Journal of Integrative Agriculture ›› 2026, Vol. 25 ›› Issue (7): 2669-2687.DOI: 10.1016/j.jia.2025.12.069

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水果产地溯源技术：十年回顾（2014-2024）与未来展望

  

  • 收稿日期:2025-03-13 修回日期:2025-12-31 接受日期:2025-11-17 出版日期:2026-07-20 发布日期:2026-06-08

Geographical origin authentication of fruits: A decadal review (2014–2024) of technological progress and outlook

Jiyun Nie1*#, Mengying Shuai1*, Yihui Liu1, Xiaoming Li1, Mingyu Liu1, An Li2, Duoyong Zhao3, Qiusheng Chen4, Xiaoli Liu1, Zhichao Li1

  

  1. 1 College of Horticulture, Qingdao Agricultural University/Laboratory of Quality & Safety Risk Assessment for Fruit (Qingdao), Ministry of Agriculture and Rural Affairs/National Technology Centre for Whole Process Quality Control of FSEN Horticultural Products (Qingdao)/Qingdao Key Laboratory of Modern Agriculture Quality and Safety Engineering, Qingdao 266109, China

    2 Institute of Quality Standards & Testing Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China

    3 Institute of Quality Standards & Testing Technology for Agro-Products, Xinjiang Academy of Agricultural Sciences, Urumqi 830091, China

    4 Institute of Agricultural Product Quality, Safety and Nutrition, Tianjin Academy of Agricultural Sciences, Tianjin 300381, China

  • Received:2025-03-13 Revised:2025-12-31 Accepted:2025-11-17 Online:2026-07-20 Published:2026-06-08
  • About author:#Correspondence Jiyun Nie, E-mail: jiyunnie@163.com * These authors contributed equally to this study.
  • Supported by:
    This work was supported by the Shandong Key R&D Plan (Agricultural Variety Project), China (2022LZGCQY008), the National Program for Quality and Safety Risk Assessment of Agricultural Products of China (GJFP20230210), the Research Foundation for Evaluation of Quality Specification & Nutritional Function of Agricultural Products, China (PJ2023-019), and the Scientific Research Foundation for High Level Talents of Qingdao Agricultural University, China (665-1120015).

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摘要： 食品欺诈是当前日益猖獗的以牟利为目的的故意欺骗行为，因此开发高效的分析方法以评估食品真实性至关重要。近年来，水果地理起源的真实性逐渐成为公众关注的焦点。水果产地的鉴别通常依赖于元素组成、稳定同位素比值以及代谢物谱等特异性指标。大量研究证实，矿质元素与稳定同位素比率作为地理溯源的有效指标，其分布模式与地理环境条件直接相关。此外，光谱技术与色谱技术等在水果产地判别及真实性验证方面也展现出显著潜力。在数据处理方面，非线性主成分分析（PCA）、偏最小二乘判别分析（PLS-DA）及线性判别分析（LDA）是当前水果产地认证中常用的化学计量学方法。随着技术进步，机器学习算法已成为现代数据分类的重要工具，包括支持向量机（SVM）、随机森林（RF）和人工神经网络（ANN）在内的多种模型，已在水果产地识别中实现较高准确率，表现出优越的判别性能。


Abstract:

Food fraud is an increasingly prevalent deliberate act of deception for profit.  Hence, it is highly necessary to develop robust analytical methods to assess the authenticity of foods.  In recent years, the geographical origin authenticity of fruits has attracted considerable public concern.  The geographical origin of fruit is generally determined based on specific indicators such as elements, stable isotopes, and metabolites.  Many studies have demonstrated that mineral elements and stable isotope ratios are effective indicators for geographical origin authentication as they are directly related to the geographical environment.  Other techniques, such as spectroscopy and chromatography, also exhibit promising potential for fruit origin discrimination and authenticity assessment.  Omics technologies have emerged as a key approach for authenticating the geographical origin of fruit.  The integration of instrumental analysis techniques with machine learning enables high-precision discrimination of fruit geographical origin, and the growing trend toward combining multiple analytical techniques further enhances identification accuracy.  Commonly used methods for geographical origin authentication include linear techniques such as PCA, PLS-DA, and LDA.  Machine learning algorithms, including SVM, RF, and ANN, have also been applied to identify fruit origin with high accuracy.  Future developments in this field should prioritize the consideration of agricultural practices to ensure reliable and practical authentication.  

Key words: fruit , authenticity ,  geographical origin ,  multivariate data analysis