Efficient feature selection for PTR-MS fingerprinting of agroindustrial products

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5164 LNCS, Issue PART 2, 2008, Pages 42-51

  Granitto, Pablo M ^a   Biasioli, Franco ^b   Furlanello, Cesare ^c   Gasperi, Flavia ^b  

^a UNIVERSIDAD NACIONAL DE ROSARIO   (Argentina)

^b Agrifood Quality Department   (Italy)

^c ITC IRST   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; BACKPROPAGATION; CHARGE TRANSFER; CHEMICAL REACTIONS; ERROR ANALYSIS; FEATURE EXTRACTION; HIGH PERFORMANCE LIQUID CHROMATOGRAPHY; LEARNING SYSTEMS; MASS SPECTROMETRY; PROTON TRANSFER; RECURSIVE FUNCTIONS; SUPPORT VECTOR MACHINES;

ARTIFICIAL NEURAL NETWORKS; DATA-SETS; EFFICIENT FEATURE SELECTION; FEATURE SELECTION; GENERALIZATION ERRORS; INDUSTRIAL PRODUCTS; INTERNATIONAL CONFERENCES; MULTI-CLASS PROBLEMS; PROTON-TRANSFER REACTIONS; RANDOM FORESTS; RECURSIVE FEATURE ELIMINATION; SVM-RFE;

NEURAL NETWORKS;

EID: 51849112769     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-87559-8_5     Document Type: Conference Paper

References (21)

1. Hansel, A., Jordan, A., Holzinger, R., Prazeller, P., Vogel, W., Lindinger, W.: Proton transfer reaction mass spectrometry: on-line trace gas analysis at the ppb level. Int. J. Mass. Spectrom. Ion Procs. 149/150, 609-619 (1995)
2. Lindinger, W., Hansel, A., Jordan, A.: On-line monitoring of volatile organic compounds at ppt level by means of Proton-Transfer-Reaction Mass Spectrometry (PTR-MS): Medical application, food control and environmental research. Int. J. Mass. Spectrom. Ion Procs. 173, 191-241 (1998)
3. Biasioli, F., Gasperi, F., Aprea, E., Colato, L., Boscaini, E., Märk, T.D.: Fingerprinting mass spectrometry by PTR-MS: heat treatment vs. pressure treatments of red orange juice - a case study. Int. J. Mass. Spectrom, 223-224, 343-353 (2003)
4. Biasioli, F., Gasperi, F., Aprea, E., Mott, D., Boscaini, E., Mayr, D., Märk, T.D.: Coupling Proton Transfer Reaction-Mass Spectrometry with Linear Discriminant Analysis: a Case Study. J. Agr. Food Chem. 51, 7227-7233 (2003)
5. Boscaini, E., Van Ruth, S., Biasioli, F., Gasperi, F., Märk, T.D.: Gas Chromatography-Olfactometry (GC-O) and Proton Transfer Reaction-Mass Spec-trometry (PTR-MS). Analysis of the Flavor Profile of Grana Padano, Parmigiano Reggiano, and Grana Trentino Cheeses. J. Agr. Food Chem. 51, 1782-1790 (2003)
6. Biasioli, F., Gasperi, F., Aprea, E., Endrizzi, I., Framondino, V., Marini, F., Mott, D., Märk, T.D.: Correlation of PTR-MS spectral fingerprints with sensory characterisation of flavour and odour profile of Trentingrana cheese. Food Qual. Prefer. 17, 63-75 (2006)
7. Guyon, I., Elisseeff, A.: An Introduction to Variable and Feature Selection. J. Mach. Learn. Res. 3, 1157-1182 (2003)
8. Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artif. Intell. 97, 273-324 (1996)
9. Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene Selection for Cancer Classification using Support Vector Machines. Mach. Learn. 46, 389-422 (2002)
10. Ambroise, C., McLachlan, G.: Selection bias in gene extraction on the basis of microarray gene-expression data. P. Natl. Acad. Sci. USA 99, 6562-6566 (2002)
11. Furlanello, C., Serafini, M., Merler, S., Jurman, G.: Entropy-Based Gene Ranking without Selection Bias for the Predictive Classification of Microarray Data. BMC Bioinformatics 4, 54 (2003)
12. Ramaswamy, S., et al.: Multiclass cancer diagnosis using tumor gene expression signatures. P. Natl. Acad. Sci. USA 98, 15149-15154 (2001)
13. Li, H., Ung, C.Y., Yap, C.W., Xue, Y., Li, Z.R., Cao, Z.W., Chen, Y.Z.: Prediction of Genotoxicity of Chemical Compounds by Statistical Learning Methods. Chem. Res. Toxicol. 18, 1071-1080 (2005)
14. Granitto, P.M., Furlanello, C., Biasioli, F., Gasperi, F.: Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products. Chemometr. Intell. Lab. 83, 83-90 (2006)
15. Breiman, L.: Random Forests. Mach. Learn. 45, 5-32 (2001)
16. Breiman, L.: Heuristics of instability and stabilization in model selection. Ann. Stat. 24, 2350-2383 (1996)
17. Hsu, C.-W., Lin, C.-J.: A comparison of methods for multi-class support vector machines. IEEE T. Neural Networ. 13, 415-425 (2002)
18. Allwein, E., Schapire, R., Singer, Y.: Reducing Multiclass to Binary: A unified Approach for Margin Classifiers. J. Mach. Learn. Res. 1, 113-141 (2000)
19. Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
20. Huang, T.-M., Kecman, V.: Gene extraction for cancer diagnosis by support vector machines. Artif. Intell. Med. 35, 185-194 (2005)
21. Gasperi, F., Biasioli, F., Framondino, V., Endrizzi, I.: Ruolo dellánalisi sensori-ale nella definizione delle caratteristiche dei prodotti tipici: lésempio dei formaggi trentini / The role of sensory analysis in the characterization of traditional products: the case study of the cheese from Trentino. Sci. Tecn. Latt.-Cas. 55, 345-364 (2004)