Section 3: Applied Aspects of Winemaking (B) Evaluation of Wine

Analytical Techniques in Oenology

Disney Ribeiro Dias1, Leonardo de Figueiredo Vilela- and Rosane Freitas Schwan2*

  • 1 Department of Food Science, Food Microbiology Sector, Federal University of Lavras, Campus Universitario, Lavras, MG, Brazil, 37.200-000
  • 2 Department of Biology, Microbiology Sector. Federal University of Lavras,

Campus Universitario, Lavras, MG, Brazil, 37.200-000

1. Introduction

Since the times of Pastern, wine production has been developing with strong research in the areas of viticulture and enology. Enology has long been said to be a science, while winemaking is an art. Viticulture is related to the study of grapes and their cultivation, while oenology is concerned with postharvesting of grapes and the elaboration of wines. It is known that the quality of the grapes varies from year, region, date of harvest and even for each type of vineyard.

There is a tendency to seek, every day, new technologies that bring, besides greater productivity, an improvement in the final product quality. Winemaking processes have been a good example of this progress. The alcoholic fermentation of grape must, which was once completely empirical, has become one of the strongest areas of agro-industrial research. Within this context, viticulture and oenology study several aspects for the improvement of production, ranging from the selection of the best cultivars and vine varieties and the microorganism, and system for fermentation, until the care to obtain the final beverage, its stabilisation, bottling and sale. The yeasts used in the fermentation process are extremely important for the final product obtained. Yeasts will transform the sugars into ethanol and their metabolism generates the other aroma compounds that characterise, together with varietals aroma-forming compounds, the quality of the wine.

The elaboration of quality beverages requires care, ranging from the ideal cultivation of the vine (climatic and soil factors), handling and hygiene in post-harvest processing and within the industry. From berry to bottling, there are several stages of wine processing. At each stage, analytical methods are involved to a greater or lesser extent, ahvays necessary for the maintenance and guarantee of the chemical, microbiological and sensorial quality of wine, as wrell as its authenticity and safety. Analytical methodologies are essential in winemaking and enology, since all commercial wines, from a wide range of geographical origins, are ruled by specific standards that determine several parameters, such as density, pH, acidity, ashes, amounts of sugars, ethanol, methanol, higher alcohols, esters and minerals.

Several methodologies and techniques are employed to analyse the various parameters in enology, some with greater or lesser operational complexity and equipment infrastructure. In this chapter, we will present an overview of the most advanced techniques in wine analysis, addressing physico-chemical, chromatographic, sensoiy, microbiological and wine analysis (Fig. 1).

2. Advances in Wine Physico-chemical Analysis

During the processes of vine production, considering the growing of the grapes until the beverage is obtained, it is necessaiy to evaluate certain chemical components that parameterise the quality of the vine. Wines are made up of a myriad of chemical compounds, which may be varietal, originating from the grape itself, synthesised or transformed by microbial metabolism or can be formed during storage (Fleet, 2007: Polaskova et al., 2008; Ebeler and Tliomgate, 2009; Puertas et al., 2018). It is certain that evaluating all the compounds present in wine would be a laborious, expensive task. To avoid unnecessary spending of time and money on wine analysis, but guaranteeing the quality and safety of wines, the regulatory bodies of each country, as well as international organisations, such as the OIY, standardised ♦Corresponding author: This email address is being protected from spam bots, you need Javascript enabled to view it

Techniques used in wine analysis

Figure 1. Techniques used in wine analysis

some of the minimum parameters of quality wine. For the determination of these parameters, the physicochemical analysis are indispensable. The physico-chemical analysis comprises the determination of important wine quality parameters and evaluates the control of the fermentative process.

2.1. Classical Techniques in Wine Analysis

The main physico-chemical parameters analysed in wines around the world comprise pH, total acidity, volatile acidity, ethanol, alkalinity of ash, sulphur dioxide, reducing sugars, alcohol strength and density, among others. To achieve the values of these parameters, several classical techniques, which use robust and low-priced apparatus, are employed, such as titrimetry, potentiometry, spectrophotometry and spectrometry (Amerine and Ough, 1980; OIY, 2015: Dias et al., 2017).

Titrimetry, also known as titration, is one of the oldest classic techniques and it is used in enological research laboratories and wineries to determine acidity (total and volatile), alkalinity of ash, carbon dioxide, hydroxymethyl furfural, sulphates, and sulphur dioxide, for example (Johansson, 1988; Dias et al., 2017). Potentiometry is an electroanalytical technique used to measure variation in electric potential in an electrochemical cell. Potentiometry is a common and reliable technique used in wine analysis and pH meter is the most common and useful potentiometric-based laboratory apparatus (Zoski, 2006; Ribereau-Gayon et al., 2006; Dias et al, 2017). The spectrophotometric analysis is based on the light propagation capacity and its absoiption/'reflection in solutions, generating spectral responses and correlating these responses with the concentration of a certain compound. Such an analysis is important in the quantification of several compounds in wines, among them are organic acids, reducing sugars, and phenolic compounds (Aleixandre-Tudo et al, 2017).

2.2. Recent Techniques in Wine Analysis

Although the classic techniques mentioned above are still used in many laboratories and small wineries, the development of more complex, robust and automated equipment, capable of analysing several physico-chemical parameters and compounds in wine samples simultaneously, has been developed in the last few decades (Schindler et ah, 1998; Serban et al., 2004; Lima and Reis, 2017). Such equipment, often referred to as ‘automatic wine analyzers’ are highly versatile, performing numerous tests on several samples over a much shorter period than would normally occur using standard equipment such as pH meters, titrators, distillers or a spectrophotometers.

Noticeably, all these benefits come at a cost, and the lab, the winery, or the industiy needs to assess whether the cost acquisition and maintenance of the automatic analyzers is worth the investment Table 1 presents the pros and cons of the use of an automatic analyser as well as the main analyses performed on commercially available analysers, manufactured by companies, such as Braker (ALPHA II Wine Analyser), Foss Analytical (WineScan™), and Skalar (San+ “ Automated Wet Chemistry Analyser).

Table 1. Merits and Demerits of the Use of Wine Analyser and the Main Parameters Analysed by this Kind of Equipment



  • • Several compounds analysed per sample
  • • Larger number of samples analysed in less time
  • • Reduced number of reagents per analysis
  • • Improves accuracy of data acquisition due to lower incidence of operator errors
  • • Acquisition and maintenance costs
  • • Cost of reagents and other consumables
  • • Requires trained personnel or supervision of a qualified technician

Main automated analyses of nines and musts

Free and total sulphur dioxide, ethanol, glucose/fructose, malic acid, volatile acid, total acid, pH, tartaric acid, brix, density, acetaldehyde, diacetyl, total alkalinity, ascorbic acid, sodium, potassium.

3. Chromatographic Analysis

The chemistry of flavour of grape wine has been the main focus of many researches due to the complexity of the volatile and non-volatile compounds that contribute to the flavor of different grape varieties (Polaskova et ah, 2008; Ebeler and Thomgate, 2009). The spectroscopic methods applied for wine and grape analyses include techniques as spanning atomic spectroscopic methods, such as atomic absoiption spectroscopy (AAS), inductively coupled plasma (ICP) and some molecular spectroscopic methods, such as infrared and ultraviolet/visible spectrophotometry, nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) (Grindlay et al., 2011). Particularly, ultraviolet-visible (UW Vis) spectrophotometry and infrared (IR) spectrometry offer good features that make them ideal for a very large volume of the routine grape and wine analyses (McGoverin et ah, 2010). Despite the power of other techniques, as spectroscopy techniques, for the high throughput analysis of a wide variety of compounds in wine samples, many applications in grape and wine analysis require individual separation of compounds, as complex organic fractions, such as the volatile compounds, phenolics and influential trace-level constituents. The most common chromatographic methods used for wine analysis are gas chromatography (GC) and high-performance liquid chromatography (HPLC) and more recently, techniques more advanced, as gas chromatography-mass spectrometry (GC-MS), liquid-chromatography- mass spectrometry (LC-MS) (De Villiers et ah, 2012).

The main compounds found in grape wines that contribute to flavour are carbohydrates, such as glucose and fructose, the major sugars present in grapes and juices (Eyduran et al., 2015). The malic, lactic and tartaric acids influence directly important parameters, as the taste balance, chemical stability and pH of the beverage (Ali et ah, 2010; Silva et ah, 2015). Proanthocyanidins (tannins), terpenoids (monoteipenoids, sesquiterpenoids and C13-norisoprenoids) and various precursors of aromatic aldehydes, esters and thiols are detectable in finished wines (Lund et ah, 2006; Coelho et ah, 2017).

3.1. High Performance Liquid Chromatography (HPLC)

The high-performance liquid chromatography (HPLC) has been one of the more studied analytic techniques, which is widely spread and used in food and beverage analysis. In grape wines analysis, the HPLC technique has been widely used for determination of sugars, alcohols, organic acids, phenolic compounds among others. Sugar analysis is performed irsing different separation methods (columns) and may be successfully used with different detection systems (detectors). The chromatographic columns commonly used in separation of sugars in foods and beverages are the NH, stationary phase and cation exchange resins. The mobile phase, water, mixtures of acetonitrile and water (80:20:75:25) or acid solutions of sulfuric acid, orthophosphoric acid and perchloric acid are used under different conditions of temperature and flow. Organic acids analysis in grape wine has also been performed, in most cases, employing ion exchange columns, acidified mobile phases and detectors based on the use of ultraviolet light as shown in Fig. 2.

The organic acids affect the flavour, enhance colour stability, limit oxidation and together with ethanol, are largely responsible for the microbial and physico-chemical stability of table wines (Waterhouse et ah, 1997; Jackson, 2000). The carbohydrates, as glucose and fructose, the major hexoses present in grapes

Chromatogram from organic acids (mix of standards). 9.55 min = citric acid; 10.80 min = tartaric acid; 12.44 min = malic acid; 18.30 min = acetic acid. Conditions

Figure 2. Chromatogram from organic acids (mix of standards). 9.55 min = citric acid; 10.80 min = tartaric acid; 12.44 min = malic acid; 18.30 min = acetic acid. Conditions: UY detector 210 nm; SCR 101H column; 100 mM perchloric acid 0.6 ml of flow rate; temperature 50°C.

are responsible for ethanol formation as well formation of secondary metabolites that determine the fermentation endpoint (De Villiers et ah, 2012).

Phenolic compounds, identified by HPLC, play an important role in the wines ageing and influence the wine flavour, affecting the organoleptic properties thr ough their contribution to astringency, bitterness and colour (Armstrong et ah, 2001). The anthocyanins are phenolic compounds responsible for the colour of red grapes and wine and are important to the health benefits. The chlorophylls and carotenoids are photosynthetic pigments and important as precursors to produce isoprenoids, which are known to be significant contributors to wine aroma (De Villiers et ah, 2012).

3.2. Gas Chromatography

In many studies, the gas-chromatograph with flame ionisation detectors (FID), GC-FID, and mass spectrometers (MS), GC-MS, are used for volatile compounds determination in grape wine; an example of the chromatogram generated by such equipment is shown in Fig. 3.

Example of the chromatogram generated by GC-MS analysis

Figure 3. Example of the chromatogram generated by GC-MS analysis

The analyses are preceded by extraction steps aiming at the sample clean up and analytes concentration. The volatile compounds extraction is performed by Liquid-Liquid Extraction (LLE) and Solid Phase Micro Extraction (SPME). For grape wine, volatile compounds are grouped as majority and minority. The major compounds are present in higher concentrations, while the minorities are the compounds presents in minor concentration (De Villiers et ah, 2012; Panighel el al., 2014; Mencarelli et ah, 2018).

According to Flamini et ah (2014), the extraction of volatile compounds is performed by solid phase micro extraction. SPME was developed in the 1990s by Pawliszyn et ah (1990) and many other papers describe different aspects of this approach and applications in different fields (Bojko et ah., 2012). This extraction technique was demonstrated to be rapid, simple and reproducible, without solvent use and needs a small sample volume (Yu et ah, 2012; Hannon et ah, 1997). For these reasons it has been used to study the volatile profile of many beverages, including grapes and wine (Vas et ah, 2004; Castro et ah, 2008; Flamini et ah, 2010). An interesting alternative to SPME, Stir Bar Sorptive Extraction (SBSE) has been recently developed (Baltussen et <7/.,1999: Sandra et ah, 2001; Zalacain et ah, 2007). In this technique, a magnetic stir bar coated with a polymeric sorbent (polymethylsiloxane, PDMS), is placed in the sample and stirred for a defined time to extract nonpolar analytes from the sample into the polymeric coating. After extraction, the stir bar is placed in a thermal desorption unit coupled online to a GC, usually equipped with an MS detector. The apparent advantage of SBSE is the relatively high content of polymeric sorbent (about 50-250 times the amount present on a SPME fiber) available for extraction of analytes, making it about 50-250 times more sensitive than SPME. Various kinds of fibres are commercially available, as PDMS, PDMS/CAR (Polydimethylsiloxane/Carboxen) and PDMS/DVB/ CAR (Polydimethylsiloxane/Divinylbenzene/Carboxen). The competition for sorptive sites on the fibre can occur so that small changes in the matrix composition can significantly changes the quantitative extraction of the analytes of interest. The time of fibre exposure, sample temperature and in the case of liquid samples, the pH, ionic strength and type of solvent or matrix composition (i.e. water and ethanol solvents in the case of grape wines) that may be present are parameters that determine the success of analysis (Murray et ah, 2001; Howard et ah, 2005; Polaskova et ah, 2008).

With the use of capillary GC, the number and quantification of compounds in a single analysis improve significantly. The phases for separation of grape wine volatiles are polyethylene glycol (PEG) or ‘WAX’ and nitroterephtalic acid-modified PEG phases (free fatty acid phases, FFAP). Non-polar phases, such as polydimethylsiloxane (PDMS), are used for the analysis of apolar compounds, such as teipenoids and volatile phenols (Pawliszyn et al., 2006). The bi-dimensional gas chromatography (GCXGC) improves separation of highly complex mixtures, such as encountered in grape wine samples. The GCXGC uses two columns to create a bi-imensional plane of separation based on volatility and polarity, thus demonstrating a high-resolution power identifying new compounds that may contribute to grape and wine aroma (Ryan et al., 2005). The volatile compounds are identified on the basis of comparisons of the mass spectra and GC retention indices (RI) to synthesised standards (Polaskova et al., 2008). Many of the peaks detected by the GC do not actually contribute to our perception of flavours or fragrances because they are present below our thresholds for detecting. Therefore, the technique, GC-olfactometry (GC-O) was a landmark development in flavour, aroma and fragrance research, as it provides valuable information on compounds and a powerful tool for identifying important odourants that contribute to grape wine aroma and for relating the contributions of individual odourants to the differences among different wines samples. In the technique GC-O, a human assessor sniffs the effluent as it emerges from the GC column and the aroma quality; the time at which the aroma is sensed and in some cases, the aroma intensity is recorded (Polaskova et al., 2008).

Grape aroma is composed of about 800 compounds and the volatile profile of wine also includes compounds produced from the fermentative process and which are the largest percentage of the total aroma composition of wine. Volatile organic compounds are responsible for the wine ‘bouquet’ and vital to wine quality, determining their aroma characteristics. The main groups of aroma and flavour compounds are organic acids, proanthocyanidins (tannins), teipenoids (monoterpenoids, sesquiterpenoids, and C13 norisoprenoids) and various precursors of aromatic aldehydes, esters and thiols. The glycosidases and peptidases being water-soluble, play a vital role in wine flavour and aroma (Gonzalez-Barreiro et al., 2015).

4. Current Techniques for Microbiological Analysis of Wines

Microorganisms belonging to several groups, including filamentous fungi, yeasts and bacteria are present in the grape since its cultivation. These microorganisms, especially yeasts, can remain during the fermentation process and are important for obtaining quality wines. In spontaneous fermentations of healthy grapes, yeasts predominate duiing fermentation, with a prevalence of Saccharomyces cerevisiae species (Fleet et al., 1984). Other yeast species, called non-Saccharomyces, are also present at the beginning of the fermentation and may contribute to the quality of the wine, by the formation of aroma-forming compounds (Ciani and Comitini, 2011). In addition to yeast, lactic acid bacteria (LAB) contribute to the improvement of wine quality, reducing the acidify of the wine during malo-lactic fermentation (Davis et al., 1985; Lasik, 2013). Filamentous fungi and acetic acid bacteria are associated with deterioration or contamination of the wine. Due to the characteristics of the microorganisms and their importance in winemaking, species identification is essential during and after vinification to control and guarantee the wine quality (Bartowsky and Henschke, 2008).

4.1. Overview of Detection and Counting of Microorganisms

The counting and detection of microorganisms or the products of their metabolism, using classical techniques and some techniques of molecular biology is established by OIV (OIV 2015). The classical microbiological methods generally involve the use of an appropriate pre-enrichment and enrichment culture media, isolation in selective media followed by morphological and/or biochemical tests. All this is laborious, obtaining results can take days or weeks and, additionally, they present low sensitivity. In addition to this, it has been demonstrated that some microbial cells can enter a viable but not cultivable state (unculturable cells), due to the processing to which the wine is subjected, making the use of culture methods limited. A series of alternative, rapid and sensitive molecular methods for the detection, identification and quantification of microorganisms have been developed to overcome these drawbacks. The best way to solve the problem of microbial identification is the use of a polyphasic approach, combining classical and molecular techniques (Ivey and Phister, 2011; Frohlich eta!., 2017). As mentioned in previous topics for physico-chemical and chromatographic techniques, several techniques in microbiology have also been developed or improved, with emphasis on molecular and spectrometric techniques, to identify microorganisms in an accurate and fast way (Fig. 4).

Evolution of microbial identification techniques

Figure 4. Evolution of microbial identification techniques

4.2. Microbial Identification Using Molecular Techniques

One of the great limitations of classical methods of microbial identification is the difficulty in describing the ecological interactions and the identification of the microbial diversity present in wine and other foods or a complex sample. In this sense, the use of indirect, for the identification of cultured microorganisms, and direct, used to profile entire microbiota or identify specific microbes in a mixed population (Fig. 4), molecular methods are of great value in the evaluation and identification of the microbiota present in the winemaking processes (Giraffa and Canninati, 2008; Cocolin el al., 2011; Ivey and Phister, 2011).

The main advances in the tests of detection of microorganisms in food, based on nucleic acids, occurred in 1990. The first molecular identification methods were DNA-DNA hybridisation, 16S rDNA sequence analysis, hybridisation with a specific probe and RFLP (restriction fragment length polymorphism) or ribotyping analysis. In contrast to physiological and biochemical characteristics, molecular identification is based on the constitutive composition of nucleic acids rather than the products of their expression (Querol and Ramon, 1996; Kurtzman, 2011).

The use of indirect molecular techniques (PFGE, pulse field gel electrophoresis; RAPD, polymorphism random amplification; AFLP, amplified fragment length polymorphism; RFLP, restriction fragment length polymorphism) allows differentiation of detection and strains, identification of species and analysis of genetic similarity even in the absence of nucleotide sequence information, has been widely used successfully in the characterisation of yeasts in different wine environments. Among the direct molecular methods, PCR (polymerase chain reaction) and associated techniques, such as rep- PCR (repetitive extragenic palindromic), ERIC-PCR (enterobacterial repetitive intergenic consensus) and q-PCR (quantitative) are recognised by simplicity in operation. The DGGE (denaturing gradient gel electrophoresis) and TGGE (temperature gradient gel electrophoresis) techniques have been increasingly used for analysis of the microbial diversity, to provide information on the diversity of microorganism populations in wines (Querol and Ramon, 1996; Cocolin et al., 2011; Ivey and Phister, 2011; Cappello et al, 2014; Lonvaud-Funel, 2015; Liu et al., 2017; Frohlich et al., 2017).

4.3. Next Generation Sequencing (NGS)

DNA sequencers are devices that read a DNA sample and generate an electronic file with symbols representing the sequence of nitrogenous bases - A, C, G, T contained in the sample. The first popular DNA sequencing methods were the chemical method of base degr adation (Gilbert’s method) and the dideoxy method or fragment termination (Sanger’s sequencing). Both methods are based on the production of a set of single strands of DNA that are separated by electrophoresis. Due to its high efficiency and low radioactivity, Sanger’s sequencing was adopted as the core technology in the ‘first generation’ of research and commercial sequencing applications. In 1987, the first automatic DNA sequencer, ABI 370, was launched by Applied Biosystems. With automation it was possible to cany out large sequencing projects, such as the human genome, the mouse and others (Liu et al., 2012). This technology was the base for complete sequencing of Sacchawmyces cerevisiae genome (Goffeau et ah, 1996). After the human genome project (Collins et al., 2003), which encouraged the development of new sequencing technologies (Next Generation Sequencing), the 454 company launched the 454 in 2005. In 2006, Solexa launched the Genome Analyser, followed by Agencourt launching the SOLiD (sequencing by oligo ligation detection), which are three more typical sequencing systems in next generation sequencing (NGS). In the year 2006, Agencort was purchased by Applied Biosystems. In 2007, the 454 was purchased by Roche and Solexa was purchased by Illumina.

Next Generation Sequencing (NGS) techniques, in comparison to Sanger sequencing, use different analytical methodologies and provide high-throughput and high resolution, producing thousands or even millions of sequences at the same time. These sequences allow precise identification of the microbial rate, including non-cultivable organisms and those present in small numbers. In specific applications, NGS provides a complete inventory of all operons and microbial genes present or expressed under different study conditions. NGS techniques are revolutionising the field of microbial ecology and have recently been used to examine various food ecosystems (Liu et al., 2012; Mayo et al., 2014). After years of evolution, the three systems, Roche 454, AB SOLiD and Illumina, exhibit better performance and their own advantages in terms of read length, accuracy, consumables, hand-power requirements and computer infrastructure (Mergulies et al., 2005; Schuster, 2007; Liu et al., 2012; Liu et al., 2017). NGS has been used, for example, for the identification of microbial diversity and dynamics during wine fermentation (del Carmen Portillo and Mas, 2016; del Carmen Portillo et al., 2016), microbial biogeography of wine grapes (Bokulich et al., 2014), wine grapes microbial terroir (Gilbert et al., 2014) and bacterial diversity in botrytised wine (Bokulich et al., 2012).

4.4. MALDI-TOF Identification of Wine Microbiota

MALDI-TOF (Matrix Associated Laser Desorption-Ionisation - Time of Flight) is a omics approach technique within the mass spectrometry area used for the identification of microorganisms in the most varied samples. The system consists of irradiation of a laser on the fresh sample of microbial colony in the presence of polymeric organic matrices (e.g. 2,5-dihydroxybenzoic acid (DHB), a-Cyano-4- hydroxycinnamic acid (a-CHCA). The material irradiated with the laser is vapourised, generating ionisation of the molecules, which fly, under vacuum, through a tube to a detector. The characteristics of the ionised molecules, especially regarding their mass to charge ratio (m/z), are responsible for the differences in the time of arrival to the detector (time of light). The detector begins to acquire data and generates spectra for each sample, which are compared with databases, in the equipment itself, leading to the rapid identification of the microorganism (Tanaka et al., 1988). The microbial identification is based on analyses of ribosomal proteins patterns, which are synthesised under all microbial growth conditions and are the most abundant cellular proteins (Ryzhov and Fenselau, 2001).

MALDI-TOF is an emerging teclmique that can contribute significantly to fast microbial identification at a low cost of analysis. However, the diffusion of this technique for routine analyses in laboratories of industrial, environmental and food microbiology and the analysis of fermented beverages, such as wine, still faces some barriers, mainly the lack of information, or database for microorganisms not common to medical clinical area (Rahi et a!., 2016). Another hairier is the value of the equipment and the database, with the main equipment available on the market being the Bmker-Biotyper (Bruker Daltonics, Germany), SAJRAMIS AnagnosTec (acquired by bioMerieux, France and restructured as Vitek-MS), and Axima Assurance (Shimadzu, Japan). These platforms use very similar analytical methodologies as they are mainly related to the sample preparation, spectra acquisition and comparison of the generated spectra with the reference spectra of the database (Cassagne et ah, 2016).

This technique of microbial identification was initially developed for application in clinical samples, especially for the identification of pathogenic bacteria and later for the identification of human pathogenic fungi (Tanaka et a!.. 1988: Cain et a!., 1994; Holland et ah, 1996). Nowadays MALDI-TOF is used in the identification of food-contaminating fungi (Lima and Santos, 2017), bacteria and yeasts present in cocoa fermentation (Miguel et ah, 2016), vinegar (Viana et ah, 2017) and microorganisms present in wines (Moothoo-Padayachie et ah, 2013; Andres-Banao et ah, 2013; Usbeck et ah, 2014; Gutierrez et ah, 2017).

5. Wine Sensory Analysis

Wine is made up of hundreds of compounds with varied chemical structures that, due to their fixed or volatile nature, will jointly define the sensory characteristics of the beverage. These compounds aoriginate from the grape variety itself and are produced during the fermentation process, by the microorganisms and in stages after fermentation (Puertas et ah, 2018). The presence of these molecules in solution, responsible for the generation of aroma and flavour, added to the colour and the density of the drink, constitute the basis of the sensorial attributes of wine. And that, to a large extent, defines consumer acceptance of wine. For these reasons, sensorial analysis is a fundamental step in wine production (Lesschaeve, 2007).

Sensoiy analysis of wines can be performed by using several methods with specific objectives, which are selected according to the purpose of the analysis, such as sensitivity methods to select or trained judges (trained panel), or affective methods to verifying the acceptability of the consumer market (Amerine et ah, 1980; Jackson, 2000; Joshi, 2006; Narasimhan and Stephen, 2011). There are several sensoiy tests used in wine analysis, such as discriminative tests (triangular, duo-trio, ordering, paired comparison and multiple comparison), descriptive tests (taste profile, texture profile and quantitative descriptive analysis) and affective tests (preference, hedonic acceptance, ideal scale acceptance and purchase intention).

The quality of wine is usually evaluated by winemakers, who are highly trained and able to detect quality attributes or possible defects (Lesschaeve, 2007), but this does not dispense the need for consumers’ affective tests (Lockshin and Corsi, 2012; Francis and Williamson, 2015).

Among the several methodologies in sensoiy analysis, some of them have been used more frequently in wine evaluation. In the case of the analysis by untrained tasters, the main methodologies are the affective tests of hedonic scale, consumer’s intention of purchase test and the Check-All-That-Apply (CATA) test. When considering evaluations from trained panels, methodologies such as Quantitative Descriptive Analysis (QDA) and Temporal Dominance of Sensations (TDS) are unusually applied (Murray et ah, 2001; Varela and Gambaro, 2006; Cadot et ah, 2010; Schlich. 2017).

5.1. Electronic Sensory Analysis of Wines

The advancement of analytical technologies in recent years has contributed to the development of new techniques for identifying compounds in wines, which tend to be cheaper, reproducible, accurate and faster. In this sense, in addition to the evolution in the development of electronic and chemical sensors, new instruments of sensorial analysis have been improved aiming to analyse wines, like the sensorial analysis by human trained panels. Among the sensors developed are the electronic nose and electronic tongue, which are capable of accurately characterising the wine sensory quality in a short time and at a low cost, when compared to the last generation chromatographs or spectrometers (Ebeler and Thomgate, 2009; Ferreira, 2010; Saenz-Navajas et ah, 2012; Smyth and Cozzolino, 2013).

The electronic nose consists of a chemo-electronic sensor array capable of interacting with volatile compounds and mimicking the human olfactory system, generating an electrical response. This response is recognised by a computerised system, or through an artificial neural network, generating a result (Fig. 5). Similar to the human nose, and unlike gas chromatographic systems, the electronic nose recognises the sample from a set of volatile compounds present in it, not only from one compound pattern (Rock et ah, 2008; Brattoli et ah, 2011; Saenz-Navajas et ah, 2012).

General scheme of electronic mimicking of human sensory

Figure 5. General scheme of electronic mimicking of human sensory

The electronic tongue is also composed of a sensor array capable of mimicking the human gustatory system and recognise the different types of flavour (umami, sweet, salt, bitter and sour). However, the sensors most commonly employed in the electronic tongue are based on electrochemical methods, such as potentiometry, amperometry and cyclic voltammetry (Winquist et ah, 2000; Vlasov et ah, 2005; Riul Jr. et ah, 2010), while the electronic nose uses sensors of a chemical nature, such as conducting polymers, electrochemical cells, piezoelectric devices, metal oxide sensors, and metal-insulator semiconductor field effect transistors (Smyth and Cozzolino, 2013). Like the electronic nose, the electronic tongue is also based on the ability of global selectivity, recognising a set of information associated with taste and generating a response related to quality (Riul Jr. et ah, 2010; Ha et ah, 2015). Both the electronic nose and the electronic tongue have been used in sensory analysis of wines, wine deterioration and wine discrimination, among others. Table 2 summarises some papers regarding the application of electronic nose and tongue in wine analyses.

6. Omics Approach in Wine Analysis

The term metabolomics refers to the complete (qualitative and quantitative) analysis of all low molecular weight (less than 1.5 kDa) metabolites present in, and around, the cells at a given time of their growth or in a sample (Dunn and Ellis, 2005; Alanon et ah, 2015). Коек et ah, 2011). Regarding its application in wine analysis, metabolomic studies allow the identification of many compounds, generating data capable of guaranteeing a wine unbiased discrimination in relation to quality assurance, authenticity, variety, vintage, origin and deterioration (Alanon et ah, 2015; Cozzolino, 2016).

A wide array of modem analytical technologies has been earned out in the metabolomic analysis of wines, such as nuclear magnetic resonance (NMR), vibration spectroscopy (MIR; near infrared, NIR; Raman), Fourier transform (FT), capillary electrophoresis (EC). Some of these technologies are used coupled to single mass spectrometers or tandem MS (MS/MS), accelerating and increasing the capacity of molecule identification, either qualitatively or quantitatively. Recently, the development of modem high- resolution mass spectrometers, mainly time-of-flight (TOF), quadrupole TOF (Q-TOF), ion-trap TOF (IT-TOF) and Orbitrap analysers, have significantly increased accuracy and the sensitivity of compound identification (Hong, 2011; Alanon et ah, 2015; Ebeler, 2015; Markley et ah, 2017).

The choice of the technology to be employed in metabolomic studies will depend on the compounds to be identified, their concentration on the sample and the physico-chemical characteristics of the sample. The metabolomic approach can be classified as targeted, or profiling and untargeted, or fingerprinting. Target metabolomics refer to the analysis of previously defined compounds that are expected to be found and quantified in the sample. Untargeted metabolomic refers to the approach of the largest possible number of compounds and their quantification in a sample, which is one of the most applied in the metabolomic analysis of an extremely complex samples such as wines (Cozzolino, 2016; Gallo and Ferranti, 2016). Figure 6 shows a ranking of the most frequent analytical techniques and the approach, targeted or untargeted, applied in wine metabolomic in the last 10 years, published in research papers.

Table 2. Application of Electronic Nose and Electronic Tongue in Wine Analyses


Electronic sensor, additional technique



Red wine


Standard chemical analytical approach

Recognition of the vineyard

Di Natale et al, 1996

Red wines


Discrimination between wines

Legin, 2003.

Barbera wine



Characterization and classification

Buratti et al., 2004

Red wines


Wine adulterations

Рапа et al., 2006

Red wine


Wine discrimination

Garcia et al., 2006

Red and white wines


Artificial neural networks

Typical aroma components

Lozano et al., 2006

Red wine




Prediction of sensorial descriptors

Buratti et al, 2007

Port wine


Chemical analyses

Prediction of wine aging

Rudnitskaya et al, 2007

Red wine


Wine aging

Lozano et al, 2008

Red wine

Nose (metal oxide-based and MS-based)

Wine spoilage

Bema et al, 2008

Red wine


Human sensory panel

Threshold of aromatic compounds

Santos el al., 2010

Red wine


Chemical analyses

Bitter taste

Rudnitskaya et al, 2010

Madeira wine



Prediction of wine aging Organic acids and phenolics quantification

Rudnitskaya et al, 2010

Red and White wines



Titratable total acidity

Wine deterioration

Gil-Sanchez et al, 2011

Red wine



Near infrared and mid infrared spectroscopies

Monitoring of alcoholic fermentation

Buratti et al, 2011




Off-vine dehydration time

De Lenna et al, 2012

Red and white wines


Artificial neural networks

Prediction and classification of wines

Aguilera et al, 2012

Red wines


Trained sensoiy panel

Discrimination based on the maturing in barrel. Prediction of the global scores

Ceto et al, 2017

Web of Science/Clarivate Analytics search result for published papers in wine metaboloinic from 2008 to 2018. Search report (26 March 2018)

Figure 6. Web of Science/Clarivate Analytics search result for published papers in wine metaboloinic from 2008 to 2018. Search report (26 March 2018): Citation report for 80 results from Web of Science Core Collection between 2008 and 2018, searched for: TOPIC: (metaboloinic*) AND TITLE: (wine*), Refined by: DOCUMENT TYPES: (ARTICLE OR REVIEW). Indexes: SCI-EXPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH, ESCI. Based on total results and on 80 papers, we performed searches using the following keywords: MS, NMR, GC-MS, LC-MS, TOF,

FT, CE, Orbitrap, Targeted, Untargeted

7. Future Prospects

Analytical teclmiques have evolved immensely in the last decade, generating new equipments and methodologies capable of identifying and quantifying hundreds of chemical compounds as well as microbial diversity, in a matrix as complex as wine, fast and accurately at a relatively low cost per analysis. However, the cost of many state-of-the-art equipment for the analysis of volatile compounds, in a metaboloinic approach, for the identification of the microbiota (MALDI-TOF or NGS), even for automation of physico-chemical analyses, is still far from reality of many winemakers. The trend, however, is that over time, these technologies will decrease the cost, allowing the acquisition and use of them, favouring the analysis and guaranteeing the wine quality.

In the coming years, it is expected that the advances in analytical techniques will make possible identification and quantification of compounds present in wine not yet chemically described.


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