Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Andrea Caratti
,
Angelica Fina
,
Fulvia Trapani
ORCID logo ,
Carlo Bicchi
ORCID logo ,
Erica Liberto
ORCID logo ,
Chiara Cordero
* ORCID logo ,
Federico Magagna
Version 1 : Received: 8 January 2024 / Approved: 9 January 2024 / Online: 9 January 2024 (09:20:56 CET)

A peer-reviewed article of this Preprint also exists.

Caratti, A.; Fina, A.; Trapani, F.; Bicchi, C.; Liberto, E.; Cordero, C.; Magagna, F. Artificial Intelligence Sensing: Effective Flavor Blueprinting of Tea Infusions for a Quality Control Perspective. Molecules 2024, 29, 565. Caratti, A.; Fina, A.; Trapani, F.; Bicchi, C.; Liberto, E.; Cordero, C.; Magagna, F. Artificial Intelligence Sensing: Effective Flavor Blueprinting of Tea Infusions for a Quality Control Perspective. Molecules 2024, 29, 565.

Abstract

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.

Keywords

flavor blueprint; black tea infusions; sensomics-based-expert-system; industrial quality control; Artificial Intelligence sensing; accurate flavor screening

Subject

Chemistry and Materials Science, Food Chemistry

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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