Phenolic profiling of grapes, fermenting samples and wines using UV-Visible spectroscopy with chemometrics

Feasibility assessment of a low-cost visible spectroscopy-based prototype for monitoring polyphenol extraction in fermenting musts

BACKGROUND

Polyphenols have long been used to evaluate grape and wine quality and it is necessary to measure them throughout various winemaking stages. They are currently assessed predominantly through analytical methods, which are characterized by time-consuming procedures and environmentally harmful practices. Non-destructive spectroscopy-based devices offer an alternative but they tend to be costly and not readily accessible for smaller wineries. This study introduces the initial steps in employing a portable, user-friendly, and cost-effective visible (VIS) spectrophotometer prototype for direct polyphenol measurement during winemaking.

RESULTS

Grapes (cv Syrah, Bobal, and Cabernet Sauvignon) at different maturation stages were fermented with or without stems. Throughout fermentation, parameters such as color intensity, total polyphenol index, total anthocyanins, and tannins were monitored. Concurrently, VIS spectra were acquired using both the prototype and a commercial instrument. Chemometric approaches were then applied to establish correlation models between spectra and destructive analyses. The prototype models demonstrated an acceptable level of confidence for only a few parameters, indicating their lack of complete reliability at this stage.

CONCLUSIONS

Visible spectroscopy is already utilized for polyphenol analysis in winemaking but the aspiration to automate the process in wineries, particularly with low-cost devices, remains unrealized. This study investigates the feasibility of a low-cost and user-friendly spectrophotometer. The results indicate that, in the early stages of prototype utilization, the goal is attainable but requires further development and in-depth assessments. © 2024 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Evaluating the Greenness of Wine Analytical Chemistry: A New Metric Approach

While the wine industry has already adopted some sustainable and environmentally friendly practices, special attention should be paid to the greenness of the analytical methodologies used. In this study, a new tool called "Green Wine Analytical Procedure Evaluation" (GWAPE) was developed to fulfill these "green" requirements. This framework offers a structured approach to evaluating the environmental impact of wine analysis processes, covering all the analytical steps from sample collection to the final results. GWAPE provides quantitative information, uses schematic representations, assigns varying levels of importance to green chemistry principles, and conducts detailed evaluations of hazard structures. Since wine analytical methods typically involve fewer highly hazardous reagents, certain criteria previously applied in green analytical chemistry should be integrated or omitted. In summary, GWAPE offers a customized and precise solution to help the laboratories of enology and the wineries assess their analytical methodologies' environmental impact. As an example of application, the proposed metric was used to evaluate the greenness of three different standard analytical methodologies to determine sugars in wine, showing good discrimination ability.

Insights on the Extraction and Analysis of Phenolic Compounds from Citrus Fruits: Green Perspectives and Current Status

Citrus fruits (CF) are highly consumed worldwide, fresh, processed, or prepared as juices and pies. To illustrate the high economic importance of CF, the global production of these commodities in 2021 was around 98 million tons. CF's composition is considered an excellent source of phenolic compounds (PC) as they have a large amount and variety. Since ancient times, PC has been highlighted to promote several benefits related to oxidative stress disorders, such as chronic diseases and cancer. Recent studies suggest that consuming citrus fruits can prevent some of these diseases. However, due to the complexity of citrus matrices, extracting compounds of interest from these types of samples, and identifying and quantifying them effectively, is not a simple task. In this context, several extractive and analytical proposals have been used. This review discusses current research involving CF, focusing mainly on PC extraction and analysis methods, regarding advantages and disadvantages from the perspective of Green Chemistry.

Exploration of Global and Specialized Near-Infrared Calibrations for the Quantification of Nutritional Content in Grapevine Organs, Berry Phenological Stages, and Shoot Lignification

Current infrared spectroscopy applications in the field of viticulture are moving toward direct in-field measuring techniques. However, limited research is available on quantitative applications using direct measurement of fresh tissue. The few studies conducted have combined the spectral data from various cultivars, growing regions, grapevine organs, and phenological stages during model development. The spectral data from these heterogeneous samples are combined into a single data set and analyzed jointly during quantitative analysis. Combining the spectral information of these diverse samples into a global data set could be an unsuitable approach and could yield less accurate prediction results. Spectral differences among samples could be overlooked during model development and quantitative analysis. The development of specialized calibrations should be considered and could lead to more accurate quantitative analyses. This study explored a model optimization strategy attempting global and specialized calibrations. Global calibrations, containing data from multiple organs, berry phenological, and shoot lignification stages, were compared to specialized calibrations per organ or stage. The global calibration for organs contained data from shoots, leaves, and berries and produced moderately accurate prediction results for nitrogen, carbon, and hydrogen. The specialized calibrations per organ yielded more accurate calibrations with a coefficient of determination in validation (R2val) at 90.65% and a root mean square error of prediction (RMSEP) at 0.32% dry matter (DM) for the berries' carbon calibrations. The leaves and shoots carbon calibrations had R2val and RMSEP at 84.99%, 0.34% DM, and 90.06%, 0.37% DM, respectively. The specialized calibrations for nitrogen and hydrogen showed similar improvements in prediction accuracy per organ. Specialized calibrations per phenological and lignification stage were also explored. Not all stages showed improvement, however, most stages had comparable or improved results for the specialized calibrations compared to the global calibrations containing all phenological or lignification stages. The results indicated that both global and specialized calibrations should be considered during model development to optimize prediction accuracy.

Multiblock data applied in organic grape juice authentication by one-class classification OC-PLS

A new strategy has been developed to enhance the assessment of the authenticity of whole grape juice within the organic class. This approach is based on the analysis of data from different analytical sources. The novel method employs a multiblock regression technique, specifically the one-class partial least squares (OC-PLS) classifier, to establish a relationship between each predictor block and the response variable. Sequential calculations are performed after orthogonalization with respect to the preceding regression scores. The proposed method has demonstrated effectiveness in detecting targeted samples. The results achieved of the best models for the test set had rates of up to 100 % sensitivity, 89 % specificity, and 83 % accuracy. To compare with the multiblock models, the DD-SIMCA method was employed, but it yielded inferior results when applied to visible data. The multiblock approach proved to be efficient in evaluating from different datasets of varied sources to classification of organic grape juice.

Effects of different maceration techniques on the colour, polyphenols and antioxidant capacity of grape juice

This study investigated the effects of different maceration techniques on the colour parameters, phenolic content and antioxidant activity of grape juice. Maceration techniques influenced colour parameters, and a* and Hue ranged from -0.77 to 0.55 and 60.90 to 104.40, respectively. The microwave and microwave and sonication combination increased the total monomeric anthocyanin, phenolic and flavonoid contents. Malvidin 3-O-glucoside increased more than twofold, and delphinidin 3-O-glucoside and cyanidin 3-O-glucoside increased one fold according to the enzymatic method in the microwave treatments. The microwave technique was the most effective technique for antioxidant capacity, but sonication, cold and thermosonication results were lower than enzymatic treatment. The microwave and microwave and sonication enhanced the polyphenols with strong antioxidant power, such as catechin from 0.87 to 37.40 and trans-resveratrol from 0.09 to 0.23 mg/100 g, by comparison with the enzymatic technique. The findings suggested these two techniques were the most effective techniques for maceration.

White wine phenolics: current methods of analysis

White wine phenolic analyses are less common in the literature than analyses of red wine phenolics. Analytical techniques for white wine phenolic analyses using spectrophotometric, chromatographic, spectroscopic, and electrochemical methods are reported. The interest of research in this area combined with the advances in technology aimed at the winemaking industry are promoting the establishment of novel approaches for identifying, quantifying, and classifying phenolic compounds in white wine. This review article provides an overview of the current research into white wine phenolics through a critical discussion of the analytical methods employed. © 2022 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Discrimination of whole grape juice using fluorescence spectroscopy data with linear discriminant analysis coupled to genetic and ant colony optimisation algorithms

In this study, a new approach was developed for classifying grape juices produced in Brazil using unfolded excitation-emission matrix (EEM) fluorescence spectroscopy and chemometrics, with respect to the agricultural production system, namely the conventional or organic agricultural one. Linear discriminant analysis (LDA) coupled to ant colony optimisation (ACO) and the genetic algorithm (GA) were used to select a more effective subset of variables to discriminate grape juice samples. The best results demonstrated highly efficient classification of grape juice samples according to a conventional or organic production process with an accuracy rate of up to 97% for the models and 94% in the prediction of these classes for samples external to the model. The models showed high selectivity and sensitivity with a rate of up to 100% for the training and test datasets, in addition to determining the most significant variables that explain the separation of classes. The proposed method proves to be viable, as it is fast and requires minimal sample preparation, allowing quality control in the food industry.

Effect of drying methods on physico-chemical and bioactive compounds of mandarin (citrus reticulata) peel

Mandarin peel, an agro waste has an immense potential for bio utilization. The present study highlights the effect of drying on the physicochemical and biochemical properties of dried mandarin peel. Microwave drying and forced air drying accomplished at three different power levels (180, 360 and 540 W) and temperatures (30, 50 and 70 °C), respectively, while freeze drying was carried out at shelf temperature of –35 °C. The results revealed highest recovery of bioactive compounds from microwave drying at 540 W viz total phenolic (43.61 mg GAE/g), flavonoid (8.08 mg QE/g), tannin (8.73 mg GAE/g), saponin (159.91 mg EE/g−1), as well as, gallic acid antioxidant activity (3.58 mg GAEAC/g) and ferric reducing antioxidant power (50.61 mg TE/g−1). Furthermore, results from HPLC and UV–vis spectroscopy revealed presence of major polyphenols in dried peel. Microwave drying can be concluded as an industrial method for the bio utilization of mandarin peel.

Non-Invasive Method to Predict the Composition of Requeijão Cremoso Directly in Commercial Packages Using Time Domain NMR Relaxometry and Chemometrics

Low Field Time-Domain Nuclear Magnetic Resonance (TD-NMR) relaxometry was used to determine moisture, fat, and defatted dry matter contents in “requeijão cremoso” (RC) processed cheese directly in commercial packaged (plastic cups or tubes with approximately 200 g). Forty-five samples of commercial RC types (traditional, light, lactose-free, vegan, and fiber) were analyzed using longitudinal (T₁) and transverse (T₂) relaxation measurements in a wide bore Halbach magnet (0.23 T) with a 100 mm probe. The T₁ and T₂ analyses were performed using CWFP-T₁ (Continuous Wave Free Precession) and CPMG (Carr-Purcell-Meiboom-Gill) single shot pulses. The scores of the principal component analysis (PCA) of CWFP-T₁ and CPMG signals did not show clustering related to the RC types. Optimization by variable selection was carried out with ordered predictors selection (OPS), providing simpler and predictive partial least squares (PLS) calibration models. The best results were obtained with CWFP-T₁ data, with root-mean-square errors of prediction (RMSEP) of 1.38, 4.71, 3.28, and 3.00% for defatted dry mass, fat in the dry and wet matter, and moisture, respectively. Therefore, CWFP-T₁ data modeled with chemometrics can be a fast method to monitor the quality of RC directly in commercial packages.

Optimisation of PLS Calibrations for Filtered and Untreated Samples towards In-Line Monitoring of Phenolic Extraction during Red-Wine Fermentations

Infrared spectroscopy provides an efficient, robust, and multivariate means to measure phenolic levels during red-wine fermentations. However, its use is currently limited to off-line sampling. In this study, partial least squares (PLS) regression was used to investigate the possibility of using spectral data from minimally pre-treated or untreated samples for the optimisation of prediction calibrations towards an in-line monitoring set-up. The evaluation of the model performance was conducted using a variety of metrics. Limits of detection and quantification of the PLS calibrations were used to assess the ability of the models to predict lower levels of phenolics from the start of fermentation. The calibrations were shown to be useful for the quantification of phenolic compounds and phenolic parameters with minimal or no sample pre-treatment during red-wine fermentation. Upon evaluation of performance, the calibrations built for attenuated-transmission Fourier-transform mid-infrared (ATR-FT-MIR) and diffuse-reflectance Fourier-transform near-infrared (DR-FT-NIR) were shown to be the most suitable spectroscopy techniques for eventual application in an automated and in-line system with values for limits of detection and quantification being suitable for the entire duration of fermentation.

Direct quantification of red wine phenolics using fluorescence spectroscopy with chemometrics

Phenolic compounds are secondary metabolites known to play crucial roles in important chemical reactions impacting the mouthfeel, colour and ageing potential of red wine. Their complexity has resulted in a number of advanced analytical methods, which often prevent routine phenolic analysis in winemaking. Fluorescence spectroscopy could be an alternative to current spectrophotometric techniques and its combination with chemometrics was investigated for its suitability in directly quantifying phenolic content of unaltered red wine and fermenting samples. Front-face fluorescence was optimised and used to build predictive models for total phenols, total condensed tannins, total anthocyanins, colour density and polymeric pigments. Machine learning algorithms were used for model development. The most successful models were built for total phenols, total condensed tannins and total anthocyanins with coefficient of determination (R²cal) and RMSECV of 0.81, 0.89, 0.80 and 5.71, 104.03 mg/L, 60.67 mg/L, respectively. The validation results showed R²val values of 0.77, 0.8 and 0.77, and RMSEP values of 7.6, 172.37 mg/L and 76.57 mg/L, respectively. A novel approach for the classification of South African red wine cultivars based on unique fluorescent fingerprints was also successful with an overall cross validation score of 0.8. The best classification ability (validation score = 0.93) was shown for the data set containing only fermenting wines for the most widely represented cultivars (>20 samples). This approach may provide a useful tool for authentication and quality control by regulatory bodies.

Identification of Tentative Traceability Markers with Direct Implications in Polyphenol Fingerprinting of Red Wines: Application of LC-MS and Chemometrics Methods

This study investigated the potential of using the changes in polyphenol composition of red wine to enable a more comprehensive chemometric differentiation and suitable identification of authentication markers. Based on high performance liquid chromatography-mass spectrometry (HPLC-MS) data collected from Feteasca Neagra, Merlot, and Cabernet Sauvignon finished wines, phenolic profiles of relevant classes were investigated immediately after vinification (Stage 1), after three months (Stage 2) and six months (Stage 3) of storage, respectively. The data were subjected to multivariate analysis, and resulted in an initial vintage differentiation by principal component analysis (PCA), and variety grouping by canonical discriminant analysis (CDA). Based on polyphenol common biosynthesis route and on the PCA correlation matrix, additional descriptors were investigated. We observed that the inclusion of specific compositional ratios into the data matrix allowed for improved sample differentiation. We obtained simultaneous discrimination according to the considered oenological factors (variety, vintage, and geographical origin) as well as the respective clustering applied during the storage period. Subsequently, further discriminatory investigations to assign wine samples to their corresponding classes relied on partial least squares-discriminant analysis (PLS-DA); the classification models confirmed the clustering initially obtained by PCA. The benefits of the presented fingerprinting approach might justify its selection and warrant its potential as an applicable tool with improved authentication capabilities in red wines.

Liquid chromatographic methods coupled to chemometrics: a short review to present the key workflow for the investigation of wine phenolic composition as it is affected by environmental factors

The guarantee of wine authenticity arises great concern because of its nutritional and economic importance. Phenolic fingerprints have been used as a source of chemical information for various authentication issues, including botanical and geographical origin, as well as vintage age. The local environment affects wine production and especially its phenolic metabolites. Integrated analytical methodologies combined with chemometrics can be applied in wine fingerprinting studies for the determination and establishment of phenolic markers that contain comprehensive and standardized information about the wine profile and how it can be affected by various environmental factors. This review summarizes all the recent trends in the generation of chemometric models that have been developed for treating chromatographic data and have been used for the investigation of critical wine authenticity issues, revealing phenolic markers responsible for the botanical, geographical, and vintage age classification of wines. Overall, the current review suggests that chromatographic methodologies are promising and powerful techniques that can be used for the accurate determination of phenolic compounds in difficult matrices like wine, highlighting the advantages of the applications of supervised chemometric tools over unsupervised for the construction of prediction models that have been successfully used for the classification based on their territorial and botanical origin.

Helium Atmospheric Pressure Plasma Jet Source Treatment of White Grapes Juice for Winemaking

In the last few years, new emerging technologies to develop novel winemaking methods were reported. Most of them pointed out the need to assess the barrel aging on the wine product, fermentation process, green technologies for wine treatment for long term storage. Among these, plasma technologies at atmospheric pressure are on the way of replacing old and expensive methods for must, wine and yeast treatment, the goal being the long-term storage, aging and even decontamination of such products, and seems to meet the requirements of the winemakers. Using the principles of dielectric barrier discharge, we power up an atmospheric pressure plasma jet in helium. This plasma is used for treatment of fresh must obtained from white grapes. Our research manuscript is focused on the correlation of plasma parameters (applied voltage, plasma power, reactive species, gas temperature) with the physico-chemical properties of white must and wine (1 and 2 years old), via ultraviolet–visible and infrared spectroscopy, and colorimetry. Two types of white must were plasma treated and studied over time. The 10 W plasma source did not exceed 40 °C during treatment, the must did not suffer during thermal treatment. A higher quantity of RONS was observed during plasma-must exposure, supporting further oxidation processes. The UV-Vis and FTIR spectroscopy revealed the presence of phenols, flavones and sugar in the wine samples. Simultaneous visualization of CIE L*a*b* and RGB in color space charts allows easier understanding of wine changing in color parameters. These experimental results supporting the possible usability of atmospheric pressure plasma for winemaking.

Evaluation of Near Infrared Spectroscopy (NIRS) for Estimating Soil Organic Matter and Phosphorus in Mediterranean Montado Ecosystem

The Montado is an agro-silvo-pastoral ecosystem characteristic of the Mediterranean region. Pasture productivity and, consequently, the possibilities for intensifying livestock production depend on soil fertility. Soil organic matter (SOM) and phosphorus (P2O5) are two indicators of the evolution of soil fertility in this ecosystem. However, their conventional analytical determination by reference laboratory methods is costly, time consuming, and laborious and, thus, does not meet the needs of current production systems. The aim of this study was to evaluate an alternative approach to estimate SOM and soil P2O5 based on near infrared spectroscopy (NIRS) combined with multivariate data analysis. For this purpose, 242 topsoil samples were collected in 2019 in eleven fields. These samples were subjected to reference laboratory analysis and NIRS analysis. For NIRS, 165 samples were used during the calibration phase and 77 samples were used during the external validation phase. The results of this study showed significant correlation between NIRS calibration models and reference methods for quantification of these soil parameters. The coefficient of determination (R2, 0.85 for SOM and 0.76 for P2O5) and the residual predictive deviation (RPD, 2.7 for SOM and 2.2 for P2O5) obtained in external validation indicated the potential of NIRS to estimate SOM and P2O5, which can facilitate farm managers’ decision making in terms of dynamic management of animal grazing and differential fertilizer application.

Near-Infrared Spectroscopy (NIRS) and Optical Sensors for Estimating Protein and Fiber in Dryland Mediterranean Pastures

Dryland pastures provide the basis for animal sustenance in extensive production systems in Iberian Peninsula. These systems have temporal and spatial variability of pasture quality resulting from the diversity of soil fertility and pasture floristic composition, the interaction with trees, animal grazing, and a Mediterranean climate characterized by accentuated seasonality and interannual irregularity. Grazing management decisions are dependent on assessing pasture availability and quality. Conventional analytical determination of crude protein (CP) and fiber (neutral detergent fiber, NDF) by reference laboratory methods require laborious and expensive procedures and, thus, do not meet the needs of the current animal production systems. The aim of this study was to evaluate two alternative approaches to estimate pasture CP and NDF, namely one based on near-infrared spectroscopy (NIRS) combined with multivariate data analysis and the other based on the Normalized Difference Vegetation Index (NDVI) measured in the field by a proximal active optical sensor (AOS). A total of 232 pasture samples were collected from January to June 2020 in eight fields. Of these, 96 samples were processed in fresh form using NIRS. All 232 samples were dried and subjected to reference laboratory and NIRS analysis. For NIRS, fresh and dry samples were split in two sets: a calibration set with half of the samples and an external validation set with the remaining half of the samples. The results of this study showed significant correlation between NIRS calibration models and reference methods for quantifying pasture quality parameters, with greater accuracy in dry samples (R2 = 0.936 and RPD = 4.01 for CP and R2 = 0.914 and RPD = 3.48 for NDF) than fresh samples (R2 = 0.702 and RPD = 1.88 for CP and R2 = 0.720 and RPD = 2.38 for NDF). The NDVI measured by the AOS shows a similar coefficient of determination to the NIRS approach with pasture fresh samples (R2 = 0.707 for CP and R2 = 0.648 for NDF). The results demonstrate the potential of these technologies for estimating CP and NDF in pastures, which can facilitate the farm manager’s decision making in terms of the dynamic management of animal grazing and supplementation needs.

Infrared Spectroscopy and Chemometric Applications for the Qualitative and Quantitative Investigation of Grapevine Organs

The fourth agricultural revolution is leading us into a time of using data science as a tool to implement precision viticulture. Infrared spectroscopy provides the means for rapid and large-scale data collection to achieve this goal. The non-invasive applications of infrared spectroscopy in grapevines are still in its infancy, but recent studies have reported its feasibility. This review examines near infrared and mid infrared spectroscopy for the qualitative and quantitative investigation of intact grapevine organs. Qualitative applications, with the focus on using spectral data for categorization purposes, is discussed. The quantitative applications discussed in this review focuses on the methods associated with carbohydrates, nitrogen, and amino acids, using both invasive and non-invasive means of sample measurement. Few studies have investigated the use of infrared spectroscopy for the direct measurement of intact, fresh, and unfrozen grapevine organs such as berries or leaves, and these studies are examined in depth. The chemometric procedures associated with qualitative and quantitative infrared techniques are discussed, followed by the critical evaluation of the future prospects that could be expected in the field.

Development of a rapid and accurate UHPLC-PDA-FL method for the quantification of phenolic compounds in grapes

Grapes are a great source of phenolic compounds, which have excellent antioxidant properties. Efficient analytical methods are necessary to selectively and precisely determine these compounds content in grapes. In this study, a reverse-phase ultra-high performance liquid chromatography (UHPLC) method with fluorescence and photodiode array detection has been developed to determine and quantify 27 of the main phenolic compounds present in grapes. An ACQUITY UPLC® BEH C18 (50 mm × 2.1 mm i.d., 1.7 mm particle size) column was employed. A gradient method was developed and column temperature (25-55 °C), as well as flow rate (0.6-0.75 mL min^-1), were optimized. The optimum conditions allowed the separation of all the compounds in less than 9 min. The method was validated and demonstrated excellent detection and quantification limits, precision, and selectivity. Finally, several grape varieties were studied in order to demonstrate the applicability of the method to the analysis of real matrix samples.

Evaluation of Near Infrared Spectroscopy (NIRS) and Remote Sensing (RS) for Estimating Pasture Quality in Mediterranean Montado Ecosystem

Pasture quality monitoring is a key element in the decision making process of a farm manager. Laboratory reference methods for assessing quality parameters such as crude protein (CP) or fibers (neutral detergent fiber: NDF) require collection and analytical procedures involving technicians, time, and reagents, making them laborious and expensive. The objective of this work was to evaluate two technological and expeditious approaches for estimating and monitoring the evolution of the quality parameters in biodiverse Mediterranean pastures: (i) near infrared spectroscopy (NIRS) combined with multivariate data analysis and (ii) remote sensing (RS) based on Sentinel-2 imagery to calculate the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI). Between February 2018 and March 2019, 21 sampling processes were carried out in nine fields, totaling 398 pasture samples, of which 315 were used during the calibration phase and 83 were used during the validation phase of the NIRS approach. The average reference values of pasture moisture content (PMC), CP, and NDF, obtained in 24 tests carried out between January and May 2019 in eight fields, were used to evaluate the RS accuracy. The results of this study showed significant correlation between NIRS calibration models or spectral indices obtained by remote sensing (NDVIRS and NDWIRS) and reference methods for quantifying pasture quality parameters, both of which open up good prospects for technological-based service providers to develop applications that enable the dynamic management of animal grazing.

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.

FT-IR, Vis spectroscopy, color and multivariate analysis for the control of ageing processes in distinctive Spanish wines

During the ageing period, diverse physicochemical changes occur affecting the quality of the final product. For this reason, it is important to study and optimize this step. Fourier transform infrared (FT-IR) and UV-visible (UV-Vis) spectroscopic techniques combined with multivariate analysis were used to obtain regression models to correlate both spectroscopic data and chromatic parameters with the ageing level of high quality Sherry wines. Three spectral ranges were obtained that contain the highest variance: two different fingerprint ranges in FT-IR (1100-2000 cm^-1 and 2300-2999 cm^-1) and one range in the visible region (380-450 nm). The regression model has enabled full differentiation between the seven levels of ageing in the wine explored. A good linear regression fit (R² above 0.95) was obtained regardless of the ranges used. The results demonstrate that both spectroscopic techniques can be used to optimize the ageing process in a simple and fast way.

Alterations on phenolic compounds and antioxidant activity during sour grape juice concentrate processing

The alterations of phenolic compounds and antioxidant capacity of sour grape juice were investigated during the concentration process stages. Phenolics and antioxidant properties of the samples changed more at the vacuum evaporation stage than that of the other stages. After evaporation, the antioxidant capacity of the samples decreased approximately between 14.2 and 17.0 % for DPPH and ABTS methods, respectively. Besides, phenolic contents also decreased approximately as 14.8%. HPLC data on phenolics of sour grape juice during concentrate processing gave 12 polyphenols, including gallic acid, (+)-catechin, (-)-epigallocatechin, vanillic acid, (-)-epigallocatechin gallate, (-)-epicatechin, caftaric acid, caffeic acid and p-coumaric acid, which were determined as 1.05-1.83 mg/100 g, 5.40-7.83 mg/100 g, 6.35-9.21 mg/100 g, 3.01-5.18 mg/100 g, 0- 1.95 mg/100 g, 2.33-3.54 mg/100 g, 12.40-37.60 mg/100 g, 1.44-2.26 mg/100 g and 0.27-0.44 mg/100 g, respectively. Ferulic, sinapic acids and quercetin were only detected at concentrate step of the sour grape juice processing and were found as 0.18 mg/100 g, 0.28 mg/100 g and 0.76 mg/100 g, respectively.