Joint neural network interpretation of infrared and mass spectra

Journal of Chemical Information and Computer Sciences

Volumn 36, Issue 2, 1996, Pages 249-257

  Klawun, Christoph ^a,b   Wilkins, Charles L ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b Braun   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0001358121     PISSN: 00952338     EISSN: None     Source Type: Journal    
DOI: 10.1021/ci9501002     Document Type: Article

References (40)

1. Rumelhart, D. E.; McClelland, J. L. Parallel Distributed Processing-Explorations in the Microstructure of Cognition. The MIT Press: Cambridge, MA, 1986; Vol. 1, pp 318-362.
2. Rumelhart, D. E.; Hinten, G. E.; Williams, R. J. Learning representations by back-propagating errors. Nature 1986, 323, 533-536.
3. Robb, E. W.; Munk, M. E. A neural network approach to infrared spectrum interpretation. Mikrochim. Acta [Wien] 1990, 1, 131-155.
4. Munk, M. E.; Madison, M. S.; Robb. E. W. Neural network models for infrared spectrum interpretation. Mikrochim. Acta [Wien] 1991, 11, 505-514.
5. Weigel, U.-M.; Herges, R. Automalic interpretation of infrared spectra: Recognition of aromatic substitution patterns using neural networks. J. Chem. Inf. Comput. Sci. 1992, 32, 723-731.
6. Meyer, M.; Weigelt, T. Interpretation of infrared spectra by artificial neural networks. Anal. Chim. Acta 1992, 265, 183-190.
7. Ricard, D.; Cachet, C.; Cabrol-Bass, D. Neural network approach to structure feature recognition from infrared spectra. J. Chem. Inf. Comput. Sci. 1993, 33, 202-210.
8. Tanabe, K.; Tamura, T.; Uesaka, H. Neural network system for the identification of infrared spectra. Anal. Sci. 1991, 7, 717-718.
9. Tanabe, K.; Tamura, T.; Uesaka, H. Neural network system for the identification of infrared spectra. Appl. Spectrosc. 1992, 46, 807-810.
10. Klawun, C.; Wilkins, C. L. Neural network assisted rapid screening of large infrared spectral databases. Anal. Chem. 1995, 67, 374-378.
11. Fessenden, R. J.; Györgyi, L. Identifying functional groups in IR spectra using an artificial neural network. J. Chem. Soc., Perkin Trans. 2 1991, 1755-1762.
12. Van Est, Q. C.; Schoenmakers, P. J.; Smits, J. R. M.; Nijssen, W. P. M. Practical implementation of neural networks for the interpretation of infrared spectra. Vib. Spectrosc. 1993, 4, 263-272.
13. Smits, J. R. M.; Schoenmakers. P.; Stehmann, A.; Sijstermans, F.; Kateman, G. Interpretation of infrared spectra with modular neural-network systems. Chemom. Intell. Lab. Syst. 1993, 18, 27-39.
14. Curry, B.; Rumelhart, D. E. MSnet: A neural network which classifies mass spectra. Tetrahedron Comput. Methodol. 1990, 3, 213-237.
15. Kwok, K.-S.; Venkataraghavan, R.; McLafferty, F. W. Computer-aided interpretation of mass spectra. III. A self-training interpretive and retrieval system. J. Am. Chem. Soc. 1973, 95, 4185-7194.
16. Werther, W.; Lohninger, H.; Stancl, F.; Varmuza, K. Classification of mass spectra. A comparison of yes/no classification methods for the recognition of simple structural properties. Chemom. Intell. Lab. Syst. 1994, 22, 63-76.
17. Sellers, J.; York, W.; Albersheim, P.; Darvill, A.; Meyer, B. Identification of the mass spectra of partially methylated alditol acetates by artificial neural networks. Carbohydrate Res. 1990, 207, c1-c5.
18. Lohninger, H. Classification of mass spectral data using neural networks. Software Development in Chemistry 5; Gmehling, J., Ed.; Springer-Verlag: Berlin, 1991; pp 159-168.
19. Lohninger, H.; Stand, F. Comparing the performance of neural networks to well-established methods of multivariate data analysis: The classification of mass spectral data. Fresenius J. Anal. Chem. 1992, 344, 186-189.
20. Harrington, P. B. Temperature-constrained backpropagation neural networks. Anal. Chem. 1994, 66, 802-807.
21. Goodacre, R.; Kell, D. B.; Bianchi, G. Rapid assessment of the adulteration of virgin olive oils by other seed oils using pyrolysis mass spectrometry and artificial neural networks. J. Sci. Food Agric. 1993, 63, 297-307.
22. Chun, J.; Atalan, E.; Kim, S.-B.; Kim, H.-J.; Hamid, M. E.; Trujillo, M. E.; Magee, J. G.; Manfio, G. P.; Ward, A. C.; Goodfellow, M. Rapid identification of streptomycetes by artificial neural network analysis of pyrolysis mass spectra. FEMS Microbiol. Lett. 1993, 114, 115-120.
23. Goodacre, R.; Neal, M. J.; Kell, D. B. Rapid and quantitative analysis of the pyrolysis mass spectra of complex binary and tertiary mixtures using multivariate calibration and artificial neural networks. Anal. Chem. 1994, 66, 1070-1085.
24. Wilkins, C. L. Linked GC/IR/MS Anal. Chem. 1987, 59, 571A-581A.
25. Curry, B. An expert system for organic structure determination. Artificial Intelligence Applications in Chemistry, Pierce, T. H., Hohne, B. A., Eds.; ACS Symposium Series 306; American Chemical Society: Washington, DC, 1986; pp 350-364.
26. Cooper, J. R.; Wilkins, C. L. Utilization of spectrometric information in linked gas chromatograph - Fourier transform infrared spectroscopy - mass spectrometry. Anal. Chem. 1989, 61, 1571-1577.
27. Williams, S. S.; Lam, R. B.; Isenhour, T. L. Search system for infrared and mass spectra by factor analysis and eigenvector projection. Anal. Chem. 1983, 55, 1117-1121.
28. Gasteiger, J.; Li, X.; Simon, V.; Novič, M.; Zupan, J. Neural nets for mass and vibrational spectra. J. Mol. Struct. 1993, 292, 141-160.
29. Munk, M. E.; Madison, M. S.; Robb, E. W. The neural network as a tool for multispectral interpretation. J. Chem. Inf. Comput. Sci. 1996, 36, 231-238.
30. Klawun, C.; Wilkins, C. L. A novel algorithm for local minimum escape in back-propagation neural networks: Application to the interpretation of matrix isolation infrared spectra. J. Chem. Inf. Comput. Sci. 1994, 34, 984-993.
31. Zupan, J. Algorithms for Chemists; John Wiley & Sons: Chichester, 1989.
32. Novič, M.; Zupan, J. Investigation of infrared spectra-structure correlation using Kohonen and counterpropagation neural networks. J. Chem. Inf. Comput. Sci. 1995, 35, 454-466.
33. Scott, D. R. Expert system for estimates of molecular weights of volatile organic compounds from low-resolution mass spectra. Anal. Chim. Acta 1991, 246, 391-403.
34. Scott, D. R. Improved method for estimating molecular weights of volatile organic compounds from low resolution mass spectra. Chemom. Intell. Lab. Syst. 1991, 12, 189-200.
35. Scott, D. R. Rapid and accurate method for estimating molecular weights of organic compounds from low resolution mass spectra. Chemom. Intell. Lab. Syst. 1992, 16, 193-202.
36. Scott, D. R.; Levitsky, A.; Stein, S. E. Large scale evaluation of a pattern recognition/ expert system for mass spectral molecular weight estimation. Anal. Chim. Acta 1993, 278, 137-147.
37. Scott, D. R. Empirical pattern recognition/expert system for molecular weight estimation of low resolution mass spectra. Anal. Chim. Acta 1994, 285, 209-222.
38. Scott, D. R. Pattern recognition/expert system for identification of toxic compounds from low resolution mass spectra. Chemom. Intell. Lab. Syst. 1994, 23, 351-364.
39. Hertz, H. S.; Hites, R. A.; Biemann, K. Identification of mass spectra by computer searching a file of known spectra. Anal. Chem. 1971, 43, 681-691.
40. Soltzberg, L. J.; Wilkins, C. L.; Kaberline, S. L.: Lam, T. F.; Brunner, T. R. Evaluation and comparison of pattern classifiers for chemical applications. J. Am. Chem. Soc. 1976, 98, 7139-7144.