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 T2 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.
Early detection of undesirable deviations in must fermentation using a portable FTIR-ATR instrument and multivariate analysis
Giussani B.;
2019-01-01
Abstract
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 T2 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.
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