Improvement of classification using robust soft classification rules for near-infrared reflectance spectral data

Chemometrics and Intelligent Laboratory Systems

Volumn 109, Issue 1, 2011, Pages 86-93

  Daszykowski, M ^a   Orzel, J ^a   Wrobel, M S ^a   Czarnik Matusewicz, H ^b   Walczak, B ^a  

^a UNIVERSITY OF SILESIA   (Poland)

^b WROCLAW MEDICAL UNIVERSITY   (Poland)

Author keywords

Chemometrics; Feeding regime; Identification; Liver; Outliers; SIMCA

Indexed keywords

FAT; LARD; PROTEIN;

ANIMAL FOOD; ANIMAL TISSUE; ARTICLE; BIOCHEMICAL COMPOSITION; CALCULATION; CLASSIFICATION ALGORITHM; CONTROLLED STUDY; FAT CONTENT; LIVER; MATHEMATICAL MODEL; MOISTURE; NEAR INFRARED REFLECTANCE SPECTROSCOPY; NONHUMAN; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; PROTEIN CONTENT; RAPESEED; SENSITIVITY AND SPECIFICITY; VALIDATION PROCESS;

EID: 80053643522     PISSN: 01697439     EISSN: 18733239     Source Type: Journal    
DOI: 10.1016/j.chemolab.2011.08.004     Document Type: Article

References (38)

1. Berzaghi P., Dalle Zotte A., Jansson L.M., Andrighetto I. Near-infrared reflectance spectroscopy as a method to predict chemical composition of breast meat and discriminate between different n-3 feeding sources. Poultry Science 2005, 84:128-136.
2. Berzaghi P., Segato S., Cozzi G., Andighetto I. Mid and near infrared spectroscopy to identify illegal treatments in beef cattle. Veterinary Research Communications 2006, 20:109-112.
3. Abbas O., Fernández Pierna J.A., Codony R., von Holst C., Baeten V. Assessment of the discrimination of animal fat by FT-Raman spectroscopy. Journal of Molecular Structure 2009, 924-926:294-300.
4. Bellorini S., Strathmann S., Baeten V., Fumière O., Berben G., Tirendi S., von Holst C. Discriminating animal fats and their origins: assessing the potentials of Fourier transform infrared spectroscopy, gas chromatography, immunoassay and polymerase chain reaction techniques. Analytical and Bioanalytical Chemistry 2005, 382:1073-1083.
5. González-Martín I., González-Pérez C., Hernández-Méndez J., Alvarez-García N. Mineral analysis (Fe, Zn, Ca, Na, K) of fresh Iberian pork loin by near infrared reflectance spectrometry-determination of Fe, Na and K with a remote fibre-optic reflectance probe. Analytica Chimica Acta 2002, 468:293-301.
6. González-Martín I., González-Pérez C., Hernández-Méndez J., Alvarez-García N., Hernández Andaluz J.-L. On-line non-destructive determination of proteins and infiltrated fat in Iberian pork loin by near infrared spectrometry with a remote reflectance fibre optic probe. Analytica Chimica Acta 2002, 453:281-288.
7. Cava R., Ruiz J., López-Bote C., Martin L., García C., Ventanas J., Antequera T. Influence of finishing diet on fatty acid profiles of intramuscular lipids, triglycerides and phospholipids in muscles of the Iberian pig. Meat Science 1997, 45:263-270.
8. Fumière O., Sinnaeve G., Dardenne P. Attempted authentication of cut pieces of chicken meat from certified production using near infrared spectroscopy. Journal of Near Infrared Spectroscopy 2000, 8:27-34.
9. Molette C., Berzaghi P., Dalle Zotte A., Remignon H., Babile R. The use of near-infrared reflectance spectroscopy in the prediction of the chemical composition of goose fatty liver. Poultry Science 2001, 80:1625-1629.
10. Cozzolino D., Chree A., Scaife J.R., Murray I. Usefulness of near-infrared reflectance (NIR) spectroscopy and chemometrics to discriminate fishmeal batches made with different fish species. Journal of Agricultural and Food Chemistry 2005, 53:4459-4463.
11. Cozzolino D., Vadell A., Galietta G., Barlocco N. Combining visible and near-infrared spectroscopy with chemometrics to trace muscles from an autochthonous breed of pig produced in Uruguay: a feasibility study. Analytical and Bioanalytical Chemistry 2006, 385:931-936.
12. Cozzolino D., Murray I. Identification of animal meat muscles by visible and near infrared reflectance spectroscopy. LWT Food Science and Technology 2004, 37:447-452.
13. Prevolnik M., Candek-Potokar M., Škrojanc D. Ability of NIR spectroscopy to predict meat chemical composition and quality-a review. Czechoslovak Journal Animal Science 2004, 49:500-510.
14. Duda R.O., Hart P.E., Stork D.G. Pattern Classification 2001, John Wiley & Sons, Toronto.
15. Naes T., Isaksson T., Fearn T., Davies T. A User Friendly Guide to Multivariate Calibration and Classification 2004, NIR Publications, Chichester.
16. Wold S. Pattern recognition by means of disjoint principal components models. Pattern Recognition 1976, 8:127-139.
17. Forina M., Casale M., Oliveri P. Application of chemometrics to food chemistry. Comprehensive Chemometrics 2009, 75-128. Elsevier, Amsterdam. S.D. Brown, R. Tauler, B. Walczak (Eds.).
18. Forina M., Lanteri S., Armiano C. Chemometrics in food chemistry. Chemometrics and Species Identification 1987, 93-143. Akademie-Verlag, Berlin. C. Armanino (Ed.).
19. Berrueta L.A., Alonso-Salces R.M., Héberger K. Supervised pattern recognition in food analysis. Journal of Chromatography A 2007, 1158:196-214.
20. Frosch Moller S., von Frese J., Bro R. Robust methods for multivariate data analysis. Journal of Chemometrics 2005, 19:549-563.
21. Daszykowski M., Kaczmarek K., Vander Heyden Y., Walczak B. Robust statistics in data analysis-a review. Basic concepts. Chemometrics and Intelligent Laborary Systems 2007, 85:203-219.
22. Daszykowski M., Kaczmarek K., Stanimirova I., Vander Heyden Y., Walczak B. Robust SIMCA-bounding influence of outliers. Chemometrics and Intelligent Laborary Systems 2007, 87:121-129.
23. Vanden Branden K., Hubert M. Robust classification in high dimensions based on the SIMCA method. Chemometrics and Intelligent Laborary Systems 2005, 79:10-21.
24. Ziólkowski T., Korniewicz A., Czarnik-Matusewicz H., Paleczek B. Feed efficiency of rapeseed in complete mixtures for fatteners. Roczniki Naukowe Zootechniki 1995, 22:215-234.
25. Kennard R.W., Stone L.A. Computer aided design of experiments. Technometrics 1969, 11:137-148.
26. Daszykowski M., Walczak B., Massart D.L. Representative subset selection. Analytica Chimica Acta 2002, 468:91-103.
27. Wold S., Esbensen K., Geladi P. Principal component analysis. Chemometrics and Intelligent Laborary Systems 1987, 2:37-52.
28. Rousseeuw P.J., van Zomeren B.C. Unmasking multivariate outliers and leverage points. Journal of the American Statistical Association 1990, 85:633-639.
29. Hubert M., Rousseeuw P.J., Vanden Branden K. ROBPCA: a new approach to robust principal component analysis. Technometrics 2005, 47:64-79.
30. Pomerantsev A.L. Acceptance areas for multivariate classification derived by projection methods. Journal of Chemometrics 2008, 22:601-609.
31. Stanimirova I., Walczak B., Massart D.L., Simeonov V. A comparison between two robust PCA algorithms. Chemometrics and Intelligent Laborary Systems 2004, 71:83-95.
32. Locantore N., Marron J.S., Simpson D.G., Tripoli N., Zhang J.T., Cohen K.L. Robust principal components for functional data. TEST 1999, 8:1-28.
33. Hössjer O., Croux C. Generalizing univariate signed rank statistics for testing and estimating a multivariate location parameter. Non-parametric Statistics 1995, 4:293-308.
34. Rousseeuw P.J., Croux C. Alternatives to the median absolute deviation. Journal of the American Statistical Association 1993, 88:1273-1283.
35. Stanimirova I., Daszykowski M., Walczak B. Dealing with missing values and outliers in principal component analysis. Talanta 2007, 72:172-178.
36. Stanimirova I., Walczak B. Classification of data with missing elements and outliers. Talanta 2008, 76:602-609.
37. Barnes R.J., Dhanoa M.S., Lister S.J. Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Applied Spectroscopy 1989, 43:772-777.
38. Rinnan A., van den Berg F., Engelsen S.B. Review of the most common pre-processing techniques for near-infrared spectra. TrAC 2009, 28:1201-1222.