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Research progress on origin traceability detection technology for high-value edible vegetable oils: A case study of camellia oil and olive oil
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Yue ZHANG1, 2, Jia-Lin ZHANG1, 3, Zhi-Yi YAO2, Zi-Juan ZHANG1, 3, *
Journal of Food Safety & Quality | 2026, 17(10) : 66 - 75
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Journal of Food Safety & Quality | 2026, 17(10): 66-75
• Special Topic: Food Authenticity and Traceability •
Research progress on origin traceability detection technology for high-value edible vegetable oils: A case study of camellia oil and olive oil
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Yue ZHANG1, 2, Jia-Lin ZHANG1, 3, Zhi-Yi YAO2, Zi-Juan ZHANG1, 3, *
Affiliations
  • 1.China National Research Center for Food Safety Inspection and Testing, Beijing 100045, China
  • 2.College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China
  • 3.Key Laboratory of Food Quality and Safety, State Administration for Market Regulation, Beijing 100045, China
doi: 10.19812/j.cnki.jfsq11-5956/ts.20251126004
Outline
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High-value edible vegetable oils, such as camellia oil or olive oil, have drawn much attention from the market for their authenticity of origin due to their unique nutritional value and economic attributes. However, issues like fraudulent origin labeling and adulteration have severely compromised the protection of geographical indication products and industry development. Currently, discriminant techniques, including near-infrared spectroscopy,inductively coupled plasma mass spectrometry, stable isotope ratio mass spectrometry, fatty acid profiling and electronic nose, etc., have widely been applied for origin traceability. Multisource data fusion combined with machine learning has demonstrated superior accuracy and robustness compared to single-technique approaches. This article systematically summarized the research progress of the aforementioned detection technologies in the traceability of high-value vegetable oils. It also focused on analyzing the main problems existing in the current research: Most of the existing studies concentrated on the identification of large-scale differences across countries or provinces, while there was a relative lack of detailed traceability research at the county-level and smaller scales, and there was also a lack of unified data preprocessing standards and a multi-source information collaboration framework. Based on this, this paper proposed that in the future, efforts should be made to establish standardized preprocessing procedures and shared databases. Through the deep integration of multi-dimensional data such as spectra, elements, isotopes and volatile components, as well as the optimization of intelligent algorithms, a leap from precise identification of small-scale production areas to dynamic verification should be achieved. The construction of an intelligent traceability platform integrating data management, feature comparison and model reasoning will provide solid technical support for ensuring product authenticity, improving regulatory efficiency and promoting the sustainable development of the high-value vegetable oil industry.

camellia oil  /  olive oil  /  origin traceability  /  chemometrics  /  machine learning  /  data fusion
张越, 张佳琳, 姚志轶, 张紫娟. 高值植物油产地溯源检测技术研究进展—以山茶油、橄榄油为例. 食品安全质量检测学报, 2026 , 17 (10) : 66 -75 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20251126004
Yue ZHANG, Jia-Lin ZHANG, Zhi-Yi YAO, Zi-Juan ZHANG. Research progress on origin traceability detection technology for high-value edible vegetable oils: A case study of camellia oil and olive oil[J]. Journal of Food Safety & Quality, 2026 , 17 (10) : 66 -75 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20251126004
Year 2026 volume 17 Issue 10
7
1
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doi: 10.19812/j.cnki.jfsq11-5956/ts.20251126004
  • Receive Date:2025-11-26
  • Online Date:2026-11-05
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  • Received:2025-11-26
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    1.China National Research Center for Food Safety Inspection and Testing, Beijing 100045, China
    2.College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China
    3.Key Laboratory of Food Quality and Safety, State Administration for Market Regulation, Beijing 100045, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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