Varrà Maria Olga, Ghidini Sergio, Zanardi Emanuela, Badiani Anna, Ianieri Adriana
Department of Food and Drug, University of Parma.
Department of Veterinary Medical Science, University of Bologna, Italy.
Ital J Food Saf. 2019 Mar 22;8(1):7872. doi: 10.4081/ijfs.2019.7872. eCollection 2019 Mar 18.
In this work, stable isotope ratio (SIR) and rare earth elements (REEs) analyses, combined with multivariate data elaboration, were used to explore the possibility to authenticate European sea bass ( L) according to: i) production method (wild or farmed specimens); ii) geographical origin (Western, Central or Eastern Mediterranean Sea). The dataset under investigation included a total of 144 wild and farmed specimens coming from 17 different European areas located in the Mediterranean Sea basin. Samples were subjected to SIR analysis (carbon and nitrogen) and REEs analysis (lanthanum, europium, holmium, erbium, lutetium, and terbium). Then, Analytical data were handled by Principal Component Analysis (PCA) and then by Orthogonal Partial Last Square Discriminant Analysis (OPLS-DA), to obtain functional classification models to qualitatively discriminate sea bass according to the conditions under study. OPLSDA models provided good correct classification rate both for production method and geographical origin. It was confirmed that chemometric elaboration of data obtained from SIR and REEs analyses can be a suitable tool for an accurate authentication of European sea bass.
在本研究中，稳定同位素比率（SIR）和稀土元素（REEs）分析，结合多变量数据处理，被用于探索根据以下因素鉴定欧洲海鲈（Dicentrarchus labrax）的可能性：i）生产方式（野生或养殖样本）；ii）地理来源（西地中海、中地中海或东地中海）。所研究的数据集总共包括来自地中海盆地17个不同欧洲地区的144个野生和养殖样本。样本进行了SIR分析（碳和氮）和REEs分析（镧、铕、钬、铒、镥和铽）。然后，分析数据先通过主成分分析（PCA）处理，再通过正交偏最小二乘判别分析（OPLS-DA）处理，以获得功能分类模型，根据所研究的条件对海鲈进行定性判别。OPLS-DA模型在生产方式和地理来源方面均提供了良好的正确分类率。证实了对SIR和REEs分析获得的数据进行化学计量学处理可以成为准确鉴定欧洲海鲈的合适工具。
