气味
芳香
风味
品味
固相微萃取
化学
感觉系统
模式识别(心理学)
感官分析
食品科学
色谱法
质谱法
气相色谱-质谱法
人工智能
计算机科学
心理学
有机化学
认知心理学
作者
Davide Bressanello,Arianna Marengo,Chiara Cordero,Giulia Strocchi,Patrizia Rubiolo,Gloria Pellegrino,Manuela R. Ruosi,Carlo Bicchi,Erica Liberto
标识
DOI:10.1021/acs.jafc.1c00509
摘要
Coffee cupping includes both aroma and taste, and its evaluation considers several different attributes simultaneously to define flavor quality and therefore requires complementary data from aroma and taste. This study investigates the potential and limits of a data-driven approach to describe the sensory quality of coffee using complementary analytical techniques usually available in routine quality control laboratories. Coffee flavor chemical data from 155 samples were obtained by analyzing volatile (headspace-solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS)) and nonvolatile (liquid chromatography-ultraviolet/diode array detector (LC-UV/DAD)) fractions, as well as from sensory data. Chemometric tools were used to explore the data sets, select relevant features, predict sensory scores, and investigate the networks between features. A comparison of the Q model parameter and root-mean-squared error prediction (RMSEP) highlights the variable influence that the nonvolatile fraction has on prediction, showing that it has a higher impact on describing acid, bitter, and woody notes than on flowery and fruity. The data fusion emphasized the aroma contribution to driving sensory perceptions, although the correlative networks highlighted from the volatile and nonvolatile data deserve a thorough investigation to verify the potential of odor–taste integration.
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