From Sensomics to AI smelling and Computer Vision: Exploring the chemical sensory code of premium chocolate

Multidimensional chromatography has been successfully applied for many years as a core technology for odorant patterns characterization in the flavor and fragrance field. Very recently a sensomics-based expert system (SEBES) capable to predict key-aroma signatures of food without using human olfaction has been implemented in a comprehensive two-dimensional gas chromatography (GC×GC) platform. The strategy, also referred to as Artificial Intelligence Smelling, conceptually opens many opportunities for odorants pattern recognition, accurate quantification avoiding time-consuming sample preparation/extraction steps, and sample sensory qualification/discrimination based on Computer Vision strategies. Moreover, due to the information density of each analysis, fingerprinting can be extended to different sample features (e.g., origin traceability, shelf-life evolution, processing impact etc.). The contribution illustrates the potential of Artificial Intelligence Smelling and Computer Vision realized by advanced multi-dimensional gas chromatography platforms in cocoa and chocolate research. Origin identitation is by pattern recognition – i.e., a computer vision approach – processing signaturing is by visual fingerprinting while aroma blueprinting is by AI smelling. A single measure answers multiple questions.