Critical evaluation of ambient mass spectrometry coupled with chemometrics for the early detection of adulteration scenarios in Origanum vulgare L.

Nowadays, most of the screening methods in food manufacturing are based on spectroscopic techniques. Ambient Mass Spectrometry is a relatively new field of analytical chemistry which has proven to offer similar speed and ease-of-use when compared to other fingerprinting techniques, alongside the advantages of good selectivity, sensitivity and chemical information. Numerous applications have been explored in food authenticity, based either on the target detection of adulteration markers or, less frequently, on the development of multivariate classification models. The aim of the present work was to evaluate and compare the capabilities of Direct Analysis in Real Time (DART) and Atmospheric Solid Analysis Probe (ASAP) Mass Spectrometry (MS) for the high-throughput authenticity screening of commercial herbs and spices products. The gross addition of bulking material to dried Mediterranean oregano was taken as case study. First, a pilot sample set, constituted by authentic dried oregano, olive leaves (a frequently reported adulterant) and mixtures thereof at different levels (i.e. 10, 20, 30 and 50% w/w) was used. Each sample was fingerprinted by both ambient-MS techniques. After appropriate pre-processing, the whole mass spectra were used for the subsequent multivariate data analysis. Soft Independent Modelling of Class Analogy was adopted as classification algorithm and the model was challenged with both new authentic oregano and in-house prepared blends. To the best of our knowledge, this is the first report of DART-MS and ASAP-MS used in full scan mode and coupled to chemometric modelling as rapid fingerprinting approach for food authentication. Although both the techniques provided satisfactory results, ASAP-MS clearly showed greater potential, leading to reproducible, diagnostic feature-rich mass spectra. For this reason, ASAP-MS was further tested under a more convoluted scenario, where the training and validation sets were enlarged with additional authentic oregano samples and a wider range of adulterant species, respectively. Overall good results were achieved, with 93% model predictive accuracy, and screening detection capability estimated between 5-20% (w/w) addition, depending on the adulterant considered with the only exception of majorana. Investigation of Q residuals could highlight the statistically-relevant chemical markers which could be tentatively annotated by coupling the ASAP probe with a high resolution mass analyser. The results from the validation study confirmed the great potential of ASAP-MS in combination with chemometrics as fast MS-based screening solution and demonstrated its feasibility for classification model building.