Artificial Intelligence Sensing: Effective Flavor Blueprinting of Tea Infusions for a Quality Control Perspective.

Tea infusions are the most consumed beverages in the world after water; their pleasant yet peculiar flavor profile drives consumer choice and acceptance and becomes a fundamental benchmark for the industry. Any qualification method capable of objectifying the product's sensory features effectively supports industrial quality control laboratories in guaranteeing high sample throughputs even without human panel intervention. The current study presents an integrated analytical strategy acting as an Artificial Intelligence decision tool for black tea infusion aroma and taste blueprinting. Key markers validated by sensomics are accurately quantified in a wide dynamic range of concentrations. Thirteen key aromas are quantitatively assessed by standard addition with in-solution solid-phase microextraction sampling followed by GC-MS. On the other hand, nineteen key taste and quality markers are quantified by external standard calibration and LC-UV/DAD. The large dynamic range of concentration for sensory markers is reflected in the selection of seven high-quality teas from different geographical areas (Ceylon, Darjeeling Testa Valley and Castleton, Assam, Yunnan, Azores, and Kenya). The strategy as a sensomics-based expert system predicts teas' sensory features and acts as an AI smelling and taste machine suitable for quality controls.

Black tea volatiles fingerprinting by comprehensive two-dimensional gas chromatography - Mass spectrometry combined with high concentration capacity sample preparation techniques: Toward a fully automated sensomic assessment.

Tea prepared by infusion of dried leaves of Camellia sinensis (L.) Kuntze, is the second world's most popular beverage, after water. Its consumption is associated with its chemical composition: it influences its sensory and nutritional quality addressing consumer preferences, and potential health benefits. This study aims to obtain an informative chemical signature of the volatile fraction of black tea samples from Ceylon by applying the principles of sensomics. In particular, several high concentration capacity (HCC) sample preparation techniques were tested in combination with GC×GC-MS to investigate chemical signatures of black tea volatiles. This platform, using headspace solid phase microextraction (HS-SPME) with multicomponent fiber as sampling technique, recovers 95% of the key-odorants in a fully automated work-flow. A group 123 components, including key-odorants, technological and botanical tracers, were mapped. The resulting 2D fingerprints were interpreted by pattern recognition tools (i.e. template matching fingerprinting and scripting) providing highly informative chemical signatures for quality assessment.