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利用稳定同位素和多元素分析结合化学计量学区分池塘集约化养殖、稻田生态养殖和自由放养的克氏原螯虾

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

作者信息

Xia Zhenzhen, Liu Zhi, Liu Yan, Cui Wenwen, Zheng Dan, Tao Mingfang, Zhou Youxiang, Peng Xitian

机构信息

Hubei Key Laboratory of Nutritional Quality and Safety of Agro Products, Institute of Agricultural Quality Standards and Testing Technology Research, Hubei Academy of Agricultural Science, Wuhan 430064, China.

College of Agriculture and Biotechnology, Hunan University of Humanities, Science and Technology, Loudi 417000, China.

出版信息

Foods. 2024 Sep 18;13(18):2947. doi: 10.3390/foods13182947.

Abstract

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 (δC, δN, δH, δO) 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 δC, δN, δH, 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.

摘要

小龙虾的养殖模式对其品质、安全性和营养有显著影响。通常,绿色环保型产品具有更高的经济价值和市场竞争力。因此,人们经常采用集约化养殖方法来取代这些环保型产品,这可能导致商业欺诈行为。在本研究中,利用稳定同位素和多元素分析并结合多变量建模来区分池塘集约化养殖、稻田生态养殖和自由放养的小龙虾。测定了88个小龙虾样本及其饲料中碳、氮、氢和氧的四种稳定同位素比率(δC、δN、δH、δO)以及20种元素,用于方差分析和相关性分析。为了识别和区分三种不同养殖模式的小龙虾,使用了无监督方法,如层次聚类分析(HCA)和主成分分析(PCA),以及有监督的多变量建模,特别是偏最小二乘判别分析(PLS-DA)。HCA和PCA在对小龙虾养殖模式进行分类方面效果有限,而PLS-DA表现出更强的性能,预测准确率为90.8%。此外,δC、δN、δH、Mn和Co等变量在PLS-DA模型中贡献相对较高,其变量投影重要性(VIP)大于1。本研究首次尝试利用稳定同位素和多元素分析来区分三种养殖模式下的小龙虾。作为小龙虾鉴别的有效策略,它具有广阔的应用前景。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e46/11431733/528d2fa6cdfe/foods-13-02947-g001.jpg

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