基于直接质谱技术的蜂蜜及其主要掺假物代谢组学研究

There are many kinds of impurities in the honey and the doping method is becoming more and more diverse, which leads to the obvious limitation as a single chemical mark can be detected in many traditional detection methods. Honey contains diversed metabolic chemicals collected by bees from nectariferous plants, therefore, metabonomics analysis could be an effective way to identify the quality, the plant source and the authenticity of honey. This project aims at honey samples from different nectariferous plants or different origins, and honey adulteration such as rice syrup, fructose syrup and high fructose syrup. Based on the optimization of ionization energy sources (such as microwave plasma, corona discharge and electric field spraying, etc.) and energy coupling, a new mass spectrometry platform for metabolism analysis will be developed for the direct analysis of honey samples without sample pretreatment. The obtained mass metabonomics fingerprint data will be processed for intelligent fingerprint recognition on the basis of chemometrics methods, such as principal component analysis, cluster analysis, discriminant analysis, factor analysis and artificial neural network. The relationship will then be clear between different honey samples and their metabonomics fingerprints, and the metabolic fingerprint of the typical samples will be distinguished. Furthermore, to identify the main typically characteristic metabolites of the natural honey samples and the adulterated ones, the metabolic chemicals that could distinguish the quality of honey samples and adulterants were analyzed by tandem mass spectrometry. Base on the above researches, a direct mass spectrometry method will be established for the rapid detection and the evaluation of honey authenticity. And this method could be used to evaluate the quality of honey samples. This project focuses on the metabonomics, and provides new technology and ideas for the research on the identification and quality evaluation of honey authenticity, using newly emerging direct mass spectrometry technology. The research results will provide the related scientific basis for the identification of honey quality and authenticity. This research will be benefit for the healthy development of the beekeeping industry in China and safeguarding the interests of consumers. Furthermore, it could provide a new method for the metabonomics study of other complex food systems.

蜂蜜掺杂物种类繁多且掺杂手段日益多样，导致单一指标检测的判别方法存在明显局限性。代谢组学分析将是蜂蜜品质识别的有效途径。本项目拟以不同来源蜂蜜及蜂蜜掺假物为实验材料，经优化电离能量源和实验条件，建立无需样品预处理的蜂蜜代谢组学直接质谱分析平台；在获得大量代谢组学指纹图谱基础上，利用化学计量学对图谱进行智能化处理和识别，明确不同样品与代谢组学指纹图谱关系，区分典型样品的代谢指纹图谱，并通过多级串联质谱分析，鉴定蜂蜜和掺假物的主要典型代谢物；在此基础上，建立基于直接质谱技术的蜂蜜真伪鉴定的快速检测技术和评价模型，并用于实际蜂蜜的品质评价。项目从代谢组学角度，利用新兴的直接质谱技术，为蜂蜜真伪鉴别和品质评价研究提供了新技术和思路，不仅有利于维护消费者利益和我国养蜂业的健康发展，也为其它复杂食品体系代谢组学的研究提供了新方法。

蜂蜜掺杂物种类繁多且掺杂手段日益多样，导致单一指标检测的判别方法存在明显局限性。代谢组学分析是蜂蜜品质识别的有效途径。本项目以不同来源蜂蜜及蜂蜜掺假物为实验材料，经优化电离能量源和实验条件，建立了无需样品预处理的蜂蜜代谢组学直接质谱分析平台（EESI-MS），结合化学计量学的方法用于快速鉴别油菜蜜、枣花蜜、椴树蜜、洋槐蜜和山乌桕蜜等5种蜂蜜；基于电喷雾萃取高分辨电离质谱（EESI-HRMS）测定花蜜和蜂蜜中的酚类化合物和氨基酸的方法，结合主成分分析（PCA）探究花蜜化合物对蜂蜜组成的影响；以三种蜂蜜（洋槐蜜、荆条蜜和枣花蜜）和三种掺假糖浆（玉米糖浆、高果玉米糖浆和转化糖浆）为研究材料，采用基质辅助激光解吸电离质谱分析了蜂蜜及其掺假样品中的糖类以及小分子代谢物，提供了一种无需样品预处理的高通量蜂蜜掺假检测和蜂蜜植物源鉴别分析方法，也为其他高品质的蜂蜜产品和高碳水化合物食品监测提供了新思路；项目从代谢组学角度，利用新兴的直接质谱技术，为蜂蜜真伪鉴别和品质评价研究提供了新技术和思路，不仅有利于维护消费者利益和我国养蜂业的健康发展，也为其它复杂食品体系代谢组学的研究提供了新方法。项目执行期间，发表标注该基金资助论文18篇（SCI 14篇，中文4篇），培养博士后1名，博士2名，硕士5名，申请专利6项（授权4项）。