ABSTRACT. The increasingly abundant food fraud cases have brought food authenticity and 1 safety into major focus. In this study, we present a fast and effective way to identify meat 2 products using rapid evaporative ionization mass spectrometry (REIMS). The experimental setup 3 was demonstrated to be able to record a mass spectrometric profile of meat specimens in a time 4 frame of less than 5 seconds. A multivariate statistical algorithm was developed and successfully 5 tested for the identification of animal tissue with different anatomical origin, breed and species 6 with 100% accuracy at species and 97% accuracy at breed level. Detection of the presence of 7 meat originating from a different species (horse, cattle, and venison) has also been demonstrated 8 with high accuracy using mixed patties with a 5% detection limit. REIMS technology was found 9to be a promising tool in food safety applications providing a reliable and simple method for the 10 rapid characterization of food products. 11 12

An automated chip-based electrospray platform was used to develop a high-throughput nanoelectrospray high resolution mass spectrometry (nESI-HRMS) method for multiplexed parallel untargeted and targeted quantitative metabolic analysis of the urine samples. The method was demonstrated to be suitable for metabolic analysis of large sample numbers and can be applied to large-scale epidemiological and stratified medicine studies. The method requires a small amount of sample (5 μL of injectable volume containing 250 nL of original sample), and the analysis time for each sample is three minutes per sample to acquire data in both negative and positive ion modes. Identification of metabolites was based on the high resolution accurate mass and tandem mass spectrometry using authentic standards.The method was validated for 8 targeted metabolites and was shown to be precise and accurate. The mean accuracy of individual measurements being of 106% and the intra-and inter-day precision (expressed as relative standard deviations) were 9% and 14%, respectively. Selected metabolites were quantified by standard addition calibration using the stable isotope labelled internal standards in a pooled urine sample, to account for any matrix effect. The multiple point standard addition calibration curves yielded correlation coefficients greater than 0.99, and the linear dynamic range was more than three orders of magnitude. As a proof-of-concept the developed method was applied for targeted quantitative analysis of a set of 101 urine samples obtained from female participants with different pregnancy outcomes. In addition to the specifically targeted metabolites, several other metabolites were quantified relative to the internal standards. Based on the calculated concentrations, some metabolites showed significant differences according to different pregnancy outcomes. The acquired high resolution full-scan data were used for further untargeted fingerprinting and improved the differentiation of urine samples based on pregnancy outcome.