Zang Q C, Wang J B, Kong W J, et al. Searching for the main anti-bacterial components in artificial Calculus bovis using UPLC and microcalorimetry coupled with multi-linear regression analysis [J].J Sep Sci, 2011, 34(23): 3330.
Kong W J, Zhao Y L, Xiao X H, et al. Spectrum-effect relationships between ultra performance liquid chromatography fingerprints and anti-bacterial activities of Rhizoma coptidis[J]. Anal Chim Acta, 2009, 634(2): 279.
[11]
Kong W J, Zhao Y L, Shan L M, et al. Investigation on the spectrum-effect relationships of EtOAc extract from Radix Isatidis based on HPLC fingerprints and microcalrimetry [J]. J Chromatogr B, 2008,871(1): 109.
[12]
朱尔一, 杨芃原. 化学计量学技术及应用[M]. 北京: 科学出版社, 2001: 84.
[13]
Wang X W, Wang Y, Cheng Y Y. Empirical study on modeling quantitative composition-activity relationships in Chinese hebal medicion-27th annual conference of the IEEE engineering in medicine and biology, Shanghai[C]. New York: IEEE, 2005: 7722.
[14]
Chau F T, Chan H Y, Cheung C Y, et al. Recipe for uncovering the bioactive components in herbal medicine [J]. Anal Chem, 2009, 81(17): 7271.
Jiang J L, Jin X L, Zhang H, et al. Identification of antitumor constituents in curcuminoids from Curcuma longa L.based on the composition-activity relationship [J].J Pharm Biomed Anal, 2012, 70: 664.
Wang Y, Jin Y C, Zhou C G, et al. Discovering active compounds from mixture of natural products by data mining approach [J]. Med Biol Eng Comput, 2008, 46(6): 605.
[22]
Cheng Y Y, Wang Y, Wang X W. A causal relationship discovery-based approach to identifying active components of herbal medicine [J]. Comput Biol Chem, 2006, 30(2): 148.
Vladimir V, Steven E G, Alex S. Support vector method for function approximation, regression estimation, and signal processing [J]. Adv Neural Inf Process Syst, 1996, 9: 140
Eskidere , Ertas F, Hanili C. A comparison of regression methods for remote tracking of Parkinson\'s disease progression [J]. Expert Syst Appl, 2012, 39(5): 5523.
[39]
Zhou H, Zhao J P, Zheng L G, et al. Modeling NOx emissions from coal-fired utility boilers using support vector regression with ant colony optimization [J]. Eng Appl Artif Intel, 2012, 25(1): 147.
[40]
Wang Y, Wang X W, Cheng Y Y. A computational approach to botanical drug design by modeling quantitative composition-activity relationship [J]. Chem Biol Drug Des, 2006, 68(3): 166.
[41]
Chen C, Li S X, Wang S M, et al. A support vector machine based pharmacodynamic prediction model for searching active fraction and ingredients of herbal medicine: naodesheng prescription as an example [J].Pharm Biomed Anal, 2011, 56(2): 443.
[42]
Yan S K, Lin Z Y, Dai W X, et al. Chemometrics-based approach to modeling quantitative composition-activity relationships for Radix Tinosporae [J]. Interdiscip Sci Comput Life Sci, 2010, 2(3): 221.
[43]
Jiang J L, Zhang H, Zhou P P, et al. Composition-activity relationship modeling to predict the antitumor activity for quality control of curcuminoids from Curcuma longa L.[J]. Anal Methods, 2013, 5(3): 641.
[44]
Jiang J L, Su X, Ding H T, et al. A novel approach to evaluate the quality and identify the active compounds of the essential oil from Curcuma longa L. [J]. Anal Lett, 2013, 46(8): 1213.
[45]
Cabrera A C, Prieto J M. Application of artificial neural networks to the prediction of the antioxidant activity of essential oils in two experimental in vitro models [J]. Food Chem, 2010, 118(1): 141.
[46]
Li Z D, Han S N, Jiang J L, et al. Antitumor compound identification from Zanthoxylum bungeanum essential oil based on composition-activity relationship [J]. Chem Res Chin Univ, doi: 10.1007/s40242-013-3223-3.
Chen C, Li S X, Wang S M, et al. Mutiple information contents derived from the chromatograms and their application to the modeling of quantitative profile-efficacy relationship[J]. Anal Chim Acta, 2012, 713: 30.
[49]
Chen C, Yuan J, Li X J, et al. Chemometrics-based approach to feature selection of chromatographic profiles and its application to search active fraction of herbal medicine [J]. Chem Biol Drug Des, 2013, 81(6): 688.
Zhu L P, Zhu L X. On distribution-weighted partial least squares with diverging number of highly correlated predictors [J]. J R Statist Soc, 2009,71: 525.
[55]
Kvalheim O M, Chan H Y, Benzie I F F, et al. Chromatographic profiling and multivariate analysis for screening and quantifying the contributions from individual components to the bioactive signature in natural products [J]. Chemometr Intell Lab, 2011, 107(1): 98.
