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.

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 industry. Any qualification method capable of objectifying the product's sensory features effectively supports industrial quality control laboratories guaranteeing high sample throughputs even without the 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 LV-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 acts as an AI smelling and taste machine predicting teas sensory features without the human panel intervention.

This study investigated whether a metabolically characterized lactic acid bacteria consortium could be effectively applied in a propagated Type IV sourdough system and how it compared with conventional sourdough starters under different fermentation regimes. Six breads produced by Type II and propagated sourdough processes, with or without bacterial protease, were compared with baker's yeast bread. Despite the shorter fermentation time, breads made with the defined consortium showed in vitro protein digestibility, predicted glycemic index, amino acid composition, and protein quality indices within the range of conventional sourdough breads. Volatile profiling revealed a shared sourdough-associated pattern dominated by organic acids and unsaturated alcohols, while also indicating a distinct Type IV profile linked to relatively greater contributions of acetic acid, (E)-2-hexenol, (E,E)-2,4-decadien-1-ol, and 1-octen-3-ol. Multivariate integration further showed that the defined consortium promoted a more acidification-associated and compositionally differentiated bread phenotype.

The microbial spoilage of raw meat is modulated by the packaging atmosphere, yet the volatile fingerprints underlying these processes remain only partially resolved. This study establishes a structured volatilome fingerprinting workflow based on comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC × GC-TOFMS) to analyze pork spoilage under air, high-N₂, and high-O₂/CO₂ packaging conditions. The applied approach achieved a more comprehensive and confident chemical characterization of the volatilome when compared to currently available results with conventional gas chromatography mass spectrometry (GC-MS) or real-time mass spectrometry. A total of 199 volatile organic compounds (VOCs) were detected, including numerous low-abundance and metabolically related compounds that were annotated by complementary orthogonal criteria. Multivariate analysis based on selected VOCs revealed atmosphere-specific volatilome trajectories and identified key spoilage markers including pyruvate-derived metabolites (acetoin, 2,3-butanedione, 2,3-butanediol), 3-methyl-1-butanol and several aldehydes and ketones. Overall, GC × GC provides enhanced chemical resolution and thus delivers new insights into raw pork spoilage mechanisms via uncovering additional trace-level metabolites not previously observed by other approaches. Expanding and rigorously annotating the meat volatilome would reinforce spoilage assessment and targeted preservation strategies, through resolving microbial metabolism and accurately discriminating spoilage trajectories.