Advances in Analytical Techniques: Determination of Toxic Components, Microelements, Compounds of Aroma and Therapeutic Significance

Simona Guerrini1,2, Silvia Mangani2, Giovanna Fia1 and Lisa Granchi1*

  • 1 Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Piazzale delle Cascine, 18, Florence, Italy
  • 2 Food Micro Team s.r.l, Academic Spin-Off of University of Florence, via Santo Spirito 14, 1-50125 - Florence (Italy)
  • 1. Analytical Chemistry Instrumental Methods
  • 1.1 Introduction

Continuous development in analytical chemistry instrumentation and methods has resulted in the increased application of advanced chromatographic and spectroscopic methods to grape and wine analysis. Innovations in Instrumentation continues to be used to provide more detailed chemical information, especially using hyphenated techniques such as gas chromatography mass spectrometry (GC-MS), liquid chromatography mass spectrometry (HPLC-MS) and advanced spectroscopic detection systems such as tandem mass instruments, etc. As instrumentation become more sensitive, detection limits are driven down, thus permitting the identification of new compounds as well as to the quantification of trace compounds.

Significant advances in instrumentation and chromatographic column technology led to the introduction of ultra-performance liquid chromatography (UPLC) which are coupled to columns packed with sub-2 micron fully porous particles and allowed achieving dramatic increases in resolution, speed and sensitivity in liquid chromatography. Modern developments in gas-phase separation technologies such as the progression from wide-bore packed to capillary columns have played a crucial role in the expansion of analytical possibilities for wine analysis. Further, important developments in sample pretreatment procedures and more sensitive and selective GC detectors have been influential in extending the application of GC for analysis of wine volatiles.

Sample preparation represents an especially important step in the analytical processes. Effective extraction and pre-concentration of the analyte from the hydro-alcoholic wine matrix is essential for the accurate quantitative analysis. The choice of the sample pre-treatment technique depends on the goals of the analysis. Solid phase extraction (SPE) is regarded as the most popular technique for extraction of natural and anthropogenic compounds from wine. It is a sample preparation technique designed to extract, partition, and/or adsorb one or more components from a liquid phase using a suitable stationary phase. The adsorbed substances can be removed from the adsorbent by step-wise increase of elution strength of the eluent. The relatively low breakthrough volumes of SPE sorbents for wine matrix can be compensated by further combination with dispersive liquid-liquid micro extraction, aiming not only to increase the obtained enrichment factors, but also to remove some undesired species from the primary SPE extract (Rodriguez-Cabo et al., 2016).

Sorptive extraction techniques such as solid phase micro-extraction (SPME) have been shown by several Authors to be advantageous for the extraction of analytes from complex matrices such as wine. SPME is a solvent-free adsorption/desorption technique, combining extraction, concentration and ♦Corresponding author: This email address is being protected from spam bots, you need Javascript enabled to view it chromatographic injection into one-step, dramatically reducing labor, materials and waste disposal. It consists of coated fibers that are used to isolate and concentrate analytes into a range of coating materials. After extraction, fibers are transferred to an analytical instrument for separation and quantification of the target analytes. A breakthrough in SPME for application in food matrices involves the development of molecularly-imprinted polymer (MIP) coatings to enhance method sensitivity and selectivity. The utilisation of task-specific MIPs as SPME coatings has demonstrated outstanding capabilities to overcome the limitations associated with low sorbent volumes, rendering enhanced selectivity and improved method selectivity of SPME (Souza-Silva et ah, 2015).

Derivatisation is a commonly used technique to augment chromatographic analysis and it is employed to permit analysis of compounds with inadequate volatility or stability as well as to improve chromatographic behavior or detectability. Derivatisation of wine constituents for example, is often used to modify non-volatile or highly polar chemical compounds not otherwise amenable to GC analysis.

Finally, spectroscopic methods applied for wine and grape analyses include a wide range of techniques, spanning atomic spectrometry methods such as atomic absorption spectroscopy (AAS) and inductively coupled plasma (ICP) and several molecular spectroscopy methods such as nuclear magnetic resonance spectrometry (NMR) and mass spectrometry (MS).

The focus of the first part of this chapter is specifically on the application of advanced instrumental techniques, including chromatography and spectroscopy, to the analysis of toxic components, microelements, aroma compounds and constituents of therapeutic significance in grape and wine.

1.2. Biogenic Amines

Biogenic amines (BAs) are nitrogenous low molecular weight organic bases that can have an aliphatic, aromatic or a heterocyclic structure and are receiving much attention in wine science because of their potential implication for human health (Silla Santos, 1996). The main BAs present in wines are histamine, tyramine, putrescine, cadaverine, 2-phenethylamine, agmatine and tryptamine mostly originating from the decarboxylation of their respective free precursor amino acids, through the action of substrate-specific microbial decarboxylases. Other amines, possibly present in wines, include the aliphatic volatile amines (methylamine, ethylamine and isoamylamine), that can be formed by the animation of non-nitrogen compounds, such as aldehydes and ketones (Bauza et ah, 1995), and the polyamines, spermine and spermidine, that can be produced from putrescine (1,4-diaminobutane), through methylation reactions involving S-adenosyl-methionine (Vincenzini et ah, 2016).

BA quantification in wine is still problematic due to their low concentration, the lack of chromophores of most BAs, the complexify of the sample matrix and the presence of potentially interfering substances. Many analytical methods have been developed to quantify these compounds in wines, including gas chromatography (Fernandes and Ferreira, 2000), capillary electrophoresis (Herrero et ah, 2010), enzymatic methods and immunoassays (Lange and Wittmann, 2002). Nowadays, HPLC is by far the most frequently used technique, due to its high resolution and sensitivity, especially when coupled to a fluorescence detector. As BAs do not show satisfactory absorption in the visible and ultraviolet range nor do they show fluorescence, chemical derivatisation is considered a necessary analytical step for their detection technology. The derivatisation methods can be mainly divided into two categories: pre-column (the derivatisation is carried out before the chr omatographic separation) or post-column (the derivatisation is carried out after the chromatographic separation) derivatisation methods. The pre-column derivatisation technique is used more frequently than the post-column derivatisation because of proriding more sensitive detection (Onal et ah, 2013). Several derivatisation reactions have been employed, as for example those using orto-phthaldialdehyde (OPA) (Vidal-Carou et ah, 2003; Kelly et ah, 2010; Arrieta and Prats-Moya,

2012), 9-fluorenylmethylchloroformate (FMOC), 2,4,6-tiinitrobenzenesulfonic acid (TNBS), diethyl ethoxymethylenemalonate (DEMM), 4-fluoro-3-dinitro-fluomethylbenzene, dansylchloride (Dns-Cl) (Zotou et ah, 2003; Hemandez-Borges et ah, 2007; Proestos et ah, 2008). dansyl chloride (Dabs-Cl) (Romero et ah, 2000), benzoyl chloride (Bnz-Cl) (Ozdestan and Uren 2009), 1,2-naphtoquinone-4- sulphonate (NQS) (Hlabangana et ah, 2006), 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate (Hemandez-Orte et ah, 2006), l-fluoro-2-nitro-l-(trifluoromethyl) benzene (Jastrzebska et ah, 2016). Currently, the International Organization of Vine and Wine (OIV) proposes derivatisation with OPA and

DEMM to determine BAs in musts and wines by HPLC using fluorimetric (OIV-MA-AS315-18) and spectrophotometric detection methods (OIV-MA-AS315-26).

Recent liquid chromatography combined with tandem mass spectrometry and ultra-performance liquid chromatography coupled to quadmpole-time of flight mass spectrometry have been shown to be very powerful techniques to increase the performance of BAs analysis (Garcia-Wlar et ah, 2009; Jia et al, 2012), also without derivatisation (Millan et ah, 2007).

A fast and reliable HPLC method for the determination of 11 BAs in beverages has been performed by Preti et ah, 2015. After pre-column derivatisation with Dns-Cla C18 core-shell particle column has been employed and the BAs were identified and quantified in a total run time of 13 minutes with ultraviolet (UY) or fluorescence detector.

1.3. Ethyl Carbamate

Ethyl carbamate (EC), commonly called urethane, is the ethyl ester of carbamic acid. It is genotoxic and a multi-site carcinogen in experimental animals and probably carcinogen to humans (EFSA, 2007). The EC occurs in wine where it is thought to be formed from non-enzymatic reaction between ethanol and a compound containing a carbamoyl group, such as urea (produced from arginine breakdown by yeasts), citrulline and carbamoylphosphate (produced from arginine breakdown by lactic acid bacteria) (Vincenzini et ah, 2016). There are currently no harmonised maximum EC levels for table wine in the EU, but Canada and USA recommend maximum values of 30 and 15 pgL'1, respectively (EFSA, 2007).

The determination presents problems owing to the low concentration of EC and to matrix interferences, The former problem can be solved by application of MS techniques, which provided sensitive determination at trace levels, while the latter can be solved by various sample clean-up procedures (Zhang and Zhang, 2008; Jiao et ah, 2014). Many methodological publications regarding EC analysis have summarised the precise quantification of EC concentrations and detailed general pretreatment steps, such as extraction and cleanup steps, detection systems, general analytical methods, and proficiency tests (Jiao et ah, 2014). Regarding the extraction steps, the most traditional method is liquid-liquid extraction, which requires a large amount of chlorinated toxic solvent (usually dichloromethane) and the use of intensive labor effort and prolonged time during the concentration step (Nobrega et ah, 2015). As an improved measure for extracting EC, solid-phase extraction provides considerable advantages over liquid-liquid extraction and it is applied in the standard method for EC determination in wine (AO AC method 994.07, AO AC, 2006), also adopted by OIV (method MA-AS315-04, ОIY, 2013), and as reference method in the Eur opean Union (commission Regulation 1999). The AO AC method involves analysis by gas-chromatography coupled to mass spectrometry in selected ion monitoring (GC-MS-SIM) after a sample preparation procedure that implies addition of propyl carbamate as internal standard, cleanup through diatomaceous earth SPE columns, EC extraction by dichloromethane, and eluate concentration. Jagerdeo et ah (2002) described a SPE method for multidimensional GC-MS (MDGC-MS) determination of EC levels in wines that eliminates the use of dichloromethane. The utilisation of SPE apparently decreases the amount of solvent and labor required but is far from satisfactory, as it is always accompanied by background interference, poor reproducibility between cartridges, and high economic cost due to the inability to reutilise cartridges during extraction (Zhang and Zhang, 2008). Another powerful technique for extracting EC is solid-phase microextraction (SPME) that combines extraction, concentration and chromatographic injection into one-step with a carbowax/divinylbenzene (CW/DVB) fiber (Wliiton and Zoecklein, 2002, Zhang and Zhang, 2008). This technique requires only a small amount of sample and shortens the time required for extraction, lowers the economic cost, and preserves the natur al content of the sample (Jiao et ah, 2014).

Although these alternative preparations have advantages over the standard procedure, they have not been extensively adopted for EC analysis in wine and are not without problems. For instance, the CW/ DVB fiber is no longer commercially available (Liu et ah, 2012), and the alcohol part in the sample may influence the SPME extraction yield (Lachenmeyer et ah, 2006). Furthermore, the method proposed by Jagerdeo et ah (2002) involves a previous time-consuming step for ethanol removal from wine by vacuum.

An improved sample preparation procedure by GC/MS/SIM was proposed by Nobrega et ah (2015). This method differs from AOAC reference procedure by the use as internal standard of deuterated ethyl carbamate (EC-d5), more similar to ethyl EC than propyl carbamate, the extraction by diethyl ether instead of more toxic dichloromethane and the concentration by vacuum automated parallel evaporation. Applicability of the method was tested by analysis of 5 wine samples. EC concentration ranged from 5.2 ±0.2 to 29.4 ± 1.5 pgL1.

1.4. N-nitrosamines

N-nitrososamines (NAms) are highly active carcinogen that have been detected in red wine. The presence of these compounds could be attributed to the nitrosable compounds present in red wine and grape juice, such as phenolic compounds, biogenic amines and flavonoids among others. There is controversial information about the presence or the absence of these compounds in wine, which could be related to the analytical method used as well as on the origin and type of red wine analyzed (Lona-Ramirez et ah, 2016).

Recent techniques have been used to detect and quantify NAms in different types of matrices, but none of these techniques was applied in wine. Jurado-Sanchez and co-workers (2007) proposed a semi automatic method for the determination of seven NAms by gas chromatography with nitrogen- phosphorus detection following automatic solid-phase extraction, but no NAms were detected in red wine. Recently the head space solid phase micro-extraction technique coupled to gas chromatography- mass spectrometry (HS-SPME-GC-MS) was applied to quantify four NAms, N-nitrosodimethylamine (NDMA), N-nitrosodiethylamine (NDEA), N-nitrosopiperazine (NPIP), N-nitrosodibutylamine (NDBA), in red wines from different types of grapes, from different countries and having undergone different aging processes (Lona-Ramirez et ah, 2016) using a polydimethylsiloxane-divinylbenzene fiber. The mass detector was equipped with a positive pole ion, single quadmpole with electron impact ionisation (El) source. The quantification was carried out with the application of the standard addition technique. The method was validated by calculating the linearity, limit of detection and quantification. Two of the four NAms analyzed, NDMA and NDBA, were foimd to be present in red wines.

1.5. Resveratrol and Other Phenolic Compounds

Resveratrol is a member of the stilbene family of phenolic compounds. Two isomers (ti ans-vssveratro 1 and m-resveratrol). and their glucosylated derivatives (/raws-piceid and m-piceid. respectively) have been detected in wine (Hashim et ah, 2013, Rodriguez-Cabo et ah, 2014).

Phenolic compounds are molecules consisting of a phenyl ring backbone with a hydroxyl group or other substituents. They are the molecules which are naturally derived from grapes and microbes. In wine, phenolic compounds are classified as non-flavonoid, such as hydroxybenzoic acids, hydroxycinnamic acids, and stilbenes, and flavonoid compounds, such as anthocyanins, flavan-3-ols and flavonols. Despite a not negligible presence of non-flavonoid compounds, flavonoids make up a significant portion of phenols in wines (Texeira et al., 2013). In wines, flavonoid contributes significantly to sensorial quality. Indeed, they are the main compounds responsible of colour, flavour, texture and astringency. Phenols contained within the skin, and flesh of grapes are extracted into wine during the vinification processes. The amount of phenols extracted during vinification is influenced by many factors, including temperature, length of skin contact, mixing, type of fermentation vessel, ethanol concentrations, S02, yeast strain, pH, and pectolytic enzymes. Extraction is ultimately limited by the amount present in the fruit, and this varies with cultivar, vintage, macro-and micro-climatic conditions, and vinification process (Romboli et ah, 2017). Besides, anthocyanins fingerprints have been widely utilised for the classification and differentiation of grape cultivars and monovarietal wines (Mangani et ah, 2011).

A recent interest in these substances has been stimulated by abundant evidence of their beneficial role in human health, such as anticarcinogenic, anti-inflammatory, and antimicrobial activity, and many of these biological functions have been attributed to their free radical scavenging and antioxidant activity. Reverse-phase HPLC with detection by UV-vis absorption with a diode array detector (DAD) represents the most popular technique. Enhancing selectivity and sensitivity for the determination of certain wine phenolics requires the application of different detection techniques, such as fluorometry, electrochemistry, chemiluminescence, and/or MS coupled with different techniques: electrospray ionisation (ESI), matrix- assisted laser desorption/ionisation (MALDI), and atmospheric pressure chemical ionisation (APCI) (Medic-Saric 2011). Due to the complexity of wine phenolics, extensive pre-fractionation is also often employed (Lorrain 2013). In the last few years, influential developments in HPLC, in terms of the use of smaller particle-packed columns, elevated temperature and multidimensional separations, have also been exploited for these compounds (Kalili and de Villiers, 2011; de Villiers, 2012; Lorrain et ah, 2013).

Silva et al. (2011) described an ultra-fast, efficient and high throughput analytical method based on UPLC equipped with a PDA detection system using a 50 mm column packed with 1.7 pm particles. After a polyphenol extraction from wines by SPE on a new hydrophilic-lipophilic balanced sorbent (N-vinylpyiTolidone-divinylbenzene copolymer), the method managed to separate and analyzed, in five minutes, fifteen bioactive phenolic compounds mainly belonging to flavonols, flavan-3-ols and phenolic acids, including /rans-resveratrol.

The stilbene content of wine has received extensive attention in literature due to the beneficial biological activity ascribed to this class of compounds (Di Donna et al., 2017; Hashim et al., 2013; Rodriguez-Cabo et al., 2014; Cacho et al., 2013; Airado-Rodriguez et al., 2010). Hashim et al. (2013) reported the application of reusable molecularly imprinted Polymers (MIPs), synthetic materials designed to have a predetermined selectivity for defined molecular targets, for the selective and robust SPE and rapid analysis of trans-resveratrol. Optimisation of the molecularly imprinted solid-phase extraction (MISPE) protocol resulted in the significant enrichment in Pww-resveratrol and several structurally related polyphenols. The metabolites were subsequently identified by capillary RP-HPLC and electrospray ionisation mass spectrometry (ESI-MS/MS) and uLC’-ESI ion trap MS/MS methods.

Regarding anthocyanin, the International Organization of Vine and Wine (OIV) proposes analysis by direct separation by RP-HPLC using spectrophotometric detection methods (OIV-MA-AS315-11).

1.6. Mycotoxins

Mycotoxins are toxins produced by fungi as secondary metabolites that may occasionally contaminate bottled wines (Bolton et al., 2017). They represent a chronic health lisk since prolonged exposure to them through diet has been linked to a range of adverse health effects (Hussein and Brasel 2001).They are colourless, odorless, and tasteless, and though primary produced in the vineyard on grapes, they have the ability to remain toxic throughout the winemaking process. More than 300 mycotoxins have been reported; currently, OchratoxinA (OTA), is the most common mycotoxin found in wine. It is produced on grapes by Aspergillus carbonarius and, to a lesser extent, Aspergillus niger. The European Union has regulated the maximum limit of OTA in wine at 2.0 pg/L (EC 123/2005, 26 Jan 2005).

As low as ppb’s concentrations are usually involved, very sensitive analytical methods for mycotoxins are needed. Conventional analytical methods are mainly based on separative instrumental techniques (CE. HPLC, GC) (Almeda et al., 2008; Soleas et al., 2001), immunological methods (ELISAs) (Soares et al., 2014; Vidal et al., 2013) and sensor-based systems (Turner et al., 2015). Among these above- mentioned methods, the most commonly used technique is based on HPLC coupled with FLD, the native fluorescence of OTA favouring the development of very sensitive methods. Before the chromatographic separation, sample preparation step involving extraction, purification and concentration of the extract must be earned out to remove the major interferences present in the sample and to pre-concentrate the analytes in order to achieve the desired sensitivity (Lee et al., 2012).

The most widely used clean-up and pre-concentration methods for OTA determination are liquid- liquid extraction (LLE) or solid-phase extraction (SPE) (Lee et al., 2012). A number of SPE columns are commercially available; the monoclonal antibody base immunoaffinity columns (IACs) are the most commonly used (Lee et al., 2012; Fabiani et al., 2010). The main advantage of these columns is that OTA is bound specifically to the antibody and the matrix interferences can be removed nearly completely. Furthermore, IACs give an optimal performance in terms of precision and accuracy within a wide range of concentrations and they reduce the use of dangerous solvents. However, these columns present several problems such as the relatively high cost; limited capacity cannot be reused, limited lifetime and in some cases, lack of specificity was observed due to cross-reaction with ochratoxin C (Aresta et al., 2006). Attempts to replace the biological recognition element by a synthetic counterpart e.g. by OTA molecularly imprinted polymers (MIP), have been proposed (Lee et al., 2012; Cao et al., 2013).

Currently, the method recommended for OTA determination in wines and beer (European Standard prEN 14133, OIV MA-AS315-10, OIV 2016) uses IACs columns to clean-up OTA after dilution of the samples in aqueous solution of polyethylene glycol and NaHC03 and the samples are analyzed by HPLC with fluorescent detection (excitation wavelength at 333 nm, emitting wavelength at 460 nm). A fast separation of OTA in red wines was obtained by Mao et al. (2013) utilising a core-shell column (Cl8, 2.6 pm, 100 A). Under optimised condition, OTA was separated in less than 5 minutes by HPLC-FLD in isocratic conditions with mobile phase constituted by acetonitrile/water (50/50 v/v, both acidified with 1% volume formic acid) and a flow rate of 0.66 mL/min after IAC cleanup.

A fast multi method was developed and optimised by Pizzutti et al. (2014) for the analysis of 36 mycotoxins in wine, based on an acetonitrile extraction, followed by a partitioning and a subsequent drying step with magnesium sulfate, and detection using UPLC-MS/MS (ESI positive mode). No cleanup was necessary, because matrix effects were kept at an acceptable level.

1.7. Microelements (Especially Cu, As, Zn, Fe)

Wine typically contain macro-elements such as K, Ca, Na and Mg (concentration >10 mg/L), microelements such as Fe, Cu, Zn Mn, Pb (concentration >10 pg/L), and ultra micro-elements such as Cr, As, Cd, and Ni (concentration <10 pg/L) (Geana et al., 2013; Grindlay et ah, 2011). Elements in wine can be classified into two groups: endogenous and exogenous. Endogenous elements are related with the type of soil on which vines are grown, the grape variety and the climatic conditions dining their growth. Exogenous elements derive from external impurities that reach wine during growth of grapes or at different stages during winemaking (from harvesting to bottling and cellaring) (Grindlay et al., 2011; Pohl, 2007).

The micro-element composition of grapes and wines is important from several standpoints. Firstly, some elements are regulated, with most countries following the maximum legal limits established by the International Organization of Vine and Wine (OIV-MA-C1-01). Examples for these are As, B, Br, Cd, Pb, and Zn, for which maximum legal limits in the upper parts per million (mg/L) to mid parts per billion (pg/L) have been defined. Secondly, many elements influence the quality of vine and wine, as they are macro-, micro-, and trace nutrients for the vine plant or are used in agrochemicals (Hopfer et al., Spectroscopy 2014). Additionally, some trace elements play a relevant role in winemaking, for example, Zn is essential at low concentrations for the correct development of alcoholic fermentation, while Cu, Fe and Mn have organoleptic effects at increased levels (Cozzolino, 2008; Ronkainen, 2016); besides some elements have detrimental effects on wine stability and need to be closely monitored during wine making (Pohl, 2007). Examples for this, are Cu and Fe, which can act as oxidation catalysts, and can also cause haze formation in wines, similar to Zn or Al. Lastly, elemental fingerprints can provide important information on the geographical origin in which the grapes were grown due to the direct relationship with soil composition as well as wine processing and storage conditions (Ebeler Chapter 1, Pyrzynska, 2007). For the determination of mineral content of wines, atomic spectrometric techniques are the most often used. Application of flame atomic absorption spectroscopy (AAS) and electrothermal AAS for metal analysis in wine have been reported (de Villiers et ah, 2012; Pohl, 2007). Recently inductively coupled plasma mass spectrometry (ICPMS) and inductively coupled plasma atomic emission spectrometry (ICPAES) have gained popularity as rapid and sensitive approaches for simultaneous screening of a large number of elements (Grindlay et ah, 2011). Hoepfer et ah (2015) determined a total of 63 elements with ICP-MS in 65 red wines from grapes harvested in five different vineyards within 40 miles of each other and processed in at least two different wineries. Based on the multi-elemental pattern, they were able to classify the wines according to their vineyard origin, their processing winery, as well as the combined effect of both origin and processing (Hoepfer et ah, 2015).

Metals may exist in wines as free ions, as complexes with organic acids as well as with large molecules ofpectic polysaccharides, peptides, proteins and polyphenols (Pyrzynska, 2007). The chemical speciation and fractionation analysis of metals are gaining interest because metals bioavailabilify and toxicity depend on the chemical forms in which they are present. Pyrzynska provided (2007) a review of the main developments in chemical speciation and fractionation of metals in wine samples.

1.8. Aroma Components

The volatile fraction of wines determines largely its aroma, which is one of the most important characteristics influencing wine qualify and the consumer acceptance. The flavour of a wine is extremely complex, and is due to the presence of several classes of compounds, such as alcohols, terpenes, hydrocarbons, ketones, esters, acids, aldehydes ethers, sulfur nitrogen compounds and lactones. More than 1000 aroma compounds have been identified, covering a wide range of polarities and volatilities and spanning few orders of magnitude in concentrations. All these compounds are responsible for the so-called “bouquet” of the wine on sniffing the head space from a glass, and the odour/aroma component of the overall flavour perceived on drinking (Cincotta et at., 2015). Several factors influence the wine aroma: grape variety, grape ripeness, climate, soil, fermentation conditions, yeast and bacteria strains, production process, and aging (Banos et al., 2012). Furthermore, many aroma compounds are chemically very unstable and can be easily oxidised or thermo degraded (Andujar-Ortiz et al., 2009).

Analysis of the base aroma compounds comprising the so-called major volatiles, which include the principal fermentation derived esters, alcohols and acids is routinely performed using generic GC methods combined with flame ionisation detector (FID) or MS (de Villiers et al., 2012). On the other hand, the analysis of specific minor volatile compounds requires dedicated methods with selective extraction and pre-concentration steps due to the complexity of wine matrix and relatively low concentrations and selective detection strategies.

For example, haloanisoles (e.g, (2,4,6-trichloroanisole, 2,3,4,6-tetrachloroanisole, 2,3,4,5,6- pentachloanisole, 2,4,6-tribromoanisole) are responsible for musty off-aromas of wines described as cork-taint. Hjelmeland et al. (2012) presented a method for the simultaneous analysis of four haloanisoles in wine by HS-SPME coupled to a GC-triple quadrupole MS. The method, fully automated, required no sample preparation other than the addition of internal standards, and was high throughput, with a 10 minutes extraction time and a 5 minutes incubation prior to extraction. Limits of detection and quantification were mainly in the sub-ng/L range.

The volatile phenols 4-ethyl phenol, 4-ethyl guaiacol,4-vinyl phenol an 4-vinyl guaiacol are mainly associated to Brettanomyces spoilage. These compounds are one of the most significant problems in modem winemaking, as they can give the wine “off-flavours”, described as phenolic, medicinal, pharmaceutical smoky and clove-like flavours (Nicolini et al., 2007). HPLC is a frequently used analytical technique; as an example a rapid method was established by Nicolini et al. (2007) using HPLC coupled with a fluorimeter detector. This method did not require sample preparation and it earned out chromatographic separation in less than 5 minutes with a detection limit of 4 pgL1. However, the most frequent approach to measuring volatile phenolsis GC-MS. In a recent work, Zhou et al. (2015) developed an ethylene glycol-polymethyl siloxane based stir bar sorptive extraction (EG/PDMS) coupled with GC-MS method, with quantification limits lower than 3 pg/L. Stir bar sorptive extraction employs a magnetic stir bar coated with a thick layer polymer for volatile extraction which increase phase volume and minimise the absorptive competition.

An automated HS-SPME combined with GC-ion trap/MS was developed by Banos et al. (2012) in order to quantify a large number of volatile compounds such as alcohols, esters, norisoprenoids, and terpenes. The procedure was optimised for SPME fiber selection, pre-incubation temperature and time, extraction temperature and time, and salt addition. The method allowed the identification of 64 volatile compounds, besides for 20 compounds considered as important aromatic contributors for the aroma of white wines calibration and validation were also performed.

Furthermore, the volatilome has been used to derive classification models for the identification of individual cultivars (de Villiers et al., 2012; Villano et al., 2017). In particular, univariate and multivariate principal component analysis-discriminant analysis statistics applied to the combined SPME-GC and 'H NMR data allowed a chemometric discrimination of270 wines from Galicia (Spain) according to the type of grape and identifying, in part, the geogr aphical subzone of origin (Martin-Pastor et al., 2016).

Considering that, the release of some impact compounds in aroma wine, depends onthe action of simple enzymatic steps promoted by microorganism, the quantification of such enzyme activities can be a useful tool. Indeed, very efficient fluorimetric methods have been developed to perform assays of esterase and p-glucosidase activities from yeasts and lactic acid bacteria of enological interest (Fia et al., 2005; Rosi et al., 2007). Commercial enzyme preparations, widely used in winemaking, were studied by the same methods (Fia et al., 2016; Fia et al., 2014). Esterase activity was assayed by measuring the amount of 5(6)-carboxyfluorescein released from 5(6)-carboxyfluorescein diacetate used as substrate after incubation at 37°C for 5 min., while p-glucosidase activity was assayed by measuring the amount of

  • 4-Methylumbelliferone liberated from 4-Methyluinbelliferyl-p-D-glucuronide after incubation at 37°C for 5 min.. Enzyme assays were earned out using black 96-well microtitre plates with flat transparent bottom by their exposition to the long-wavelength UV light of a transilluminator. The images were acquired with Gel Doc 2000 System and analysed by Quantity One v.4.3.0 (Bio-Rad) software.
  • 2. Microbial and Biomolecular Analysis
  • 2.1. Introduction

The yeast and bacterial microbiota occurring in grape must and duiing alcoholic or malolactic fermentation have been investigated by several Authors (Baleiras Couto et al., 2005; Fernandez et ah, 1999; Gangaand Martinez, 2004; Gonzalez et al., 2007; Hierro et al., 2006b; Lopaudic etal., 2008; Spano and Torriani, 2017) and different techniques have been used with this purpose (Ivey and Phister, 2011; Zott et ah, 2010; Longin et ah, 2017). Before the advent of molecular biology, microbial population size and diversity were analyzed using methods mainly based on growth of cultivable microbiota on nutrient media (Lafon-Lafourcade and Joyeux, 1979). Molecular biology has brought forth significant new advances in microbiological analysis of grape must and wine (Mils et ah, 2008). In particular, traditional methods for microbial identification (morphological and/or physiological criteria) have been almost completely replaced by ribosomal DNA - based methods. Moreover, in the past few years, successful culture-independent methods, such as Polymerase Chain Reaction (PCR) real Time, PCR-DGGE, in situ hybridisation, flow cytometry with fluorescent antibodies, or microbial genome sequences have been used to describe the microbial ecology of musts and wines (Capece et ah, 2003; Doare-Lebmn et ah, 2006; Rodriguez and Thornton, 2008; Xufreetal, 2006; Longin et ah, 2017). The following paragraphs describe not only the various methods to quantify, identify and characterise the wine microorganisms, but also to detect their metabolic capabilities of technological and healthy interest.

2.2. Microbial Techniques to Quantify Wine Microorganisms

Monitoring of the microbial changes occurring during the winemaking is fundamental to cany out a process under control. In fact, this information is necessary to not only promote and guide the growth of yeasts during alcoholic fermentation and of lactic acid bacteria during malolactic fermentation, but also to ensure the proper aging and stability of the wine before bottling and storage (Delfini and Formica, 2001). Microbial enumeration in wine can involve two kind of methods: “indirect” and “direct”. The indirect methods, such as plate count or most probable number, do not enumerate the original cells in the sample but their progeny, as enriched in a specific medium. On the contrary, the direct methods allow the counting of microbial cells directly into must and wine using microscopy techniques or the more complex flow cytometry. The following paragraphs briefly describe the main methods for counting microorganisms in wine.

2.2.1. Microscopic Count, Plate Count, Most Probable Number

Classical microbiological techniques (microscopic technique, plate count, most probable number) to monitor wine fermentation and to detect undesirable microorganisms are well described in the resolution OlV-Oeno 206/2010. The resolution also reports, for each technique, a description of the principles, reagents and materials, installations and equipment, sampling procedures, and finally quality tests. In Table 1, a schematic description of various techniques shown in the resolution 206/2010, is reported.

2.2.2. Direct Epifluorescence Technique (DEFT)

Direct epifluorescence technique (DEFT) is a particularly successful direct analysis when applied to aging wines or to control filtered wines after bottling. This technique exploits the microbial-based cleavage of a fluorescent substrate, which enables direct counting of viable cells through a fluorescent microscope. Therefore, DEFT enables to quantify viable cells of both bacteria and yeast in aging wine, including those viable but non-cultivable (VBNC) that cannot be quantified by the culture dependent methods (Millet and Lonvoud-Fimel. 2000; du Oit et ah, 2005; Divol and Lonvoud-Funel, 2005). To analyze aging wine,

Table 1. Schematic Description and Aim of Various Techniques Reported in the Resolution 206/2010




Microscopic techniques (culture independent technique)

Microscopic examination of liquids or deposits

To detect and differentiate the yeasts from the bacteria in terms of their size and shape. Microscopic observation cannot distinguish between viable and non-viable microorganisms.

Gram staining for the differentiation of bacteria isolated from colonies

To differentiate between lactic acid bacteria (Gram positive)and acetic bacteria (Gram negative) and also to observe their morphology

Catalase Test for the differentiation of bacteria isolated from colonies

To differentiate between acetic and lactic acid bacteria. Acetic bacteria have a positive reaction. Lactic acid bacteria give a negative response

Yeast cell count by haemocytometry

To determinate yeast cell population in fermenting musts, wines, and active dry yeasts (starter cultures)

Yeast cell count after methylene blue staining of yeast cells

To allow a rapid estimation of the percentage of viable yeast cells,which are not stained, while dead cells are blue- stained

Plate count (culture dependent technique)

Enumeration of viable and cultivable yeasts, moulds and lactic or acetic bacteria in musts, concentrated musts, partially fermented musts, wines (including sparkling wines) during their manufacture and after bottling, by counting the colonies grown on solid differential and,7 or selective media after suitable incubation

To control the winemaking process and prevent microbial spoilage of musts or wines

Most probable number (culture dependent technique)

Estimation of the number of viable microorganisms in selective liquid media, starting fi'om the principle of its normal distribution in the sample

To evaluate the number of viable and cultivable microorganisms in wines having high contents of solid particles in suspension and/or high incidence of plugging

DEFT methods provides that a homogenised sample is filtered through a 0.2 pm pore polycarbonate filter where the wine microorganisms are concentrated and remained on. Then the microorganisms are stained with fluorochromes and the filter examined under a fluorescence microscope to count viable yeast and/' or bacteria cells. Siegrist et al. (2015) report an exhaustive list of fluorochromes for rapid detection not only viable, but also damage and death microbial cells. DEFT is particularly useful for rapid detection of spoilage yeasts such as Brettanomyces spp. which may negatively affect wine quality producing off- flavours and which is more difficult to be revealed by traditional plate count method because of long time of incubation and the possible presence of cells in VBNC state (Granchi et al., 2006). DEFT, with some modifications, can be also used to quantify viable yeasts during alcoholic fermentation. This procedure, named Thoma-Epifluorescence-Microscopy-Technique (TEMT), is based on the combined use of Thoma-counting chamber and epifluorescence microscopy (Granchi et al., 2006). TEMT is a suitable tool for monitoring yeast populations during the winemaking process. Moreover, the rapid assessment of inadequate Saccharomyces cerevisiae cell concentrations during alcoholic fermentation, allowing timely interventions, can contribute in preventing stuck fermentations.

2.2.3. Flow Cytometry

Flow cytometry (FCM) can allow the rapid acquisition of multi-parametric data, such as cell numbers, as well as shape, size, and cell viability regarding individual cells suspended in a fluid stream (Diaz et ah, 2010; Longobardi-Givan, 2001; O’Neill et ah, 2013; Longin et al., 2017). A flow cytometer is mainly composed of a fluidic, an optical, and an electronic part (Longin et ah, 2017). The fluidic component is composed of a flow chamber that separates and aligns the cells by passing them through the light source with the refraction or scattering of light (laser, arc lamp or light emitting diode) at all angles. The light emitted from the cells after they are irradiated in the flow chamber is directed to appropriate detectors. The magnitude of “forward scatter” (FSC: the amount of light scattered in the forward direction when the light strikes the cell) is roughly proportional to the size of the cell. In addition to this, a system of focusing lens is used to direct the light rays to a set of filters separating the various wavelengths present. If the cells are labelled with fluorochromes, they generate a fluorescence signal crossing the path of light. The fluorescent light is then directed to an appropriate detector. Finally, the light, which passes through an optical system, will be converted into electronic signals generated by photodiodes and photomultiplier tubes and collected to enable data acquisition and analysis (Longin et al., 2017).

Most flow cytometry protocols applied in enology concern detection (presence/absence) and/or enumeration of viable microorganisms. Using special dyes coupled to flow cytometry, physiological analysis of wine yeasts and bacteria can be also performed. Even if only viable cells of yeasts and bacteria are able to perform alcoholic and malolactic fermentations respectively, they could have a weak metabolic activity that can negatively affect the fermentation rate. For this reason it could be important to measure also the microbial “vitality”, which reflects metabolic activity of the cells. Vitality dyes, such as Fluorescein Di-Acetate (FDA), point out life essential functions such as enzymatic activity. In particular, FDA staining highlights the metabolic activity of cells through esterase activity. This dye has provided excellent results by measuring the cell vitality of S. cerevisiae during alcoholic fermentation in synthetic wine, in grape must, in red and white wines (Malacrino et al., 2001; Salma et ah, 2013; Malacrino et ah, 2001; Gerbaux and Thomas, 2009). On the contrary, viability dyes determine if cells are in a physiological state sufficient to ensure their survival,for example measuring the membrane integrity in terms of permeability. Propidium Iodide (PI) is a dye frequently used with this aim (Delobel et ah, 2012). Finally, comparing the results of cell viability obtained with FCM and culture dependent methods such as plate counts, it is possible quantify for difference the VBNC cells of yeasts (Andorra et ah, 2011; Divol and Lonvaud-Funel, 2005; Seipaggi et ah, 2012; Salma et ah, 2013) and bacteria (Herrerp et ah, 2006; Quirds et ah, 2009; Oliver, 2005) in wine. Table 2 reports the dyes used in FCM to monitor enological microorganisms.

2.3. Molecular Techniques to Identify and Characterise Wine Microorganisms

This section will focus on molecular techniques used to identify and characterise yeasts, lactic acid bacteria and acetic bacteria obtained from must and wine after an enrichment phase in plates containing specific growth media. These molecular methods can be grouped in three categories: those that are aimed to identify species, those that are suitable to differentiate strains of the same species, and finally those that highlight special enzymatic abilities of the strains assayed.

2.3.1. Species Identification and Strain Characterisation


One of the most significant systems to identify yeast species is the sequencing of the ribosomal genes, followed by the comparison of the sequences experimentally obtained with those deposited in special database (Mills et ah, 2008). The 5.8S, 18S, and 26S ribosomal genes are grouped in tandem to form transcription units that are repeated 100-200 times throughout the genome of the yeasts. Each transcription unit contains the internal transcribed spacer (ITS), while non-transcribed spacers (NTSs) separate the coding regions. The sequences of 5.8S, 18S, and 26S ribosomal genes and the ITS and NTS spacers can be

Table 2. Dyes Used in FCM and Related Function to Monitor Enological Microorganisms (modified from Longin et ah, 2017)






Fluorescein diacetate (FDA)

ЛЪЖ- State (Flow cytometiy versus plate count)

Spoilage yeasts

Seipaggi et at., 2012


Alcoholic fermentation microorganisms

Malacrino et at., 2001 Salma et ah, 2013 Gerbaux and Thomas, 2009

Malolactic fermentation microorganisms

Boiux and Ghorbal, 2013

Salma et ah, 2012 Malacrino et al, 2001

Carboxyfluorescein diacetate (cFDA)

Alcoholic fermentation microorganisms

Bouix and Leveau, 2001; Bouchez et al., 2004

2-chloro-4-(2,3-dihydro-3- methyl-(benzo-1,3-thiazol-2- yl)-methylidene)-1 - phenylquinolinium iodide (FUN-1)

Alcoholic fermentation microorganisms

Salma et al., 2013

Propidium iodide (PI)

Viability (membrane integrity)

Microorganisms of sediments

Gerbaux and Thomas, 2009

Alcoholic fermentation microorganisms

Landolfo et al., 2008 Mannazzu et al., 2008 Branco et ah, 2012 Delobel et al., 2012 Chaney et al., 2006 Farthing et al., 2007

Malolactic fermentation microorganisms

Bouix and Ghorbal, 2013

Salma et al., 2012

Chemchrom Y6 and Propidium iodide (CY6/PI)

YBNC State (Flow cytometry versus plate count)

Malolactic fermentation microorganisms

Herrero et al., 2006

Vitality and Viability

Alcoholic fermentation microorganisms

Herrero et al., 2006

Malolactic fermentation microorganisms

Da Silveira et al, 2002 Bouix and Ghorbal, 2013

Carboxyfluorescein diacetate and Propidimn iodide (cFDA/ PI)

Alcoholic fermentation microorganisms

Montheard et al, 2012

used to identify yeasts species because of their conservation and concerted evolution (Femandez-Espinar et al., 2011). In other words, the sequence similarity between repeated transcription units within a given species is greater than between units belonging to different species (Femandez-Espinar et al., 2011). This sequence similarity within the species makes the ribosomal genes a powerful tool with which to identify yeasts. In particular, the D1 and D2 regions at the 5' end of the genes encoding the 26S (Kurtzman and Robnett 1998) and 18S (James et al., 1997) ribosomal subunits are the two most commonly used regions to identify yeasts also thanks to a wide availability of deposited sequences in DNA databases. The sequencing of D1/D2 region allows identifying the yeasts species when the homology of the sequences experimentally obtained with those deposited in the database is greater than 99% (Kurtzman and Robnett, 1998). The sequence homology is performed by using the program WU-BLAST2 ( Blas2/index.html).

Unfortunately, sequencing of ribosomal genes (or portions thereof) are expensive and time consuming, two aspects that make this method inadequate for large-scale ecological studies (Mils et al., 2008). The rRNA gene sequence analysis combined with PCR methods have enabled the rapid identification of species yeast isolated from wine. A common approach is to isolate yeasts based on colony or microscopic morphology, and then to perform the identification, by rRNA gene sequencing, of only the isolates representatives of each morphology.

Another economical method to identify yeasts is the ITS-Restriction Fragment Length Polymorphism (RFLP) (Mils et al., 2008). This method consists in PCR amplification of rDNA regions followed by restriction analysis of the amplified products (Guillamon et al., 1998; Esteve-Zarzoso et al., 1999; Granchi et al., 1999). The fragments generated are separated by electrophoresis in agarose gel and their size is compared to databases occurring in literature to recognise the species. Despite rITS-RFLP is not as discriminatory as 26S rRNA gene sequences (Alias et ah, 2002), several authors successfully used this method to describe the yeast ecology during wine fermentations (Fernandez et al., 1999; Granchi et al., 1999; Pramateftaki et al., 2000; Esteve-Zarziso et al., 2001; Jemec et al., 2001: Raspor et al., 2002; Romancino et al., 2008).

The most commonly used molecular methods to characterise yeast strains are the restriction analysis of mitochondrial DNA (mtDNA) and Polymerase Chain Reaction (PCR)-based methods such as Random Amplification of Polymorphic DNA (RAPD), PCR-Analysis of Repetitive Genomic DNA (Microsatellites and Minisatellites), Amplification of d Sequences, and Amplified Fragment Length Polymorphism (AFLP). (Femandez-Espinar et al., 2011). Restriction analysis of mtDNA exploit the high polymorphism of mtDNA to differentiate S. cerevisiae strains. The DNA is extracted and digested by restriction enzymes providing specific patterns for each strain. Several Authors used this technique to describe wine yeast biodiversity and ecology (Femandez-Gonzalez et al., 2001; Torija et al., 2001; Beltran et al., 2002: Lopes et al., 2002; Sabate et al., 2002; Granchi et al., 2003; Torija et al., 2003; Esteve-Zarzoso et al., 2001; Martinez et al., 2004; Lopes et al., 2006; Gonzalez et al., 2007; Lopes et al., 2007; Capece et al., 2016).With the same purpose, PCR-based methods have been developed to detect DNA polymorphisms without using restriction enzymes. All of these techniques use oligonucleotide primers, which bind to target sequences of the DNA. The target sequences are amplified by PCR and the amplification products are visualised in agarose gels. Thanks to the strain specific nature of these amplification products, different strains show different profiles. The two most frequently PCR-based techniques used to differentiate yeast strains are the RAPD (Lopandic et al., 2008; Urso et al., 2008; Tofalo et al., 2009) and microsatellite analysis (Caruso et al., 2002; Capece et ah, 2003; Howell et ah, 2004; Ayoub et ah, 2006; Capece et ah, 2016). Finally, the amplification of d sequences and intron splice sites have been used to differentiate between wine strains belonging to the species S. cerevisiae (Pramateftaki et ah, 2000; Femandez-Espinar et ah, 2001; Lopes et ah, 2002; Ciani et ah, 2004; Le Jeune et ah, 2006). To conclude, another approach have been used to differentiate wine yeast strains: whole or sub-genomic analysis trough pulse field gel electrophoresis (PFGE) In PFGE, two transverse electric fields are alternated forcing the chromosomes to continually change the direction of their migration. Consequently, these large fragments of DNA can be separated in the agarose gel matrix. This method of karyotype analysis has been demonstrated to be highly efficient for the differentiation of S. cerevisiae strains and numerous studies have used karyotype analysis to characterise wine strains of 5. cerevisiae (Marinez et ah, 2004; Rodriguez et ah, 2004; Schuller et ah, 2004; Naumov et ah, 2002).


Various molecular teclmiques are available to identify bacteria (lactic acid bacteria and acetic bacteria) from wine after an enrichment phase in plate containing selective media. As already shown for yeasts, also for bacteria a common approach is to randomly withdraw from the plates a significant number of isolates and identify them by molecular methods. The most significant method is certainly the direct sequencing of the 16S and 23S rDNA gene (Cloe et al., 2005; Yamada and Yukphan, 2008), or parts of it, through comparison to existing database. As previously underlined, sequencing of ribosomal genes are expensive and time consuming. Therefore, rRNA gene RFLP approaches have been used also to identify bacteria of musts and wines. In particular, the most rapid and reliable method is the Amplified 16S rDNA restriction analysis (ARDRA) which provides for the amplification of the 16S rRNA gene with appropriate primers and the cut of amplicon with restriction enzymes. The digestion patterns obtained are compared with literature (Rodas et al., 2003; Ventura et al., 2000; Poblet et al., 2000; Ruiz et al., 2000; Ruiz et al., 2000; Gonzalez et al., 2006b: Guillamon and Mas, 2011).

Among the lactic acid bacteria (LAB) present in wine, Oenococcus oeni is the main species associated with malolactic fermentation, therefore PCR methods have been developed for rapid detection and identification of this species using specific primers (Zapparoli et al., 1998) or multiplex RAPD-PCR (Reguant and Bordons, 2003). The RAPD analysis is also useful for distinguishing between different strains of the same species (Guerrini et al., 2003; Rodas et ah, 2005; Solieri et al., 2010; Marques et al., 2011), as well as PFGE (Larisica et al., 2008; Lopez et al., 2008; Pramateftak et al., 2012: Zapparoli et al., 2012). Although less frequently, other molecular teclmiques are used for typing strains of O. oeni: AFLP (Cappello et al., 2014), Variable Number of Tandem Repeats ("NTR) (Garofalo et al., 2015; Claisse and Lonvaud-Funel, 2014), and Multilocus Sequence Typing (MLST) (Garofalo et al., 2015; Bridier et al., 2010). Croz-Pio et al. (2017) demonstrated that the use of molecular fingerprints (RAPD and VNTR) is in many cases enough to discriminate O. oeni strains and to quantify diversity even if some isolates sharing the same genomic profiles can have different fermentative profiles, and vice versa. Consequently, the polyphasic approach, combining phenotypic (such as carbohydrates degradation) and genotypic profiles (molecular fingerprint), provides an optimum typing of O. oeni strains.

Metabolic Capabilities of Microorganisms

A reliable tool to identify microbial populations able to produce unwanted substances in wine is to use PCR screenings after an enrichment phase in plate containing selective media. This approach allow understanding the ecological distribution of specific genes (Mills et al., 2008). Genes responsible for ropiness (Gindreau et al., 2001), acrolein taint (Claisse and Lonvoud-Fimel. 2001) and biogenic amines (BA) production (Landete et al., 2005; Costantini et al., 2006) have been identified, sequenced and used as target of PCR with properly designed primers. The gene occurs in the assayed isolate when an amplification product is obtained. This approach is particularly important in order to assess for example the potential risk of BA accumulation in wine. PCR screens to detect BA producing LAB are described in RESOLUTION OIV-OENO 449-2012. The methods described in this document consist in detecting LAB that have the genes of amino acids decarboxylases and/or agmatine deiminase using the primers listed in Table 3. Obviously, the results obtained with these methods are not able to predict the final BA concentrations in wine, but identify the risk of BA formation due to the presence of the decarboxylases and agmatine deiminase genes in the LAB population (Lucas et al., 2008). Assessing the potential risk of a BA accumulation in wine at an early stage of the winemaking, these methods can assist in managing the fermentation process in order to reduce the BA formation (Vmcenzini et al., 2017).

2.4. Molecular Techniques for Direct Microbial Species or Strain Detection

The molecular methods previously described to identify or characterise yeasts and bacteria in wine involve enrichment techniques. Unfortunately, enrichment procedures are not only time consuming, but also underestimate the viable but not cultivable cells, which are unable to grow on plates but are still metabolically active. Alternatively, various molecular teclmiques have been developed for microbial species or strain detection directly in must and wine. The following paragraphs briefly describe these methods.

Table 3. Oligonucleotide Primers for the Detection of BA Producing LAB in Wine reported by RESOLUTION OIY-OENO 449-2012 (Yincenzini et al, 2017)


Primers 5'—>3'sequence


Histamine decarboxylases


Coton and Coton, 2005

Tyramine decarboxylases


Marcobal et al., 2005 Coton et al, 2004

Putrescine decarboxylases


Marcobal et al, 2005 Granchi et al, 2006

2.4.1. PCR - DGGE and TGGE

Muyzer et al. (1993) have introduced for the first time denaturing gradient gel electrophoresis (DGGE) of PCR products to study microbial ecology. Unlike simple gel electrophoresis above reported, DGGE allows to separate DNA amplicons of the same length based on sequence differences. In fact, the separation of these DNA fragments is based on the different electrophoretic mobility of a partially melted double stranded DNA molecule in polyacrylamide gels containing a linear gradient of denaturing agents (usually a mixture of urea and fonnamide). DNA migration is retarded when the DNA strands dissociate and the strand dissociation at a specific concentration of denaturing agent depends on the basis sequence. A similar technique is the temperature gradient gel electrophoresis (TGGE), which is based on a linear temperature gradient for separation of DNA molecules. DNA bands in DGGE and TGGE methods can be visualised using ethidium bromide or SYBR Green I. Moreover, PCR fragments can be extracted directly from the gel and sequenced for species identification.

Both methods have been used for yeast identification in wine fermentations (Andorra et al., 2008; Cocolin et al., 2000; di Maro et al., 2007; Prakitchaiwattana et al., 2004; Reuouf et al., 2007; Stringini et al., 2009; Urso et al., 2008). In particular-, Cocolin et al. (2000) developed primers that amplified a portion of D1-D2 region ofrDNA. Using these primers in DGGE-PCR, the Authors described the ecology of various wine yeasts species (both Saccharomyces and non-Saccharomyces spp.) during alcoholic fermentation. PCR-DGGE is also useful to detect spoilage yeast such as Brettanomyces bruxellensis (Renouf et al., 2006). Moreover, some Authors concluded that PCR-DGGE analysis is less sensitive than agar culture at least for determining the yeast ecology of grapes (Andorra et al., 2008).

As far as LAB are concerned, PCR-DGGE in wine is complicated by inherent problems associated with primer specificity (Mills et al., 2008). Indeed, several Authors observed how primers, commonly used for bacterial PCR-DGGE in other foods, amplify incorrectly DNAs of yeast, moulds, or plants (Lopez et al., 2003; Dent et al., 2004). Anyway, recently some authors have successfully used this technique to describe the ecology of O. oeni in wine (Gonzalez-Arenzana et al., 2017).

2.4.2. Direct PCR Approaches and Real Time PCR

In recent years, Authors reported the use of real-time quantitative PCR (Q-PCR) to detect and quantify micro-organisms in different foods (Blackstone et al., 2003; Bleve et al., 2003; Hein et al.,2001). The main advantage of this method is the low detection level, theoretically as low as 1 cell/mL (Zot et al., 2010). In Q-PCR the logarithmic amplification of a DNA target sequence is linked to the fluorescence of reporter molecules. These molecules binds double stand DNA and, when linked, they have excitation and emission at accurate wavelengths. This fluorescence, which is measured after each cycle of DNA amplification, may either be compared to an external standard curve (absolute quantification) or to an internal or external control sample (relative quantification). Relative quantification is primarily used to follow gene expression, while absolute quantification is the most common type of Q-PCR employed in wine ecology (Mills et ah, 2008). Several different reporter molecules exist, but the most common for detection of wine microorganisms is S YBER Green, which has an excitation wavelength of about 250 nm and an emission wavelength of about 497 nm.

In recent years, Q-PCR has been applied in many aspects of wine microbial ecology (Hierro et ah, 2007). It was used to quantify the total yeast population (Hierro et ah, 2006a; Salinas et ah, 2009), Saccharomyces cerevisiae (Martorell et ah, 2005), Zygosaccharomyces bailii (Rawsthome and Phister, 2006), Brettanomyces bruxellensis/Dekkera bruxellensis (Delaherche et ah, 2004; Phister and Mills, 2003; Tessonniere et ah, 2009), lactic acid bacteria (Neely et ah, 2005), O. oeni (Pinzaui et ah, 2004), acid acetic bacteria (Gonzalez et ah, 2006).

At present, the current use of Q-PCR to describe wine microbial ecology is limited (Mills et ah, 2008). Despite the large number of Q-PCR assays developed and the diffusion of these to service laboratories, few wineries use this approach to keep under control the wine fermentations. It is an exception the quantification of Brettanomyces bruxellensis/Dekkera bruxellensis by Q-PCR. Indeed, the enumerations of this yeast required from 5 to 10 days with conventional analysis by plate counts (Phister and Mills, 2003), while only 1-2 horns with Q-PCR (Mills et ah, 2008). This time difference provides winemakers the possibility of intervening before the damage caused by this yeast occurs. Another exception is represented by the rapid detection and quantification of В A producing LAB in wine with Q-PCR. Indeed, in the RESOLUTION OIY-OENO 449-2012, in order to assess the potential risk of BA accumulation in wine, Q-PCR methods to detect LAB that have the genes of amino acids decarboxylases by targeting the suitable genes are described.

2.4.3. Oligonucleotide Probes and Specific Antibodies

Another approach to detect microorganisms in wine is fluorescence in situ hybridisation (FISH). FISH allows the direct detection and identification of microorganisms combining fluorescence microscopy with the reliability of molecular methods. Indeed, in this technique, a set of specific probes are used to detect different microorganisms directly in the sample. The probes can be labelled with different fluorophores, thereby allowing detection of several species simultaneously. When FISH probes are designed to target ribosomal RNA, only living cells are detected (Bottari et ah, 2006). In wine, this technique has been used for the rapid monitoring of LAB (Sohier and Lonvaud-Funell998; Blasco et ah, 2003), Brettanomyces bruxellensis/Dekkera bruxellensis (Stender et ah, 2001), S. cerevisiae and various non-Saccharomyces yeasts (Xufre et ah, 2006). The D1/D2 domains at the 5' end of the 26S rRNA subunit provide an excellent basis to develop species-specific FISH probes for yeasts, while the 16S rRNA subunit is suitable for LAB. FISHmay be coupled with FCM to specifically quantify wine microorganisms (Longin et ah, 2017).Thanks to the species-specific properties of the FISH probes, the FISH-FCM technique has been used to monitor fermentation directly in wine quantifying S. cerevisiae, H. guilliermondii, H. uvarum, Stannerella bacillaris, B. bruxellensis (Andorra et ah, 2011; Branco et ah, 2012; Wang et ah, 2014b, Seipaggi et ah, 2010). FCM may be also coupled with polyclonal antibodies. Rodriguez and Thornton (2008) developed a method to distinguish and quantify S. cerevisiae from other yeasts such as the genus Hanseniaspora in must fermentation. First, they incubated grape must with an anti-Saccharomyces polyclonal antibody and then a second incubation was performed with a secondary antibody coupled with Alexa Fluor® 488 before FCM analysis. These Authors developed also a green fluorescent polyclonal antibody for O. oeni to monitor malolactic fermentation. Recently, an immune- cytometric test has been developed also to detect and quantify B. bruxellensis (Chaillet et ah, 2014). This method consists in using an anti -Brettanomyces polyclonal antibody conjugated with a fluorochrome to specifically distinguish and quantify this yeast among other yeast species with a greater efficiency and rapidity than plate count methods.

2.5. Next-generation DNA Sequencing Applied to Wine Microbiota

Recently, the study of genetic material recovered directly from environmental samples (metagenomic approach) has been applied to the study of microbial communities in ecosystems, providing a great insight into the processes responsible for microbial diversity. In this contest, DNA sequence represents a single format onto which a broad range of biological phenomena can be projected for high throughput data collection. Over the past ten years, DNA sequencing platforms have become widely available, reducing considerably the cost of DNA sequencing. These new technologies are rapidly evolving, and near-term challenges include the development of robust protocols for generating sequencing libraries and building effective new approaches to data-analysis (Shendure and Ji, 2008). In particular, High- Throughput Sequencing (HTS) technologies such as the 454 pyrosequencing of amplicons (Shendure and Ji, 2008), can be used to characterise more precisely the microbial diversity of complex environmental ecosystems, including food samples (Ercolini, 2013; Galimberti et al., 2015; Solieri, et ah, 2013). In the 454 pyrosequencing, libraries may be constructed by any method that gives rise to a mixture of short, adaptor-flanked fragments. Clonal sequencing features are generated by emulsion PCR (Dressman et al., 2003), with amplicons captured to the surface of 28-pm beads. After breaking the emulsion, beads are treated with denaturant to remove untethered strands, and then subjected to a hybridisation-based enrichment for amplicon bearing beads. A sequencing primer is hybridised to the universal adaptor at the appropriate position and orientation, that is, immediately adjacent to the start of unknown sequence. Finally, sequencing is performed by the pyrosequencing method (Ronaghi et al., 1996).

HTS technology has recently been used to determine the bacterial diversity of botrytised wines (Bokulich et al., 2012), to monitor seasonal changes in winery-resident microbiota (Richardson and Mills, 2013) or to analyze the microbial biogeography of grapes from a Californian region (Bokulich et al., 2014). Using HTS techniques, Bokulich et al. (2014) showed that the microbial population in wine fermentation is strongly relatedto climatic conditions, grape variety, and vineyard environmental conditions. In other words, the authors confirmed what was already known by using other molecular methods: there is a unique microbial pattern that influences the wine quality and asserts the existence of non-random “microbial terroir”. Likewise De Filippis et al. (2017) monitored yeast and mould populations involved in spontaneous fermentations of Aglianico and Greco di Tufo grape must by high- throughput sequencing (HTS) of 18S rRNA gene amplicons. As expected, the Authors found a complex microbiota at the beginning of the fermentation, mainly characterised by non-Saccharomyces yeasts and several moulds, with differences between the two types of grapes.

To conclude, HTS techniques can be used for monitoring microbial changes in wine fermentations and winemakers could exploit this information to drive fermentation, but the too expensive instrumentation and bioinformatic knowledge needed to use these techniques confine, at least in the near future, this method to research laboratories.


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