Origin Of Wines Research Articles

From the Soil to the Wine—Elements’ Migration in Monovarietal Bulgarian Wines

Bulgarian wines are renowned worldwide and serve as a symbol of the country. However, ensuring wine authenticity and establishing reliable methods for its assessment are critical challenges in wine quality control. This study investigates the migration of chemical elements within the soil/grape/wine system and utilizes the findings to develop a method for identifying specific elements capable of distinguishing the geographical origin of wine. Additionally, it explores the potential to determine its botanical origin. Thirty monovarietal Bulgarian wines, specifically produced for this study with precisely known geographical and botanical origins, were analyzed for 20 chemical elements. These included macroelements such as Al, B, Ba, Ca, Cu, Fe, K, Mg, Mn, Na, P, Sr, and Zn, as well as microelements like As, Cd, Co, Cr, Li, Ni, and Pb. The study encompassed white wines from Chardonnay, Muscat Ottonel, Sauvignon Blanc, Tamyanka, and Viognier varieties, as well as red wines from Egiodola, Broad-Leaved Melnik, Cabernet, Cabernet Franc, Cabernet Sauvignon, Marselan, Melnik, Merlot, Pinot Noir, and Syrah. The chemical composition was determined in soil extracts (using acetate and EDTA extract to represent the bioavailable fraction), vine leaves, primary musts, and raw wines before clarification and stabilization. Statistically significant correlation coefficients were calculated for the soil/leaves, soil/must, and must/wine systems, enabling an analysis of the migration of chemical elements from soil to wine and the concentration changes throughout the process. The results identified elemental descriptors capable of indicating the geographical origin of wine.

Vintage and terroir are the strongest determinants of grapevine carposphere microbiome in the viticultural zone of Drama, Greece.

The role of microbial terroir for enhancing the geographical origin of wines is well appreciated. Still, we lack a good understanding of the assembly mechanisms driving carposphere grapevine microbiota. We investigated the role of cultivar, vintage, terroir units (TUs) and vineyard geographic location on the composition of the carpospheric microbiota of three important cultivars in the viticultural zone of Drama, Greece using amplicon sequencing. Our strategy to define TUs based on georeferencing analysis allowed us to disentangle the effects of TU and vineyards geographic location, considered as a lumped factor in most studies to date. We hypothesized that: (i) these factors contribute differently on the assembly of the carposphere microbiome and that (ii) fungal and bacterial communities follow different assembly mechanisms. Vintage and TU were the stronger determinants of the carposphere fungal and bacterial communities, although the latter showed weaker response. The stronger effect of TU over vineyard geography and cultivar reinforces the role of microbial terroir in viticulture. We identified fungi (Cladosporium, Aureobasidium, Alternaria) and bacteria (Pseudomonas, Methylobacterium, Sphingomonadaceae) as main members of the core microbiome. These microorganisms were associated with specific cultivars and TUs, a feature that could be pursued towards a new microbiome-modulated paradigm of viticulture.

Recent uses and applications of nuclear magnetic resonance, isotope ratio mass spectrometry and high-resolution mass spectrometry for authenticity and geographical origin of wines

Recent uses and applications of nuclear magnetic resonance, isotope ratio mass spectrometry and high-resolution mass spectrometry for authenticity and geographical origin of wines

The Identification of Ancient Wine through Organic Residue Analysis of Ceramic Vessels

The Identification of Ancient Wine through Organic Residue Analysis of Ceramic Vessels

How European consumers value wine credence attributes: a cross-country comparison of France, Greece and Italy

Several attributes can be used to differentiate wine products to meet consumer interest and thus increase producer visibility, attractiveness and revenues. The perception towards the same attribute may depend on various factors that characterise the subjects, such as individual, behavioural and situational characteristics, like their country of origin. This study aims to identify which credence attributes and related levels motivate consumers the most to buying wine, by comparing the results obtained in three different European countries: France, Greece and Italy. A conjoint experiment based on linear assumption was administered using price, production method, geographical indication and wine origin as product attributes. The conjoint data were analysed in three steps: performing a model with the whole sample; performing three models using national data to compare results between the countries; performing a cluster analysis using the Ward method to associate consumer characteristics with product attributes. Results show that origin is the most valued attribute for choosing wine, followed by the production method. Cross-country evaluation reveals several significant differences among the attributes of the production method, geographical indication and origin. The cluster analysis identified three groups named: High price and national wine seekers; Certification seekers and Price-sensitive seekers. This paper provides several implications for both academicians and enterprises. Indeed, it is the first evaluation comparing the role given by consumers to biodynamic certification in a cross-country evaluation. Several indications are also provided for producers who can help differentiate better wine production by earning a higher income.

Enhancing wine authentication: leveraging 12,000+ international mineral wine profiles and artificial intelligence for accurate origin and variety prediction

For the wine industry, ensuring quality and authenticity hinges on the precise determination of wine origin. In our study, we developed a fast semi-quantitative method to analyse 41 chemical elements in wine, employing inductively coupled plasma mass spectrometry (ICP-MS). This methodology characterises what we term the mineral wine profile (MWP). In contrast to an organic molecular profile, the mineral composition of a wine remains constant from the moment it is bottled. Mineral elements play a crucial role in the terroir of wine: they pass primarily from soil to grape and are then influenced by various vinification techniques. Indeed, it is widely recognised that the original soil characteristics are altered by a multitude of winemaking procedures, presenting a considerable challenge when endeavouring to extract origin-related information in a typical scenario. Our study demonstrates that statistical analyses and artificial intelligence (AI) could be a tool for accurately deciphering origin information within the MWP, provided sufficient mineral elements are measured and a comprehensive database of wine samples is employed to establish effective learning. In this study, a dataset comprising 12,966 MWPs was created in just over a year. The first analysis revealed correlations between the elements in wine, especially between rare earth elements, between macronutrients and between micronutrients. A machine learning method was then developed to assess wine origin and principal grape variety. Six models were tested by comparing the area under the receiver operating characteristic curve (AUC), with eXtreme Gradient Boosting as the chosen model. Mean accuracies of 92 % for country classification, 91 % for the French wine region, and 85 % for the main grape variety were obtained, and mean AUC scores of 0.964 for country classification, 0.961 for the French wine region and 0.914 for the main grape variety. This study represents the first comprehensive investigation at this scale on wine samples, and underscores the importance of using a comprehensive MWP dataset for AI applications when verifying wine origin. The authentication of a wine with over 99 % specificity could be routinely achievable through this approach.

Chemometric Approach Application in Modern Wine Studies

The wine market is affected by the origin of wines, but the current wine traceability system has some limitations. The idea of geographical authenticity and quality has increased as one of the most important parameters influencing consumers’ preferences. Chemical parameters such as total polyphenolic content (TPC), alcohol and organic acid content (total acids, malic and tartaric acids), and antioxidant activity were observed in this work at 15 dry white wines of varieties Pinot blanc, Riesling, Sauvignon blanc, and Chardonnay, which originated from three different countries. FTIR and spectrophotometry methods (TPC and DPPH) were used and chemometric approaches such as ANOVA and PCA were selected as the most important for evaluation. In general, high levels of malic acid, TPC, and antioxidant activity were detected in Austrian wines. Pinot blanc, Chardonnay, and Sauvignon from the same producing region in Austria showed higher results. The higher overall acid concentration was a distinguishing feature of the Slovakian Sauvignon and Riesling varieties, particularly in the case of tartaric acid. Austrian samples showed significantly higher antioxidant activity compared to Hungarian Chardonnay (p = 0.002) and Sauvignon (p = 0.007), comparable to TPC. The application of statistical analysis was useful in demonstrating many discrepancies, particularly by application of the PCA method.

Variation and fractionation of δ2H, δ18O, and δ17O stable isotopes from irrigation water to soil, grapes, and wine for the traceability of geographical origins

To investigate the variation and fractionation of stable isotopes from irrigation water to soil, grapes, and wine, δ2H, δ18O, and δ17O in different samples from 10 regions in China were determined using a water isotope analyser. The values were significantly different among regions according to the chemometric analysis. All isotopes were significantly and positively correlated with irrigation water–soil and grape–wine. A significant water isotopic fractionation effect was observed from the irrigation water to the soil, grapes, and wine. Stable isotope distribution characteristics correlated with longitude, latitude, altitude, temperature, precipitation, station pressure and wind speed. The linear discriminant analysis (LDA), random forest (RF), support vector machine (SVM), and feed-forward neural network (FNN) models 58.33–100 %, 80–100 %, 53.33–100 %, and 73.33–100 % accurate for distinguishing the geographical origins of all samples from training and test data, respectively. These findings provide a theoretical basis for authenticating the geographic origin of Chinese wines using stable isotope analysis.

Generation of wine sensory descriptors by one-step method based on free profile and their use to establish the typicity of PDO wines

The typicity of wines is related to their sensory characteristics, which allow them to be differentiated from other wines. Different sensory methods have been applied to try to detect the sensory characteristics responsible for the typicity of Protected Designation of Origin wines (PDO). However, so far, none of them has been completely successful. In this study, a new one-step method based on free profile and comparative analysis by pairs was applied to generate sensory descriptors of wines. The discriminant power of the sensory attributes generated among wines from 4 different PDO wines from the Spanish Autonomous Community of Castilla y León was evaluated. The study was carried out with wine experts and 159 white, rosé, and red wines were tasted. The judges generated 245 attributes which were reduced to 95 (11 defects, 20 visual, 48 olfactory and 16 taste and mouthfeel). A Multifactorial Analysis was carried out with the data of contingency matrices based on the frequency of citation. The results obtained showed that the sensory descriptors generated allowed the wines to be distinguished according to their origin, being the discriminant power significantly higher when the visual, olfactory and gustatory attributes were considered separately.

New archaeochemical insights into Roman wine from Baetica

Although ancient wines adsorbed on vessel walls or their remains can be identified with the help of specific biomarkers, no analysis of such wines in the liquid state appears to have been reported to date. Therefore, the 2019 finding in a Roman mausoleum in Carmona, southern Spain, of an ash urn roughly 2000 years old, containing a reddish liquid, was rather exceptional and unexpected. An archaeochemical study of the liquid allowed it to be deemed the oldest ancient wine conserved in the liquid state. The study used inductively coupled plasma mass spectrometry (ICP-MS) to determine the chemical elements in the mineral salts of the wine, and high-performance liquid chromatography-mass spectrometry (HPLC-MS) to identify the polyphenols it contained. The mineral salt profile and, especially, the detection and quantification of some typical polyphenols, allowed the liquid to be identified as white wine.

Research of 2D-COS with metabolomics modifications through deep learning for traceability of wine

To tackle the difficulty of extracting features from one-dimensional spectral signals using traditional spectral analysis, a metabolomics analysis method is proposed to locate two-dimensional correlated spectral feature bands and combine it with deep learning classification for wine origin traceability. Metabolomics analysis was performed on 180 wine samples from 6 different wine regions using UPLC-Q-TOF-MS. Indole, Sulfacetamide, and caffeine were selected as the main differential components. By analyzing the molecular structure of these components and referring to the main functional groups on the infrared spectrum, characteristic band regions with wavelengths in the range of 1000–1400 nm and 1500–1800 nm were selected. Draw two-dimensional correlation spectra (2D-COS) separately, generate synchronous correlation spectra and asynchronous correlation spectra, establish convolutional neural network (CNN) classification models, and achieve the purpose of wine origin traceability. The experimental results demonstrate that combining two segments of two-dimensional characteristic spectra determined by metabolomics screening with convolutional neural networks yields optimal classification results. This validates the effectiveness of using metabolomics screening to determine spectral feature regions in tracing wine origin. This approach effectively removes irrelevant variables while retaining crucial chemical information, enhancing spectral resolution. This integrated approach strengthens the classification model's understanding of samples, significantly increasing accuracy.

Toward the authentication of wines of Itata valley denomination of origin through total reflection x-ray fluorescence (TXRF) method for testing adulteration. A novel valuable tool by using Compton/Rayleigh scattering signals in wines

The evaluation of wine adulteration is often a cumbersome process not exempt from multiple difficulties. In this work, a valuable methodology to rapidly assess wine adulteration was used. Harnessing the ratio of Compton and Rayleigh scattering signals obtained in TXRF (total reflection X-ray fluorescence). The Compton/Rayleigh signal ratio as a sensitive way to estimate the average effective atomic number (Zeff) of a sample was used. Thus, any addition made into the wine, would cause a change in its Zeff which could be detected by TXRF. Non-adulterated and adulterated wines were selected and its Zeff was estimated. The method was developed using X-ray excitation Molybdenum tube. Deconvolution of independent Compton and Rayleigh signals was performed by non-Gaussian and Gaussian curve resolution methods, and the area ratio was evaluated. A calibration curve for Compton/Rayleigh signal ratio versus Zeff was established and wine adulterated samples were tested in a Zeff range between 4.3 and 7.2. Wine adulteration was detected in all cases. The method is simple, fast, sensitive, precise and non-destructive. It procedure is usefulness as an important tool for wine industry and for the maintenance of origin and quality of wines.

A Study of the Elemental Profiles of Wines from the North-Eastern Coast of the Black Sea

Due to the increasing consumer interest in wines with a controlled place of origin, PGI (Protected Geographical Indication) and PDO (Protected Designation of Origin), the most acute question is how to identify them. One of the most effective ways to confirm the place of origin of wine in global practice is a comprehensive study of the elemental profile using statistical analysis methods. In the period from 2020 to 2023, 152 grape samples of grapes were collected from various wineries in Crimea and Kuban. The grape must that was obtained from them was fermented in laboratory conditions. The elemental profile was determined in the prepared wines, which included 71 indicators. In the conducted work, it was revealed that wines from Crimea and Kuban differ statistically significantly in the concentration of the elements B, Ca, Cu, Mn, Na, Ni, Re, Si, Sn and U. At the same time, the contents of the elements U, Sn and Re prevail in wines from Crimea, and those of B, Ca, Cu, Mn, Na, Ni and Si prevail in wines from Kuban. At the same time, methods of univariate and multivariate statistics do not allow us to reliably classify wine samples from Crimea and Kuban by their place of origin. In order to reveal the non-linear dependence of the studied indicators in wines on the geographical place of grape growing, the method of a supervised learning Random Forest was used. After training the model on the dataset, the proportion of its correct predictions was 96%. The model used 61 parameters, among which the most important were Ni, Re, Ba, Rb, Na, U, Sb, Zn, Bi, Ag and Ti.

Review: A History of Italian Wine: Culture, Economics, and Environment in the Nineteenth through Twenty-First Centuries, by Manuel Vaquero Piñeiro, Paolo Tedeschi and Luciano Maffi

Review: <i>A History of Italian Wine: Culture, Economics, and Environment in the Nineteenth through Twenty-First Centuries</i>, by Manuel Vaquero Piñeiro, Paolo Tedeschi and Luciano Maffi

An Artificial Intelligence-based tool to predict “unhealthy” wine and olive oil

The application of Artificial Intelligence (AI) in the agri-food industry has witnessed significant advancements, particularly in healthy food, quality assessment, and geographical origin determination of agri-products such as wine and olive oils, whose international market is constantly growing. This paper explores the applicability of Machine Learning (ML) models for predicting “unhealthy” wines and olive oils. As for the healthy factor, two main characteristics of wines and olive oils were evaluated: their quality and their geographical origin. In the study, 3 ML algorithms were compared (Random Forest, Linear Discriminant Analysis, and K-Nearest Neighbors) to predict just by observing their chemical characteristics: i) the quality of red and white wine and ii) the geographical origins of wine and olive oils. Real datasets were used for these case studies. The Synthetic Minority Over-sampling Technique (SMOTE) was used to manage imbalanced data, and well-adopted AI metrics were collected to evaluate the accuracy of the predictions made by the 3 ML algorithms. 3 datasets were analyzed: dataset #01 contains ∼7000 data entries related to 2 types of wine (red and white Portuguese wines) and 11 wine's chemical characteristics; dataset #02 contains 178 data entries related to 3 different Italian vineyards and 13 wine's chemical characteristics; dataset #03 contains 572 data entries related to 9 different Italian geographical regions for olive oil and 7 fatty acid characteristics. A total of ∼7700 data points, 5 wines and 9 olive oil types, and 31 chemical characteristics were analyzed. 12 quality metrics were collected per each dataset analysis. The designed ML models achieved good accuracy (>0.65) in predicting the wine quality, a very high accuracy (>0.99) in predicting its vineyard, and a very good accuracy (>0.97) in predicting the origin of olive oil. Moreover, an AI-based tool has been developed that stakeholders can use to predict the quality and geographical origin of wine and olive oil to avoid possible fraudulent and unhealthy product labels. The obtained prediction results showcase AI and ML's potential to help the agri-food industry, providing valuable insights and innovations for producers, distributors, and consumers alike. Integrating AI and ML in the agri-food industry may enhance product quality, consumer trust, and market transparency.

Geographical identification of Chinese wine based on chemometrics combined with mineral elements, volatile components and untargeted metabonomics

Identifying the geographic origin of a wine is of great importance, as origin fakery is commonplace in the wine industry. This study analyzed the mineral elements, volatile components, and metabolites in wine using inductively coupled plasma-mass spectrometry, headspace solid phase microextraction gas chromatography–mass spectrometry, and ultra-high-performance liquid chromatography-quadrupole-exactive orbitrap mass spectrometry. The most critical variables (5 mineral elements, 13 volatile components, and 51 metabolites) for wine origin classification were selected via principal component analysis and orthogonal partial least squares discriminant analysis. Subsequently, three algorithms—K-nearest neighbors, support vector machine, and random forest —were used to model single and fused datasets for origin identification. These results indicated that fused datasets, based on feature variables (mineral elements, volatile components, and metabolites), achieved the best performance, with predictive rates of 100% for all three algorithms. This study demonstrates the effectiveness of a multi-source data fusion strategy for authenticity identification of Chinese wine.

Comprehensive analysis of colloid formation, distribution, and properties of monovarietal red wines using asymmetrical flow field-flow fractionation with online multidetection

Red wine colloids, crucial in determining wine quality and stability, are understudied due to inadequate techniques for studying them effectively in the natural wine environment. Recently, Asymmetrical Flow Field-flow Fractionation (AF4) with online multidetection has emerged as a novel analytical tool for quantifying, fractionating, and characterizing red wine colloids in their native state. This study aimed to characterize the colloidal composition of 24 monovarietal Italian wines produced without filtration, oak contact, fining treatments, malolactic fermentation, macerating enzymes or ageing on yeast lees. AF4 analysis allowed quantification and characterization of wine colloids based on light scattering signal (MALS; gyration radius − Rg), size (hydrodynamic radius – Rh) and absorbance (A280 & A520 nm).The results showed that each wine contained up to five distinct colloids’ populations, varying in size and gyration radii. Despite possessing very similar Rh, most colloids exhibited great differences in compactness, as indicated by their varying Rg values. Comparing the A280 signal of whole wines to those of wines containing only species larger than 5 kDa (considered colloids) allowed to calculate the percentage of molecules involved in colloidal particles assembly, ranging from 1 to 44 % of the total A280 absorbing compounds, reflecting the diversity among wines. The A520 signal indicated the presence of polymeric pigments in the colloidal fraction. Notably, colored colloids all had Rg > 20 nm, indicating their association with other colloidal-forming compounds. This observation led to the conclusion that, apart from free anthocyanins and polymeric pigments, the color of red wines is also due to colloidal particles formed by the latter bound to proteins, with their quantity being highly variable across wines of different origin. These findings, which highlight the fundamental role of proteins in shaping the colloidal status of red wines, were utilized to propose an updated hypothetical model for colloidal aggregation in red wine.

Study on Clarification Effect and Stability of Fragrans Mead by Clarification Process

In order to study the best clarification conditions of osmanthus honey wine, single factor experiment and orthogonal experiment were used. The experimental results show that: When the type of clarifying agent was chitosan, the concentration of clarifying agent was 0.25%, and the clarifying time was 14 h, the maximum light transmittivity was 80.8±0.10%. After cold and hot treatment, standing at room temperature and oxidation stability test results showed that the stability of osmanthus honey wine with chitosan and potassium pyrosulfite was the best, and the wine body could not only maintain the flavor of the original wine, but also maintain the flavor of the original wine. The storage time is longer.After clarification, the sensory evaluation of the product was 86.9±0.69 points, the light transmittance was 80.8±0.10%, and the alcohol content was 6.4±0.05 %Vol.

The influence of perceived country of origin image on Chinese consumers’ purchase intention of imported wine

PurposeThis study aims to examine how consumers’ propensity to purchase imported wines is influenced by their attitudes and perceptions toward the countries of origin (COO) of those wines.Design/methodology/approachThe questionnaires were distributed online and 298 valid completed questionnaires were received. This study measured the perception of the wines’ countries of origin by adopting two independent dimensions of competence and warmth in the stereotype content model.FindingsThe results show a relationship between the purchase intention and the perception of the country of origin of the wine. Furthermore, the perceived image of the country of origin impacts the brand image of the wine and the quality of wine from its country of origin.Research limitations/implicationsThis study’s questionnaire was distributed online. Future research would benefit from in-depth qualitative investigation and a wider range of sample sizes across countries.Practical implicationsThe results of this study guide imported wine companies in product marketing design and advertising. By promoting the countries of origin of premium wines to target consumers, trust in the quality of imported wine can be improved, thereby increasing consumers’ purchase intention.Originality/valueThis study contributes to the understanding of consumer perception of the country of origin in the context of wine marketing. It provides valuable implications for wine companies’ marketing positioning and strategy, benefiting wine marketers, distributors and importers.

Design and implementation of popular quality analysis system for dry red wines originating from Northwest China

SummaryChinese wine, especially those from the Northwest region, has received worldwide attention and consumer recognition. However, mislabelling the year and origin of wine is a major fraud challenging the wine industry, such as the deception on year or origin. Inspired by the Appellation d'Origine Contrôlée (A.O.C) system, a feasible wine popular quality analysis system (WPQAS) has been established to supervise the selling. First, an investigation for all potential users was carried out to analyse their requirements for WPQAS. Then, the system framework was designed with the Browser/Server (B/S) 3‐tier architecture, including the data layer for saving data, the business logic layer for running the models, and the presentation layer for interacting with users. Meanwhile, a unified modelling language (UML) sequence diagram was used to monitor the dynamic behaviour of the WPQAS. The core for driving system operation was generated based on a series of machine learning algorithms, including K‐nearest neighbour (KNN) for detecting outlines, random forest (RF) for weighting the chemical indexes, and artificial neural network (ANN) for training the models. Finally, the WPQAS, implemented as a software, was applied to estimate the popular quality of 600 dry red wines from Northwest China. The mode of decoupling models from the system could ensure WPQAS simplicity and scalability, and make the models reusable. Thus, the present work proposes a lightweight system that can be easily popularised and applied in diverse scenarios, and would play a significant positive impact on the correct meeting the challenges associated with counterfeit wine.