建立地理标志大米的蛋白质指纹技术用于监测与应对食品造假

Food risks is consisted of illegal intentional addition and food fraud or 'Economically motivated adulteration'(EMA), integrity and authenticity issues are becoming the first priority of consumers' concerns and research hot spot instead of traditionally safety issues. Those who commit food fraud want to avoid detection and do not necessarily intend to cause physical harm. Thus, the vast majority of fraud incidents do not pose a public health risk, but in some cases have resulted in food safety incident. Rice is the staple food of more than half of the world's population, meanwhile, one of the most historical and important Chinese local grains. The challenge in rice quality and safety securing is inseparable with our national security, economy and the people's livelihood. Traditional testing methods and standards are overmuch outdated to fulfill the need against rice adulteration and authenticity detection nowadays, which proposed an urgent demand for developing new technologies of currently existing scanty toolbox in rice authenticity discrimination. Targeted and non-targeted proteomic strategies by using mass spectrometry based on the applicant's previous research experiences in dairy protein studies will be introduced into this project to identify rice variety, quality and origin for the first time. The central idea is to start from developing peptide fingerprint database for rice authentic samples through non-targeted LC-MS proteomic studies, then define unique peptide sequence for each individual variety as specifically quantitative basis. Stable isotope labeled peptide will also be applied to minimize the influence of matrix effect and enzyme digestion efficiency, which made it possible to achieve accurate quantification of different rice proteins. In the meantime, 'Near infrared' (NIR) spectrum instruments will be used to develop rice authentic spectrum database and chemometrics modeling for rapid on-site purpose. Evidence of aromatic small molecules and ester compounds will be used to generate rice flavor and smell fingerprinting database. Eventually this toolbox combining all parts together is expected to implement rice authenticity detection by integration of protein properties, flavors and screening rapidly and accurately. The attempt of this project in methodology discovering brings new thoughts and insights into rice fraud detection, provides theoretical basis and technical support for food safety supervision which has remarkable significance theoretically and practically.

国际食品安全已从传统Food Safety 到Food Integrity。大米是中国人主食，其质量安全关乎国计民生与国家安全。而目前针对大米真实性检测研究非常有限，传统的检测指标与检测技术，已经无法适应现今对大米真实性保障及掺假检测技术的需求。因此，开发有效大米真实性检测技术刻不容缓。本项目结合申请人前期研究基础，首次将非靶向与靶向蛋白质组学技术引进大米品种、品质与产地指纹鉴定，并结合近红外光谱技术的现场快速判断能力，构建大米真实性样品近红外光谱学和非靶向蛋白质组学数据库；鉴定出特征蛋白，建立特异性肽段的选择与靶向蛋白质组学方法；基于地标大米的分子特征向量差异的寻找，构建芳香族小分子与酯类化合物质谱特征库；最终形成蛋白质与特征风味小分子特征相关联的快速、有效大米真实性检测技术。本项目研究将为我国大米真实性检测提供新思路、新方法，为食品安全监管提供理论依据与技术支持，具重要的理论和现实意义

国际食品安全近年来已从传统食品安全观念逐步扩展到食品完整性概念。大米作为中国人的主食，其质量与安全关乎国计民生与国家长期发展战略。目前现有的针对大米真实性检测相关研究非常有限，传统的检测指标与检测技术，已经无法应对现今对大米真实性保障及掺假复杂程度的需求。本项目通过在与英国贝尔法斯特女王大学（Queen’s University of Belfast）搭建的国际合作研究框架下，从原产地采集了数个中国本土著名地理标识大米与其他国际市场知名亚洲国家出产的主流高价值大米的真实性样品。随后使用多种光谱与质谱学检测分析技术手段，对样品进行了多维度、多层级的非靶向性指纹图谱分析，并结合化学计量学统计建模手段，建立了一系列不同的地理来源分类鉴别模型。对大米样品中的相关蛋白，建立了相应的提取分离与多肽的采集回收方法，但相关的分析建模结果并不理想，模型准确度受到大米加工过程中的研磨程度、贮藏、基质干扰差异等变量影响较大。改为使用代谢组学相关分析，通过建立代谢组学指纹图谱模型为大米地理溯源提供理论依据。同时，还通过引入拉曼光谱作为信号检测器，并以金纳米星作为增强基底，结合纳米酶技术，使用新型检测技术手段对蛋白质分析进行了探索。通过研发新型的共价有机框架材料（COFs）复合金纳米颗粒（AuNPs doped COFs）作为纳米酶，实现了β乳球蛋白的超高灵敏度表面增强拉曼（SERS）检测，并为食品中致敏原检测提供了新的技术手段与支持。更进一步通过与智能手机终端交互，实现蛋白检测的可视化与便携化。相关结果在2018-2020连续三年在国际食品保护协会（lnternational Association of Food Protection）在华举办的中国国际食品安全与质量控制会议（CIFSQ）以及2019 年食品真实性技术与产业发展国际论坛暨食品真实性技术国际联合研究中心年会上通过大米真实性分论坛与报告的形式进行展示，并以国际合作发表形式发表于高水平期刊。