Chemistry behind pleasure: exploring aroma quality by multidimensional analytical techniques

Over the last decades, consumer preferences have favored healthier and more flavorsome food with higher nutritional value: the driving force has been food quality. Although the primary condition for food quality is safety, an equally important role is played by its sensory impact, in particular flavor and appearance. Food constituents with their peculiar quali-quantitative distribution concur to define a characteristic chemical fingerprint which encrypts comprehensive information on nutritional properties, sensory attributes, quality, authenticity and safety. Modern omics disciplines, as food metabolomics and sensomics [1,2], are the elective strategies for a comprehensive evaluation of food attributes, they combine multi-dimensional analytical approaches in sensu lato to include in the investigation all constituents considered collectively (primary and specialized metabolites, compounds generated by thermal treatments and/or enzymatic activity, flavour active compounds). High quality hazelnuts (Corylus avellana L.) from different botanical/geographical origin are here considered as challenging test bench for multidimensional investigations while trajectories from Academic research to industy application will be discussed as virtuous interactions to achieve a modern concept of food quality. Targeted and un-targeted fingerprinting [3,4] aiming to reveal hazelnuts aroma potential, to define aroma blueprint or spoilage traits will be presented emphasizing the advantages of a true multidimensionality, from sample preparation (the zeroth dimension of an analytical system) to separation (two-dimensional comprehensive chromatography) and analyte detection and identification (mass spectrometry). Reliable, effective and intuitive data analysis approaches conclude the analytical investigation enabling a productive and comprehensive interpretation of the complex information array in particular by linking analytical data to hazelnut quality attributes. Computer Vision and Artificial Intelligence smelling [5] might be effective decision makers with strong correlation to distinctive chemical patterns. References 1. Dunkel, A.; Steinhaus, M.; Kotthoff, M.; Nowak, B.; Krautwurst, D.; Schieberle, P.; Hofmann, T. Nature’s Chemical Signatures in Human Olfaction: A Foodborne Perspective for Future Biotechnology. Angew. Chemie - Int. Ed. 2014, 53 (28), 7124–7143. 2. Cordero, C.; Kiefl, J.; Schieberle, P.; Reichenbach, S. E.; Bicchi, C. Comprehensive Two-Dimensional Gas Chromatography and Food Sensory Properties: Potential and Challenges. Analytical and Bioanalytical Chemistry. Springer Verlag 2015, pp 169–191. 3. Cordero, C.; Kiefl, J.; Reichenbach, S. E.; Bicchi, C. Characterization of Odorant Patterns by Comprehensive Two-Dimensional Gas Chromatography: A Challenge in Omic Studies. TrAC - Trends in Analytical Chemistry. 2019, pp 364–378. 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. 5. 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 (14), 4011–4022.