Jimin Jeong , KyoungHye Baek , Hwang-Ju Jeon , Hyoyoung Kim , Yong-Kyoung Kim , Ho Jin Kim
{"title":"利用电感耦合等离子体光谱和多元统计分析方法对韩国和中国黄花菊的地理来源进行了分析","authors":"Jimin Jeong , KyoungHye Baek , Hwang-Ju Jeon , Hyoyoung Kim , Yong-Kyoung Kim , Ho Jin Kim","doi":"10.1016/j.foodchem.2025.146708","DOIUrl":null,"url":null,"abstract":"<div><div>The fruit of <em>Hovenia dulcis</em>, a popular Korean hangover remedy, is marketed as Korean and Chinese products. To protect domestic producers, ensure accurate labeling, and safeguard consumers, this study discriminated origin through inorganic element analysis using inductively coupled plasma-optical emission spectroscopy and mass spectrometry. Orthogonal partial least squares-discriminant analysis showed high model quality (R<sup>2</sup> = 0.956, Q<sup>2</sup> = 0.883). Twenty-five elements had variable importance in projection (VIP) values ≥1, and receiver operating characteristic analysis confirmed 100 % accuracy. Coefficient plots revealed Ca (−0.453081), <sup>103</sup>Rh (−0.222397), <sup>175</sup>Lu (−0.2084) significant for Chinese, while <sup>11</sup>B (0.137669) and <sup>88</sup>Sr (0.0954821) were significant for Korean. Linear discriminant analysis showed intergroup distance of 19, indicating strong discrimination. Findings highlight ICP-based elemental profiling with chemometrics and machine learning as a robust tool for authenticating <em>H. dulcis</em> origin, offering advantages over traditional visual methods and supporting regulatory monitoring, labeling accuracy, and consumer protection.</div></div>","PeriodicalId":318,"journal":{"name":"Food Chemistry","volume":"496 ","pages":"Article 146708"},"PeriodicalIF":9.8000,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Determination of geographical origin of Hovenia dulcis found in Korea and China via inorganic element analysis using inductively coupled plasma spectroscopy and multivariate statistical analysis\",\"authors\":\"Jimin Jeong , KyoungHye Baek , Hwang-Ju Jeon , Hyoyoung Kim , Yong-Kyoung Kim , Ho Jin Kim\",\"doi\":\"10.1016/j.foodchem.2025.146708\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The fruit of <em>Hovenia dulcis</em>, a popular Korean hangover remedy, is marketed as Korean and Chinese products. To protect domestic producers, ensure accurate labeling, and safeguard consumers, this study discriminated origin through inorganic element analysis using inductively coupled plasma-optical emission spectroscopy and mass spectrometry. Orthogonal partial least squares-discriminant analysis showed high model quality (R<sup>2</sup> = 0.956, Q<sup>2</sup> = 0.883). Twenty-five elements had variable importance in projection (VIP) values ≥1, and receiver operating characteristic analysis confirmed 100 % accuracy. Coefficient plots revealed Ca (−0.453081), <sup>103</sup>Rh (−0.222397), <sup>175</sup>Lu (−0.2084) significant for Chinese, while <sup>11</sup>B (0.137669) and <sup>88</sup>Sr (0.0954821) were significant for Korean. Linear discriminant analysis showed intergroup distance of 19, indicating strong discrimination. Findings highlight ICP-based elemental profiling with chemometrics and machine learning as a robust tool for authenticating <em>H. dulcis</em> origin, offering advantages over traditional visual methods and supporting regulatory monitoring, labeling accuracy, and consumer protection.</div></div>\",\"PeriodicalId\":318,\"journal\":{\"name\":\"Food Chemistry\",\"volume\":\"496 \",\"pages\":\"Article 146708\"},\"PeriodicalIF\":9.8000,\"publicationDate\":\"2025-10-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Food Chemistry\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0308814625039603\",\"RegionNum\":1,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, APPLIED\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Food Chemistry","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0308814625039603","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, APPLIED","Score":null,"Total":0}
Determination of geographical origin of Hovenia dulcis found in Korea and China via inorganic element analysis using inductively coupled plasma spectroscopy and multivariate statistical analysis
The fruit of Hovenia dulcis, a popular Korean hangover remedy, is marketed as Korean and Chinese products. To protect domestic producers, ensure accurate labeling, and safeguard consumers, this study discriminated origin through inorganic element analysis using inductively coupled plasma-optical emission spectroscopy and mass spectrometry. Orthogonal partial least squares-discriminant analysis showed high model quality (R2 = 0.956, Q2 = 0.883). Twenty-five elements had variable importance in projection (VIP) values ≥1, and receiver operating characteristic analysis confirmed 100 % accuracy. Coefficient plots revealed Ca (−0.453081), 103Rh (−0.222397), 175Lu (−0.2084) significant for Chinese, while 11B (0.137669) and 88Sr (0.0954821) were significant for Korean. Linear discriminant analysis showed intergroup distance of 19, indicating strong discrimination. Findings highlight ICP-based elemental profiling with chemometrics and machine learning as a robust tool for authenticating H. dulcis origin, offering advantages over traditional visual methods and supporting regulatory monitoring, labeling accuracy, and consumer protection.
期刊介绍:
Food Chemistry publishes original research papers dealing with the advancement of the chemistry and biochemistry of foods or the analytical methods/ approach used. All papers should focus on the novelty of the research carried out.