杨庆春,王芳,刘玮,姚人勇,刘纯,赵宇航,邢益玮.食用植物油掺假检测技术的最新研究进展[J].中国粮油学报,2026,41(7):199-206
食用植物油掺假检测技术的最新研究进展
The latest research progress on detection technologies for adulteration in edible vegetable oils
投稿时间:2026-03-25  修订日期:2026-06-11
DOI:
中文关键词:  食用植物油  掺假  检测技术
英文关键词:edible vegetable oil  adulteration  detection technology
基金项目:
作者单位邮编
杨庆春 武汉长江粮油储备有限公司 武汉 430416 430416
王芳 武汉长江粮油储备有限公司 武汉 430416 430416
刘玮 武汉长江粮油储备有限公司 武汉 430416 430416
姚人勇 武汉长江粮油储备有限公司 武汉 430416 430416
刘纯 武汉长江粮油储备有限公司 武汉 430416 430416
赵宇航* 武汉长江粮油储备有限公司 武汉 430416 430416
邢益玮 武汉长江粮油储备有限公司 武汉 430416 430416
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中文摘要:
      近年来,食用植物油掺假问题已成为社会关注的焦点,其质量安全对消费者健康及市场秩序至关重要。本文系统梳理了当前食用植物油掺假检测的主要技术手段及其局限性,并对未来发展方向进行展望,旨在为食用植物油掺假相关科研、标准化工作及市场监管提供参考。当前应用比较广泛的食用植物油掺假检测技术主要包括分子生物学、化学“指纹”、光谱学,但其分别具有DNA易降解,假阴性率高,对精炼油效果差;设备昂贵,操作复杂耗时,专业性要求高;灵敏度较低,依赖模型,对微量掺假识别难等局限性。为更有力的保障食用植物油的真实性,需将当前检测技术与人工智能深度融合,构建“多维感知、快速筛查、智能判别、精准确证、全程追溯”的综合性技术监管体系。
英文摘要:
      In recent years, the issue of adulteration in edible vegetable oils has become a focus of public concern. The quality and safety of edible vegetable oils are of vital importance to consumer health and market order. This article systematically reviews the main technical means for detecting adulteration in edible vegetable oils and their limitations, and looks forward to the future development direction, aiming to provide references for related scientific research, standardization work, and market supervision on edible vegetable oil adulteration. Currently, the widely used detection technologies for edible vegetable oil adulteration mainly include molecular biology, chemical "fingerprints", and spectroscopy. However, they respectively have limitations such as DNA being easily degraded, high false negative rate, and poor effect on refined oils; expensive equipment, complex and time-consuming operation, and high professional requirements; low sensitivity, dependence on models, and difficulty in identifying trace adulteration. To more effectively ensure the authenticity of edible vegetable oils, it is necessary to deeply integrate current detection technologies with artificial intelligence, and build a comprehensive technical supervision system featuring "multi-dimensional perception, rapid screening, intelligent discrimination, precise confirmation, and full traceability".
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