SinS - Abstract

Abstract Information :

Comprehensive two-dimensional gas chromatography (GC×GC) is a powerful analytical platform for effective investigations in food-omics [1]: volatilomics, sensomics, and metabolomics. It combines the information capacity of profiling with the opportunities and flexibility of fingerprinting strategies. Compared to mono-dimensional (1D)-GC, the comprehensive combination of two separation dimensions results in analytical platforms with remarkable separation power and enhanced sensitivity. In addition, the retention logic for structurally correlated components generates 2D patterns that are distinctive sample fingerprints that can be explored by Computer Vision tools [2]. GC×GC offers a perspective on samples’ compositional complexity that is particularly useful in challenging situations [3]. The contribution discusses, through applications developed in the author’s laboratory, how Artificial Intelligence algorithms and concepts can act as a booster in food metabolomics research enabling easy access to higher level information [4]. Within the complex volatilome of high-quality hazelnuts, it will be shown how Augmented Visualization strategies can “see” and detect spoilage patterns. By targeting robust markers of quality and key-aroma compounds, the potential of Artificial Intelligence smelling based on sensomic principles [6], will be discussed in the perspective of AI decision-making tools for food quality and traceability in an industrial perspective. References 1. Stilo, F.; Bicchi, C.; Reichenbach, S.E.; Cordero, C. Comprehensive two‐dimensional gas chromatography as a boosting technology in food‐omic investigations. J. Sep. Sci. 2021, 44, 1592–1611, doi:10.1002/jssc.202100017. 2. Danuser, G. Computer vision in cell biology. Cell 2011. 3. Stilo, F.; Bicchi, C.; Robbat, A.; Reichenbach, S.E.; Cordero, C. Untargeted approaches in food-omics: The potential of comprehensive two-dimensional gas chromatography/mass spectrometry. TrAC Trends Anal. Chem. 2021, 135, 116162, doi:10.1016/j.trac.2020.116162. 4. Stilo, F.; Bicchi, C.; Jimenez-Carvelo, A.M.; Cuadros-Rodriguez, L.; Reichenbach, S.E.; Cordero, C. Chromatographic fingerprinting by comprehensive two-dimensional chromatography: Fundamentals and tools. TrAC Trends Anal. Chem. 2021, 134, 116133, doi:10.1016/j.trac.2020.116133. 5. Cuadros-Rodríguez, L.; Ruiz-Samblás, C.; Valverde-Som, L.; Pérez-Castaño, E.; González-Casado, A. Chromatographic fingerprinting: An innovative approach for food “identitation” and food authentication – A tutorial. Anal. Chim. Acta 2016, 909, 9–23, doi:10.1016/j.aca.2015.12.042. 6. Nicolotti, L.; Mall, V.; Schieberle, P. Characterization of Key Aroma Compounds in a Commercial Rum and an Australian Red Wine by Means of a New Sensomics-Based Expert System (SEBES) - An Approach to Use Artificial Intelligence in Determining Food Odor Codes. J. Agric. Food Chem. 2019, 67, 4011–4022, doi:10.1021/acs.jafc.9b00708.