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Differentiating Pond-Intensive, Paddy-Ecologically, and Free-Range Cultured Crayfish (Procambarus clarkii) Using Stable Isotope and Multi-Element Analysis Coupled with Chemometrics

文献类型： 外文期刊

作者： Xia, Zhenzhen 1 ; Liu, Zhi 2 ; Liu, Yan 3 ; Cui, Wenwen 1 ; Zheng, Dan 1 ; Tao, Mingfang 1 ; Zhou, Youxiang 1 ; Peng, Xitian 1 ;

作者机构： 1.Hubei Acad Agr Sci, Inst Agr Qual Stand & Testing Technol Res, Hubei Key Lab Nutr Qual & Safety Agro Prod, Wuhan 430064, Peoples R China

2.Hunan Univ Humanities Sci & Technol, Coll Agr & Biotechnol, Loudi 417000, Peoples R China

3.Wuhan Polytech Univ, Coll Food Sci & Engn, Wuhan 430023, Peoples R China

关键词： crayfish; farming pattern; stable isotope; multi-element; PLS-DA; authentication

期刊名称：FOODS （ 影响因子：5.1； 五年影响因子：5.6 ）

ISSN：

年卷期： 2024 年 13 卷 18 期

页码：

收录情况： SCI

摘要： The farming pattern of crayfish significantly impacts their quality, safety, and nutrition. Typically, green and ecologically friendly products command higher economic value and market competitiveness. Consequently, intensive farming methods are frequently employed in an attempt to replace these environmentally friendly products, leading to potential instances of commercial fraud. In this study, stable isotope and multi-element analysis were utilized in conjunction with multivariate modeling to differentiate between pond-intensive, paddy-ecologically, and free-range cultured crayfish. The four stable isotope ratios of carbon, nitrogen, hydrogen, and oxygen (delta C-13, delta N-15, delta H-2, delta O-18) and 20 elements from 88 crayfish samples and their feeds were determined for variance analysis and correlation analysis. To identify and differentiate three different farming pattern crayfish, unsupervised methods such as hierarchical cluster analysis (HCA) and principal component analysis (PCA) were used, as well as supervised multivariate modeling, specifically partial least squares discriminant analysis (PLS-DA). The HCA and PCA exhibited limited effectiveness in classifying the farming pattern of crayfish, whereas the PLS-DA demonstrated a more robust performance with a predictive accuracy of 90.8%. Additionally, variables such as delta C-13, delta N-15, delta H-2, Mn, and Co exhibited relatively higher contributions in the PLS-DA model, with a variable influence on projection (VIP) greater than 1. This study is the first attempt to use stable isotope and multi-element analysis to distinguish crayfish under three farming patterns. It holds promising potential as an effective strategy for crayfish authentication.

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