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.
| 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 |