A comparison of different chemometrics approaches for the robust classification of electronic nose data

Analytical and Bioanalytical Chemistry

Volumn 406, Issue 29, 2014, Pages 7581-7590

  Gromski, Piotr S ^a   Correa, Elon ^a   Vaughan, Andrew A ^a   Wedge, David C ^b   Turner, Michael L ^a   Goodacre, Royston ^a  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

^b WELLCOME TRUST SANGER INSTITUTE   (United Kingdom)

Author keywords

Bootstrapping; Cross validation; Linear discriminant analysis; Partial least squares discriminant analysis; Random forests; Support vector machines

Indexed keywords

CHEMICAL DETECTION; DECISION TREES; DIAGNOSIS; DISCRIMINANT ANALYSIS; LEAST SQUARES APPROXIMATIONS; PATTERN RECOGNITION;

BOOTSTRAPPING; CROSS VALIDATION; LINEAR DISCRIMINANT ANALYSIS; PARTIAL LEAST SQUARES - DISCRIMINANT ANALYSIS; RANDOM FORESTS;

SUPPORT VECTOR MACHINES;

GAS;

ALGORITHM; ANALYSIS; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BIOMIMETICS; COMPARATIVE STUDY; CONDUCTOMETRY; EVALUATION STUDY; GAS; HUMAN; NOSE; ODOR; PROCEDURES; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL ANALYSIS; VALIDATION STUDY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BIOMIMETICS; CONDUCTOMETRY; DATA INTERPRETATION, STATISTICAL; GASES; HUMANS; NOSE; ODORS; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84925486858     PISSN: 16182642     EISSN: 16182650     Source Type: Journal    
DOI: 10.1007/s00216-014-8216-7     Document Type: Article

References (46)

1. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1:67-82
2. Rock F, Barsan N, Weimar U (2008) Electronic nose: current status and future trends. Chem Rev 108:705-725
3. Scott SM, James D, Ali Z (2006) Data analysis for electronic nose systems. Microchim Acta 156:183-207
4. Manly BFJ (1986) Multivariate statistical methods: a primer. Chapman and Hall
5. Jurs PC, Bakken GA, McClelland HE (2000) Computational methods for the analysis of chemical sensor array data from volatile analytes. Chem Rev 100:2649-2678
6. Dobrokhotov V, Oakes L, Sowell D, Larin A, Hall J, Kengne A, Bakharev P, Corti G, Cantrell T, Prakash T, Williams J, McIlroy DN (2012) Toward the nanospring-based artificial olfactory system for trace-detection of flammable and explosive vapors. Sensors Actuators B Chem 168:138-148
7. Dragonieri S, Schot R, Mertens BJA, Le Cessie S, Gauw SA, Spanevello A, Resta O, Willard NP, Vink TJ, Rabe KF, Bel EH, Sterk PJ (2007) An electronic nose in the discrimination of patients with asthma and controls. J Allergy Clin Immunol 120:856-862
8. Wold S, Sjostrom M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chemometr Intell Lab 58:109-130
9. Cynkar W, Dambergs R, Smith P, Cozzolino D (2010) Classification of Tempranillo wines according to geographic origin: combination of mass spectrometry based electronic nose and chemometrics. Anal Chim Acta 660:227-231
10. Di Natale C, Macagnano A, Martinelli E, Paolesse R, D'Arcangelo G, Roscioni C, Finazzi-Agro A, D'Amico A (2003) Lung cancer identification by the analysis of breath by means of an array of non-selective gas sensors. Biosens Bioelectron 18:1209-1218
11. Bernabei M, Pennazza G, Santortico M, Corsi C, Roscioni C, Paolesse R, Di Natale C, D'Amico A (2008) A preliminary study on the possibility to diagnose urinary tract cancers by an electronic nose. Sens Actuators B-Chem 131:1-4
12. Brereton RG (2009) Chemometrics for pattern recognition. Wiley, Chichester
13. Breiman L (2001) Random forests. Mach Learn 45:5-32
14. Pardo M, Sberveglieri G (2008) Random forests and nearest shrunken centroids for the classification of sensor array data. Sens Actuators B-Chem 131:93-99
15. Vapnik VN (1999) An overview of statistical learning theory. IEEE Trans Neural Netw 10:988-999
16. Pardo M, Sberveglieri G (2005) Classification of electronic nose data with support vector machines. Sensors Actuators B Chem 107:730-737
17. Gualdron O, Brezmes J, Llobet E, Amari A, Vilanova X, Bouchikhi B, Correig X (2007) Variable selection for support vector machine based multisensor systems. Sensors Actuators B Chem 122:259-268
18. Machado RF, Laskowski D, Deffenderfer O, Burch T, Zheng S, Mazzone PJ, Mekhail T, Jennings C, Stoller JK, Pyle J, Duncan J, Dweik RA, Erzurum SC (2005) Detection of lung cancer by sensor array analyses of exhaled breath. Am J Respir Crit Care Med 171:1286-1291
19. Sattlecker M, Bessant C, Smith J, Stone N (2010) Investigation of support vector machines and Raman spectroscopy for lymph node diagnostics. Analyst 135:895-901
20. Distante C, Ancona N, Siciliano P (2003) Support vector machines for olfactory signals recognition. Sensors Actuators B Chem 88:30-39
21. Wedge DC, Das A, Dost R, Kettle J, Madec MB, Morrison JJ, Grell M, Kell DB, Richardson TH, Yeates S, Turner ML (2009) Real-time vapour sensing using an OFET-based electronic nose and genetic programming. Sensors Actuators B Chem 143:365-372
22. Gilbert RJ, Goodacre R, Woodward AM, Kell DB (1997) Genetic programming: a novel method for the quantitative analysis of pyrolysis mass spectral data. Anal Chem 69:4381-4389
23. Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge
24. Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2:121-167
25. Kohavi R (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the 14th international joint conference on artificial intelligence, Montreal. Morgan Kaufmann, p 7
26. Efron B (1979) 1977 Rietz lecture-bootstrap methods: another look at the jackknife. Ann Stat 7:1-26
27. Pearce TC, Manuel SM (2003) Chemical sensor array optimization: geometric and information theoretic approaches. In: T.C. P, S. SS, T NH, W GJ (eds) Handbook of machine olfaction - electronic nose technology. Wiley, Weinheim
28. Team RDC (2008) R: A language and environment for statistical computing. R Foundation for Statistical Computing. http://www.R-project.org.
29. Brereton RG (2006) Consequences of sample size, variable selection, and model validation and optimisation, for predicting classification ability from analytical data. Trac-Trend Anal Chem 25:1103-1111
30. Brereton RG, Lloyd GR (2014) Partial least squares discriminant analysis: taking the magic away. J Chemometrics 28:213-225
31. Dixon SJ, Brereton RG (2009) Comparison of performance of five common classifiers represented as boundary methods: Euclidean distance to centroids, linear discriminant analysis, quadratic discriminant analysis, learning vector quantization and support vector machines, as dependent on data structure. Chemometr Intell Lab 95:1-17
32. Efron B, Tibshirani R (1997) Improvements on cross-validation: the 632 + bootstrap method. JASA 92:548-560
33. Jain AK, Dubes RC, Chen CC (1987) Bootstrap techniques for error estimation. IEEE Trans Pattern Anal Mach Intell 9:628-633
34. Xu Y, Zomer S, Brereton RG (2006) Support vector machines: a recent method for classification in chemometrics. Crit Rev Anal Chem 36:177-188
35. Gunn SR (1998) Support vector machines for classification and regression. Technical Report. http://ce.sharif.ir/courses/85-86/2/ce725/resources/root/LECTURES/SVM.pdf.
36. Ben-Hur A, Weston J (2010) A user's guide to support vector machines. Technical report. http://pyml.sourceforge.net/doc/howto.pdf. 609
37. Westerhuis JA, Hoefsloot HCJ, Smit S, Vis DJ, Smilde AK, van Velzen EJJ, van Duijnhoven JPM, van Dorsten FA (2008) Assessment of PLSDA cross validation. Metabolomics 4:81-89
38. Goodacre R, Timmins EM, Burton R, Kaderbhai N, Woodward AM, Kell DB, Rooney PJ (1998) Rapid identification of urinary tract infection bacteria using hyperspectral whole-organism fingerprinting and artificial neural networks. Microbiology 144:1157-1170
39. Goodacre R, Broadhurst D, Smilde AK, Kristal BS, Baker JD, Beger R, Bessant C, Connor S, Calmani G, Craig A, Ebbels T, Kell DB, Manetti C, Newton J, Paternostro G, Somorjai R, Sjostrom M, Trygg J, Wulfert F (2007) Proposed minimum reporting standards for data analysis in metabolomics. Metabolomics 3:231-241
40. Venables WN, Ripley BD (2002) Modern applied statistics with S. Springer, New York
41. Kuhn M (2008) Building predictive models in R using the caret package. J Stat Softw 28:1-26
42. Liaw A, Wiener M (2002) Classification and regression by randomforest. R News 2:18-22
43. Karatzoglou A, Meyer D, Hornik K (2006) Support vector machines in R. J Stat Softw 15:1-28
44. Gromski PS, Xu Y, Correa E, Ellis DI, Turner ML, Goodacre R (2014) A comparative investigation of modern feature selection and classification approaches for the analysis of mass spectrometry data. Anal Chim Acta 829:1-8
45. Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46:389-422
46. Chapelle O, Vapnik V, Bousquet O, Mukherjee S (2002) Choosing multiple parameters for support vector machines. Mach Learn 46:131-159