Classifications

• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
  • G01N33/02—Food
  • G01N33/14—Beverages
  • G01N33/146—Beverages containing alcohol
• • C—CHEMISTRY; METALLURGY
  • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
  • C12G—WINE; PREPARATION THEREOF; ALCOHOLIC BEVERAGES; PREPARATION OF ALCOHOLIC BEVERAGES NOT PROVIDED FOR IN SUBCLASSES C12C OR C12H
  • C12G1/00—Preparation of wine or sparkling wine
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
  • G06N3/00—Computing arrangements based on biological models
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
  • G06Q30/00—Commerce
  • G06Q30/018—Certifying business or products
  • G06Q30/0185—Product, service or business identity fraud

Definitions

• the invention falls within the field of the production of alcoholic beverages, in particular wines, from a raw material derived from agriculture, in this case from viticulture in the case of wines.
• the invention relates more specifically to a technology which aims to improve the quality and traceability of alcoholic beverages, in particular wines (the term "wine” hereinafter designating any alcoholic beverage), by automatic or semi-automatic methods. Automatic based on the statistical processing (of the datamining type), possibly carried out using artificial intelligence tools, of analytical data relating to the mineral composition, in particular metallic, of these drinks (wines).
• this technology comprises a process, in particular implemented by computer, for management and/or monitoring:
• the invention also relates to a device for the implementation of this method.
• Alcoholic beverages and in particular wines have a significant economic impact.
• the global alcoholic beverages market corresponds to a turnover of 1.470 billion dollars.
• At the top are beers, followed by spirits, wines and finally ciders.
• the combined annual sales of spirits and wines represent approximately 38 billion euros, in 2020.
• consumer expectations relate to the quality of alcoholic beverages, to health safety, to access to information relating to production conditions and to guarantees in terms of origin, identity and authenticity of these drinks.
• Traceability is also a requirement of the standards relating to the ISO 9000 & 9001 quality management system.
• Traceability also makes sense with regard to the health and safety of consumers. It provides them with reliable information on the substances present in alcoholic beverages. It also guarantees consumers perfect consumer safety, from purchase to the end of the product's life.
• Traceability is also crucial with regard to the origin, provenance and logistics of alcoholic beverages. It gives access to information on the production region of the agricultural raw material of the alcoholic beverage, for example wine, as well as on the places and conditions of manufacture, storage and transport of the alcoholic beverage.
• Alcoholic beverages in particular wines, contain mineral elements, in particular metals and metalloids.
• Wines usually contain (i) major elements such as Ca, K, Mg, and Na -10-1000 mg/L-, (ii) minor elements: Al, Fe, Cu, Mn, Rb, Sr and Zn, -0.1-10 mg/L- and (iii) traces -i.a- Ba, Cd, Co, Cr, Li, Ni, Rb, and V -0.1-1000 ⁇ g/L.
• the invention aims to satisfy at least one of the following objectives:
• [0022] - provide a high-performance process, in particular implemented by computer, for managing and/or monitoring the production of agricultural raw materials useful for the manufacture of alcoholic beverages, the manufacture of these alcoholic beverages, the quality of these alcoholic beverages, the organoleptic properties of these alcoholic beverages, the authenticity of these alcoholic beverages, and/or the traceability of these alcoholic beverages.
• [0023] providing a high-performance process, in particular implemented by computer, for managing and/or monitoring wine production, wine production, wine quality, organoleptic properties of wines, authenticity of wines, and/or wine traceability.
• [0024] - provide a high-performance and economical process, in particular implemented by computer, for managing and/or monitoring the production of agricultural raw materials useful for the manufacture of alcoholic beverages, the manufacture of these alcoholic beverages, the quality of these alcoholic beverages, the organoleptic properties of these alcoholic beverages, the authenticity of these alcoholic beverages, and/or the traceability of these alcoholic beverages.
• [0025] providing a high-performance and economical method, in particular implemented by computer, for managing and/or monitoring wine production, wine production, wine quality, organoleptic properties of wines, authenticity of wines, and/or traceability of wines.
• [0026] for a high-performance, economical and reliable process, in particular implemented by computer, for managing and / or monitoring the production of agricultural raw materials useful for the manufacture of alcoholic beverages, the manufacture of these alcoholic beverages , the quality of these alcoholic beverages, the organoleptic properties of these alcoholic beverages, the authenticity of these alcoholic beverages, and/or the traceability of these alcoholic beverages.
• [0027] - for a high-performance, economical and reliable process, in particular implemented by computer, for managing and/or monitoring wine production, wine production, wine quality, organoleptic properties of wines, the authenticity of the wines, and/or the traceability of the wines.
• [0028] - provide a high-performance, economical and reliable device for implementing the method referred to in the above objectives.
• alcoholic drink drink prepared by alcoholic fermentation of a vegetable raw material, preferably agricultural. It may in particular be a wine, a beer, a cider, a liqueur, a spirit, a whisky, a brandy, a tequila, a vodka, a rum, a cognac, an armagnac, an Asian alcohol, etc.
• inert container with respect to the alcoholic beverage container which retains its physical integrity and good mechanical properties (breaking limit and plasticity still sufficient for reliable use as a container for liquids) after 10 years of contact with the alcoholic beverage and does not react with the latter, in particular the pollutant in particular with metallic elements - "quality of the alcoholic beverage”; this covers in particular the organoleptic properties, such as the taste, the smell, the structure, the texture, the balance, the color, the aspect, the consistency, the length in the mouth..., but also the sanitary qualities independently ethanol-related toxicity, such as the content of heavy metals such as lead or cadmium;
• the invention satisfies at least one of the above objectives and relates, according to a first aspect, to a method, in particular implemented by computer, for managing and/or monitoring at least one factor *f x * chosen from a set of factors including:
• each sample data on the alcoholic beverage chosen from the group comprising - advantageously consisting of - data on the origin, data on the terroir, data on the manufacture, data on the conservation and the maturation, consumption data, physico-chemical data, qualitative data, in particular organoleptic data, economic data, commercial data... and combinations of these data;
• step (c) storing at least some of the samples collected in step (a), under determined conditions;
• step (e) constituting a database relating to the samples and resulting from step (b) and from step (d);
• step (f) optionally, complete and/or update the data assigned in step (b), at least once, on all or part of the samples;
• step (g) optionally, completing and/or repeating the analyzes carried out in step (d), at least once, on all or part of the samples;
• the inventors have judiciously used inert containers, non-contaminating and forming a barrier, with respect to the mineral profile of the alcoholic beverage, and moreover, to containers each containing a sample of alcoholic beverages and suitable to be sealed tightly with a cap.
• This new and inventive approach gives access to a mineral profile, preferably metallic, of the alcoholic beverage, eg wine, which constitutes a reliable marker allowing the control, the management and/or the monitoring of a plurality of stages of the complete chain, from agricultural production to commercial distribution, of the alcoholic beverage, as well as of a certain number of states of the latter, before its consumption.
• This reliable marker can be made up of several dozen different chemical elements, for example more than 50 different chemical elements which provide a considerable mine of information.
• the stages of the sector include the cultivation of the vine, the harvest and all the processes of vinification, breeding, aging, packaging (bottling) and storage.
• the mineral (metallic) profile of wines is also a reliable reflection of the characteristics, sanitary, regulatory, origin and authenticity, as well as the quality and excellence of the wines.
• the mineral (metallic) profile of the alcoholic beverage -wine- is all the more interesting according to the invention, as it is refined/optimized by statistical analysis, or even preferably by artificial intelligence algorithms, and that it is used as a tool for classifying and predicting the properties of the alcoholic beverage, in particular the vinic properties when it comes to wine.
• This fine characterization gives access to a whole range of improved statistical or predictive models for the alcoholic beverage.
• the method according to the invention also represents substantial progress in the quest for traceability, which makes it a daunting anti-counterfeiting weapon and an unequaled means of monitoring the quality of alcoholic beverages, in particular wines. .
• the alcoholic beverages, in particular wines, obtained by implementing this process undoubtedly have an increased commercial value.
• Alcoholic beverages are characterized by a signature or a mineral, in particular metallic, profile.
• This profile originates, in particular, in the plant raw material, in its culture medium and/or in its cultivation and in all or part of the stages in the manufacture of the alcoholic beverage, namely in the case of wine: vinification, aging , aging, maturation, conservation, until consumption.
• This evolution of the mineral profile, in particular metallic results in the appearance and disappearance, through variations in concentration, of the metallic elements of the alcoholic beverage.
• the method according to the invention is based:
• the method according to the invention is intended to use the profile of the metallic signature of the alcoholic beverage for what it is, namely a tracer of the health & food state and a marker of the identity alcoholic drink.
• this step it is a question of taking at least one, or even at least 2 samples, of at least 1 ml, for example 50 ml, of the alcoholic beverage and placing each sample in an inert container with respect to the alcoholic beverage, then to close this container hermetically with a stopper, also inert vis-à-vis the alcoholic beverage.
• the volume -Ve- of alcoholic beverage taken for each sample is such that, in increasing order of preference with Ve expressed in milliliters: 0.5 ⁇ Ve ⁇ 100; 1 ⁇ Ve ⁇ 50; 10 ⁇ Ve ⁇ 30.
• the container is filled to more than 50%, and preferably between 60 and 75%, of its capacity, with 1% nitric acid, and maintained at 50°C for 12 hours, the solution obtained is then taken for elemental analysis.
• He flow rate 4.3 mL/min
• Each sample is then nebulized using a Micromist® nebulizer then introduced into the ICP-MS after passing through a Scott chamber, cooled to 2°C.
• An equilibration and signal stabilization time of 60 s is programmed before proceeding to the actual measurement.
• the container and its stopper are made of a material chosen from thermoplastic polymers, preferably polyolefins, and more preferably still from the group comprising -preferably consisting of - polyethylene, polypropylene and mixtures thereof, polyethylene being a preferred material for the cap and polypropylene being a preferred material for the container.
• the container could be made of silica.
• the samples are taken at different stages of development of the alcoholic beverage. For example, in the wine sector, samples are taken: during storage in vats, before bottling and/or after bottling and/or at different times after bottling (several months or several years after bottling).
• the container preserves the purity of the sample, in particular its inorganic purity, and also effectively collects the data related to this sample.
• barcode / QR Code labeling is an interesting solution.
• the data of the sample are in particular: date, sampler, technician in charge of the analysis, results... They can be read, for example, from a simple "scan" in all conditions.
• step (a) the taking of a sample from a container, such as a tank, a barrel or a bottle, consists in emptying at least 10%, preferably at least 1 % and at most 50% of the volume of alcoholic beverage present in the sampling container, before taking the sample.
• the data assigned to the samples in this step are preferably:
• origin data includes name of alcoholic beverage, name of producer, name of estate, year of production, date of sampling, name of cuvée, batch number, and/or type of alcoholic beverage;
• the data on the terroir includes the designation of origin, the geographical indication, the country, the region, the place, the plot, the grape varieties, the distribution of the grape varieties, the exposure, the sunshine, the density of planting (in vines/ha), the type of vine pruning, the cultivation method, the fertilization of the vine, the grass cover, the pest control, the watering, the average age of the vine, the relief, soil type, water source, and/or vine irrigation;
• the data on production includes the type of harvest, the date of the harvest, the type of sorting and destemming, the type of vinification, the type of press, the maceration time of the skins and pips in the must, the material tanks, the addition of yeast or not, the type of fining and clarification, the filtration system, and/or the assembly;
• storage data includes storage time, successive container types, capacity, temperature, humidity, closure type, and/or packaging date;
• consumption data includes the presence and level of sulphites, the presence and level of phenolic compounds, the percentage of alcohol, and/or the presence and level of aromatic compounds;
• organoleptic * qualitative data, in particular organoleptic, include evaluations of balance, length, intensity, complexity (and/or concentration) and typicity [ELIC(T) method];
• the organoleptic data includes the color, the aromas, the tastes, and/or the duration of expression in the mouth of the aromas of the wine, preferably expressed in caudalies,
• * economic data includes price, sales volume, and/or sales amount
• This step (c) involves collecting several samples of alcoholic beverages, in particular different wines (more than 100, or even more than 1,000, or even more than 10,000) in a cupboard or storage room to establish a data bank. reference samples, usable for several years (for example at least 5 years, or even at least 10 years).
• a sample library of alcoholic beverages, in particular wines is thus constituted, in which each sample retains its elemental mineral, in particular metallic, content. This stability opens the door to applications for health safety (dosage of heavy metals, toxicity), for the fight against fraud or counterfeiting or for subsequent additional analyses, in connection with the quality of a wine. Indeed, all the analyzes are not necessarily carried out soon after the sampling, for reasons of economy.
• the storage conditions for the samples collected in step (a) are as follows: temperature ⁇ 40° C.; pressure ⁇ 1 to 2 bar; humidity £90%; duration at one month, preferably ⁇ one year, and, even more preferably at 5 years.
• mineral elements chosen from B, Na, Mg, P, S, Cl, K, Ca, * at least the following metallic elements (called trace metals): Fe; Cu; Zn; Min;
• At least 10, preferably at least 30, and, even more preferably, at least 40 elements for example between 50 and 100, chosen from the following trace mineral elements: Rb, Cs, Sr, Ba, Ce, Ti, V , Cr, Co, Ni, Zr, Mo, Ag, Al, Ga, Sn, As, Br, I, Se; and/or from the following ultra-trace mineral elements: La, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, Th, U, Sc, Y, Nb, Ru, Rh, Pd, Hf, Ta, W, Re, Os, Ir, Pt, Au, Kg, Tl, Bi, Sb;
• trace mineral elements Rb, Cs, Sr, Ba, Ce, Ti, V , Cr, Co, Ni, Zr, Mo, Ag, Al, Ga, Sn, As, Br, I, Se
• ultra-trace mineral elements La, Pr, Nd, Sm, Eu, Gd, Tb, Dy,
• iron, copper, zinc and manganese are the main trace elements that have an impact on the life of the wine.
• the analytical data can be collected on an analytical sheet specific to each alcoholic beverage (wine).
• the elements chosen for the analysis are the same for all the drinks analyzed.
• This relative analytical variant is particularly appropriate for countering the effects of dilution or evaporation. It also allows better sensitivity in the detection of stable areas of metal concentrations in wine, areas which would be less influenced by bottle contamination and/or by precipitation which could lead to selective modifications of some metal concentrations over time.
• the solvent used for the dilution is advantageously an aqueous solvent, e.g. chosen from the group comprising aqueous acid solutions, such as nitric acid.
• the dilutions used can be: a 1v/Xv dilution and a 1v/2X dilution, with positive natural integer X between 1 and 10. For example, we can have: 1v/5v dilution and a 1v/ 10v and/or 1v/10v dilution and a 1v/20v dilution.
• the analysis of the concentration of the mineral element considered is reliable if the ratio between the concentration c 1v/Xv measured at the dilution 1 v/Xv on the concentration C 1v/2xv measured at the 1 v/2xv dilution, is such that:
• the database formed during step (e) groups together data relating to at least 500, preferably at least 1000, and, more preferably still, to at least 10,000 alcoholic beverages different.
• the database gathers the analytical data from stage (d) and the data attributed to stage (b) on the origin, on the terroir, on the production, on the conservation, consumption, quality, organoleptic properties, economic aspects and commercial aspects.
• This database advantageously operates dynamically, thanks to regular updates.
• Step (h) Advantageously, the processing of the data according to step (h) essentially consists in
• ⁇ use at least one of the following means:
• PCA Principal Component Analysis
• * predictive model algorithm preferably chosen from the group comprising -ideally constituted by-: Random forest (RF) Decision Tree Forest and/or Artificial neural networks (ANN) and/or Support - Support vector machines (SVM); to perform at least one of the following actions:
• RF Random forest
• ANN Artificial neural networks
• SVM Support - Support vector machines
• PCA Principal component analysis
• KLT Karhunen-Loève transformation
• LDA Linear Discriminant Analysis
• Linear discriminant analysis can be compared to supervised methods developed in machine learning and to logistic regression developed in statistics.).
• Such analyzes can be performed using toolboxes or libraries in many software environments such as Python (Sdkit Leam library), Matlab (Statistics and Machine Learning Toolbox toolbox), R (statistical package for PCA, MASS package for LDA and net package for logistic regression).
• Python Systemdkit Leam library
• Matlab Matlab
• R statistical package for PCA
• MASS package MASS package for LDA
• net package for logistic regression a software environment
• the analysis techniques used here are simple, they are based on a basis that allows easy understanding, allow an interpretation of the relationship between the selected elements since the techniques are based on a linear analysis and they are widely available in most statistical packages.
• Random forest The “Random forest” (RF) decision tree forest analysis technique has been widely used in many scientific fields in recent years (Ga'al et al., 2012). This statistical learning theory was proposed by Breiman in 2001 (Breiman, 2001; Tian et al., 2017). Random forests consist of a group of predictor trees where each tree describes a subset of data, which have been sampled according to different observations and according to different variables (Breiman, 2001). The final prediction obtained by the classification forest is the majority vote obtained by consulting for all the decision trees (Tian et al., 2017; Zahiri et al., 2013).
• ANNs constitute automatic learning methods that can improve their behavior with new observations, ie with experience (Anjos et al., 2015).
• ANNs are made up of an interconnected group of nodes (called artificial neurons) that process information (Anjos et al., 2015).
• a neuron in the network operates according to a simple rule that combines its inputs into outputs. For example, a neuron can calculate the sum of its input signals and responds with an output signal by comparing whether this sum is greater than or equal to a threshold value.
• the organization of connections makes it possible to define different types of networks: proactive, recurrent networks, etc.
• neurons can be connected by layers, an input layer (which receives the input data), one or more intermediate layers and a final layer which generates the output (variable) signals (Anjos et al., 2015).
• 'hidden' neurons Linares-Rodriguez et al., 2013. They are able to extract meaningful features from data and they can 'learn' the relationship between inputs and outputs when there is enough training data (in quantity and complexity) (Chiang and Chang, 2009) .
• these models can be used for a wide variety of applications, for example discriminating the botanical origin of different honey samples (Anjos et al., 2015), modeling rainfall runoff (Chiang and Chang, 2009) for finally predict the best choice of tomato cultivar, their type of production and their harvest date (Suarez et al., 2015), among others.
• the SVM model uses the input data to construct a hyperplane (or hyperplanes) in high-dimensional space, to perform classification, regression or other tasks (RapidMiner, 2020a).
• a classification problem it is a question of determining the optimal separating hyperplane which maximizes the margin (distance between the hyperplane and the restricted subset of the samples closest to the hyperplane, samples also called "support vectors" .
• SVM models can be applied in many applications such as gear fault diagnosis (Xing et al., 2017) or to assess pavement condition (Hadjidemetriou et al., 2018).
• the LibSVM library of Chang and Lin was used to develop the SVM models for the study of wines (Hsu et al, 2016)
• Step (h) Preferably, the data processed in step (h) comprises:
• mineral elements chosen from B, Na, Mg, P, S, Cl, K, Ca, * at least the following metallic elements (called trace metals): Fe; Cu; Zn; Min;
• the concentration ratios of these elements, and possibly of all or part of their isotopes and at least 1, preferably at least 5 physicochemical parameters of the alcoholic beverage chosen from the group of parameters comprising - advantageously consisting of - TAV (Alcoholic Strength by Volume), Glucose + Fructose, AT (Total Acidity), Acetic Acid, SO 2 Free, Total SO 2 , pH, Active SO 2 , Ethanal, Malic Acid, Lactic Acid, CO 2 , Tartaric Acid, Gluconic Acid, Glycenol, Optical Density, and all combinations of these parameters (preferably measured within one week before or after bottling, or at a specific time in relation to bottling).
• TAV Alcoholic Strength by Volume
• Glucose + Fructose AT (Total Acidity)
• Acetic Acid SO 2 Free, Total SO 2 , pH, Active SO 2 , Ethanal, Malic Acid, Lactic Acid, CO 2 , Tartaric Acid, Gluc
• the parameter is chosen from the group comprising - ideally constituted by -: the tasting parameters, preferably, balance, length, intensity, complexity, concentration and/or typicity; the overall composition parameters, preferably, alcoholic strength by volume, glucose & fructose, total acidity, acetic acid, free SO 2 , total SO 2, pH, active SO 2 , ethanal, malic acid, lactic acid, CO 2 , tartaric acid , gluconic acid and/or glycerol; the parameters for the content of specific aromatic and/or coloring molecules.
• the tasting parameters preferably, balance, length, intensity, complexity, concentration and/or typicity
• the overall composition parameters preferably, alcoholic strength by volume, glucose & fructose, total acidity, acetic acid, free SO 2 , total SO 2, pH, active SO 2 , ethanal, malic acid, lactic acid, CO 2 , tartaric acid , gluconic acid and/or glycerol
• a use of the data processed in step (h) for the management and / or monitoring the quality of the alcoholic beverage consisting essentially of:
• the method according to the invention thus makes it possible to detect reliably, and possibly in advance, among alcoholic beverages, for example wines, during aging (barrel tank) and/or during maturation/conservation ( vat, barrel or bottle), real nectars or future nectars.
• the invention thus offers a screening means making it possible to select alcoholic beverages, for example wines of high quality, present or future.
• the method according to the invention thus gives access to traceability of alcoholic beverages, in particular wines, for the greatest benefit of the health and safety of consumers, from purchase to the end of the beverage's life.
• the consumer and the producer have a lot of information on the substances present in the drink as well as on the origin and on the conditions of production and delivery of this drink.
• this parameter a tasting Pa 1 , the overall composition Pa 3 and the contents of aromatic molecules Pa 3 and/or specific colorants Pa 4 .
• Pa 1 Tasting (balance, length, intensity, complexity and typicality)
• Balance is fundamental. It is the main component of quality. We generally understand by balance of a wine a harmony between the different components of the texture in the mouth: acidity, alcohol and smoothness, tannins and sugars... It is often easier to define an unbalanced wine. It is a wine of which one of the components is in excess or insufficient in the wine. Thus, a wine that is too green with biting and acerbic acidity, or a wine that is too extracted with astringent and bitter tannins will be unbalanced wines. An unbalanced wine is generally judged as unsatisfactory (or mediocre) by tasters.
• the length of the wine is understood from the aromatic point of view, and it is preferable for the aromas of the wine to remain in the mouth for a long time once it has been swallowed (or spat out in professional tasting). We often bet on a long wine in the mouth when we can feel for many seconds, or minutes, the aromas of a wine.
• Complexity is also a quality parameter. It expresses the number of aromas that we believe we detect when smelling the wine. The more the wine is rich in different aromas, the more it is considered to be of quality. A simple entry-level Muscadet from the Loire is always less complex than a Meursault from Burgundy. For some wines, perhaps still young, which have not yet revealed their full aromatic potential, we can then focus on the concentration of flavors. The richer a wine is, the more it is judged to be of high quality.
• typicality is the most complex notion to judge.
• the typicity of a wine can be understood as a unique and recognizable character of its place of production. Obviously, it is by dint of tasting wines from all regions of the world that one gradually builds up one's memory of the terroirs.
• a typical wine is a wine that inevitably has a “taste of the place”.
• typicity often comes to the aid of fine and subtle wines, perhaps not as demonstrative and aromatic as certain "bombs" of the new world, made from from aromatic grape varieties...
• a Muscadet on lees, because of its terroir, is, for example, never very concentrated.
• it has this crystalline freshness, this saline minerality contributing to its typical and recognizable taste, in short, a great typicality.
• These analytical data are chosen from the group comprising -advantageously consisting of-: TAV (Alcoholic strength by volume), glucose + fructose, AT (Total Acidity), acetic acid, free SO 2 , total SO 2 , pH, active SO 2 , ethanal , malic acid, lactic acid, CO 2 , tartaric acid, gluconic acid, glycerol and all combinations of these data.
• IRTF Infrared spectroscopy with Fourier transform
• visible spectroscopy automated colorimetry
• capillary electrophoresis colorimetry
• titrimetry capillary electrophoresis
• aromatic molecules have a positive effect on taste, others have a negative effect or are considered faults and/or contaminants, and others contribute greatly to the nose of wines.
• step (h) it is therefore possible, in accordance with the invention, to implement, with regard to the data processed in step (h), by statistical analysis and, possibly with the assistance of artificial intelligence, corrective actions aimed at:
• esters acetaldehyde (fresh apple), isoamyl acetate (banana), ethyl acetate (acesant character), ethyl butyrate, isobutyrate Ethyl Butyrate, Ethyl 2-Hydroxy-4-Mepentanoate, Ethyl Octanoate, Ethyl Decanoate, Ethyl Hexanoate, Ethyl Isovalerate, Isoamyl Acetate, 2-Phenylethanol, 3-isobutyl-2-methoxypyrazine (IBMP) and isopropyl-methoxypyrazine (IPMP), sec-butyl-methoxypyrazine (SBMP)-green pepper and vegetable aromas-, Terpenes and Norisoprenoids (Terpenols: Geraniol, linalool, a-terpine
• Geosmin compound with strong odor power, which has an earthy smell - very marked moldy
• OTA ochratoxin A
• biogenic amines Histamine, Methylamine, Ethylamine, Tyramine, Phenylethylamine, Putrescine, Isoamylamine, Cadaverine
• benzaldehyde (benzoic aldehyde) with a bitter almond smell
• benzyl alcohol (benzyl alcohol from the plasticizer present in the epoxy resin coatings used in certain packaging for bottling)
• ⁇ type (iii) aromatic molecules Propan-1-ol, 2-methylpropan-1-ol, Isopentanols, 2-Methyl-butanol, 3-Methyl-butanol, Butan-1-ol, Butan-2-ol, But -2-ene-1-ol.
• step (h) it is possible, in accordance with the invention, to set up, with regard to the data processed in step (h), by statistical analysis and, possibly, with the assistance of artificial intelligence, actions correctors aiming to adjust the quantity and/or the evolution of the quantity over time, of coloring molecules acting on the appearance of the color.
• tannins and anthocyanins are examples that may be mentioned.
• the latter is a polyphenol present mainly in the skin of grapes. Resveratrol content depends on the grape variety (Pinot Noir, Grenache, Mourvèdre and Merlot contain more), vinification, geographical origin and exposure to cryptogamic diseases. This powerful antioxidant is said to have beneficial effects on human health.
• this may consist in choosing the location of the vines, the type of vines, the type of crop, the type of harvest and finally the date of harvest and the state of maturity of the grapes at the time of the harvest.
• the location, for example with the GPS coordinates, of the vine plots can be extended to the estates, appellation areas, towns, regions.
• the location chosen also determines the relief [Plain - Slope - Steep slope (>20°) - Extreme slope (>30°)], the exposure/sunshine and the type of soil: Acid, basic, Clay - Limestone - Gravel - Pond - Chalk - Granite - Rolled pebbles - Schist - Silica - Silt - Sand - Gneiss - Sandstone - Others.
• the type of vines is defined by the grape varieties (type and estimated distribution %), the age of the plans, the planting density.
• the cultivation may be reasoned, conventional, organic, or biodynamic. It can also be defined in particular by the following elements:
• Modalities of the vinification and/or blending and/or aging process making it possible to influence at least one preference parameter
• These methods relate to several stages in the manufacture of the alcoholic beverage that is wine, corresponding to a particular mode of implementation of the process according to the invention, namely: reception of the harvest and preferential operations, alcoholic fermentation and fermentation operation, cuvaison and alcoholic fermentation, aging and operation following vinification, decision to end vinification and follow-up control of alcoholic and malolactic fermentation.
• pre-fermentation crusher made (to release the juice more easily);
• juice can be removed in order to increase the grape skin/juice ratio, in order to obtain more concentrated wines, bleeding is then used for rosé or a less qualitative batch);
• the juice is composed of a large quantity of alcohol, which accentuates the extraction and in particular that of undesirable compounds, the extraction will then be limited to the minimum, we simmer.
• the cap of marc is now only lightly watered (very little pumping over) daily, or even every two days, in order to renew the juice present in the marc and to prevent it from getting pricked.
• the tasting is essential, when the cellar master and the oenologist consider that the material and the fat have been extracted enough, the tank is drained to draw wine with bitterness, greenness and/or of drought.
• the duration of vatting varies according to the quality of the grapes and the desired wine, it generally varies from 10 to 30 days.
• the malolactic fermentation generally takes place after the alcoholic fermentation. It reduces the acidity of certain wines.
• Aging can be done in different ways: aging in vats - aging the wines in wood - type of vat - time and conditions; wine aging with micro-oxygenation and/or macro-oxygenation: oxygenation conditions
• red wine fining stabilization and clarification fining, tartaric stability, clogging index and filtration behavior
• a blend can be made at any time, between free run and press wine, between two cuvées, between varietal wines to make a blended wine.
• the control and monitoring of alcoholic and malolactic fermentation is based on the following indicators: Glucose + Fructose, TAV acquired, Probable Degree, AT, Acetic acid, free SO 2 , total SO 2 , pH, Malic acid, Acid Lactic,
• a balance is made after racking, on the basis of the following indicators: total SO 2 , pH, active SO 2 , Ethanal, Malic acid, Lactic acid, CO 2 , Tartaric acid, Gluconic acid, Glycerol, Iron, Copper ( whites and rosés) - Tasting - OTA index, coloring intensity on request),
• step (I) of the method may comprise a sub-step (i c ) consisting in an action of correction of the mineral profile which may consist of:
• the method according to the invention takes advantage of focusing on the mineral/metallic analysis of alcoholic beverages, in particular wines, by using suitable containers for collecting samples, by processing the data statistically, from preferably by targeting learning processes based on artificial intelligence and by collecting the data collected in a database provided for this purpose.
• Alcoholic beverages, in particular wines can thus be distinguished from each other, according to their quality at a time t, but also by taking into account the prospects for the evolution of this quality, in view of the mineral profile (eg metal) of the alcoholic drink.
• the fact of having access, thanks to the process according to the invention, to the evolutionary potential of the alcoholic drink, is a very interesting new criterion, which facilitates its evaluation and makes it possible to assign scores with greater fairness and impartiality.
• the rapid analysis of the data in the database using the methods and tools according to the invention constitutes an effective aid for informed, and above all objective, decision-making for the classification or evaluation of alcoholic beverages, in particular wines. , but also upstream to provide all desirable preventive and/or corrective actions at all stages of the production of alcoholic beverages.
• the invention relates to a device for implementing the method according to the invention, characterized in that it comprises a sample library comprising at least one enclosure in which the samples collected are stored and kept. in step (a) in inert containers each closed by a stopper and in that this chamber is capable of placing these samples under given temperature, pressure, humidity and atmosphere conditions.
• the invention also relates to a computerized management system for the sample library and the database.
• Figure 1 is a distribution of the wines analyzed in the examples, according to their grape varieties.
• Figure 2 is a distribution of the wines analyzed in the examples, according to their origins - their regions.
• Figure 3 is a distribution of the wines analyzed in the examples, according to their cultivation methods.
• Figure 4 is a graph in which:
• the ordinate corresponds to the number of wines analyzed in the examples which fall into a price category and which are counted
• Figure 5 is a graph in which:
• the ordinate corresponds to the number of wines analyzed in the examples which fall into a category (red wine or white wine) and which are counted,
• Figure 6 is a graph in which:
• the ordinate corresponds to the number of wines analyzed in the examples which fall into a price category and which are counted
• FIG. 7 is a graph representing the distribution of the Mg concentrations of wines analyzed in the examples, as a function of the quality of the wine (here translated by a selection of the scores obtained).
• FIG. 8 is a graph representing the distribution of the K concentrations of wines analyzed in the examples, as a function of the quality of the wine (here translated by a selection of the scores obtained).
• FIG. 9 is a graph representing the distribution of the Ca concentrations of wines analyzed in the examples, as a function of the quality of the wine (here translated by a selection of the scores obtained).
• FIG. 10 is a graph representing the distribution of the Na concentrations of wines analyzed in the examples, as a function of the quality of the wine (here translated by a selection of the scores obtained).
• Figure 11 shows a statistical correlation matrix between the mineral elements analyzed (lanthanides and W, S, Nb).
• Figure 12 presents the mineral profile of a wine from the 2 nd series of examples, according to a graphical star representation (Kiviat diagram).
• Figure 13 is a graph giving the distribution of metallic and mineral elements on the different concentration classes of the wines of the 3rd series of examples
• Figures 14 to 17 show the mineral profiles represented according to Kiviat diagrams of the red, white, rosé and sparkling wines of the 3 rd series of examples.
• sample tubes are then placed in a storage area at room temperature.
• step (b) Each tube is referenced and the available information concerning the wine is entered into a data table. This information is listed below in the passage relating to step (e) creation of the database.
• the reagents used are of ultra-pure quality: 35% HNO 3 (Suprapur, Merck), 30% HCl (Suprapur, Merck) and doubly deionized water (resistivity 18.2 MQ.cm) dispensed via a purification system (Pure La b, Elga)
• the obtaining of semi-quantitative data is ensured by the use of one-point calibration using a multi-element standard (5 ppb/element). This was prepared from 1000 ppm monoelemental solutions of Li, Mg, Co, Y, Ce, Tl (Plasma CAL ICP-MS) diluted in 1% nitric acid.
• sampler needle is rinsed for 30 s with water, then 30 s with 2% HCl and finally, 30 s with 1% HNO 3 .
• a blank sample (HNO 3 1%) is injected in order to ensure the absence of cross-contamination.
• dilution tests were carried out on a sample of wine (ref Wine No. 20) at 3 levels: dilution by 20, by 10 and by 5 in nitric acid.
• the semi-quantitative analysis was carried out on the main elements. For each element, the measurements obtained were compared (dilution value 5/dilution value 10) and (dilution value 10/dilution value 20). When for these two values, the ratio is equal to 2+-0.5, it can be considered that the measurement of the element is reliable in the domain and that the element can be considered as reliable to use.
• 50 reliable elements to measure for this sample Na, Mg, Al, S, K, Ca, Sc,
• the element can be measured more specifically by adjusting the dilution setting, and can be considered interesting to interpret.
• 11 additional elements were found for this sample: Si, Ti, Zn, Ga, Br, Nb, In, Sn, Sm, Ho, Er.
• the appended Figures 1 to 3 show, by way of examples, the distribution obtained according, respectively, to information on the grape varieties, the origins-regions and the cultivation methods.
• the Python language is used with the numpy, pandas, seabo and matplotlib libraries (non-exhaustive list).
• the working method begins with a first data processing by analyzing the basic properties of all the data. Then comes the phase of viewing certain characteristics according to classes defined beforehand (for example, grouping according to price ranges, rating ranges, etc.)
• the following libraries are imported under Python: pandas, seabo (for graphical representation), numpy, matplotlib and scipy.stats.
• the excel files are loaded in the form of Pandas DataFrame: the first contains the concentrations of metals, the second the information relating to the origins, descriptions and quality of the wine. These files are merged to have a single DataFrame, grouped using the common information: “Sample Name”.
• Seabom.plot.pie is then implemented to represent qualitative variables such as origin, type of wine, method of cultivation, etc.
• subsets of interest are created. Examples of subsets studied are: metals by category, prices (by setting lower and upper bounds) and Vivinos ratings.
• FIGS 4, 5 and 6 are examples of graphs obtained, in which:
• the ordinate corresponds to the number of wines falling into the category (price for figures 4&5 and red wine or white wine for figure 6) and counted,
• the graph in FIG. 4 shows that the chlorine content which increases with the price of the wine.
• the graph of Figure 6 shows a difference between white and red wine on the potassium level.
• the 4 graphs of FIGS. 7 to 10 representing the distribution of the concentrations of Mg, K, Ca and Na, respectively, according to the quality of the wine (id translated by a selection of the scores obtained).
• step (i) Use of statistically analyzed data From the different measurements performed on the ultra-traces, we obtain a correlation matrix shown in Figure 11.
• the scale on the right in FIG. 11 corresponds to the Pearson coefficient (the value -1 means that the variables are inversely correlated, +1 linear correlation and zero no correlation). It is calculated according to the expression covariance of the matrix (X, Y) divided by std(X)*std(Y) (standard deviation of X times standard deviation of Y): cov(X,Y)/std(X) std(Y)Pearson's coefficient is an index reflecting a linear relationship between two continuous variables.
• the correlation coefficient varies between -1 and +1, 0 reflecting a zero relationship between the two variables, a negative value (negative correlation) meaning that when one of the variables increases, the other decreases; while a positive value (positive correlation) indicates that the two variables vary together in the same direction.
• Figure 12 shows the mineral profile of the wine submitted for analysis, in the form of a Kiviat diagram which presents the results in deciles and with reference to all of the 590 red wines from France analyzed in the same terms.

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