繁殖
小马驹
主成分分析
食品科学
数学
化学
动物科学
生物
统计
遗传学
作者
Ainara López-Maestresalas,K. Insausti,Carmen Jarén,Claudia Pérez-Roncal,O. Urrutia,María José Beriáin Apesteguía,Silvia Arazuri Garín
出处
期刊:Food Control
[Elsevier]
日期:2019-04-01
卷期号:98: 465-473
被引量:66
标识
DOI:10.1016/j.foodcont.2018.12.003
摘要
The aim of this work was to investigate the feasibility of near-infrared spectroscopy (NIRS), combined with chemometric techniques, to detect fraud in minced lamb and beef mixed with other types of meats. For this, 40 samples of pure lamb and 30 samples of pure beef along with 160 samples of mixed lamb and 156 samples of mixed beef at different levels: 1-2-5-10% (w/w) were prepared and analyzed. Spectral data were pre-processed using different techniques and explored by a Principal Component Analysis (PCA) to find out differences among pure and mixed samples. Moreover, a PLS-DA was carried out for each type of meat mixture. Classification results between 78.95 and 100% were achieved for the validation sets. Better rates of classification were obtained for samples mixed with pork meat, meat of Lidia breed cattle and foal meat than for samples mixed with chicken in both lamb and beef. Additionally, the obtained results showed that this technology could be used for detection of minced beef fraud with meat of Lidia breed cattle and foal in a percentage equal or higher than 2 and 1%, respectively. Therefore, this study shows the potential of NIRS combined with PLS-DA to detect fraud in minced lamb and beef.
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