Download PDF
Planta Med 2016; 82(01/02): 154-162
DOI: 10.1055/s-0035-1558085
Analytical Studies
Original Papers
Georg Thieme Verlag KG Stuttgart · New York

Comparison of Ensemble Strategies in Online NIR for Monitoring the Extraction Process of Pericarpium Citri Reticulatae Based on Different Variable Selections

Zheng Zhou
1   Beijing University of Chinese Medicine, Beijing, China
4   Fujian University of Traditional Chinese Medicine, Fujian, China
,
Yang Li
1   Beijing University of Chinese Medicine, Beijing, China
,
Qiao Zhang
1   Beijing University of Chinese Medicine, Beijing, China
,
Xinyuan Shi
1   Beijing University of Chinese Medicine, Beijing, China
2   Key Laboratory of TCM-information Engineering of State Administration of TCM, Beijing, China
3   Beijing Key Laboratory for Basic and Development Research on Chinese Medicine, Beijing, China
,
Zhisheng Wu
1   Beijing University of Chinese Medicine, Beijing, China
2   Key Laboratory of TCM-information Engineering of State Administration of TCM, Beijing, China
3   Beijing Key Laboratory for Basic and Development Research on Chinese Medicine, Beijing, China
,
Yanjiang Qiao
1   Beijing University of Chinese Medicine, Beijing, China
2   Key Laboratory of TCM-information Engineering of State Administration of TCM, Beijing, China
3   Beijing Key Laboratory for Basic and Development Research on Chinese Medicine, Beijing, China
› Author Affiliations
Further Information

Publication History

received 08 February 2015
revised 10 July 2015

accepted 20 August 2015

Publication Date:
20 October 2015 (online)

Also available at
    Permissions and Reprints

Abstract

Different ensemble strategies were compared in online near-infrared models for monitoring active pharmaceutical ingredients of Traditional Chinese Medicine. Bagging partial least square regression and boosting partial least square regression were adopted to near-infrared models, to determine hesperidin and nobiletin content during the extraction process of Pericarpium Citri Reticulatae in a pilot scale system. Different pretreatment methods were investigated, including Savitzky-Golay smoothing, derivatives, multiplicative scatter correction, standard normal variate, normalize, and combinations of them. Two different variable selection methods, including synergy interval partial least squares and backward interval partial least squares algorithms, were performed. Based on the result of the synergy interval partial least squares algorithm, bagging partial least square regression and boosting partial least square regression were adopted into the quantitative analysis. The results demonstrated that the established approach could be applied for rapid determination and real-time monitoring of hesperidin and nobiletin in Pericarpium Citri Reticulatae (Citrus reticulata) during the extraction process. Comparing the results, the boosting partial least square regression provided a slightly better accuracy than the bagging partial least square regression. Finally, this paper provides a promising ensemble strategy on online near-infrared models in Chinese medicine.

Key words

Citrus reticulata - Rutaceae - Pericarpium Citri Reticulatae - on-line near-infrared - partial least square regression - bagging-PLS - boosting-PLS
 

  • References

  • 1 Wu ZS, Sui CL, Xu B, Ai L, Ma Q, Shi XY, Qiao YJ. Multivariate detection limits of on-line NIR model for extraction process of chlorogenic acid from Lonicera japonica . J Pharm Biomed Anal 2013; 77: 16-20
    CrossrefPubMedSearch in Google Scholar
  • 2 McClure WF. 204 years of near infrared technology: 1800–2003. J Near Infrared Spec 2003; 11: 487-518
    CrossrefPubMedSearch in Google Scholar
  • 3 Li YK, Shao XG, Cai WS. A consensus least squares support vector regression (LS-SVR) for analysis of near-infrared spectra of plant samples. Talanta 2007; 72: 217-222
    CrossrefPubMedSearch in Google Scholar
  • 4 Tsymbal A, Pechenizkiy M, Cunningham P. Diversity in search strategies for ensemble feature selection. Inf Fusion 2005; 6: 83-98
    CrossrefPubMedSearch in Google Scholar
  • 5 Breiman L. Bagging predictors. Mach Learn 1996; 24: 123-140
    PubMedSearch in Google Scholar
  • 6 Galar M, Fernandez A, Barrenechea E, Bustince H, Herrera F. A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches. IEEE T Syst Man Cy C 2012; 42: 463-484
    CrossrefPubMedSearch in Google Scholar
  • 7 Viscarra Rossel RA. Robust modelling of soil diffuse reflectance spectra by “bagging-partial least squares regression”. J Near Infrared Spec 2007; 15: 39
    CrossrefPubMedSearch in Google Scholar
  • 8 Schapire RE. The strength of weak learnability. Mach Learn 1990; 5: 197-227
    PubMedSearch in Google Scholar
  • 9 Webb GI. Multiboosting: A technique for combining boosting and wagging. Mach Learn 2000; 40: 159-196
    CrossrefPubMedSearch in Google Scholar
  • 10 Tan C, Wang J, Wu T, Qin X, Li M. Determination of nicotine in tobacco samples by near-infrared spectroscopy and boosting partial least squares. Vib Spectrosc 2010; 54: 35-41
    CrossrefPubMedSearch in Google Scholar
  • 11 Zhang MH, Xu QS, Massart DL. Boosting partial least squares. Anal Chem 2005; 77: 1423-1431
    CrossrefPubMedSearch in Google Scholar
  • 12 Chinese Pharmacopoeia Commission. Pharmacopeia of Peopleʼs Republic of China. Beijing: China Medical Science Press; 2010: 176-177
    Search in Google Scholar
  • 13 Yi LZ, Yuan DL, Liang YZ, Xie PS, Zhao Y. Quality control and discrimination of Pericarpium Citri Reticulatae and Pericarpium Citri Reticulatae Viride based on high-performance liquid chromatographic fingerprints and multivariate statistical analysis. Anal Chim Acta 2007; 588: 207-215
    CrossrefPubMedSearch in Google Scholar
  • 14 Food and Drug Administration. Guidance for industry PAT–a framework for innovative pharmaceutical development, manufacturing, and quality assurance. Rockville, MD: DHHS; 2004
    Search in Google Scholar
  • 15 Li Y, Shi XY, Wu ZS, Guo MY, Xu B, Pan XN, Ma Q, Qiao YJ. Near-infrared for on-line determination of quality parameter of Sophora japonica L. (formula particles): From lab investigation to pilot-scale extraction process. Pharmacogn Mag 2015; 11: 8
    CrossrefPubMedSearch in Google Scholar
  • 16 Wu ZS, Xu B, Du M, Sui CL, Shi XY, Qiao YJ. Validation of a NIR quantification method for the determination of chlorogenic acid in Lonicera japonica solution in ethanol precipitation process. J Pharm Biomed Anal 2012; 62: 1-6
    CrossrefPubMedSearch in Google Scholar
  • 17 Wang P, Zhang H, Yang HL, Nie L, Zang HC. Rapid determination of major bioactive isoflavonoid compounds during the extraction process of kudzu (Pueraria lobata) by near-infrared transmission spectroscopy. Spectrochim Acta A Mol Biomol Spectrosc 2015; 137: 1403-1408
    CrossrefPubMedSearch in Google Scholar
  • 18 Büning PH. Analysis of water in food by near infrared spectroscopy. Food Chem 2003; 82: 107-115
    CrossrefPubMedSearch in Google Scholar
  • 19 Tan C, Wu T, Xu ZH, Li WY, Zhang KS. A simple ensemble strategy of uninformative variable elimination and partial least-squares for near-infrared spectroscopic calibration of pharmaceutical products. Vib Spectrosc 2012; 58: 44-49
    CrossrefPubMedSearch in Google Scholar
  • 20 Zhang QH, Li QB, Zhang GJ. A strategy of small sample modeling for multivariate regression based on improved Boosting PLS. Anal Methods – UK 2012; 4: 2039-2047
    PubMedSearch in Google Scholar
  • 21 Williams P. Tutorial: The RPD statistic: a tutorial note. NIR news 2010; 21: 22
    CrossrefPubMedSearch in Google Scholar
  • 22 Esbensen K, Geladi P, Larsen A. The RPD myth…. NIR news 2014; 25: 24
    PubMedSearch in Google Scholar
  • 23 Bauer E, Kohavi R. An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Mach Learn 1999; 36: 105-139
    CrossrefPubMedSearch in Google Scholar
  • 24 Lu WZ, Yuan HF, Xu GT. Modern near infrared spectroscopy analytical technology. Beijing: China Petrochemical Press; 2007
    Search in Google Scholar
  • 25 Liu EH, Zhao P, Duan L, Zheng GD, Guo L, Yang H, Li P. Simultaneous determination of six bioactive flavonoids in Citri Reticulatae Pericarpium by rapid resolution liquid chromatography coupled with triple quadrupole electrospray tandem mass spectrometry. Food Chem 2013; 141: 3977-3983
    CrossrefPubMedSearch in Google Scholar
  • 26 Zheng GD, Yang DP, Wang DM, Zhou F, Yang X, Jiang L. Simultaneous determination of five bioactive flavonoids in pericarpium Citri reticulatae from china by high-performance liquid chromatography with dual wavelength detection. J Agric Food Chem 2009; 57: 6552-6557
    CrossrefPubMedSearch in Google Scholar