Abstract
Food authentication is very important to protect consumers, sellers, and producers from fraud. Although several methods have been developed using a wide range of analytical techniques, most of them require sample destruction and do not allow in situ sampling or analysis, nor reliable quantification of hundreds of molecules at the same time. To overcome these limitations, we have developed and validated a new noninvasive analytical workflow for food authentication. The method uses a functionalized strip to adsorb small molecules from the surface of the food product, followed by gas chromatography–mass spectrometry analysis of the desorbed analytes. We validated the method and applied it to the classification of five different apple varieties. Molecular concentrations obtained from the analysis of 44 apples were used to identify markers for apple cultivars or, in combination with machine learning techniques, to perform cultivar classification. The overall reproducibility of the method was very good, showing a good coefficient of variation for both targeted and untargeted analysis. The approach was able to correctly classify all samples. In addition, the method was also used to detect pesticides and the following molecules were found in almost all samples: chlorpyrifos-methyl, deltamethrin, and malathion. The proposed approach not only showed very good analytical performance, but also proved to be suitable for noninvasive food authentication and pesticide residue analysis.
Highlights
Interest in and awareness of food traceability and authenticity is steadily increasing.At the same time, new analytical methods are urgently needed to help enforce regulations and detect illegal activities while protecting consumers from fraudulent and dangerous practices
Several spectroscopic techniques have been developed to characterize lytes were determined by gas chromatography–mass spectrometry
Among the most important features selected by the algorithm for classification, the best biomarkers identified by monovariate analysis were alpha-farnesene, anthrallinic acid, 1-undecanol, and heneicosane 3-methyl. These results suggest that our sampling technique using the strips, combined with a gas chromatography–mass spectrometry method and a machine learning approach, can be used for noninvasive classification of apple cultivars, counteracting food fraud in the agricultural products field
Summary
Interest in and awareness of food traceability and authenticity is steadily increasing.At the same time, new analytical methods are urgently needed to help enforce regulations and detect illegal activities while protecting consumers from fraudulent and dangerous practices. Several new methods for the chemical classification of food products have been developed, including spectroscopic techniques [1,2], gas chromatography and liquid chromatography coupled with mass spectrometry [3,4,5], direct analysis in real-time mass spectrometry (DART-MS) [6,7], and electronic nose approaches [8]. All of these methods have always been coupled with multivariate techniques [9] and, more recently, with machine learning methods [10] to perform the automatic and increasingly accurate classification of foods based on chemical information
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