Monitoring large scale wine fermentations with infrared spectroscopy

Fourier transform infrared spectroscopy in monitoring the wine production

The complexity of the wine matrix makes monitoring of the winemaking process from the grapes to the final product crucial for the wine industry. In this context, analytical methodologies that can combine good accuracy, robustness, high sample throughput, “green character”, and by preference real-time analysis, are on-demand to create high-quality vitivinicultural products. In the last years, Fourier-transform Infrared Spectroscopy (FTIR) combined with chemometric analysis has been evaluated in several studies as an effective analytical tool for the wine sector. Some applications of FTIR spectroscopy have been already accepted by the wine industry, mainly for the prediction of basic oenological parameters, using portable and non-portable instruments, but still many others are waiting to be thoroughly developed. This literature review aims to provide a critical synopsis of the most important studies assessing grape and wine quality and authenticity, and to identify possible gaps for further research, meeting the needs of the modern wine industry and the expectations of most demanding consumers. The FTIR studies were grouped according to the main sampling material used - 1) leaves, stems, and berries; 2) grape must and wine applications - along with a summary of the basic limitations and future perspectives of this analytical technique.

Prediction of quality parameters in straw wine by means of FT-IR spectroscopy combined with multivariate data processing

This study represents the first attempt to combine mid infrared (MIR) spectroscopy and multivariate data processing for prediction of alcohol degree, sugars content and total acidity in straw wine. 302 Italian samples, representing different vintages, production regions and grape varieties, were analysed using FT-MIR spectroscopy and reference methods. New regression functions based on a combination of Orthogonal Signal Correction and Partial Least Squares regression are proposed for prediction of quality parameters: this approach allows overcoming the issue of matrix complexity, reducing spectral interferences and enhancing the information embodied in fingerprinting data. The models proposed are characterised by an excellent reliability, with low error in prediction (alcohol: 0.28%; sugars: 9.9 g/L; acidity: 0.29 g/L) comparable both to reference methods and table wine models. Results demonstrate that vibrational spectroscopy, combined with a proper multivariate data strategy, represents a suitable strategy for the quick and non-destructive assessment of quality parameters of straw wine.

Non-destructive determination of ethanol levels in fermented alcoholic beverages using Fourier transform mid-infrared spectroscopy

BACKGROUND

Traditional fermented alcoholic beverages are indigenous to a particular area and are prepared by the local people using an age-old techniques and locally available raw materials. The main objective of this work was the direct determination of ethanol in traditional fermented alcoholic beverages using mid infrared spectroscopy with partial least squares regression, verifying the robustness of the calibration models and to assess the quality of beverages.

RESULTS

The level of ethanol determination in Ethiopian traditional fermented alcoholic beverages was done using mid infrared spectroscopy with partial least squares regression (MIR-PLS). The calibration and validation sets, and real samples spectra were collected with 32 scans from 850-1200 cm-1. A total of 25 synthetic standards (calibration and validation sets) with ethanol (2-10% w/w) and sugars (glucose, fructose, sucrose and maltose) (0-5% w/w) compositions were used to construct and validate the models. Twenty-five different calibration models were validated by cross-validation approach with 25 left out standards. A large number of pre-treatments were verified, but the best pre-treatment was subtracting minimum + 2nd derivative. The model was found to have the highest coefficients of determination for calibration and cross-validation (0.999, 0.999) and root mean square error of prediction [0.1% (w/w)]. For practical relevance, the MIR-PLS predicted values were compared against the values determined by gas chromatography. The predicted values of the model were found to be in excellent agreement with gas chromatographic measurements. In addition, recovery test was conducted with spiking 2.4-6.4% (w/w) ethanol. Based on the obtained recovery percentage, 85.4-107% (w/w), the matrix effects of the samples were not considerable.

CONCLUSION

The proposed technique, MIR-PLS at 1200-850 cm-1 spectral region was found appropriate to quantify ethanol in fermented alcoholic beverages. Among the studied beverages (Tella, Netch Tella, Filter Tella, Korefe, Keribo, Borde and Birz), the average ethanol contents ranged from 0.77-9.1% (v/v). Tej was found to have the highest ethanol content whereas Keribo had the least ethanol content. The developed method was simple, fast, precise and accurate. Moreover, no sample preparation was required at all. However, it should be noted that the present procedure is probably not usable for regulatory purposes (e.g. controlling labelling).

Sample presentation, sources of error and future perspectives on the application of vibrational spectroscopy in the wine industry

Vibrational spectroscopy encompasses a number of techniques and methods including ultra-violet, visible, Fourier transform infrared or mid infrared, near infrared and Raman spectroscopy. The use and application of spectroscopy generates spectra containing hundreds of variables (absorbances at each wavenumbers or wavelengths), resulting in the production of large data sets representing the chemical and biochemical wine fingerprint. Multivariate data analysis techniques are then required to handle the large amount of data generated in order to interpret the spectra in a meaningful way in order to develop a specific application. This paper focuses on the developments of sample presentation and main sources of error when vibrational spectroscopy methods are applied in wine analysis. Recent and novel applications will be discussed as examples of these developments.

Quantification of phenolic compounds during red winemaking using FT-MIR spectroscopy and PLS-regression

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.

Monitoring of alcoholic fermentation using near infrared and mid infrared spectroscopies combined with electronic nose and electronic tongue

Effective fermentation monitoring is a growing need due to the rapid pace of change in the wine industry, which calls for fast methods providing real time information in order to assure the quality of the final product. The objective of this work is to investigate the potential of non-destructive techniques associated with chemometric data analysis, to monitor time-related changes that occur during red wine fermentation. Eight micro-fermentation trials conducted in the Valtellina region (Northern Italy) during the 2009 vintage, were monitored by a FT-NIR and a FT-IR spectrometer and by an electronic nose and tongue. The spectroscopic technique was used to investigate molecular changes, while electronic nose and electronic tongue evaluated the evolution of the aroma and taste profile during the must-wine fermentation. Must-wine samples were also analysed by traditional chemical methods in order to determine sugars (glucose and fructose) consumption and alcohol (ethanol and glycerol) production. Principal Component Analysis was applied to spectral, electronic nose and electronic tongue data, as an exploratory tool, to uncover molecular, aroma and taste modifications during the fermentation process. Furthermore, the chemical data and the PC1 scores from spectral, electronic nose and electronic tongue data were modelled as a function of time to identify critical points during fermentation. The results showed that NIR and MIR spectroscopies are useful to investigate molecular changes involved in wine fermentation while electronic nose and electronic tongue can be applied to detect the evolution of taste and aroma profile. Moreover, as demonstrated through the modeling of NIR, MIR, electronic nose and electronic tongue data, these non destructive methods are suitable for the monitoring of must-wine fermentation giving crucial information about the quality of the final product in agreement with chemical parameters. Although in this study the measurements were carried out in off-line mode, in future these non destructive techniques could be valid and simple tools, able to provide in-time information about the fermentation process and to assure the quality of wine.

Technical solutions for analysis of grape juice, must, and wine: the role of infrared spectroscopy and chemometrics

Information about constituents of grape juice, must, and wine can be used for management and decision support systems in order to improve, monitor, and adapt grape and wine production to new challenges. Numerous sensors that gather this information are either currently available or in development. Nevertheless there is still a need to adapt these sensors to special requirements, for example robustness, calibration and maintenance, operating costs, duration, sensitivity, and specificity to a particular application. The sensors commonly used by the wine industry are those that are based on mid-infrared (MIR), near-infrared (NIR), visible (VIS) and ultraviolet (UV) spectroscopy. This article reviews some recent technical solutions for analysis of juice, must and wine based on the combination of infrared spectroscopy and chemometrics.

Application of FT-MIR spectroscopy for fast control of red grape phenolic ripening

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.

Data Mining Techniques in Agricultural and Environmental Sciences

Data mining techniques are largely used in different sectors of the economy and they increasingly are playing an important role in agriculture and environment-related areas. This paper aims to show our vision on the importance of knowing and efficiently using data mining and machine learning-related techniques for knowledge discovery in the field of agriculture and environment. Efforts for searching hidden patterns in data are not a recent phenomenon. History shows that extensive observations on data have helped discover empirical laws in different fields of research. Therefore, it is important to provide researchers in agriculture and environmental-related areas with the most advanced knowledge discovery techniques. Data mining is the process of extracting important and useful information from large sets of data. This information can be converted into useful knowledge that could help to better understand the problem in study and to better predict future developments. The paper presents the state of the art in data mining and knowledge discovery techniques and provides discussions for future directions.

Coupling kinetic expressions and metabolic networks for predicting wine fermentations

Problematic fermentations are commonplace and cause wine industry producers substantial economic losses through wasted tank capacity and low value final products. Being able to predict such fermentations would enable enologists to take preventive actions. In this study we modeled sugar uptake kinetics and coupled them to a previously developed stoichiometric model, which describes the anaerobic metabolism of Saccharomyces cerevisiae. The resulting model was used to predict normal and slow fermentations under winemaking conditions. The effects of fermentation temperature and initial nitrogen concentration were modeled through an efficiency factor incorporated into the sugar uptake expressions. The model required few initial parameters to successfully reproduce glucose, fructose, and ethanol profiles of laboratory and industrial fermentations. Glycerol and biomass profiles were successfully predicted in nitrogen rich cultures. The time normal or slow wine fermentations needed to complete the process was predicted accurately, at different temperatures. Simulations with a model representing a genetically modified yeast fermentation, reproduced qualitatively well literature results regarding the formation of minor compounds involved in wine complexity and aroma. Therefore, the model also proves useful to explore the effects of genetic modifications on fermentation profiles.

Comparative study of wine tannin classification using Fourier transform mid-infrared spectrometry and sensory analysis

Wine tannins are fundamental to the determination of wine quality. However, the chemical and sensorial analysis of these compounds is not straightforward and a simple and rapid technique is necessary. We analyzed the mid-infrared spectra of white, red, and model wines spiked with known amounts of skin or seed tannins, collected using Fourier transform mid-infrared (FT-MIR) transmission spectroscopy (400-4000 cm(-1)). The spectral data were classified according to their tannin source, skin or seed, and tannin concentration by means of discriminant analysis (DA) and soft independent modeling of class analogy (SIMCA) to obtain a probabilistic classification. Wines were also classified sensorially by a trained panel and compared with FT-MIR. SIMCA models gave the most accurate classification (over 97%) and prediction (over 60%) among the wine samples. The prediction was increased (over 73%) using the leave-one-out cross-validation technique. Sensory classification of the wines was less accurate than that obtained with FT-MIR and SIMCA. Overall, these results show the potential of FT-MIR spectroscopy, in combination with adequate statistical tools, to discriminate wines with different tannin levels.

Quantitative analysis of red wine tannins using Fourier-transform mid-infrared spectrometry

Tannin content and composition are critical quality components of red wines. No spectroscopic method assessing these phenols in wine has been described so far. We report here a new method using Fourier transform mid-infrared (FT-MIR) spectroscopy and chemometric techniques for the quantitative analysis of red wine tannins. Calibration models were developed using protein precipitation and phloroglucinolysis as analytical reference methods. After spectra preprocessing, six different predictive partial least-squares (PLS) models were evaluated, including the use of interval selection procedures such as iPLS and CSMWPLS. PLS regression with full-range (650-4000 cm(-1)), second derivative of the spectra and phloroglucinolysis as the reference method gave the most accurate determination for tannin concentration (RMSEC = 2.6%, RMSEP = 9.4%, r = 0.995). The prediction of the mean degree of polymerization (mDP) of the tannins also gave a reasonable prediction (RMSEC = 6.7%, RMSEP = 10.3%, r = 0.958). These results represent the first step in the development of a spectroscopic methodology for the quantification of several phenolic compounds that are critical for wine quality.

Managing high-density commercial scale wine fermentations

AIMS

To investigate the effects of grape juice dilution and different temperature/nitrogen addition regimes on commercial-scale, high-density Shiraz and Chardonnay fermentations.

METHODS AND RESULTS

Duplicated fermentations (30 hl) were conducted at two temperatures for Shiraz and for Chardonnay. Two additional tanks of Chardonnay and Shiraz were diluted. Nitrogen was added once at inoculation or in aliquots over several days. Yeast concentration and viability was determined by flow cytometry. Fermentation chemistry was monitored by Fourier transform infrared spectroscopy. Fermentations arrested in both of the undiluted, higher temperature duplicate tanks of Shiraz. Different fermentation temperature resulted in sensorially different Shiraz, but not Chardonnay, wines made from undiluted musts. The converse was observed for wines made from diluted musts.

CONCLUSIONS

High-density musts can be fermented completely using reduced fermentation temperature coupled with incremental nitrogen addition.

SIGNIFICANCE AND IMPACT OF THE STUDY

This is the first study in duplicated, commercial-scale, high-density grape juice fermentations to address temperature, nitrogen addition, and juice dilution effects on stuck fermentation potential and wine sensory properties.

Chemometrics and visible-near infrared spectroscopic monitoring of red wine fermentation in a pilot scale

The modern wine industry needs tools for process control and quality assessment in order to better manage fermentation or bottling processes. During wine fermentation it is important to measure both substrate and product concentrations (e.g. sugars, phenolic compounds), however, the analysis of these compounds by traditional means requires sample preparation and in some cases several steps of purification are needed. The combination of visible/near-infrared (Vis/NIR) spectroscopy and chemometrics potentially provides an ideal solution to accurately and rapidly monitor physical or chemical changes in wine during processing without the need for chemical analysis. The aim of this study was to assess the possibility of combining spectral and multivariate techniques, such as principal component analysis (PCA), discriminant partial least squares (DPLS), or linear discriminant analysis (LDA), to monitor time-related changes that occur during red wine fermentation. Samples (n = 652) were collected at various times from several pilot scale fermentations with grapes from either Cabernet Sauvignon or Shiraz varieties, over three vintages (2001-2003) and scanned using a monochromator instrument (Foss-NIRSystems 6500, Silver Spring, MD) in transmission mode (400-2,500 nm). PCA was used to demonstrate consistent progressive spectral changes that occur through the time course of the fermentation. LDA using PCA scores showed that regardless of variety or vintage, samples belonging to a particular time point in fermentation could be correctly classified. This study demonstrates the potential of Vis/NIR spectroscopy combined with chemometrics, as a tool for the rapid monitoring of red wine fermentation.