Discrimination of meat paté s according to the animal species by means of near infrared spectroscopy and chemometrics

Restaino, E, Fassio, A and Cozzolino, D 2011, 'Discrimination of meat paté s according to the animal species by means of near infrared spectroscopy and chemometrics', CyTA - Journal of Food, vol. 9, no. 3, pp. 210-213.


Document type: Journal Article
Collection: Journal Articles

Title Discrimination of meat paté s according to the animal species by means of near infrared spectroscopy and chemometrics
Author(s) Restaino, E
Fassio, A
Cozzolino, D
Year 2011
Journal name CyTA - Journal of Food
Volume number 9
Issue number 3
Start page 210
End page 213
Total pages 4
Publisher Taylor & Francis Inc.
Abstract Commercial meat paté samples, comprised of 100% pork (n = 7), 100% beef (n = 5) meat, and binary mixtures (beef and pork, w/w) (n = 18) were used. Fresh samples were analysed in a scanning spectrophotometer NIRSystems 6500 in reflectance mode (1100-2500 nm). Principal component analysis (PCA) and stepwise linear discriminant analysis (SLDA) were used to classify samples according to the animal species based on their near infrared reflectance (NIR) spectra. Full cross validation was used as validation method when classification models were developed. Both beef and pork paté samples were classified correctly (100%) while binary mixture samples only achieved 72% of correct classification using SLDA technique. The results demonstrated the usefulness of NIR spectra combined with chemometrics as an objective and rapid method to classify paté samples according to meat type. Nevertheless, NIR spectroscopic methods might provide initial screening in the food chain and enable more costly methods to be used more efficiently.
Subject Food Sciences not elsewhere classified
Sensor Technology (Chemical aspects)
Keyword(s) Beef
Near infrared
Paté
Pork
Principal component analysis
Spectroscopy
Stepwise linear discriminant analysis
DOI - identifier 10.1080/19476337.2010.512396
Copyright notice © 2011 Taylor & Francis
ISSN 1947-6337
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