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Early detection of undesirable deviations in must fermentation using a portable FTIR‐ATR instrument and multivariate analysis
Author(s) -
Cavaglia Julieta,
Giussani Barbara,
Mestres Montserrat,
Puxeu Miquel,
Busto Olga,
Ferré Joan,
Boqué Ricard
Publication year - 2019
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.3162
Subject(s) - fermentation , partial least squares regression , principal component analysis , multivariate statistics , chemistry , yeast , linear discriminant analysis , multivariate analysis , mathematics , chromatography , food science , biochemistry , statistics
A portable FTIR‐ATR spectrometer was used to monitor small‐scale must fermentations (microvinifications) with the aims to describe the process and to early detect problematic fermentations. Twenty fermentations at normal operation conditions (NOC) and three fermentations that were intentionally deviated from NOC (yeast assimilable nitrogen deficiency—YAN) were monitored. FTIR‐ATR spectra were registered after a minimum sample pretreatment during the fermentation process. In addition, density, sugars (glucose and fructose), and acetic acid contents were determined by traditional methods. Different multivariate analysis strategies (global and local models) were applied to the spectroscopic data to describe the evolution of the NOC fermentation and to early detect the abnormal fermentations. Global models based on principal component analysis (PCA) and partial least squares‐discriminant analysis (PLS‐DA) allowed to describe the evolution of fermentations in time and to correctly classify NOC and YAN fermentations. Abnormal deviations were successfully detected by developing one model for each sampling time. YAN experiments could be identified 49 hours after the beginning of the fermentations by means of Hotelling T 2 and residual F statistics. In conclusion, ATR‐FTIR coupled to multivariate analysis showed great potential as a fast and simple at‐line analysis tool to monitor wine fermentation and to early detect fermentation problems.