食品与发酵工业 >

2022 , Vol. 48 >Issue 1: 261 - 266

DOI: https://doi.org/10.13995/j.cnki.11-1802/ts.027230

分析与检测

基于稳定同位素比值的不同产地枸杞特征分析及判别

  • 连思雨 ,
  • 朱姗姗 ,
  • 张紫娟 ,
  • 谢瑜杰 ,
  • 范春林 ,
  • 陈辉 ,
  • 王明林
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  • 1(山东农业大学 食品科学与工程学院,山东 泰安,271018)
    2(中国检验检疫科学研究院,北京,100176)
硕士研究生(陈辉副研究员和王明林教授为共同通信作者,E-mail:ciqhuichen@163.com;mlwang1963@163.com)

收稿日期: 2021-03-12

  修回日期: 2021-04-27

  网络出版日期: 2022-01-27

基金资助

特色高值农产品产地判别技术研究(2017YFF0211302)

收起

Characteristics analysis of Chinese wolfberry from different origins based on stable isotope ratio

  • LIAN Siyu ,
  • ZHU Shanshan ,
  • ZHANG Zijuan ,
  • XIE Yujie ,
  • FAN Chunlin ,
  • CHEN Hui ,
  • WANG Minglin
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  • 1(College of Food Science and Engineering,Shandong Agricultural University,Taian 271018,China)
    2(Chinese Academy of Inspection and Quarantine,Beijing 100176,China)

Received date: 2021-03-12

  Revised date: 2021-04-27

  Online published: 2022-01-27

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摘要

原产地差异导致枸杞价格悬殊,为促进国内枸杞市场健康有序发展,该文应用元素分析仪-稳定性同位素质谱(elemental analyzer-stable isotope mass spectrometry,EA-IRMS)测定了来自青海、宁夏和新疆3个地区共97份枸杞样品δ13C、δ15N、δ2H、δ18O和δ34S 5种稳定同位素组成,结合箱线图、单因素方差分析以及主成分分析(principal component analysis,PCA)对不同产地枸杞中稳定同位素比值分布规律和相关性进行了讨论,应用线性判别分析(linear discriminant analysis,LDA)和支持向量机(support vector machine,SVM)2种算法建立不同产地枸杞判别模型。结果显示,枸杞样品中δ13C、δ15N、δ2H和δ18O 4种稳定同位素对产地判别贡献率较大,δ34S在判别不同产地枸杞效用较弱,PCA前2个主成分的累积方差贡献率为75.3%。在LDA模型中,验证集和训练集的总体准确率分别为89.7%和93.9%;在SVM模型中模型验证和模型训练的总体准确率分别为95.9%和96.9%,相比而言,SVM模型的判别效果优于LDA。研究表明,不同产地枸杞样品中稳定同位素比值存在一定差异,可为实现枸杞产地判别提供依据。

关键词： 枸杞; 稳定同位素比值; 特征分析; 产地判别

本文引用格式

连思雨 , 朱姗姗 , 张紫娟 , 谢瑜杰 , 范春林 , 陈辉 , 王明林 . 基于稳定同位素比值的不同产地枸杞特征分析及判别[J]. 食品与发酵工业, 2022 , 48(1) : 261 -266 . DOI: 10.13995/j.cnki.11-1802/ts.027230

Abstract

The geographic origins of Chinese wolfberry were different prices. A total of 97 Chinese wolfberry samples was collected from three provinces/autonomous regions (Qinghai, Ningxia and Xinjiang) and their δ13C, δ15N, δ2H, δ18O and δ34S were obtained using elemental analyzer-stable isotope mass spectrometry (EA-IRMS). The correlation of stable isotope ratios and geographical origins was explored with the combination of box-plot, one-way analysis of variance and principal component analysis (PCA). Linear discriminant analysis (LDA) and support vector machine (SVM) were applied to construct the discriminant models of Chinese wolfberry for the geographic origins respectively. The results showed that the contribution of stable isotope δ13C, δ15N, δ2H, and δ18O was more than that of isotope δ34S to geographic origin discriminant. The cumulative variance contribution of the first two principal components was up to 75.3%. In the LDA model, the overall accuracy of training and verification were 93.9% and 89.7% respectively. In the SVM model, the overall accuracy of training and verification were 96.9% and 95.9%. Therefore, the SVM model was better than that of LDA in the discrimination of Chinese wolfberry from different origins. In summary, the difference in the stable isotope of Chinese wolfberry samples could be applied to identify the geographical origins.

Key words： Chinese wolfberry; stable isotope ratios; characteristics analysis; geographical origin discriminant

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