利用多元素指纹图谱和化学计量学鉴定冬枣的地理产地

IF 4.6 1区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY
Xiabing Kong, Qiusheng Chen, Min Xu, Yihui Liu, Xiaoming Li, Lingxi Han, Qiang Zhang, Haoliang Wan, Lu Liu, Xubo Zhao, Jiyun Nie
{"title":"利用多元素指纹图谱和化学计量学鉴定冬枣的地理产地","authors":"Xiabing Kong, Qiusheng Chen, Min Xu, Yihui Liu, Xiaoming Li, Lingxi Han, Qiang Zhang, Haoliang Wan, Lu Liu, Xubo Zhao, Jiyun Nie","doi":"10.1016/j.jia.2024.03.065","DOIUrl":null,"url":null,"abstract":"Winter jujube ( ‘Dongzao’) is greatly appreciated by consumers for its excellent quality, but brand infringement frequently occurs in the market. Here, we first determined a total of 38 elements in 167 winter jujube samples from the main winter jujube producing areas of China by inductively coupled plasma mass spectrometer (ICP-MS). As a result, 16 elements (Mg, K, Mn, Cu, Zn, Mo, Ba, Be, As, Se, Cd, Sb, Ce, Er, Tl, and Pb) exhibited significant differences in samples from different producing areas. Supervised linear discriminant analysis (LDA) and orthogonal projection to latent structures discriminant analysis (OPLS-DA) showed better performance in identifying the origin of samples than unsupervised principal component analysis (PCA). LDA and OPLS-DA had a mean identification accuracy of 87.84 and 94.64% in the testing set, respectively. By using the multilayer perceptron (MLP) and C5.0, the prediction accuracy of the models could reach 96.36 and 91.06%, respectively. Based on the above four chemometric methods, Cd, Tl, Mo and Se were selected as the main variables and principal markers for the origin identification of winter jujube. Overall, this study demonstrates that it is practical and precise to identify the origin of winter jujube through multi-element fingerprint analysis with chemometrics, and may also provide reference for establishing the origin traceability system of other fruits.","PeriodicalId":16305,"journal":{"name":"Journal of Integrative Agriculture","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Geographical origin identification of winter jujube (Ziziphus jujuba ‘Dongzao’) by using multi-element fingerprinting with chemometrics\",\"authors\":\"Xiabing Kong, Qiusheng Chen, Min Xu, Yihui Liu, Xiaoming Li, Lingxi Han, Qiang Zhang, Haoliang Wan, Lu Liu, Xubo Zhao, Jiyun Nie\",\"doi\":\"10.1016/j.jia.2024.03.065\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Winter jujube ( ‘Dongzao’) is greatly appreciated by consumers for its excellent quality, but brand infringement frequently occurs in the market. Here, we first determined a total of 38 elements in 167 winter jujube samples from the main winter jujube producing areas of China by inductively coupled plasma mass spectrometer (ICP-MS). As a result, 16 elements (Mg, K, Mn, Cu, Zn, Mo, Ba, Be, As, Se, Cd, Sb, Ce, Er, Tl, and Pb) exhibited significant differences in samples from different producing areas. Supervised linear discriminant analysis (LDA) and orthogonal projection to latent structures discriminant analysis (OPLS-DA) showed better performance in identifying the origin of samples than unsupervised principal component analysis (PCA). LDA and OPLS-DA had a mean identification accuracy of 87.84 and 94.64% in the testing set, respectively. By using the multilayer perceptron (MLP) and C5.0, the prediction accuracy of the models could reach 96.36 and 91.06%, respectively. Based on the above four chemometric methods, Cd, Tl, Mo and Se were selected as the main variables and principal markers for the origin identification of winter jujube. Overall, this study demonstrates that it is practical and precise to identify the origin of winter jujube through multi-element fingerprint analysis with chemometrics, and may also provide reference for establishing the origin traceability system of other fruits.\",\"PeriodicalId\":16305,\"journal\":{\"name\":\"Journal of Integrative Agriculture\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-03-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Integrative Agriculture\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://doi.org/10.1016/j.jia.2024.03.065\",\"RegionNum\":1,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AGRICULTURE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Integrative Agriculture","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1016/j.jia.2024.03.065","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

摘要

冬枣以其优良的品质深受消费者喜爱,但市场上经常出现品牌侵权行为。在此,我们首先利用电感耦合等离子体质谱仪(ICP-MS)测定了来自中国冬枣主产区的 167 份冬枣样品中的 38 种元素。结果表明,16 种元素(Mg、K、Mn、Cu、Zn、Mo、Ba、Be、As、Se、Cd、Sb、Ce、Er、Tl 和 Pb)在不同产区的样品中存在显著差异。与无监督主成分分析法(PCA)相比,有监督线性判别分析(LDA)和正交投影潜结构判别分析(OPLS-DA)在确定样品来源方面表现出更好的性能。在测试集中,LDA 和 OPLS-DA 的平均识别准确率分别为 87.84% 和 94.64%。通过使用多层感知器(MLP)和 C5.0,模型的预测准确率分别达到 96.36% 和 91.06%。根据上述四种化学计量学方法,选择 Cd、Tl、Mo 和 Se 作为冬枣产地鉴定的主要变量和主要标记。总之,本研究表明,利用化学计量学的多元素指纹分析方法对冬枣进行产地鉴定是切实可行和准确的,也可为建立其他水果的产地溯源体系提供参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Geographical origin identification of winter jujube (Ziziphus jujuba ‘Dongzao’) by using multi-element fingerprinting with chemometrics
Winter jujube ( ‘Dongzao’) is greatly appreciated by consumers for its excellent quality, but brand infringement frequently occurs in the market. Here, we first determined a total of 38 elements in 167 winter jujube samples from the main winter jujube producing areas of China by inductively coupled plasma mass spectrometer (ICP-MS). As a result, 16 elements (Mg, K, Mn, Cu, Zn, Mo, Ba, Be, As, Se, Cd, Sb, Ce, Er, Tl, and Pb) exhibited significant differences in samples from different producing areas. Supervised linear discriminant analysis (LDA) and orthogonal projection to latent structures discriminant analysis (OPLS-DA) showed better performance in identifying the origin of samples than unsupervised principal component analysis (PCA). LDA and OPLS-DA had a mean identification accuracy of 87.84 and 94.64% in the testing set, respectively. By using the multilayer perceptron (MLP) and C5.0, the prediction accuracy of the models could reach 96.36 and 91.06%, respectively. Based on the above four chemometric methods, Cd, Tl, Mo and Se were selected as the main variables and principal markers for the origin identification of winter jujube. Overall, this study demonstrates that it is practical and precise to identify the origin of winter jujube through multi-element fingerprint analysis with chemometrics, and may also provide reference for establishing the origin traceability system of other fruits.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of Integrative Agriculture
Journal of Integrative Agriculture AGRICULTURE, MULTIDISCIPLINARY-
CiteScore
7.90
自引率
4.20%
发文量
4817
审稿时长
3-6 weeks
期刊介绍: Journal of Integrative Agriculture publishes manuscripts in the categories of Commentary, Review, Research Article, Letter and Short Communication, focusing on the core subjects: Crop Genetics & Breeding, Germplasm Resources, Physiology, Biochemistry, Cultivation, Tillage, Plant Protection, Animal Science, Veterinary Science, Soil and Fertilization, Irrigation, Plant Nutrition, Agro-Environment & Ecology, Bio-material and Bio-energy, Food Science, Agricultural Economics and Management, Agricultural Information Science.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信