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贝类中的无机元素:ICP-MS 数据多元分析的地理溯源。

Inorganic Elements in Shells: Geographic Traceability by Multivariate Analysis of ICP-MS Data.

机构信息

Department of Chemistry, University of Bari "Aldo Moro", 70125 Bari, Italy.

Department of Chemistry "Giacomo Ciamician", University of Bologna, 40126 Bologna, Italy.

出版信息

Molecules. 2021 Apr 30;26(9):2634. doi: 10.3390/molecules26092634.

Abstract

The international seafood trade is based on food safety, quality, sustainability, and traceability. Mussels are bio-accumulative sessile organisms that need regular control to guarantee their safe consumption. However, no well-established and validated methods exist to trace mussel origin, even if several attempts have been made over the years. Recently, an inorganic multi-elemental fingerprint coupled to multivariate statistics has increasingly been applied in food quality control. The mussel shell can be an excellent reservoir of foreign inorganic chemical species, allowing recording long-term environmental changes. The present work investigates the multi-elemental composition of mussel shells, including Al, Cu, Cr, Zn, Mn, Cd, Co, U, Ba, Ni, Pb, Mg, Sr, and Ca, determined by inductively-coupled plasma mass-spectrometry in collected along the Central Adriatic Coast (Marche Region, Italy) at 25 different sampling sites (18 farms and 7 natural banks) located in seven areas. The experimental data, coupled with chemometric approaches (principal components analysis and linear discriminant analysis), were used to create a statistical model able to discriminate samples as a function of their production site. The LDA model is suitable for achieving a correct assignment of >90% of individuals sampled to their respective harvesting locations and for being applied to counteract fraud.

摘要

国际海鲜贸易基于食品安全、质量、可持续性和可追溯性。贻贝是生物蓄积性固着生物,需要定期进行控制,以保证其安全食用。然而,即使多年来已经进行了几次尝试,也没有建立完善和经过验证的方法来追踪贻贝的来源。最近,一种无机多元素指纹与多元统计分析相结合,越来越多地应用于食品质量控制。贻贝壳可以成为外来无机化学物质的绝佳储存库,允许记录长期的环境变化。本研究调查了贻贝壳中的多元素组成,包括 Al、Cu、Cr、Zn、Mn、Cd、Co、U、Ba、Ni、Pb、Mg、Sr 和 Ca,这些元素通过电感耦合等离子体质谱法在意大利马尔凯地区中央亚得里亚海沿岸的 25 个不同采样点(18 个养殖场和 7 个自然养殖场)采集的样本中进行了测定。将实验数据与化学计量学方法(主成分分析和线性判别分析)相结合,用于创建一个统计模型,能够根据其生产地点对样本进行区分。LDA 模型适合对 >90%的抽样个体进行正确的归属,适用于打击欺诈行为。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/02d0/8125296/332477801e86/molecules-26-02634-g001.jpg

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