Quantitative analysis of multivariate data using artificial neural networks: A tutorial review and applications to the deconvolution of pyrolysis mass spectra

Zentralblatt fur Bakteriologie

Volumn 284, Issue 4, 1996, Pages 516-539

  Goodacre, Royston ^a   Neal, Mark J ^a   Kell, Douglas B ^a  

^a ABERYSTWYTH UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0030221932     PISSN: 09348840     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0934-8840(96)80004-1     Document Type: Article

References (114)

1. Aleksander, I. and H. Morton: An Introduction to Neural Computing. Chapman & Hall, London (1990)
2. Amit, D. J.: Modeling Brain Function; The World of Attractor Neural Networks. Cambridge University Press, Cambridge (1989)
3. Aries, R. E., C. S. Gutteridge, and T. W. Ottley: Evaluation of a low-cost, automated pyrolysis-mass spectrometer. J. Anal. Appl. Pyrol. 9 (1986) 81-98
4. Ball, J. W. and P. C. Jurs: Automated selection of regression-models using neural networks for C-13 NMR spectral predictions. Anal. Chem. 65 (1993) 505-512
5. Baum, E. B. and D. Haussler: What size net gives valid generalization? Neural Computation 1 (1989) 151-160
6. Beale, R. and T. Jackson: Neural Computing: An Introduction. Adam Hilger, Bristol (1990)
7. Beebe, K. R., W. W. Blaser, R. A. Bredeiveg, J. P. Chauvel, R. S. Harner, M. LaPack, A. Leugers, D. P. Martin, L. G. Wright, and E. D. Yalvac: Process analytical-chemistry. Anal. Chem. 65 (1993) 199R-216R
8. Berkeley, R. C. W., R. Goodacre, R. J. Helyer, and T. Kelley: Pyrolysis-MS in the identification of micro-organisms. Lab. Pract. 39 (1990) 81-83
9. Blank, B. T. and S. D. Brown: Nonlinear multivariate mapping of chemical data using feed-forward neural networks. Anal. Chem. 65 (1993) 3081-3089
10. Boddy, L. and C. W. Morris: Neural network analysis of flow cytometry data. In: Flow Cytometry in Microbiology, pp. 159-169 (D.Lloyd, Ed.). Springer-Verlag, London (1993)
11. Borggaard, C. and H. H. Thodberg: Optimal minimal neural interpretation of spectra. Anal. Chem. 64 (1992) 545-551
12. Bos, A., M. Bos, and W. E. van der Linden: Artificial neural networks as a tool for softmodeling in quantitative analytical-chemistry - the prediction of the water-content of cheese. Anal. Chim. Acta 256 (1992) 133-144
13. Bourlard, H. A. and N. Morgan: Connectionist Speech Recognition: A Hybrid Approach. Kluwer Academic Publishers, Boston, MA (1994)
14. Brereton, R. G.: Chemometrics: Applications of Mathematics and Statistics to Laboratory Systems. Ellis Horwood, New York (1990)
15. Brereton, R. G.: Multivariate Pattern Recognition in Chemometrics. Elsevier, Amsterdam (1992)
16. Broomhead, D. and D. Lowe: Multivariate function interpolation and adaptive networks. Complex Systems 2 (1988) 321-355
17. Brown, S. D., R. S. Bear, and T. B. Blank: Chemometrics. Anal. Chem. 64 (1992) 22R-49R
18. Bruchmann, A., P. Zinn, and C. M. Haffer: Prediction of gas-chromatographic retention index data by neural networks. Anal. Chim. Acta 283 (1993) 869-880
19. Buntine, W. L. and A. S. Weigend: Bayesian back-propagation. Complex Systems 5 (1991) 603-643
20. Carpenter, G. A. and S. Grossberg: Pattern Recognition by Self-Organizing Neural Networks. MIT Press, Cambridge, MA (1991)
21. Causton, D. R.: A Biologist's Advanced Mathematics, pp. 48-72. Allen & Unwin, London (1987)
22. Chatfield, C. and A. J. Collins: Introduction to Multivariate Analysis, pp. 57-81. Chapman & Hall, London (1980)
23. Chun, J., E. Atalan, A. C. Ward, and M. Goodfellow: Artificial neural network analysis of pyrolysis mass-spectrometric data in the identification of Streptomyces strains. FEMS Microbiol. Lett. 107 (1993) 321-325
24. Cowan, J. D. and D. H. Sharp: Neural nets. Q. Rev. Biophys. 21 (1988) 365-427
25. Curry, B. and D. E. Rumelhart: MSnet: a neural network which classifies mass spectra Tetra. Comput. Meth. 3 (1990) 213-237
26. Cybenko, G.: Approximation by superposition of a sigmoidal function. Mathemat. Control Signals Syst. 2 (1989) 303-314
27. Drennen, J. K., E. G. Kraemer, and R. A. Lodder: Advances and perspectives in near-infrared spectrophotometry. Critical Rev. Anal. Chem. 22 (1991) 441-475
28. Eberhart, R. C. and R. W.Dobbins: Neural Network PC Tools. Academic Press, London (1990)
29. Erwin, E., K. Obermayer, and K. Schulten: Self-organizing maps: Ordering, convergence properties and energy functions. Biolog. Cybernet. 67 (1992) 47-55
30. Everitt, B. S.: Cluster Analysis. Edward Arnold, London (1993)
31. Fahlman, S. E.: An Empirical Study of Learning Speed in Back Propagation Networks. Carnegie-Mellon University Technical Report CMU-CS-88-162 (1988)
32. Fahlman, S. E. and C. Lebiere: The Cascade-Correlation Learning Architecture. Report CMU-CS-90-100, Carnegie-Mellon University 1990
33. Finoff, W., F. Hergert, and H. G. Zimmermann: Improving model selection by nonconvergent methods. Neural Networks 6 (1993) 771-783
34. Flury, B. and H. Riedwyl: Multivariate Statistics: A Practical Approach, pp. 181-233. Chapman & Hall, London (1988)
35. Frank, I. E.: A nonlinear PLS model. Chemom. Intell. Lab. Sys. 8 (1990) 109-119
36. Frean, M.: The upstart algorithm: A method for constructing and training feedforward neural networks. Neural Computation 2 (1990) 198-209
37. Freeman, R., R. Goodacre, P. R. Sisson, J. G. Magee, A. C. Ward, and N. F. Lightfoot: Rapid identification of species within the Mycobacterium tuberculosis complex by artificial neural network analysis of pyrolysis mass spectra. J. Med. Microbiol. 40 (1994) 170-173
38. Freeman, R., F. K. Gould, R. Wilkinson, A. C. Ward, N. F. Lightfoot, and P. R. Sisson: Rapid inter-strain comparison by pyrolysis mass spectrometry of coagulase-negative staphylococci from persistent CAPD peritonitis. Epidem. Infect. 106 (1991) 239-246
39. Funahashi, K.: On the approximate realization of continuous-mappings by neural networks. Neural Networks 2 (1989) 183-192
40. Gallant, S. I.: Neural Network Learning. MIT Press, Cambridge, MA (1993)
41. Geman, S., E. Bienenstock, and R. Doursat: Neural networks and the bias/variance dilemma. Neural computation 4 (1992) 1-58
42. Gemperline, P. J., J. R. Long, and V. G. Gregoriou: Nonlinear multivariate calibration using principal components regression and artificial neural networks. Anal. Chem. 63 (1991) 2313-2323
43. Goodacre, R.: Characterisation and quantification of microbial systems using pyrolysis mass spectrometry: Introducing neural networks to analytical pyrolysis. Microbiol. Eur. 2(2) (1994) 16-22
44. Goodacre, R. and R. C. W. Berkeley: Detection of small genotypic changes in Escherichia coli by pyrolysis mass spectrometry. FEMS Microbiol. Lett. 71 (1990) 133-138
45. Goodacre, R. and D. B. Kell: Rapid and quantitative analysis of bioprocesses using pyrolysis mass spectrometry and neural networks: Application to indole production. Anal. Chim. Acta 279 (1993) 17-26
46. Goodacre, R., J. E. Beringer, and R. C. W. Berkeley: The use of pyrolysis-mass spectrometry to detect the fimbrial adhesive antigen F41 from Escherichia coli HB101 (pSLM204). J. Anal. Appl. Pyrol. 22 (1991) 19-28
47. Goodacre, R., A. N. Edmonds, and D. B. Kell: Quantitative analysis of the pyrolysismass spectra of complex mixtures using artificial neural networks: Application to casamino acids in glycogen. J. Anal. Appl. Pyrol. 26 (1993) 93-114
48. Goodacre, R., S. A. Howell, W. C. Noble, and M. J. Neal: Sub-species discrimination using pyrolysis mass spectrometry and self-organising neural networks of Propionibacterium acnes isolated from normal human skin. Zbl. Bakt., in this volume (1996, pp. 501-515)
49. 5 in Escherichia coli. J. Biotechnol. 34 (1994) 185-193
50. Goodacre, R., D. B. Kell, and G. Bianchi: Neural networks and olive oil. Nature 359 (1992) 594
51. Goodacre, R., D. B. Kell, and C. Bianchi: Rapid assessment of olive oil adulteration using pyrolysis mass spectrometry and artificial neural networks. J. Sci. Food Agric. 63 (1993) 297-307
52. Goodacre, R., M. J. Neal, and D. B. Kell: Rapid and quantitative analysis of the pyrolysis mass spectra of complex binary and tertiary mixtures using multivariate calibration and artificial neural networks. Anal. Chem. 66 (1994) 1070-1085
53. Goodacre, R., M. J. Neal, D. B. Kell, L. W. Greenham, W. C. Noble, and R. G. Harvey: Rapid identification using pyrolysis mass spectrometry and artificial neural networks of Propionibacterium acnes isolated from dogs. J. Appl. Bact. 76 (1994) 124-134
54. Goodacre, R., S. Trew, C. Wrigley-Jones, M. J. Neal, J. Maddock, T. W. Ottley, N. Porter, and D. B. Kell: Rapid screening for metabolite overproduction in fermentor broths using pyrolysis mass spectrometry with multivariate calibration and artificial neural networks. Biotechn. Bioengin. 44 (1994) 1205-1216
55. Gutteridge, C. S.: Characterization of microorganisms by pyrolysis mass spectrometry. Meth. Microbiol. 19 (1987) 227-272
56. Gutteridge, C. S., L. Vallis, and H. J. H. MacFie: Numerical methods in the classification of microorganisms by pyrolysis mass spectrometry. In: Computer-assisted Bacterial Systematics, pp. 369-401 (eds., M. Goodfellow, D. Jones, F. G. Priest). Academic Press, London (1985)
57. Hassibi, B. and D. G. Stork: Second order derivatives for network pruning: Optimal brain surgeon. In: Advances in Neural Information Processing Systems 5, pp. 164-171 (eds., S. J. Hanson, J. D. Cowan and C. L. Giles). Morgan Kaufmann, San Mateo, CA (1993)
58. Hassibi, B., D. G. Stork, and G. J. Wolff: Optimal Brain Surgeon And General Network Pruning. IEEE Internat. Confer. Neural Networks 1-3 (1993) 293-299
59. Hecht-Nielsen, R.: Neurocomputing. Addison-Wesley, Massachusetts (1990)
60. Hertz, J., A. Krogh, and R. G. Palmer: Introduction to the Theory of Neural Computation. Addison-Wesley, California (1991)
61. Hornik, K., M. Stinchcombe, and H. White: Multilayer feedforward networks are universal approximators. Neural Networks 2 (1989) 359-368
62. Hornik, K., M. Stinchcombe, and H. White: Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks. Neural Networks 3 (1990) 551-560
63. Höskuldsson, A.: Quadratic PLS regression. J. Chemom. 6 (1992) 307-334
64. Irwin, W. J.: Analytical Pyrolysis: A Comprehensive Guide. Marcel Dekker, New York (1982)
65. Kell, D. B. and C. L. Davey. On fitting dielectric spectra using artificial neural networks. Bioelectrochem. Bioenerg. 28 (1992) 425-434
66. Kohonen, T.: Self-Organization and Associative Memory. Springer-Verlag, Berlin (1989)
67. 13C nuclear magnetic resonance spectra. Anal. Chem. 57 (1985) 2858-2864
68. LeCun, Y., J. S. Denker, and S. A. Solla: Optimal brain damage. In: Advances in Neural Information Processing Systems 1 (ed., D. S.Touretzky), pp. 598-605. Morgan Kaufmann, New York (1989)
69. Long, J. R., H. T. Mayfield, M. V. Henley, and P. R. Kromann: Pattern recognition of jet fuels Chromatographic data by artificial neural networks with back-propagation of error. Anal. Chem. 63 (1991) 1256-1261
70. MacFie, H. J. H., C. S. Gutteridge, and J. R. Norris: Use of canonical variates in differentiation of bacteria by pyrolysis gas-liquid chromatography. J. Gen. Microbiol. 104 (1978) 67-74
71. Magee, J. T.: Whole-organism fingerprinting, in Handbook of New Bacterial Systematics, pp. 383-427 (M. Goodfellow and A. G. O'Donnell, Eds.). Academic Press, London (1993)
72. Magee, J. T., J. M. Hindmarch, B. I. Duerden, and L. Goodwin: A pyrolysis mass spectrometry study of oral pigmented bacteroides. J. Med. Microbiol. 37 (1992) 56-61
73. Mark, H.: Principles and practice of spectroscopic calibration. John Wiley & Sons, New York (1991)
74. Martens, H. and T. Nces: Multivariate Calibration. John Wiley & Sons, New York (1989)
75. Martin, K. A.: Recent advances in near-infrared reflectance spectroscopy. Appl. Spect. Revs. 27 (1992) 325-383
76. Massart, D. L., B. G, M. Vandeginste, S. N. Deming, Y. Michotte, and L. Kaufmann: Chemometrics: A Textbook. Eisevier, Amsterdam (1988)
77. McAvoy, T. J., H. T. Su, N. S. Wang, M. He, J. Horvath, and H. Semerjian: A comparison of neural networks and partial least-squares for deconvoluting fluorescence-spectra. Biotechn. Bioeng. 40 (1992) 53-62
78. McClelland, J. L. and R. E. Rumelhart: Explorations in Parallel Distributed Processing: A Handbook of Models, Programs and Exercises. MIT Press, Cambridge, MA (1988)
79. Meloun, M., J. Militky, and M. Forina: Chemometrics for Analytical Chemistry. Vol 1: PC-aided Statistical Data Analysis. Ellis Horwood, Chichester, UK (1992)
80. Meuzelaar, H. L. C., J. Haverkamp, and F. D. Hileman: Pyrolysis Mass Spectrometry of Recent and Fossil Biomaterials. Elsevier, Amsterdam (1982)
81. Miller, A. J.: Subset Selection in Regression. Chapman & Hall, London (1990)
82. Mjolsness, E., D. H. Sharp, and B. K. Alpert: Scaling, machine learning, and genetic neural nets. Adv. Appl. Math. 10 (1989) 137-163
83. Moody, J. and C. Darken: Fast learning in networks of locally-tuned processing units. Neural Computation 1 (1989) 281-294
84. Mozer, M. C. and P. Smolensky: Skeletonization: A technique for trimming fat from a network via relevance assessment. In: Advances in Neural Information Processing Systems 1, pp. 107-115 (ed., D. S.Touretzky). Morgan Kaufmann, New York (1989)
85. Næs, T., K. Kvaal, T. Isaksson, and C. Miller: Artificial neural networks in multivariate calibration. J. Near Infrared Spectrosc. 1 (1993) 1-11
86. Heal, M. J., R. Goodacre, and D. B. Kell: On the analysis of pyrolysis mass spectra using artificial neural networks. Individual input scaling leads to rapid learning. Proc. World Congress on Neural Networks 1994, San Diego, California, (1994) I-318, I-323
87. Pao, Y.-H.: Adaptive Pattern Recognition and Neural Networks. Addison-Wesley, Reading, MA (1989)
88. Peretto, P.: An Introduction to the Modelling of Neural Networks. Cambridge University Press, Cambridge (1992)
89. Rataj, T. and J. Schindler: Identification of bacteria by a multilayer neural network. Binary 3 (1991) 159-164
90. Rawlings, J. O.: Applied Regression Analysis. Wadsworth & Brooks, Pacific Grove, CA (1988)
91. Reed, R.: Pruning algorithms - a survey. IEEE Trans. Neural Networks 4 (1993) 740-747
92. Richard, D., C. Cachet, D. Cabrol-Bass, and T. P. Forrest: Neural network approach to structural feature recognition from infrared-spectra. J. Chem. Inf. Comput. Sci. 33 (1993) 202-210
93. Rumelhart, D. E., J. L. McClelland, and The PDF Research Group: Parallel Distributed Processing. Experiments in the Microstructure of Cognition. MIT Press, Cambridge, Massachusetts (1986)
94. H.-R. Schulten and R. P. Lattimer: Applications of mass-spectrometry to polymers. Mass Spec. Rev. 3 (1984) 231-315
95. Seasholtz, M. B. and B. R. Kowalski: The parsimony principle applied to multivariate calibration. Anal. Chim. Acta 277 (1993) 165-177
96. Shadmehr, R., D. Angell, P. B. Chou, G. S. Oebrlein, and R. S. Jaffe: Principal component analysis of optical-emission spectroscopy and mass-spectrometry - application to reactive ion etch process parameter-estimation using neural networks. J. Electrochem. Soc. 139 (1992) 907-914
97. Simpson, P. K.: Artificial Neural Systems. Pergamon Press, Oxford (1990)
98. Smith, P. B. and A. P. Snyder: Characterization of bacteria by quartz tube pyrolysis-gas chromatography ion trap mass-spectrometry. J. Anal. Appl. Pyrol. 24 (1992) 23-38
99. Smits, J. R. M., P. Shoenmaker, A. Stehmann, F. Sijstermans, and G. Kateman: Interpretation of infrared spectra with modular neural-network systems. Chemom. Intell. Lab. Syst. 18 (1993) 27-39
100. Taavitsainen, V. M. and P. Korhonen: Nonlinear data analysis with latent variable. Chemom. Intell. Lab. Sys. 14 (1992) 185-194
101. D. Van de Meenl, J. W. de Leeuw, P. A. Schenck, W. Windig, and J. Haverkamp: Quantitative analysis of polymer mixtures by pyrolysis mass spectrometry/discriminant analysis. J. Anal. Appl. Pyrol. 4 (1982) 133-142
102. Wasserman, P. D.: Neural Computing: Theory and Practice, Van Nostrand Reinhold, New York (1989)
103. Wasserman, P. D. and R. M. Oetzel: Neural Source: the Bibliographic Guide to Artificial Neural Networks. Van Nostrand Reinhold, New York (1989)
104. Weigend, A. S., D. E. Rumelhart, and B. A. Huberman: Generalization by weight-elimination with application to forecasting. In: Neural Information Processing Systems 3, pp. 875-882 (eds., R. P. Lippmann, E. Moody and D. S. Touretzky). Morgan Kaufmann, San Mateo, CA (1991)
105. Weiss, S. H. and C. A. Kulikowski: Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Networks, Machine Learning, and Expert Systems. Morgan Kaufmann Publishers, California (1991)
106. Werhos, P. J.: Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. M. Sc. Thesis, Harvard University, USA (1974)
107. Werbos, P. J.: The Roots of Back-Propagation: From Ordered Derivatives to Neural Networks and Political Forecasting. John Wiley & Sons, Chichester (1993)
108. White, H.: Connectionist nonparametric regression - multilayer feedforward networks can learn arbitrary mappings. Neural Networks 3 (1990) 535-549
109. Windig, W., J. Haverkamp, and P. G. Kistemaker: Interpretation of a set of pyrolysis mass spectra by discriminant analysis and graphical rotation. Anal. Chem. 55 (1983) 387-391
110. Wold, S.: Nonlinear partial least squares modelling. II. Spline inner relation. Chemom. Intell. Lab. Sys. 14 (1992) 71-84
111. Wold, S., N. Kettaneh-Wold, and B.Skagerberg: Nonlinear PLS modelling. Chemom. Intell. Lab. Sys. 7 (1989) 53-65
112. Wythoff, B. J.: Orthonet: Orthogonal latent variable neural network. Chemom. Intell. Lab. Sys. 20 (1993) 129-148
113. Zupan, J. and J. Gasteiger: Neural Networks for Chemists: An Introduction. VCH Verlagsgesellschaft, Weinheim (1993)
114. Zupan, J., M. Novic, X. Li, and J. Gasteiger: Classification of multicomponent analytical data of olive oils using different neural networks. Anal. Chim. Acta 292 (1994) 219-234