文章摘要
陈胜军,刘先进,杨贤庆,李来好,黄卉,吴燕燕,李春生.不同产地鲍鱼特征元素分析与主成分评价模型的建立.渔业科学进展,2019,40(2):83-90
不同产地鲍鱼特征元素分析与主成分评价模型的建立
Analysis of Characteristic Elements and Establishment of Principal Component Evaluation Model of Abalone from Different Habitats
投稿时间:2018-08-13  修订日期:2018-09-11
DOI:
中文关键词: 鲍鱼  特征元素  主成分评价  不同产地
英文关键词: Abalone  Characteristic elements  Principal component analysis  Different habitats
基金项目:中央级公益性科研院所基本科研业务费专项资金(中国水产科学研究院基本科研业务费)(2016HY-ZD0802)、2017年国家农产品质量安全风险评估计划(GJFP201700904)和农业部财政重大专项(NFZX2013)共同资助
作者单位
陈胜军 农业农村部水产品加工重点实验室 国家水产品加工技术研发中心 中国水产科学研究院南海水产研究所 广州 510300 
刘先进 农业农村部水产品加工重点实验室 国家水产品加工技术研发中心 中国水产科学研究院南海水产研究所 广州 510300上海海洋大学食品学院 上海 201306 
杨贤庆 农业农村部水产品加工重点实验室 国家水产品加工技术研发中心 中国水产科学研究院南海水产研究所 广州 510300 
李来好 农业农村部水产品加工重点实验室 国家水产品加工技术研发中心 中国水产科学研究院南海水产研究所 广州 510300 
黄卉 农业农村部水产品加工重点实验室 国家水产品加工技术研发中心 中国水产科学研究院南海水产研究所 广州 510300 
吴燕燕 农业农村部水产品加工重点实验室 国家水产品加工技术研发中心 中国水产科学研究院南海水产研究所 广州 510300 
李春生 农业农村部水产品加工重点实验室 国家水产品加工技术研发中心 中国水产科学研究院南海水产研究所 广州 510300 
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中文摘要:
      为了找出不同产地鲍鱼(Haliotis Spp. Abalone)的区域性差异,并探究一种有效的鲍鱼产地的鉴别方法,采用主成分分析法对对广东、福建、山东、辽宁4个主要养殖省份鲍鱼样品肌肉中的特征元素(Na、K、Mg、Ca、Fe、Zn、Cu、Ni、As、Al、Mn、Cr、Se)进行分析。结果显示,鲍鱼样品的元素含量存在差异,Mn的变异程度最大,变异系数为74%,Ni次之,为65%,其次是Se (60%),其余元素的变异系数均高于10%。同时,通过对这些数据进行降维处理,有效地从13个特征元素中提取了6个元素作为主成分,累计方差贡献率达89.87%;同时发现Ca、Se、Na、Fe、Mn、K、Ni这7种元素是不同产地鲍鱼的特征元素,并建立了主成分综合评价模型:F=0.2777F1+0.2652F2+ 0.1295F3+0.1066F4+0.0656F5+0.0541F6。模型的建立可以为利用特征元素对不同产地鲍鱼的产地溯源提供一定的理论参考。
英文摘要:
      This study was done to assess regional differences among abalone from different habitats and develop a new and efficient method to identify abalone from different habitats of origin. To do this, the content and composition of 13 major elements and trace elements (Na, K, Mg, Ca, Fe, Zn, Cu, Ni, As, Al, Mn, Cr, and Se) in the muscle tissue of 18 kinds of abalone samples from the four major breeding provinces of Guangdong, Fujian, Shandong, and Liaoning, China, were determined and analyzed using principal component analysis (PCA). The results showed that the elemental content differed among the 18 abalone samples. The variation in Manganese (Mn) was the largest [the coefficient of variation (CV) was 74% among samples], followed by Nickel (Ni) (CV = 65%), and then by Selenium (Se) (CV = 60%); all the rest of the analyzed elements had CVs higher than 10%. At the same time, after using the PCA method to reduce the dimensions of these data, six elements were effectively extracted from among the 13 elements examined that cumulatively explained 89.87% of the variance among samples. These characteristic elements of abalone from different habitats were Ca, Se, Na, Fe, Mn, K, and Ni, and a comprehensive model of the six principal components including them was established as follows: F = 0.2777F1 + 0.2652F2 + 0.1295F3 + 0.1066F4 + 0.0656F5 + 0.0541F6, where F1 represents the first principal component, and F2~F6 represent the second to sixth principal components, respectively. On the basis of the comprehensive PCA score, the top six samples were samples No. 2, 6, 17, 13, 7, and 15, and the lowest one was sample No. 5. Among these, sample No. 2 had a higher content of all characteristic elements and better quality than all others. The PCA approach was found to be quite suitable for the evaluation of the nutritive quality of abalone. The establishment of this PCA model provided an empirical basis for the theoretical determination of the origin of abalone samples.
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