Abstract
Current concern for the safety and traceability of food, as well as the desire of oyster farmers, for marketing reason, to emphasize the geographical origin of their production, requires new methods to make possible a real product identification. In this study, 181 oyster samples were analyzed to determine their origin area. These samples were collected in nine French rearing areas at four different times of the year (spring, summer, and the beginning and end of autumn) and from four to eight sites in each area to provide a variability parameter. Analysis of fingerprints after Curie point pyrolysis-mass spectrometry, by an artificial neural network gave a mean classification rate of 89 %. Although the technique requires further improvements, it appears to be a useful discriminative tool for rapid identification of an oyster production area.
Recommended Citation
J. Krupcík et al., "Pyrolysis-mass Spectrometry for Rapid Classification of Oysters According to Rearing Area," Analusis, vol. 28, no. 9, pp. 825 - 829, EDP Sciences; Wiley-VCH, Jan 2000.
The definitive version is available at https://doi.org/10.1051/analusis:2000150
Department(s)
Chemistry
Publication Status
Free Access
Keywords and Phrases
Classification; Neural network; Oyster; Production area; Pyrolysis-mass spectrometry
International Standard Serial Number (ISSN)
0365-4877
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 EDP Sciences, Wiley -VCH, All rights reserved.
Publication Date
01 Jan 2000
