Rapid method for the discrimination of red wine cultivars based on mid-infrared spectroscopy of phenolic wine extracts

Physicochemical Characterization, Storage Stability Behavior, and Intestinal Bioaccessibility of Clove Extract Encapsulated Using Varying Combinations of Gum Arabic and Maltodextrin

Clove (Syzygium aromaticum, L.) is a rich source of polyphenols and antioxidants, but its intense flavor, poor solubility, and instability may limit its widespread and efficient use in industrial applications. In a series of laboratory-scale experiments, gum Arabic (GA) and maltodextrin (MD) were used as coating agents in various proportions (ranging from 0MD:100GA to 100MD:0GA) for encapsulation of clove extract using a freeze-drying method. The encapsulates were assessed for the physicochemical properties, storage stability behavior, and intestinal bioaccessibility of phenolics using an in vitro gastrointestinal digestion test. The freeze-dried encapsulates were characterized as having low water activity (<0.3, which is a critical threshold to ensure chemical and microbiological stability), high water solubility (>90%), solid (product) recovery (mean 93.1 ± 1.77%), and encapsulation efficiency (91.4-94.9%). Hygroscopicity increased as the GA:MD proportion increased in the encapsulation formulations. Encapsulation was effective in protecting bioactive components of clove extract during storage at room (up to 40 days) or high temperature (60 °C for 7 days) and minimized the loss of antioxidant activity during storage, as compared to the clove extract in a non-encapsulated form. All encapsulation formulations were characterized by a negative zeta potential (from -22.1 to -29.7 mV) and a polydispersity index ranging from 0.47 to 0.68, classifying the formulations as having a mid-range polydisperse particle size distribution. The FTIR analysis demonstrated that the freeze-drying encapsulation process resulted in no evident chemical interaction between coating and core materials. Intestinal bioaccessibility of total phenolics after the in vitro-simulated gastrointestinal digestion was greater in the encapsulated clove extract compared to the non-encapsulated clove extract. In conclusion, the encapsulation process was effective in protecting the bioactivity of the polyphenol-rich clove extract during storage and improved the phenolic bioaccessibility, potentially supporting the application of the encapsulated clove extract for use in functional food development.

Wound healing effects of biogenic gold nanoparticles synthesized using red wine extracts

Gold nanoparticles (AuNPs) were synthesized using three red wine extracts (RW-Es); by varying temperature, pH, concentrations of RW-Es and gold salt. The RW-AuNPs were characterized by UV-vis, transmission electron microscopy (TEM), dynamic light scattering (DLS), and the Fourier Transform Infra-red Spectroscopy (FT-IR). Their stability was evaluated in water, foetal bovine serum (FBS), phosphate-buffered saline (PBS), and Dulbecco's Modified Eagle Medium (DMEM) by UV-Vis. The effect of the RW-Es and RW-AuNPs on KMST-6 cell cell viability was evaluated by MTT assay; and their wound healing effects were monitored by scratch assay. RW-AuNPs synthesis was observed by colour change, and confirmed by UV-Vis spectrum, with an absorption peak around 550 nm. The hydrodynamic sizes of the RW-AuNPs ranged between 10 and 100 nm. Polyphenols, carboxylic acids, and amino acids are some of functional groups in the RW-Es that were involved in the reduction of RW-AuNPs. The RW-AuNPs were stable in test solutions and showed no cytotoxicity to the KMST-6 cells up to 72 h. AuNPs synthesized from Pinotage and Cabernet Sauvignon enhanced proliferation of KMST-6 cells and showed potential as wound healing agents. Further studies are required to investigate the molecular mechanisms involved in the potential wound-healing effect of the RW-AuNPs.

Deep reinforcement learning classification of sparkling wines based on ICP-MS and DOSY NMR spectra

An approach that combines NMR spectroscopy and inductively coupled plasma mass spectrometry (ICP-MS) and advanced tensor decomposition algorithms with state-of-the-art deep learning procedures was applied for the classification of Croatian continental sparkling wines by their geographical origin. It has been demonstrated that complex high-dimensional NMR or ICP-MS data cannot be classified by higher-order tensor decomposition alone. Extension of the procedure by deep reinforcement learning resulted in an exquisite neural network predictive model for the classification of sparkling wines according to their geographical origin. A network trained on half of the sample set was able to classify even 94% of all samples. The model can particularly be useful in cases where the number of samples is limited and when simpler statistical methods fail to produce reliable data. The model can further be exploited for the identification and differentiation of sparkling wines including a high potential for authenticity or quality control.

Preparation and Physicochemical Analysis of Camellia sinensis cv. 'Ziyan' Anthocyanin Microcapsules

The new tea cultivar Ziyan has a high content of anthocyanin and ester catechins in the raw material, but the conventional processing and application methods are limited. To explore its application potential, the freeze-drying method was used to prepare microcapsules with an embedding time of 30 min, solid content of 30%, and core to wall ratio of 1:10 (g/g). The anthocyanin recovery was 95.94 ± 0.50%, and the encapsulation efficiency was 96.15 ± 0.11%. The stability of microcapsules and composite wall materials was evaluated in the simulation system. Results showed that microcapsules employing a maltodextrin-gum arabic ratio of 2:8 (w/w) as the wall material significantly reduced degradation rates, extending anthocyanin half-life under various storage conditions. Characterization indicated improved physical properties of Ziyan anthocyanin powder post-microencapsulation. FT-IR and DSC- revealed the formation of a new phase between anthocyanins and wall materials, leading to increased enthalpy and enhanced thermal stability. The microencapsulation results of this experiment proved that the storage stability of anthocyanin was effectively enhanced.

FT-Raman Methodology Applied to Study the Effect of Time and Type of Seasoning in the Crafting of Sherry Casks® Used in the Aging of Brandy De Jerez

Brandy de Jerez is a grape-derived spirit produced in Southern Spain with specific characteristics that come from the casks where it is produced, which must have previously contained some type of Sherry wine for at least 12 months. These casks are known as Sherry Cask®. In this work, Brandies de Jerez aged for different aging times (0, 3, 6 and 12 months) in casks seasoned with three different types of Sherry wines (Fino, Oloroso and Amontillado) have been studied. The samples have been analyzed using FT-Raman spectroscopy, and their chemical characterization has also been realized by studying their total content of organic acid, volatile compounds, and phenolic and furanic compounds. Their chemical study showed that the main differences between the studied samples were due to the duration and the type of seasoning performed. However, the spectra obtained through FT-Raman presented noticeable differences according to cask seasoning time and the Sherry wine used for the process. A PCA (Principal Component Analysis) confirmed that the Brandies de Jerez presented significant differences depending on the seasoning time and type that the casks were subjected to. A PLS-R (Partial Least Squares Regression) study enabled establishing a close correlation between specific regions of the FT-Raman spectra and cask seasoning time.

Application of Fourier transform infrared (FT-IR) spectroscopy and multivariate analysis for detection of adulteration in honey markets in Ethiopia

Honey is a highly susceptible food item to adulteration in national and international trade. Spectrum screening by FTIR coupled with multivariate analysis was investigated as an alternate analytical technique for honey adulterations and authentication. This technique was evaluated using pure honey samples that were blended at a ratio of 0-50% with commonly known adulterant materials and honey samples that were readily available for purchase in the Addis Ababa markets channel. Holeta Bee Research's bee farm pure honey, which is authentic honey, is employed as the control in this experiment. In the region, 4000-400 cm-1, spectral data of honey samples and five adulterant materials were recorded. The combination of spectra measurement with multivariate analyses resulted in the visualization of honey grouping and classification based on their functional group. The bands at 1800-650 cm-1 spectral region were selected for successful discrimination of clusters. Based on spectral differences, cluster analysis (CA) is also capable of grouping and separating pure from contaminated honey. Principle component analysis was able to visualize the differentiation of deliberately adulterated honey and commercially available from authentic ones. According to the results of our investigation, using FTIR analysis methods along with multivariate statistical analysis of the data could be considered useful fingerprinting procedures for identifying samples of pure and adulterated honey.

Chemometric and sensometric techniques in enological data analysis

Enological evaluations capture the chemical and sensory space of wine using different techniques; many sensory methods as well as a variety of analytical chemistry techniques contribute to the amount of information generated. Data fusion, especially integrating data sets, is important when working with complex systems. The success reported when trying to integrate different modalities is generally low and has been attributed to the lack of statistically considerate strategies focusing on the data handling process. Multiple stages of data handling must be carefully considered when dealing with multi-modal data. In this review, the different stages in the data analysis process were examined. The study revealed misconceptions surrounding the process and elucidated rules for purpose-driven approaches by examining the complexities of each stage and the impact the decisions made at each stage have on the resulting models. The two major modeling approaches are either supervised (discrimination, classification, prediction) or unsupervised (exploration). Supervised approaches were emphatic on the pre-processing steps and prioritized increasing performance. Unsupervised approaches were mostly used for preliminary steps. The review found aspects often neglected when it came to the data collection and capturing which in the end contributed to the low success in combining sensory and chemistry data.

Efficiency of carboxymethylcellulose in red wine tartaric stability: Effect on wine phenolic composition, chromatic characteristics and colouring matter stability

In this work, the effect of carboxymethylcellulose structural features on the efficiency to prevent potassium hydrogen tartrate precipitation in red wines and on the phenolic composition, chromatic characteristics and colouring matter stability was studied. The degree of substitution of carboxymethylcellulose was important for its efficiency in highly unstable wines. Application of carboxymethylcellulose doesn't result in a significant change in the phenolic, monomeric anthocyanin composition, colour intensity, and chromatic characteristics of red wines. Sensory analysis also showed that carboxymethylcellulose doesn't have a significant impact on wine sensory attributes. Carboxymethylcellulose doesn't decrease the colouring matter stability. The use of turbidity for evaluating the colouring matter stability of wines has severe drawbacks as the turbidity value measured might not be related to the amount of suspended material. Therefore, the application of carboxymethylcellulose in red wines is efficient in increasing tartaric stability without impacting on the phenolic composition, sensory characteristics, and colouring matter stability.

Clustering in Wineinformatics with Attribute Selection to Increase Uniqueness of Clusters

Wineinformatics is a new data science research area that focuses on large amounts of wine-related data. Most of the current Wineinformatics researches are focused on supervised learning to predict the wine quality, price, region and weather. In this research, unsupervised learning using K-means clustering with optimal K search and filtration process is studied on a Bordeaux-region specific dataset to form clusters and find representative wines in each cluster. 14,349 wines representing the 21st century Bordeaux dataset are clustered into 43 and 13 clusters with detailed analysis on the number of wines, dominant wine characteristics, average wine grades, and representative wines in each cluster. Similar research results are also generated and presented on 435 elite wines (wines that scored 95 points and above on a 100 points scale). The information generated from this research can be beneficial to wine vendors to make a selection given the limited number of wines they can realistically offer, to connoisseurs to study wines in a target region/vintage/price with a representative short list, and to wine consumers to get recommendations. Many possible researches can adopt the same process to analyze and find representative wines in different wine making regions/countries, vintages, or pivot points. This paper opens up a new door for Wineinformatics in unsupervised learning researches.

Secondary Metabolic Profile as a Tool for Distinction and Characterization of Cultivars of Black Pepper (Piper nigrum L.) Cultivated in Pará State, Brazil

This study evaluated the chemical compositions of the leaves and fruits of eight black pepper cultivars cultivated in Pará State (Amazon, Brazil). Hydrodistillation and gas chromatography-mass spectrometry were employed to extract and analyze the volatile compounds, respectively. Sesquiterpene hydrocarbons were predominant (58.5-90.9%) in the cultivars "Cingapura", "Equador", "Guajarina", "Iaçará", and "Kottanadan", and "Bragantina", "Clonada", and "Uthirankota" displayed oxygenated sesquiterpenoids (50.6-75.0%). The multivariate statistical analysis applied using volatile composition grouped the samples into four groups: γ-Elemene, curzerene, and δ-elemene ("Equador"/"Guajarina", I); δ-elemene ("Iaçará"/"Kottanadan"/"Cingapura", II); elemol ("Clonada"/"Uthirankota", III) and α-muurolol, bicyclogermacrene, and cubebol ("Bragantina", IV). The major compounds in all fruit samples were monoterpene hydrocarbons such as α-pinene, β-pinene, and limonene. Among the cultivar leaves, phenolics content (44.75-140.53 mg GAE·g^-1 FW), the enzymatic activity of phenylalanine-ammonia lyase (20.19-57.22 µU·mL^-1), and carotenoids (0.21-2.31 µg·mL^-1) displayed significant variations. Due to black pepper's susceptibility to Fusarium infection, a molecular docking analysis was carried out on Fusarium protein targets using each cultivar's volatile components. F. oxysporum endoglucanase was identified as the preferential protein target of the compounds. These results can be used to identify chemical markers related to the susceptibility degree of black pepper cultivars to plant diseases prevalent in Pará State.

Wineinformatics: Using the Full Power of the Computational Wine Wheel to Understand 21st Century Bordeaux Wines from the Reviews

Although wine has been produced for several thousands of years, the ancient beverage has remained popular and even more affordable in modern times. Among all wine making regions, Bordeaux, France is probably one of the most prestigious wine areas in history. Since hundreds of wines are produced from Bordeaux each year, humans are not likely to be able to examine all wines across multiple vintages to define the characteristics of outstanding 21st century Bordeaux wines. Wineinformatics is a newly proposed data science research with an application domain in wine to process a large amount of wine data through the computer. The goal of this paper is to build a high-quality computational model on wine reviews processed by the full power of the Computational Wine Wheel to understand 21st century Bordeaux wines. On top of 985 binary-attributes generated from the Computational Wine Wheel in our previous research, we try to add additional attributes by utilizing a CATEGORY and SUBCATEGORY for an additional 14 and 34 continuous-attributes to be included in the All Bordeaux (14,349 wine) and the 1855 Bordeaux datasets (1359 wines). We believe successfully merging the original binary-attributes and the new continuous-attributes can provide more insights for Naïve Bayes and Supported Vector Machine (SVM) to build the model for a wine grade category prediction. The experimental results suggest that, for the All Bordeaux dataset, with the additional 14 attributes retrieved from CATEGORY, the Naïve Bayes classification algorithm was able to outperform the existing research results by increasing accuracy by 2.15%, precision by 8.72%, and the F-score by 1.48%. For the 1855 Bordeaux dataset, with the additional attributes retrieved from the CATEGORY and SUBCATEGORY, the SVM classification algorithm was able to outperform the existing research results by increasing accuracy by 5%, precision by 2.85%, recall by 5.56%, and the F-score by 4.07%. The improvements demonstrated in the research show that attributes retrieved from the CATEGORY and SUBCATEGORY has the power to provide more information to classifiers for superior model generation. The model build in this research can better distinguish outstanding and class 21st century Bordeaux wines. This paper provides new directions in Wineinformatics for technical research in data science, such as regression, multi-target, classification and domain specific research, including wine region terroir analysis, wine quality prediction, and weather impact examination.

Valorization of Gleditsia triacanthos Invasive Plant Cellulose Microfibers and Phenolic Compounds for Obtaining Multi-Functional Wound Dressings with Antimicrobial and Antioxidant Properties

Gleditsia triacanthos is an aggressive invasive species in Eastern Europe, producing a significant number of pods that could represent an inexhaustible resource of raw material for various applications. The aim of this study was to extract cellulose from the Gleditsia triacanthos pods, characterize it by spectrophotometric and UHPLC-DAD-ESI/MS analysis, and use it to fabricate a wound dressing that is multi-functionalized with phenolic compounds extracted from the leaves of the same species. The obtained cellulose microfibers (CM) were functionalized, lyophilized, and characterized by ATR-FTIR and SEM. The water absorption and retention capacity as well as the controlled release of phenolic compounds with antioxidant properties evaluated in temporal dynamics were also determined. The antimicrobial activity against reference and clinical multi-drug-resistant Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, Acinetobacter baumannii, Enterobacter cloacae, Candida albicans, and Candida parapsilosis strains occurred immediately after the contact with the tested materials and was maintained for 24 h for all tested microbial strains. In conclusion, the multi-functionalized cellulose microfibers (MFCM) obtained from the reproductive organs of an invasive species can represent a promising alternative for the development of functional wound dressings with antioxidant and antimicrobial activity, as well as being a scalable example for designing cost-effective, circular bio-economy approaches to combat the accelerated spread of invasive species.

Application of gum Arabic and maltodextrin for encapsulation of eggplant peel extract as a natural antioxidant and color source

Our aim was to produce an encapsulated powder loaded with eggplant peel extract as a natural source of color and antioxidants through gum Arabic and maltodextrin. The effect of spray drying inlet temperature (140-170°C) and various carriers (maltodextrin, gum Arabic, and their combination) on powder production yield, physical properties, flowability, color, total phenolic content (TPC), antioxidant activity, infrared spectroscopy (FTIR), microstructure and particle size were investigated. Our results revealed that physicochemical properties of powders were influenced by the carrier type and inlet temperature. Obtained powders by maltodextrin at 170°C showed the highest TPC (5.2mg/g), DPPH (73.4%), ABTS (90.5%), TEAC (2. 5mM), hydroxyl radicals scavenging activity (79.1%) and reducing power (1.2 Abs₇₀₀) among all samples. FTIR spectroscopy indicated that the extract was encapsulated by the carriers. Microstructure evaluation of powders showed some hollow particles with matrix-type structures. Sensory evaluation indicated that addition of encapsulated eggplant extract into the formulation of gummy candy improved its color and overall acceptability.

Wineinformatics: A Quantitative Analysis of Wine Reviewers

Data Science is a successful study that incorporates varying techniques and theories from distinct fields including Mathematics, Computer Science, Economics, Business and domain knowledge. Among all components in data science, domain knowledge is the key to create high quality data products by data scientists. Wineinformatics is a new data science application that uses wine as the domain knowledge and incorporates data science and wine related datasets, including physicochemical laboratory data and wine reviews. This paper produces a brand-new dataset that contains more than 100,000 wine reviews made available by the Computational Wine Wheel. This dataset is then used to quantitatively evaluate the consistency of the Wine Spectator and all of its major reviewers through both white-box and black-box classification algorithms. Wine Spectator reviewers receive more than 87% accuracy when evaluated with the SVM method. This result supports Wine Spectator’s prestigious standing in the wine industry.

Study of the Grape Cryo-Maceration Process at Different Temperatures

This research aimed to determine the effects of cryo-maceration at different temperatures on polyphenol content during the winemaking process of Chardonnay wine. Samples of Chardonnay grapes were subjected to rapid cooling processes by direct injection of liquid CO₂ to obtain final temperatures of 10.0, 8.0, 6.0 and 4.0 °C and yield different batches of grape mash. Subsequently, each batch underwent the winemaking process to produce four different wines. The wines obtained were characterized by chemical analyses. We observed higher extraction of polyphenolic compounds with low-temperature cold maceration, particularly when the temperature was reduced from 10.0 to 6.0 °C. Conversely, when the temperature was reduced below 6.0 °C, the increase in polyphenol content in wine was negligible, whereas CO₂ consumption increased. Furthermore, a numerical simulation was performed to determine the pipe length, L₀, after which the temperature was constant. This condition is very important because it guarantees that after the length L₀, the thermodynamic exchange between liquid CO₂ and is complete, eliminating the possibility of liquid CO₂ pockets in the cyclone.

Red Wine Age Estimation by the Alteration of Its Color Parameters: Fourier Transform Infrared Spectroscopy as a Tool to Monitor Wine Maturation Time

Color, phenolic content, and chemical age values of red wines made from Cretan grape varieties (Kotsifali, Mandilari) were evaluated over nine months of maturation in different containers for two vintages. The wines differed greatly on their anthocyanin profiles. Mid-IR spectra were also recorded with the use of a Fourier Transform Infrared Spectrophotometer in ZnSe disk mode. Analysis of Variance was used to explore the parameter's dependency on time. Determination models were developed for the chemical age indexes using Partial Least Squares (PLS) (TQ Analyst software) considering the spectral region 1830-1500 cm-1. The correlation coefficients (r) for chemical age index i were 0.86 for Kotsifali (Root Mean Square Error of Calibration (RMSEC) = 0.067, Root Mean Square Error of Prediction (RMSEP) = 0,115, and Root Mean Square Error of Validation (RMSECV) = 0.164) and 0.90 for Mandilari (RMSEC = 0.050, RMSEP = 0.040, and RMSECV = 0.089). For chemical age index ii the correlation coefficients (r) were 0.86 and 0.97 for Kotsifali (RMSEC 0.044, RMSEP = 0.087, and RMSECV = 0.214) and Mandilari (RMSEC = 0.024, RMSEP = 0.033, and RMSECV = 0.078), respectively. The proposed method is simpler, less time consuming, and more economical and does not require chemical reagents.

Merging vibrational spectroscopic data for wine classification according to the geographic origin

The wine making procedure is no longer a secret and it is nowadays well described and repeated around the world. Nevertheless, wines present unique features, strongly associated with their geographic origin. Classification systems were developed to catalogue wines according to the provenance, and are currently established by official authorities in order to ensure wine authenticity. The use of near-infrared (NIR), mid-infrared (MIR) and Raman spectroscopy for tracing the origin of wine samples, has been reported with different levels of success. This work evaluated and compared the performance of these techniques, as well as their joint use, in terms of geographic origin classification. NIR, MIR and Raman spectra of wine samples belonging to four Portuguese wine regions (Vinhos Verdes, Lisboa, Açores and Távora-Varosa) were analyzed by partial least squares discriminant analysis (PLS-DA). Results revealed the better suitability of MIR spectroscopy (87.7% of correct predictions) over NIR (60.4%) and Raman (60.8%). The joint use of spectral sets did not improve the predictive ability of the models. The best models were achieved by combining MIR and NIR spectra resulting in 86.7% of correct predictions. Multiblock partial least squares (MB-PLS) models were developed to further explore the combination of spectral data. Although these models did not improve the percentage of correct predictions, they demonstrated the higher contribution of MIR spectroscopic data, in the development of the models.

Authenticity Determination of Greek-Cretan Mono-Varietal White and Red Wines Based on their Phenolic Content Using Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy and Chemometrics

The assessment of wine authenticity is a critical issue that has gained a lot of interest internationally. A simple and fast method was developed for the varietal classification of Greek wines according to grape cultivar using attenuated total reflectance (ATR) Fourier transform infrared (FT-IR) spectroscopy. The phenolic content and color parameters of wine samples (n=88) made by two white (Vilana and Dafni) and two red (Kotsifali and Mandilari) grape varieties were measured and their FT-IR spectra were recorded. Principal Component Analysis (PCA) of their chemical parameters indicated that the wines can be discriminated based on their different phenolic content. The spectroscopic analysis combined with discriminant analysis of the fingerprint region of the spectra (1800-900 cm-1) resulted in complete discrimination of the grape varieties. The proposed method in comparison with the rest analytical methods is simpler, less time consuming, more economical and requires reduced quantities of chemical reagents prior to analysis.

An electrochemical biosensing platform based on 1-formylpyrene functionalized reduced graphene oxide for sensitive determination of phenol

A novel electrochemical biosensing platform is proposed. New tyrosinase-based biosensor can be used to detect phenols.

Classification of Sparkling Wine Style and Quality by MIR Spectroscopy

In this study, the suitability of attenuated total reflection (ATR) mid-infrared (MIR) spectroscopy, combined with principal component analysis (PCA) and partial least squares (PLS) regression, was evaluated as a rapid analytical technique for the classification of sparkling wine style and quality. Australian sparkling wines (n = 139) comprising a range of styles (i.e., white, rosé, red, Prosecco and Moscato) were analyzed by ATR-MIR spectroscopy combined with multivariate data analysis. The MIR spectra of 50 sparkling white wines, produced according to four different production methods (i.e., Carbonation, Charmat, Transfer and Methodé Traditionelle) were also evaluated against: (i) quality ratings determined by an expert panel; and (ii) sensory attributes rated by a trained sensory panel. Wine pH, titratable acidity (TA), residual sugar (RS), alcohol and total phenolic content were also determined. The sparkling wine styles were separated on the PCA score plot based on their MIR spectral data; while the sparkling white wines showed separation based on production method, which strongly influenced the style and sensory properties of wine (i.e., the intensity of fruit versus yeast-derived characters). PLS calibrations of 0.73, 0.77, 0.82 and 0.86 were obtained for sweetness, tropical fruit, confectionary and toasty characters (on the palate), respectively.

Study of phenolic profile and antioxidant activity in selected Moravian wines during winemaking process by FT-IR spectroscopy

Wine belongs to a family of products where the quality matters. Its quality can be in principle verified using diverse physicochemical approaches, including the determination of various chemical compounds generally accepted as chemical markers of product quality. Example of such applicable compounds is a family derived from phenols. Next to a more classical approach, infrared spectroscopy can play an important role in this game. Here we sought to develop an easy to use, ultra-fast and robust method based on FT-IR with some important advantages including lower sample and solvent consumptions. The tested and evaluated method was consequently applied in a monitoring of changes in a content of total phenolic compounds (TPC) and total antioxidant activity (TAA) during a process of wine-making. It was found out that total amount of phenolic compounds differs both for individual kind of wines, namely red, white and rose, at each processing stage of the production. The content of phenolic compounds of red and white wine increased while an opposite trend was observed in rose wine. TAA values of analysed wines showed difference between individual kind of wine and indicate the same trend like phenolic profile. Antioxidant activity values relate to changes of phenolic content during production process.

Direct and simultaneous quantification of tannin mean degree of polymerization and percentage of galloylation in grape seeds using diffuse reflectance fourier transform-infrared spectroscopy

The direct and simultaneous quantitative determination of the mean degree of polymerization (mDP) and the degree of galloylation (%G) in grape seeds were quantified using diffuse reflectance infrared Fourier transform spectroscopy and partial least squares (PLS). The results were compared with those obtained using the conventional analysis employing phloroglucinolysis as pretreatment followed by high performance liquid chromatography-UV and mass spectrometry detection. Infrared spectra were recorded in solid state samples after freeze drying. The 2nd derivative of the 1832 to 1416 and 918 to 739 cm(-1) spectral regions for the quantification of mDP, the 2nd derivative of the 1813 to 607 cm(-1) spectral region for the degree of %G determination and PLS regression were used. The determination coefficients (R(2) ) of mDP and %G were 0.99 and 0.98, respectively. The corresponding values of the root-mean-square error of calibration were found 0.506 and 0.692, the root-mean-square error of cross validation 0.811 and 0.921, and the root-mean-square error of prediction 0.612 and 0.801. The proposed method in comparison with the conventional method is simpler, less time consuming, more economical, and requires reduced quantities of chemical reagents and fewer sample pretreatment steps. It could be a starting point for the design of more specific models according to the requirements of the wineries.

Isolation, characterization, spectroscopic properties and quantum chemical computations of an important phytoalexin resveratrol as antioxidant component from Vitis labrusca L. and their chemical compositions

In this study, isolation and characterization of trans-resveratrol (RES) as an antioxidant compound were carried out from VLE, VLG and VLS. Furthermore, antioxidant activities were evaluated by using six different methods. Finally, total phenolic, flavonoid, ascorbic acid, anthocyanin, lycopene, β-carotene and vitamin E contents were carried out. In addition, the FT-IR, (13)C and (1)H NMR chemical shifts and UV-vis. spectra of trans-resveratrol were experimentally recorded. Quantum chemical computations such as the molecular geometry, vibrational frequencies, UV-vis. spectroscopic parameters, HOMOs-LUMOs energies, molecular electrostatic potential (MEP), natural bond orbitals (NBO) and nonlinear optics (NLO) properties of title molecule have been calculated by using DFT/B3PW91 method with 6-311++G(d,p) basis set in ground state for the first time. The obtained results show that the calculated spectroscopic data are in a good agreement with experimental data.