Application of hand-held near-infrared and Raman spectrometers in surface treatment authentication of cork stoppers

The aim of this paper was to evaluate the potential of using near-infrared (NIR) spectroscopy and multivariate analysis as a rapid tool to non-destructively determine the presence of surface treatments applied to cork stoppers. Density and dimensions of 6 closure varieties were characterized and the extraction force was measured on those produced for still wines. Cork stoppers were also analyzed using hand-held NIR and Raman spectrometers. Soft independent modelling of class analogy (SIMCA) models showed significant differences among treated and untreated samples, linked to components of the coating agents applied (silicone and paraffin). SIMCA model''s classification performance was tested and high sensitivity (93.33 %) and specificity (100 %) values were obtained. Partial least squares regression (PLSR) model accurately predicted the extraction forces measured with low standard error of prediction (SEP = 4.0 daN). Our results are promising for the future application of this technology in cork industry, reducing time and economic losses. © 2021 The Author(s

Early detection of undesirable deviations in must fermentation using a portable FTIR-ATR instrument and multivariate analysis

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\u2014YAN) 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