A Platform for High‐Throughput Chemical Analysis of Foods: Characterization of Extra Virgin Olive by Direct Mass Spectrometry and Machine Learning

ABSTRACT

Poor diet is now the leading cause of early death globally. In part, this is because our complex food supply chains are increasingly at risk of overprocessing, contamination, low nutrient content, and economically motivated fraud. Chemical testing can offer insights into these concerns, but testing methods are frequently impractical. Extra virgin olive oil (EVOO) is a premium food of high nutritional value, but because of its growing popularity and high price, it can be a target for mislabeling, substitution, dilution, and/or false claims of origin. Rapid and accurate testing methods for its characterization are therefore increasingly important. We used two direct forms of mass spectrometry (MS)—laser desorption/ionization (LDI) MS and direct analysis in real time (DART) MS—to obtain complex chemical signatures of edible oils. The data generated on a set of reference samples were then used to develop and train three independent machine learning (ML) models that assess key characteristics of a test oil. We also developed a proof‐of‐concept DART‐MS/MS assay add‐on for the quantification of bioactive phenols in EVOO. Our approach accurately predicts several attributes of an edible oil based on novel markers and intricate patterns within the acquired data. Further, pure reference standards and an isotopically‐labeled internal standard allow accurate quantification of the constituent phenols. Because there is no chromatography, both the mass fingerprints and quantification can be performed in seconds–minutes. The method uses low (milliliter) volumes of sample and green solvents, and when combined with ML, it offers rapid data analysis and comprehensive result interpretation.