Artificial neural networks in chemometrics: History, examples and perspectives

Microchemical Journal

Volumn 88, Issue 2, 2008, Pages 178-185

  Marini, F ^a   Bucci, R ^a   Magri A L ^a   Magri A D ^a  

^a SAPIENZA UNIVERSITY OF ROME   (Italy)

Author keywords

Artificial neural networks (ANNs); Chemometrics; Classification; Modeling; Nonlinear regression

Indexed keywords

EID: 39149085365     PISSN: 0026265X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.microc.2007.11.008     Document Type: Article

References (30)

1. McCulloch W.S., and Pitts W.H. A logical calculus of the ideas imminent in nervous activity. Bull. Math. Biophys. 5 (1943) 115-133
2. Hebb D.O. The Organization of Behavior: A Neuropsychological Theory (1949), John Wiley Ed., New York
3. Rosenblatt F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65 6 (1958) 386-408
4. Rosenblatt F. Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms (1962), Spartan Books, Washington DC ASIN: B0006AXUII
5. B. Widrow, M.E. Hoff, Adaptive switching circuits, IRE WESCON Convention Record, 4 (1960) 96-104. Reprinted in J.A. Anderson, E. Rosenfeld (eds), Neurocomputing: foundations of research, MIT Press Ed., Cambridge (1988), ISBN: 0-262-01097-6, pp.123-134.
6. Minsky M., and Papert S. Perceptrons: An Introduction to Computational Geomety. 3rd ed. (1987), MIT Press Ed., Cambridge. 0-262-631113
7. Hopfield J. Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. U. S. A. 79 8 (1982) 2554-2558
8. Bishop C.M. Neural Networks for Pattern Recognition (1996), Clarendon Press, Oxford. 978-0198538646
9. Zupan J., and Gasteiger J. Neural Networks in Chemistry and Drug Design. 2nd ed. (1999), Wiley VCH Ed., Weinheim. 978-3527297795
10. Dreyfus G. Neural networks: an overview. In: Dreyfus G. (Ed). Neural Networks - Methodology and Applications (2005), Springer, Heidelberg. 978-3540229803 1-84
11. Hornik K., Stinchcombe M., and White H. Multilayer feedforward networks are universal approximators. Neural Netw. 2 5 (1989) 359-366
12. Hornik K., Stinchcombe M., and White H. Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks. Neural Netw. 3 5 (1990) 551-560
13. Hornik K. Approximation capabilities of multilayer feedforward networks. Neural Netw. 4 2 (1991) 251-257
14. Rumelheart D.E., Hinton G.E., and Williams R.J. Learning internal representations by error back-propagation. In: Rumelheart D.E., and McClelland J.L. (Eds). Parallel Distributed Processing. Explorations in the Microstructure of Cognition (1986), MIT Press Ed., Cambridge. 978-0262181204 318-362
15. Rumelheart D.E., Durbin R., Golden R., and Chauvin Y. Backpropagation: the basic theory. In: Chauvin Y., and Rumelheart D. (Eds). Backpropagation: Theory, Architectures and Applications (1995), Lawrence Erlbaum, Hillsdale. 978-0805812596
16. Rumelhart D.E., Hinton G.E., and Williams R.J. Learning internal representations by back-propagating errors. Nature 323 6088 (1986) 533-536
17. T. Kohonen, Self Organizing Maps, 3rd ed, Springer, New York (2001) ISBN: 3-540-67921-9.
18. Zupan J., Novič M., and Ruisánchez I. Kohonen and counterpropagation artificial neural networks in analytical chemistry. Chemometr. Intell. Lab. 38 1 (1997) 1-23
19. Hecht-Nielsen R. Counterpropagation networks. Appl. Opt. 26 23 (1987) 4979-4984
20. Stork D.G. Counterpropagation networks: adaptive hierarchical networks for near optimal mappings. Synapse Connect. 1 2 (1988) 9-17
21. Marini F., Magrì A.L., Balestrieri F., Fabretti F., and Marini D. Supervised pattern recognition applied to the discrimination of the floral origin of Italian honey samples. Anal. Chim. Acta 515 1 (2004) 117-125
22. Simon V., Gasteiger J., and Zupan J. A combined application of two different neural network types for the prediction of chemical reactivity. J. Am. Chem. Soc. 115 20 (1993) 9148-9159
23. F. Marini, A.L. Magrì, R. Bucci, A.D. Magrì, submitted for publication. Use of different artificial neural networks to resolve binary blends of monocultivar Italian olive oils, Anal. Chim. Acta. 599 (2) (2007) 232-240.
24. Kennard R.W., and Stone L.A. Computer-aided design of experiments. Technometrics 11 1 (1969) 137-148
25. Marini F., Balestrieri F., Bucci R., Magrì A.L., and Marini D. Supervised pattern recognition to authenticate Italian olive oil varieties. Chemometr. Intell. Lab. 73 1 (2004) 85-93
26. Marini F., Magrì A.L., Marini D., and Balestrieri F. Characterization of the lipid fraction of Niger seeds (Guizotia Abyssinica Cass.) from different regions of Ethiopia and India and chemometric authentication of their geographical origin. Eur. J. Lipid Sci. Technol. 105 11 (2003) 697-704
27. Marini F., Balestrieri F., Bucci R., Magrì A.L., and Marini D. Supervised pattern recognition to discriminate the geographical origin of rice bran oils: a first study. Microchem. J. 74 3 (2003) 239-248
28. Marini F., Zupan J., and Magrì A.L. On the use of counterpropagation artificial neural networks to characterize Italian rice varieties. Anal. Chim. Acta 510 2 (2004) 231-240
29. Marini F., Zupan J., and Magrì A.L. Class-modeling using Kohonen artificial neural networks. Anal. Chim. Acta 544 1-2 (2005) 306-314
30. Marini F., Magrì A.L., and Bucci R. Multilayer feed-forward neural networks for class-modeling. Chemometr. Intell. Lab. 88 1 (2007) 118-124