo-like approach for variable selection with applications to gene selection and disease classification[J]. Analytica Chimica Acta,2012,740:20.
[30] LI H,LIANG Y,XU Q,et al. Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration[J]. Analytica Chimica Acta,2009,648(1):77.
[31] AND V C,MASSART D,NOORD O E D,et al. Elimination of uninformative variables for multivariate calibration[J]. Analytical Chemistry,1996,68(21):3851.
[32] OUSSAMA A,ELABADI F,PLATIKANOV S,et al. Detection of olive oil adulteration using FT-IR spectroscopy and PLS with variable importance of projection (VIP)scores[J]. Journal of the American Oil Chemists' Society,2012,89(10):1807-1812.
[33] LU B,CASTILLO I,CHIANG L,et al. Industrial PLS model variable selection using moving window variable importance in projection[J]. Chemometrics & Intelligent Laboratory Systems,2014,135(15):90-109.
[34] TETKO I V,LIVINGSTONE D J,LUIK A I. Neural network studies. 1. Comparison of overfitting and overtraining[J]. Journal of Chemical Information & Computer Sciences,1995,35(5):826-833.
[35] 刘秀明,王家俊,李源栋,等. GC/MS指纹图谱结合Hotelling T2检验用于香精品质控制[J]. 烟草科技,2015,48(7):36-40. LIU X M,WANG J J,LI Y D,et al. Flavor quality control by combining GC/MS fingerprint with hotelling T2 test[J]. Tobacco Science & Technology,2015,48(7):36-40.

近红外光谱技术应用于中草药口服液在线质量控制的化学计量学建模

Development of chemometric models based on near infrared spectroscopy for on-line quality control of a Chinese herbal liquid tonic

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