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The content of phenolic compounds determines the state of phenolic ripening of red grapes and is a key criterion in setting the harvest date to produce quality red wines. In this study, the feasibility of Fourier transform mid-infrared (FT-MIR) spectroscopy combined with partial least-squares (PLS) regression to quantify phenolic compounds is reported. The reference methods used for quantifying these compounds (which were evaluated as total phenolic compounds, total anthocyanins, and condensed tannins) were the usual ones used in cellars that employed UV−vis spectroscopy. To take into account the high natural variability of grapes when building the calibration models, fresh grapes from six varieties, at different phenolic ripening states were harvested during three vintages. Destemmed and crushed grapes were subjected to an accelerated extraction process and used as calibration standards. A total of 192 extracts (objects) were obtained, and these were divided into a training set (106 objects) and a test set (86 objects) to evaluate the predictive ability of the models. Among the different MIR regions of the extract raw spectra, those that provided the highest variability on the absorption were selected. The results showed that the best PLS regression model was the one obtained when working in the region of 1168−1457 cm−1 because it gave the most accurate and robust prediction for total phenolic compounds (RMSEP % = 4.3 and RPD = 4.5), total anthocyanins (RMSEP % = 5.9 and RPD = 3.5), and condensed tannins (RMSEP % = 5.8 and RPD = 3.8). Therefore, it can be concluded that FT-MIR spectroscopy can be a fast and reliable technique for monitoring the phenolic ripening in red grapes during the harvest period.

An electronic nose based on coupling of headspace (HS) with a mass spectrometer (MS) has been used in this study to classify and characterize a series of beers according to their production site and chemical composition. With this objective, we analyzed 67 beers of the same brand and preparation process but produced in different factories. The samples were also subjected to sensory evaluation by a panel of experts. Linear discriminant analysis (LDA) was used as the classification technique and stepwise LDA based on Wilk's lambda criterion was used to select the most discriminating variables. To interpret the aroma characteristics of the beers from the m/z ions obtained, score and loading bi-plots were obtained by applying canonical variables. Because the beers analyzed were marketed with the same name and brand, we expected to be working with the same product irrespective of its origin. However, results from both sensory evaluation and use of the e-nose revealed differences between factories. With the e-nose it was possible to relate these differences to the presence (and abundance) of characteristic ions of different compounds typically found in beer. These results demonstrate that the HS-MS e-nose is not only an aroma sensor capable to classify and/or differentiate samples but it can also provide information about the compounds responsible for this differentiation.

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We present a rapid method to quantify phenolic compounds all during the red winemaking process using Fourier transform mid-infrared (FT-MIR) spectroscopy and chemometrics. To get the reference values, we used the usual UV–vis spectroscopy methods, and the compounds studied were evaluated as total phenolic compounds (TPC), total anthocyanins (TA), and condensed tannins (CT). Sampling from five different grape varieties (Merlot, Tempranillo, Syrah, Cariñena, and Cabernet sauvignon), harvested at different ripening states, and monitored over 10 days of vinification produced a total of 600 spectra. These were used to build and validate four different predictive models by partial least-squares (PLS) regression. The spectral regions selected for each model were between 979 and 2989 cm(–1), and when selecting the most suitable one in each case, good values of performance parameters were obtained (R2(val) > 0.95 and RPD > 4.0 for TPC; R2(val) > 0.90 and RPD > 3.0 for TA; R2(val) < 0.8 and RPD < 3.0 for CT). Furthermore, also more specific PLS regression models for each phenolic parameter and each grape variety were developed using different regions with results similar to those obtained when dealing with all of the grape varieties. It is concluded that FT-MIR spectroscopy together with multivariate calibration could be a rapid and valuable tool for wineries to carry out the monitoring of phenolic compound extraction during winemaking.

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