Real-Time Food Authentication Using a Miniature Mass Spectrometer

Abstract

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.