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NMR spectroscopy combined with chemometrics for quality assessment of common vegetable oils: A review

文献类型： 外文期刊

作者： Ting Shi;Tenghui Dai;Tao Zhang;Xiaodong Ma;Xinjie Wang

作者机构：

关键词： （1-1-1）Authenticity;Chemometrics;NMR;Nutritional quality parameters;Oxidation evaluation;Vegetable oil

期刊名称： Trends in Food Science and Technology

ISSN： 0924-2244

年卷期： 2025 年 157 卷

页码：

收录情况： SCIE（2025版） ; ; EI（2025版）

摘要： Background: Vegetable oils have gained more attention accounting for their high nutrition value. The common 11 vegetable oils can be classified into four types: palmitic acid oil (palm oil); high oleic acid oils (camellia oil, olive oil, and high oleic acid sunflower oil); linoleic acid oils (sunflower oil, corn oil, sesame oil, peanut oil, and rice bran oil); linolenic acid oils (soybean oil and rapeseed oil). For its unique advantage, each vegetable oil faces challenges in nutritional quality parameters (fatty acid, triglyceride, sterols, squalene), oxidation evaluation (oxidative stability, oxidation products), and authenticity (authentication, adulteration, traceability). Thus a rapid method should be urgently constructed from above three major aspects. Scope and approach: The nuclear magnetic resonance (NMR) with different nuclei absorption (1H, 13C, 31P), affords comprehensively quantitative information, for fatty acids, triglycerides, minor components, and oxidation products analysis. Combined with chemometrics, the NMR-based methods offer robust models, covering qualitative, quantitative, and optimization strategies. The present review provides a recent critical overview of NMR-chemometric applications for 11 vegetable oils in quality evaluation. Key findings and conclusion: NMR coupled to chemometric models along with optimization procedure, has been utilized to predict nutritional quality parameters, oxidation evaluation, and authenticity of common vegetable oils. Compared with traditional MS-chromatographic techniques and other spectroscopy techniques, the NMR-based method is an effective alternative tool. As the development of more accurate machine learning models, the further investigation will be broadened into the on-site detection, using portable technology, for vegetable oils quality control.

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