Open AccessFast High–Resolution Mass Spectrometry and Chemometrics for Evaluation of Sensory Parameters of Commercial Coffee Blends
Author(s) -
Victor Gustavo Kelis Cardoso,
Guilherme P. Sabin,
Leandro Wang Hantao
Publication year - 2021
Publication title -
brjac brazilian journal of analytical chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.131
H-Index - 6
eISSN - 2179-3433
pISSN - 2179-3425
DOI - 10.30744/brjac.2179-3425.ar-11-2021
Subject(s) - chemometrics , partial least squares regression , mass spectrometry , chemistry , chromatography , sensory analysis , linear discriminant analysis , roasting , analytical chemistry (journal) , mathematics , pattern recognition (psychology) , artificial intelligence , food science , computer science , statistics
This study presents a fast method to estimate sensory parameters of commercial capsules of roasted coffee using flow injection analysis coupled to high–resolution mass spectrometry (FIA–HRMS) as an alternative to traditional sensory analysis, which is a laborious and subjective method. Over 25 types of coffee capsules were studied. The samples were partitioned into an aqueous and organic extract, which were analyzed by FIA–HRMS in the positive and negative ionization modes. Data fusion of such mass spectra was performed to explore the complementary information of sample preparation and ionization conditions. Orthogonalized partial least square discriminant analysis (OPLS–DA) models were built and trained to determine the type of capsule and to estimate important coffee parameters (e.g., acidity, bitterness, body, intensity, and roasting level), achieving accuracy values higher than 91.1%. In addition, variable importance in projection (VIP) scores enabled assignment of the elemental composition and, in some cases, putative identification of compounds in coffee (e.g., caffeine, caffearine, and quinides) that exhibited an important role in class discrimination.