Food adulteration is a threat to public health and the economy. In order to determine food adulteration efficiently, rapid and easy-to-use on-site analytical methods are needed. In this study, a miniaturized mass spectrometer in combination with three ambient ionization methods was used for food authentication. The chemical fingerprints of three milk types, five fish species, and two coffee types were measured using electrospray ionization, desorption electrospray ionization, and low temperature plasma ionization. Minimum sample preparation was needed for the analysis of liquid and solid food samples. Mass spectrometric data was processed using the laboratory-built software MS food classifier, which allows for the definition of specific food profiles from reference data sets using multivariate statistical methods and the subsequent classification of unknown data. Applicability of the obtained mass spectrometric fingerprints for food authentication was evaluated using different data processing methods, leave-10%-out cross-validation, and real-time classification of new data. Classification accuracy of 100% was achieved for the differentiation of milk types and fish species, and a classification accuracy of 96.4% was achieved for coffee types in cross-validation experiments. Measurement of two milk mixtures yielded correct classification of >94%. For real-time classification, the accuracies were comparable. Functionality of the software program and its performance is described. Processing time for a reference data set and a newly acquired spectrum was found to be 12 s and 2 s, respectively. These proof-of-principle experiments show that the combination of a miniaturized mass spectrometer, ambient ionization, and statistical analysis is suitable for on-site real-time food authentication.

中文翻译：

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使用微型质谱仪进行实时食品认证

食品掺假对公共卫生和经济构成威胁。为了有效地确定食品掺假，需要快速且易于使用的现场分析方法。在这项研究中，结合了三种环境电离方法的小型质谱仪用于食品认证。使用电喷雾电离，解吸电喷雾电离和低温等离子体电离测量了三种牛奶，五种鱼类和两种咖啡的化学指纹。分析液体和固体食物样品需要最少的样品准备。使用实验室内置的MS食品分类器处理质谱数据，该方法允许使用多元统计方法从参考数据集中定义特定的食物概况，并随后对未知数据进行分类。使用不同的数据处理方法，留出10％的交叉验证以及对新数据进行实时分类，评估了所获得的质谱指纹图谱在食品认证中的适用性。在交叉验证实验中，牛奶类型和鱼类种类的分类精度达到100％，咖啡类型的分类精度达到96.4％。两种牛奶混合物的测量结果得出正确分类率> 94％。对于实时分类，准确性是可比较的。描述了软件程序的功能及其性能。发现参考数据集和新获取的光谱的处理时间分别为12 s和2 s。这些原理验证实验表明，将微型质谱仪，环境电离和统计分析相结合，适用于现场实时食品认证。