{"title":"牡蛎养殖区热裂解-质谱法快速分类。","authors":"M. Cardinal, C. Viallon, C. Thonat, J. Berdagué","doi":"10.1051/ANALUSIS:2000150","DOIUrl":null,"url":null,"abstract":"Current concern for the safety and traceability of food, as well as the desire of oyster farmers, for marketing reason, to emphasise the geographical origin of their production, requires new methods to make possible a real product identification. I n this study, 181 oyster samples were analysed to determine their origin area. These samples were collected in nine French rear- ing 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 a rti- ficial 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.","PeriodicalId":8221,"journal":{"name":"Analusis","volume":"6 1","pages":"825-829"},"PeriodicalIF":0.0000,"publicationDate":"2000-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Pyrolysis-mass spectrometry for rapid classification of oysters according to rearing area.\",\"authors\":\"M. Cardinal, C. Viallon, C. Thonat, J. Berdagué\",\"doi\":\"10.1051/ANALUSIS:2000150\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Current concern for the safety and traceability of food, as well as the desire of oyster farmers, for marketing reason, to emphasise the geographical origin of their production, requires new methods to make possible a real product identification. I n this study, 181 oyster samples were analysed to determine their origin area. These samples were collected in nine French rear- ing 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 a rti- ficial 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.\",\"PeriodicalId\":8221,\"journal\":{\"name\":\"Analusis\",\"volume\":\"6 1\",\"pages\":\"825-829\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2000-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Analusis\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1051/ANALUSIS:2000150\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Analusis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1051/ANALUSIS:2000150","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Pyrolysis-mass spectrometry for rapid classification of oysters according to rearing area.
Current concern for the safety and traceability of food, as well as the desire of oyster farmers, for marketing reason, to emphasise the geographical origin of their production, requires new methods to make possible a real product identification. I n this study, 181 oyster samples were analysed to determine their origin area. These samples were collected in nine French rear- ing 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 a rti- ficial 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.