A chemometric study of chromatograms of tea extracts by correlation optimization warping in conjunction with PCA, support vector machines and random forest data modeling

Analytica Chimica Acta

Volumn 642, Issue 1-2, 2009, Pages 257-265

  Zheng, L ^a   Watson, D G ^a   Johnston, B F ^a   Clark, R L ^a   Edrada Ebel, R ^a   Elseheri, W ^a  

^a UNIVERSITY OF STRATHCLYDE   (United Kingdom)

Author keywords

Correlation optimization warping; Prediction; Principle component analysis; Random forest; Support vector machines; Tea; Warping

Indexed keywords

CORRELATION OPTIMIZATION WARPING; PREDICTION; PRINCIPLE COMPONENT ANALYSIS; RANDOM FOREST; TEA; WARPING;

ALIGNMENT; CHROMATOGRAPHIC ANALYSIS; CORRELATION METHODS; HIGH PERFORMANCE LIQUID CHROMATOGRAPHY; HIGH PRESSURE LIQUID CHROMATOGRAPHY; IMAGE RETRIEVAL; OPTIMIZATION; SOLVENT EXTRACTION; SUPPORT VECTOR MACHINES; VECTORS; WEAVING;

PRINCIPAL COMPONENT ANALYSIS;

ALGORITHM; ARTICLE; CHEMICAL ANALYSIS; CHEMOMETRIC ANALYSIS; CHROMATOGRAPHY; CORRELATION OPTIMIZATION WARPING; PREDICTION; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; RANDOM FOREST; SUPPORT VECTOR MACHINE; TEA;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CHROMATOGRAPHY, HIGH PRESSURE LIQUID; MASS SPECTROMETRY; PATTERN RECOGNITION, AUTOMATED; PRINCIPAL COMPONENT ANALYSIS; TEA; TIME FACTORS;

EID: 67349228760     PISSN: 00032670     EISSN: 18734324     Source Type: Journal    
DOI: 10.1016/j.aca.2008.12.015     Document Type: Article

References (43)

1. Wipawee P., Eiichiro F., Takeshi B., and Tsutomu Y. J. Agric. Food Chem. 55 (2007) 231
2. Hsieh M.M., and Chen S.M. Talanta 73 (2007) 326
3. Zhao J., Chen Q.S., and Huang X.Y. J. Pharm. Biomed. Anal. 41 (2006) 1198
4. Lillo M.P., Sanderson P.N., Wantling E.L., and Pudney D.A. Dev. Food Sci. 43 (2006) 537
5. Wang D.M., Lu J.L., Miao A.Q., Xie Z.Y., and Yang D.P. J. Food Comp. Anal. 21 (2008) 361
6. Lv Y., Yang X.B., Zhao Y., Ruan Y., and Yang Y. Food Chem. (2008)
7. Xiang L., Xing D.M., Lei F., Wang W., Xu L.Z., Nie L., and Du L.J. Tsinghua Sci. Technol. 13 (2008) 460
8. Daszykowski M., Heyden Y.V., and Walczak B. J. Chromatogr. A 1176 (2007) 12
9. Kumar S.D., Narayan G., and Hassarajani S. Food Chem. 111 (2008) 784-788
10. Daszykowski M., Wu W., Nicholls A.W., Czekaj T., and Walczak B. J. Chemometr. 21 (2007) 292
11. Nielsen N.V., Carstensen J.M., and Smedsgaard J. J. Chromatogr. A 805 (1998) 17
12. Katajamaa M., and Oresic M. J. Chromatogr. A 1158 (2007) 318
13. Chae M., Reis R.J.S., and Thaden J.J. BMC Bioinform. 9 (2008) (available online)
14. Johnson K.J., Wright B.W., Jarman K.H., and Synovec R.E. J. Chromtatogr. A 996 (2003) 141
15. Smith C.A., Want E.J., Maille G.O., Abagyan R., and Siuzdak G. Anal. Chem. 78 (2006) 779
16. Machine learning, http://hunch.net/.
17. Kim H.C., Pang S., Je H.M., Kim D., and Bang S.Y. Pattern Recogn. 36 (2003) 2757
18. Vapnik V.N., and Chervonenkis A.Y. Theory Prob. Appl. 17 (1971) 26
19. Vapnik V.N. The Nature of Statistical Learning Theory (2000), Springer-Verlag
20. Kam H.T. Proceedings of the 3rd International Conference on Document Analysis and Recognition (1995) 278
21. Kim H.T. IEEE Trans. Pattern Anal. Mach. Intell. 20 (2005) 832
22. Yali A., and Donald G. Neural Comput. 9 (1997) 1545
23. H.T. Kim, S.S. Kim, S.J. Kim, Proceedings of the 2005 IEEE Engineering in Medicine and Biology, 27th Annual Conference, 2005.
24. Yélamos I., Escudero G., Graells M., and Puigjaner L. Comp. Aided Chem. Eng. 24 (2007) 1253
25. Kim H.T., and Park H. Protein Eng. 16 (2003) 553
26. Guyon I., Weston J., Barnhill S., and Vapnik V. Mach. Learn. 46 (2002) 389
27. Wu D., Chen X.J., and He Y. International Conference on Computational Intelligence and Security Workshops (2007)
28. Breiman L. Mach. Learn. 24 (1996) 123
29. Breiman L. Mach. Learn. 45 (2001) 5
30. Brown S., and Lugo A.E. Plant Soil 124 (1990) 53
31. L. Breiman, A. Cutler, Random Forest-Manual, http://www.stat.berkeley.edu/∼breiman/RandomForests/cc_manual.htm, 2004.
32. Breiman L., Friedman J., Olshen R., and Stone C. Classification and Regression Trees (1984), Wadsworth International, Belmont, California
33. Grimm R., Behrens T., Märker M., and Elsenbeer H. Geoderma 146 (2008) 102-113
34. Perdiguero-Alonso D., Montero F.E., Kostadionva A., Raga J.A., and Barrett J. Int. J. Parasitol. (2008) (available online)
35. Pravdova V., Walczak B., and Massart D.L. Anal. Chim. Acta 456 (2002) 77
36. Tomasi G., van den Berg F., and Anderson C. J. Chemometr. 18 (2004) 231
37. G. Tomasi, COW codes, http://www.models.kvl.dk/source/DTW_COW/index.asp.
38. Aizerman M., Braverman E., and Rozonoer L. Automat. Rem. Control 25 (1964) 821
39. Burges C.J.C. Data Mining Knowl. Disc. 2 (1998) 121
40. Meyer D., Leisch F., and Hornik K. Neurocomputing 55 (2003) 169
41. Cortes C., and Vapnik V. Mach. Learn. 20 (1995) 273-297
42. Breiman L. Looking inside the black box. Wald Lecture II (2002)
43. Angulo C., Parra X., and Català A. Neurocomputing 55 (2003) 57