Ultra-high pressure homogenization (UHPH) is a fast and efficient technique that can sterilize fluid foods at low temperatures or even under cooling conditions. A white must (Vitis vinifera L.) was processed at 300 MPa (inlet temperature 20 ºC, in-valve temperature 98 ºC, outlet temperature 25 ºC, and time in valve 0.02 s) and their performance was compared with two untreated controls, a must that underwent a spontaneous fermentation (without SO2 addition) and another must that was sulfited with 35 mg/L of total SO2 and inoculated with the same Saccharomyces cerevisiae yeast as the UHPH-treated must. UHPH treatment led to the total elimination of grape microorganisms considering an initial population of 1x10 6 CFU/mL in average of wild yeasts and fungi in must, and approximately 7x10 3 CFU/mL of background bacteria. In a parallel assay, UHPH-processed must without yeast inoculation showed absence of fermentation for eight days at 18 ºC. The musts treated with UHPH showed a lighter appearance (10%) before fermentation compared to the control. The triangular test verified the existence of sensory differences between the wines obtained and the preference tests showed that the judges found the wine obtained from the UHPH-treated must more fruity (3.5/5 compared with 1.5-2 in controls) and with better aroma. Industrial relevance UHPH is an interesting way to process the must before fermentation allowing the reduction of sulfite addition while controlling wild and spoilage microorganisms.

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Production of sparkling wines involve a second alcoholic fermentation and contact with yeast less over an extended period of time, which influences the aroma composition and sensory quality of the resulting wines. Sparkling wines obtained with two yeast strains inoculated as free cells, immobilized in alginate bed and bioimmobilized as biocapsules, were aged during 32 months. Among the volatile compounds, high Odor Activity Values were obtained with isoamyl acetate, ethyl propanoate, ethyl butanoate, ethyl 3-methylbutanoate, ethyl hexanoate, ethyl octanoate, hexanol, 2-methoxy-4-vinylphenol, decanal, octanoic acid, decanoic acid and TDN. Taken together these contribute more than 70% of the overall aromatic series value. Although some results rely more on the yeast strain than the inoculation format, specific aroma compounds were associated with the immobilization format, allowing the classification of sparkling wines by PCA. As a result the aroma quality of sparkling wines could be improved using immobilized yeasts.

Please cite this article in press as: X. Cetó, et al., Instrumental measurement of wine sensory descriptors using a voltammetric electronic tongue, Sens. Actuators B: Chem. (2014), http://dx. a b s t r a c tThe approach presented herein reports the application of a voltammetric electronic tongue (ET), in contrast with a wine tasting sensory panel, as a tool for standardized wine tasting; concretely, to achieve the discrimination of different wine DOs (Denominación de Origen, a mark related to its geographical region and ensuring high-quality levels) and the prediction of the global score assigned by the trained sensory panel. To this aim, a voltammetric array of sensors based on metallic and bulk-modified graphite electrodes was used as the sensing part, while chemometric tools such as linear discriminant analysis (LDA) and artificial neural networks (ANNs) were used as the qualitative and quantitative modelling tools. Departure information was the set of voltammograms, which were first preprocessed employing fast Fourier transform(FFT), followed by removal of less-significant coefficients employing a stepwise inclusion method and pruning of the inputs. The trend, in global scores, was modelled successfully with a 92.9% of correct identification for the qualitative application, and a correlation coefficient of 0.830 for the quantitative one (with 14 and 20 samples for the external test subsets, respectively).

This work reports the application of an electronic tongue as a tool towards the analysis of wine in tasks such as its discrimination based on the maturing in barrels or the prediction of the global scores assigned by a sensory panel. To this aim, red wine samples were first analysed with the voltammetric sensor array, without performing any sample pretreatment. Afterwards, obtained responses were preprocessed employing fast Fourier transform (FFT) for the compression and reduction of signal complexity, and obtained coefficients were then used as inputs to build the qualitative and quantitative models employing either linear discriminant analysis (LDA) or partial least squares regression (PLS), respectively. Satisfactory results were obtained overall, with a classification rate of 100% in the discrimination of the type of barrel used during wine maturing, a normalized NRMSE of 0.077 in the estimation of ageing time (months) or 0.11 in the prediction of the scores (0-10) from a trained sensory panel (all for the external test subset).