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Application of Stable Isotopes and Multi Elemental Fingerprints to Verify the Origin of Premium Chinese Hainan Bananas

Yurong Huang,Hanyi Mei,Yongzhi Zhang, Mingyue Wang,Zhibo Huan,Jing Nie,Karyne M Rogers, Bayan Nuralykyzy, Chunlin Li,Yuwei Yuan

Foods (Basel, Switzerland)(2025)

College of Agriculture and Animal Husbandry | Analysis and Testing Center

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Abstract
China is the world’s largest consumer and second largest producer of bananas. This strong domestic demand consistently provides a reliable income for Chinese banana growers. The geographical origin of food is usually associated with product quality and safety, and this is especially noted for Hainan origin-labeled bananas, which are grown offshore on China’s largest tropical island. Hainan banana is recognized as a premium variety within China’s banana market, but there have been recent impacts on branding, profits, and a reduction in income for banana farmers due to the fraudulent in-market substitution of non-Hainan bananas. In this study, stable isotope and elemental chemometric models were used to differentiate bananas grown in Hainan province (HN) from non-Hainan provinces (NHN). The results showed that HN bananas had a specific isotopic and elemental fingerprint compared to NHN bananas. Bananas sampled from HN and NHN regions showed significant differences in δ13C values (HN: −22.2‰ to −27.7‰, NHN: −22.3‰ to −24.3‰), Al content (HN: 0.00 mg/kg to 0.10 mg/kg, NHN: 0.00 mg/kg to 0.02 mg/kg), Na content (HN: 0.00 mg/kg to 0.09 mg/kg, NHN: 0.00 mg/kg to 0.07 mg/kg), and other elements (p < 0.05). Overall, 14 key variables reflecting climate and soil properties were selected from a group of 53 variables to improve a partial least squares discriminant analysis (PLS-DA) chemometric model. The discrimination accuracy of the test set increased from 84.60% to 90.93% after variable reduction. The use of stable isotopes and elements combined with PLS-DA models provided an effective method for distinguishing Chinese HN bananas from NHN bananas and would be useful as a screening or regulatory tool to confirm instances of origin fraud.
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Lour.,elements,chemometrics,PLS-DA,model,traceability
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要点】:本研究运用稳定同位素和多元素指纹技术,结合偏最小二乘判别分析(PLS-DA)模型,成功区分了海南产与非海南产香蕉,为打击市场上假冒海南香蕉的欺诈行为提供了有效的检测手段。

方法】:通过收集香蕉样本的稳定同位素比值(如δ13C)和多元素含量数据,构建PLS-DA模型,对数据进行降维处理并提取关键变量,最终实现对不同产地香蕉的准确分类。

实验】:研究采集了海南(HN)和非海南(NHN)地区的香蕉样本,使用稳定同位素比值和多元素分析技术进行测试,通过比较δ13C值、铝(Al)含量、钠(Na)含量等14个关键变量,最终在测试集上实现了90.93%的分类准确率。