Bedoukian   RussellIPM   RussellIPM   Piezoelectric Micro-Sprayer


Home
Animal Taxa
Plant Taxa
Semiochemicals
Floral Compounds
Semiochemical Detail
Semiochemicals & Taxa
Synthesis
Control
Invasive spp.
References

Abstract

Guide

Alphascents
Pherobio
InsectScience
E-Econex
Counterpart-Semiochemicals
Print
Email to a Friend
Kindly Donate for The Pherobase

« Previous AbstractChemometric Modeling of Coffee Sensory Notes through Their Chemical Signatures: Potential and Limits in Defining an Analytical Tool for Quality Control    Next Abstract"Characterization of bioactive, chemical, and sensory compounds from fermented coffees with different yeasts species" »

J Agric Food Chem


Title:Chromatographic Fingerprinting Strategy to Delineate Chemical Patterns Correlated to Coffee Odor and Taste Attributes
Author(s):Bressanello D; Marengo A; Cordero C; Strocchi G; Rubiolo P; Pellegrino G; Ruosi MR; Bicchi C; Liberto E;
Address:"Dipartimento di Scienza e Tecnologia del Farmaco, Universita degli Studi di Torino, Via Pietro Giuria 9, 10125 Turin, Italy. Lavazza S.p.A., Strada Settimo 410, 10156 Turin, Italy"
Journal Title:J Agric Food Chem
Year:2021
Volume:20210407
Issue:15
Page Number:4550 - 4560
DOI: 10.1021/acs.jafc.1c00509
ISSN/ISBN:1520-5118 (Electronic) 0021-8561 (Linking)
Abstract:"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"
Keywords:Coffee Gas Chromatography-Mass Spectrometry *Odorants/analysis Solid Phase Microextraction Taste *Volatile Organic Compounds/analysis Hs-spme-gc-ms Lc-uv/dad chemometrics sensory data;
Notes:"MedlineBressanello, D Marengo, A Cordero, C Strocchi, G Rubiolo, P Pellegrino, G Ruosi, M R Bicchi, C Liberto, E eng 2021/04/08 J Agric Food Chem. 2021 Apr 21; 69(15):4550-4560. doi: 10.1021/acs.jafc.1c00509. Epub 2021 Apr 7"

 
Back to top
 
Citation: El-Sayed AM 2024. The Pherobase: Database of Pheromones and Semiochemicals. <http://www.pherobase.com>.
© 2003-2024 The Pherobase - Extensive Database of Pheromones and Semiochemicals. Ashraf M. El-Sayed.
Page created on 22-11-2024