Volatile Compounds Related to 'Stone Fruit' Aroma ... - ACS Publications

Article Cite This: J. Agric. Food Chem. XXXX, XXX, XXX−XXX

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Volatile Compounds Related to ‘Stone Fruit’ Aroma Attributes in Viognier and Chardonnay Wines Tracey E. Siebert,*,†,‡ Alice Barker,†,§ Wes Pearson,† Sheridan R. Barter,† Miguel A. de Barros Lopes,‡ Philippe Darriet,⊥,¶ Markus J. Herderich,† and I. Leigh Francis†,‡ †

The Australian Wine Research Institute, P.O. Box 197, Glen Osmond (Adelaide) SA 5064, Australia School of Pharmacy and Medical Science, University of South Australia, G.P.O Box 2471, Adelaide, SA 5001, Australia ⊥ Unité de Recherche Œnologie EA 4577, ISVV, University of Bordeaux, Villenave d’Ornon cedex 33882, France ¶ USC Œnologie, ISVV, INRA, Villenave d’Ornon cedex 33882, France ‡

S Supporting Information *

ABSTRACT: A ‘stone fruit’ aroma is important in many white wine varieties and styles, but little is known about the chemical basis of this wine aroma attribute. A set of Viognier and Chardonnay wines that featured ‘stone fruit’ aroma attributes were selected by a panel of wine experts. The selected wines were characterized by sensory descriptive analysis and detailed volatile chemical composition analyses. This comprehensive data also allowed Viognier wine to be profiled for the first time. By partial least-squares regression, several esters and fatty acids and benzaldehyde were indicated as contributing to the ‘peach’ attribute; however, a reconstitution sensory study was unsuccessful in mimicking this attribute. A mixture of γ-lactones, monoterpenes, and aldehydes were positively correlated to the ‘apricot’ aroma, which were generally higher in the Viognier wines. Reconstitution studies confirmed that the monoterpenes linalool, geraniol, and nerol were the most important compounds for the mixture being perceived as having an ‘apricot’ aroma. KEYWORDS: wine, aroma composition, sensory descriptive analysis, PLS regression, aroma reconstitution, stone fruit, apricot, peach, Viognier and Chardonnay

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INTRODUCTION ‘Stone fruit’, ‘peach’, and ‘apricot’ are terms often listed as varietal aroma attributes for white wines, notably Viognier and Chardonnay wines,1 but very little is known about the chemical basis of ‘stone fruit’ aromas in wine. As Chardonnay has the largest market share for white wine in the Australian wine industry and is made in all wine-producing countries, it is highly valuable to understand the flavor compounds that confer its distinct varietal characters. Viognier wine has a relatively small market share, but the variety is now grown in most wine regions around the world.2 It is a well-regarded premium wine variety in France, especially in Condrieu and Château-Grillet, Appellation d’Origine Contrôlée (AOC) Rhône Valley, and also in Virginia, California, and Australia. Also, in some wine regions, a small amount of Viognier grapes (up to 10%) are cofermented with Shiraz grapes to produce a more aromatic Shiraz wine, often with an ‘apricot’ aroma.1 Chardonnay wines have often been the subject of wine aroma studies,3 but Viognier wine aroma has received little attention. A recent exploratory study investigating white wines with ‘stone fruit’ aroma attributes using gas chromatography− olfactometry−mass spectrometry (GC-O-MS) did not find a common aroma compound responsible for the ‘stone fruit’ character in the wine varieties studied. However, the data suggested that some monoterpenes, linalool, α-terpineol, and geraniol, were involved in the ‘stone fruit’ aroma in Viognier wines, as was a combination of esters in Chardonnay wines and γ-nonalactone in botrytis Semillon wines.4 This was the first study directed at wines specifically with ‘stone fruit’ aroma © XXXX American Chemical Society

attributes. In a subsequent investigation, the role of the littlestudied ‘dairy lactone’ was assessed.5 Sensory descriptive analysis of food and beverage products is a highly developed quantitative methodology that has been used for many years to provide comprehensive sensory evaluations of products.6 The methodology is sensitive and can be applied to a specific group of wines, providing word descriptions and their intensities to compare similarities or differences between the wines. Some individual aroma compounds can have strong impacts on wine aromas.7 Even though the aromas of varietal wines can often be perceived as very distinctive, the volatile aroma compositions of wines made from different varieties might only vary in the proportions of those compounds.8 Relating the compositional differences among wines to their sensory attributes can help determine if a sensory difference is due to variation in the concentrations of aroma compounds, the absence or presence of certain aroma compounds, or an additive or synergistic effect of several aroma compounds.9−11 However, relating sensory data to chemical composition is not always successful.12 This may be due to the complexity of flavors resulting from interactions between the aroma compounds themselves and nonvolatile components7,13 and the absolute need for reliable sensory data and wide-ranging Received: November 16, 2017 Revised: February 13, 2018 Accepted: February 14, 2018

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DOI: 10.1021/acs.jafc.7b05343 J. Agric. Food Chem. XXXX, XXX, XXX−XXX

Article

Journal of Agricultural and Food Chemistry

Chemicals. All the chromatographic solvents were of GC grade; all of the chemicals were of analytical-reagent grade unless otherwise stated. Water was obtained from a Milli-Q purification system (Millipore, North Ryde, NSW, Australia). Ethanol was purchased from VWR (Murrarie, QLD, Australia); tartaric acid, potassium hydroxide, and sodium chloride were from Merck (Bayswater, VIC, Australia); potassium metabisulfite was from Chem-Supply (Gillman, SA, Australia); D-(−)-fructose was from Scharlau (Chem-Supply); and potassium L-tartrate monobasic, D-(+)-glucose, glycerol, DL-malic acid, and succinic acid were supplied by Sigma-Aldrich. Food coloring was obtained from AmeriColor (Complete Cake Decorating Supplies, Panorama, SA, Australia). The aroma compounds used as the reference standards for the quantitation and reconstitution studies were purchased from Sigma-Aldrich (Castle Hill, NSW, Australia) unless stated otherwise. The purities of all of the aroma compounds were ≥97%, except (E)-2-hexenal (≥95%), (E)-2-octenal (≥94%), and (E)-2-nonenal (≥93%). Hydrogen sulfide and methanethiol were derived from the sodium salts of these analytes (sodium hydrosulfide hydrate, purity >70%, and sodium thiomethoxide, purity >95%), which were dissolved in cooled water and used immediately.22 β-Ionone was supplied by Merck, 4-ethylguaiacol was supplied by Apin Chemicals (Abingdon, UK), 4-methylguaiacol was supplied by TCI (Chemsupply, Gilman, SA, Australia), (Z)-7-decen-5-olide was supplied by Penta International (Livingston, NJ), and 6-pentyl-α-pyrone was supplied by Pyrazine Specialties (Ellenwood, GA). (Z)-6-Dodeceno-γlactone was kindly donated by Symrise (Holzminden, Germany), and γ-methyldecalactone was kindly donated by Pyrazine Specialties. Ethyl 9-decenoate was synthesized in-house in a similar manner to that described previously.23 Briefly, 9-decen-1-oic acid was esterified by refluxing in ethanol with sulfuric acid as the catalyst to ethyl 9decenoate (98% yield). The 1H NMR (600 MHz) spectroscopic data and GC-MS spectrum obtained were consistent with those found in the literature. Other compounds previously synthesized in-house included 3-mercaptohexanol and 3-mercaptohexyl acetate24 and 4mercapto-4-methylpentan-2-one.25 The deuterated analogues used for the stable-isotope dilution analyses (SIDA) (d8-ethyl acetate, d10-butanol, d13-hexanol, d3-acetic acid, d5-propanoic acid, d7-butanoic acid, d11-hexanoic acid, d15octanoic acid, d19-decanoic acid, and d4-acetaldehyde) were purchased from Sigma-Aldrich (Castle Hill, Australia); d4-furfural was purchased from CDN Isotopes (SciVac Pty Ltd., Hornsby, NSW, Australia). Other deuterated analogues had been synthesized in-house previously: d5-ethyl lactate, d9-2-methylpropyl acetate, d5-2-methylbutyl acetate, d9-3-methylbutyl acetate, d7-2-methylpropanoic acid, d7-3-methylbutanoic acid, d9-2-methylpropanol, and d9-3-methylbutanol;26 d7-acetoin and d8-2,3-butanediol;27 n-alkyl d7-γ-lactones (C8−C12);28 d6-αterpineol;29 d4-β-damascenone, d3-α-ionone, d3-β-ionone, and d8naphthalene;30 d4-(E)-2-hexenol;31 d10-3-mercaptohexanol, d5-3-mercaptohexyl acetate, and d10‑4-mercapto-4-methylpentan-2-one;24 d5-2furfurylthiol and d5-benzenemethanethiol;32 d3-guaiacol and d3-4methylguaiacol;33 d3-vanillin;34 d4-trans-oak lactone and d4-cis-oak lactone;35 d4-4-ethylphenol;36 and d5-methionol, d3-methional, d6furaneol, d5-homofuraneol, d4-sotolon, d9-(E)-2-octenal, d8-(E)-2hexenal, 2-phenyl-d5-acetaldehyde, and d5-benzaldehyde.37 Quantitation of Aroma Compounds. The aroma compounds in the wines were quantified by targeted analyses using previously published methods. All of the targeted analytical methods used SIDA with the deuterated analogues as the internal standards and MS in the selected ion monitoring (SIM) mode or MS/MS with multiple reaction monitoring (MRM), except one which instead used two compounds chemically similar to the analytes.22 The fermentation-derived aroma compounds were analyzed by headspace−solid-phase microextraction−gas chromatography−mass spectrometry (HS-SPME-GC-MS). The ethyl esters, acetate esters, fatty acids, and alcohols were analyzed as described by Siebert et al.,26 except a polyacrylate (PA, white) 85 μm SPME fiber (Agilent Technologies Australia Pty Ltd., Mulgrave, VIC, Australia) was used. Both 2- and 3-methylbutanol used d9-3-methylbutanol for SIDA, and both 2- and 3-methylbutanoic used d7-3-methylbutanoic for SIDA. trans-Ethyl cinnamate was analyzed according to Smyth,11 except a

chemical compositional data. Obtaining a comprehensive quantitative chemical data set could be considered as targeted metabolomics. Noble and Ebeler14 reviewed the statistical techniques utilized for complex wine data sets, such as principal component analysis (PCA), generalized Procrustes analysis (GPA), and partial least-squares (PLS) regression. The techniques are relatively robust for large numbers of variables with relatively small numbers of samples and are used to assess which compounds are related to particular aroma attributes.11,15 To assist with confirming or dismissing these associations, reconstitution, addition, and omission experiments are required.16 Accurate quantitation of the wine aroma compounds is required to enable valid results from reconstitution studies. Stable-isotope dilution analysis (SIDA) is the preferred technique for accurate and precise measurements of compounds in complex matrixes such as wine.17 The aim of this study was to identify the ‘stone fruit’ aroma characteristics of Viognier and Chardonnay wines using a combination of sensory analysis and aroma compound quantitation with multivariate analysis by PLS to relate the data sets, and to perform model reconstitution studies to confirm the predicted outcomes. Previous studies using this strategy have focused on wine typicality, characterizing certain varieties or comparing treatments or regions, or used limited sensory attributes or limited compounds.18−20 This is the first time to our knowledge that this comprehensive approach has been used to identify the aroma compounds responsible for a specific wine aroma attribute using wines explicitly selected as having that attribute.

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MATERIALS AND METHODS

Wine. Seventy-five commercially available prospective Viognier and Chardonnay wines were preselected because they had been described as having ‘stone fruit’ aromas either on their back label or on the winery’s tasting notes. The prospective wines were evaluated independently, under blind conditions, by a group of experienced wine tasters (n = 6). The wine samples (30 mL) were presented to the assessors in transparent, covered, International Organization for Standardization (ISO) wine glasses with up to 12 wines assessed per session in a dedicated open-plan sensory laboratory. The tasters used free-choice notes and then had a group discussion to categorize each wine’s intensity of ‘stone fruit’ aromas, that is, none, low, medium, or high, and overall acceptable wine quality. From these assessments, a set of 18 wines with a range of ‘stone fruit’ aroma descriptors and an array of intensities, from low to high, were selected from the 75 prospective wines: 6 Australian Chardonnay; 6 Australian Viognier; and 6 French Viognier. The selected wines were up to 5 years old. Five of the wines were donated by the respective wineries, seven were purchased from wine retail outlets in Adelaide, South Australia, or directly from the winery in Australia, and six were purchased from wine retail outlets or direct from the winery in France. Basic Wine Composition. The basic chemical composition of all of the white wines was determined by the Australian Wine Research Institute (AWRI) Commercial Services using the methods detailed in Iland et al.21 The titratable acidity (TA), volatile acidity, pH, residual sugar, and alcohol were measured using FTIR WineScan (FOSS, Hillerød, Denmark); the free and total sulfur dioxide (F/T SO2) were measured using flow-injection analysis (FIA Quickchem 8000 series, Lachat, Colorado); and the optical density, total phenolics, hydroxycinnamates, and flavonoids were measured by UV−vis spectroscopy (Cary60, Agilent, Petaling Jaya, Malaysia; Supporting Information, Table S1). For the reconstituted control wine, the F/T SO2 were measured by aspiration, and the TA was measured with a combined pH meter and autotitrator (TIM840, Radiometer Analytical, Dusseldorf, Germany).21 B


Article

Journal of Agricultural and Food Chemistry divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS, gray, 2 cm) 50/30 μm SPME fiber (Agilent) was used, and ethyl 9decenoate was included. Acetaldehyde-related volatiles were analyzed as per Varela et al.27 The carbowax/DVB 65 μm (CW/DVB, orange) SPME fiber utilized in the published HS-SPME-GC-MS protocols11,26 was no longer commercially available, but the PA and DVB/CAR/ PDMS fibers had been used as suitable replacements previously for similar wine aroma compound analyses.4,38 Volatile sulfur compound analysis was performed according to Siebert et al.22 utilizing staticheadspace−gas chromatography−sulfur chemiluminescence detection (SHS-GC-SCD). n-Alkyl γ-lactones were quantified using solid-phase extraction (SPE) and then GC-MS according to Cooke et al.,28 while γ-methyldecalactone, 6-amyl-α-pyrone, (Z)-7-decen-5-olide, and nalkyl δ-lactones were also monitored. (Z)-6-Dodeceno-γ-lactone was analyzed by direct-immersion (DI)-SPME-GC-MS/MS following Siebert et al.5 Grape-derived aroma compounds were determined by liquid-liquid extraction (LLE)-GC-MS. Monoterpenes were analyzed as described by Pedersen et al.,29 except cis-rose oxide was included and d6-αterpineol was used as the SIDA standard. Norisoprenoids were analyzed as described by Ugliano et al.39 C6 alcohols and aldehydes were determined by HS-SPME-GC-MS as detailed in Capone et al.31 Polyfunctional thiols were analyzed by high-performance liquid chromatography (HPLC)-MS/MS after their derivatization and SPE as described by Capone et al.32 The oak-derived aroma compound quantitation was performed according to Pollnitz et al., except all of the compounds were analyzed by LLE-GC-MS.33 The oxidative aroma compounds were analyzed by GC-MS/MS after derivatization and SPE as described by Mayr et al.37 Sensory Descriptive Analysis. Sensory descriptive analysis was conducted on the 18 white wines. A panel of ten assessors (three male) with an average age of 54 (SD = 9.8) was convened for this study, all of whom were part of the AWRI trained descriptive analysis panel. The panel was not informed about the specific context of the study and generated the attribute terms by consensus. The panel attended four 2 h training sessions to determine the attributes and agree upon the reference standards appropriate for describing and rating the wines. Details of the appearance, aroma, and palate attributes used in this study and their definitions are listed in Table 1. The intensity of each attribute was rated using an unstructured 15 cm line scale from 0 to 10 with indented anchor points of ‘low’ and ‘high’ placed at 10 and 90%, respectively. The wine samples (30 mL) were presented to the assessors in threedigit-coded, covered, ISO wine glasses at 22−24 °C in isolated booths under daylight lighting with a randomized presentation order on each tray of samples for each judge. In a booth training session, six wines were assessed in duplicate on four trays of three wines per tray. During the formal sessions, the wines were assessed in triplicate on five trays of three wines per tray for the first 3 days and three trays of three wines on the fourth and final day. A 60 s rest between each sample and a 10 min rest between each tray was enforced. During the 10 min break, the assessors were requested to leave the booths and were directed to a different booth for each tray. The data was acquired using the Fizz sensory software (version 2.48B, Biosystemes, Couternon, France). Aroma Reconstitution. Sensory descriptive analysis was conducted on eight addition and omission models plus the control and blank models. A panel of eight assessors (one male) with an average age of 53 (SD = 8.4) was convened for this study. Only the aroma of the reconstituted wines was evaluated by the panel. The details of the aroma attributes used in this study and their definitions are listed in Table 2. For the sensory experiments, a blank wine-like model was prepared in water with ethanol (10% v/v), potassium L-tartrate monobasic (2 g/ L), D-(+)-glucose (1 g/L), D-(−)-fructose (1 g/L), glycerol (4.6 g/L), citric acid (0.4 g/L), malic acid (2.6 g/L), succinic acid (0.6 g/L), and potassium metabisulfite (20 mg/L) and adjusted to pH 3.33 using an aqueous potassium hydroxide solution (20% w/v). Yellow-orange food coloring was added to closely match the appearance of a white wine. As detailed in Table 3, 55 aroma compounds were added to the blank

Table 1. Attributes and Compositions of the Reference Standards and Definitions of the Terms Used in the Sensory Descriptive Analyses of the 18 Viognier and Chardonnay Wines attribute yellow color overall fruit passion-fruit pineapple apricot peach lemon lime floral grassy vegetal box hedge honey butter nutty flint sweaty/ cheesy kerosene pungent overall fruit tropical fruit stone fruit citrus floral green honey wood sweet viscosity acid hotness astringency bitter fruit aftertaste

description (reference-standard compositiona) Appearance intensity of the yellow color Aroma overall intensity of the fruit aroma in the wine aroma of passion-fruit (1 tsp of passion-fruit pulp, John West) aroma of pineapple (4 × 2 cm2 of fresh pineapple, 5 mL of tinned pineapple juice, Golden Circle) aroma of apricot: fresh, dried (20 mL of tinned apricot pieces, Goulburn Valley) aroma of peach (4 × 2 cm2 of fresh white peach, no skin) aroma of lemon (1 × 2 cm2 of fresh lemon with rind) aroma of lime (1 × 2 cm2 of fresh lime with rind) aroma of flowers: violets, blossoms, musk (130 μg/L linalool, 170 μg/L 2-phenylethanol) aroma of green grass, green beans, green leaves (10 × 1 cm of fresh cut grass, 5 × 1 cm of green beans, no wine) aroma of cooked green vegetables: asparagus, green beans (5 mL of water from tinned asparagus, Edgell) aroma of box hedge (box hedge leaves, no wine) aroma of honey (5 mL of honey, Beechworth) aroma of butter, buttered popcorn (5 mL of soft butter, Coles) aroma of nuts (5 g of mixed nuts, no wine) aroma of flint, wet stones, metal (0.7 μg/L benzenemethanethiol) aroma of sweat, cheese, blue cheese, cheddar cheese (35 mg/L hexanoic acid, 40 mg/L 3-methylbutanoic acid) aroma of kerosene, machine oil, petroleum, wax, burnt rubber (3 μg/L 1,1,6-trimethyl-1,2-dihydronaphthalene) aroma and effect of alcohol (4 mL of ethanol) Palate overall intensity of the fruit flavors in the wine flavor of tropical fruits: pineapple, passion-fruit, melon, mango, guava, lychee flavor of stone fruits: peach, apricot, nectarine flavor of citrus fruits: lemon, lime flavor of flowers: blossoms, musk flavor of green stalks, green leaves, grass, green vegetables flavor of honey flavor of wood, oak, toast taste of sucrose perception of the body, weight, or thickness of the wine in the mouth taste of acid in the mouth, including the aftertaste alcohol hotness perceived in the mouth after expectoration drying and mouth-puckering sensation in the mouth bitter taste perceived in the mouth, before or after expectoration lingering fruit flavor perceived in the mouth after expectorating

a

The standards were presented in 30 mL of a young, neutral white wine unless otherwise noted.

model to produce the control model. The concentrations of the 55 aroma compounds added were equivalent to the means determined across the 18 wines from this study, except for that of oak lactone, which was added as the racemate (cis/trans combined, 42 μg/L); that of sotolon, which was decreased slightly (8 μg/L); and those of 2methylbutanoic acid and 3-methylbutanoic acid, which were added at concentrations within the ranges reported previously (500 and 320 μg/L, respectively).40 The aroma compounds that were found in a C


Article


For the chemical compositional data, significant differences between the means (P = 0.05) were determined with Fisher’s multiple comparisons test using Minitab. For relating the sensory attribute ratings to the chemical compositions by partial least-squares (PLS) regression, the Unscrambler X (version 10.3, CAMO ASA, Oslo, Norway) was used with the chemical components as the x variables and the sensory aroma attributes as the y variables. The data were standardized (1/Sdev), and the optimal number of components for the model was determined from an inspection of the residual variance explained by each component. Models were generated using full cross-validation, and an uncertainty test was applied to assess the statistical significance of each x variable. Human Ethics. The Human Research Ethics Committee of the University of South Australia approved the methods of the sensory testing and data collection used in this study (Protocol Number 0000031967). Informed consent was obtained from each individual prior to the sensory analysis.

Table 2. Attributes and Compositions of the Reference Standards and Definitions of the Terms Used in the Sensory Descriptive Analyses of the Omission and Reconstitution Samples aroma attribute overall aroma apricot peach citrus tropical apple/pear honey confection/ floral green sweaty/ cheesy yeasty cardboard nail polish remover pungent

description (reference standard compositiona) overall intensity of the aroma in the sample aroma of apricot: tinned, fresh (20 mL of tinned apricot pieces, Goulburn Valley) aroma of fresh white peach (4 × 2 cm2 of fresh white peach, no skin) aroma of citrus fruits: lemon, lime (1 × 2 cm2 of fresh lime, 1 × 2 cm2 of fresh lemon with rind) aroma of tropical fruits: passion-fruit, pineapple, banana (4 × 2 cm2 of fresh pineapple, 5 mL of tinned pineapple juice, Golden Circle; 1 tsp of passion-fruit pulp, John West) aroma of apple, pear, bruised apple (8 × 2 cm2 of nashi pear, no wine) aroma of honey (5 mL of honey, Beechworth) aroma of confection and flowers: banana candy, musk, red candy, bubble gum (1 banana candy, 1 red raspberry candy, Allen’s; no wine) aroma of green vegetables: beans, peas, green capsicum, grass (50 ng/L isobutyl methoxypyrazine) aroma of sweat, cheese, yoghurt (35 mg/L hexanoic acid, 40 mg/L 3-methylbutanoic acid) aroma of yeast, bread, toast, butter (warm water, dried yeast, 5g of sugar) aroma of damp cardboard, chemicals, plastic, crayons, linoleum (130 ng/L 2,6-dichlorophenol) aroma of nail polish remover

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RESULTS AND DISCUSSION

Quantitation of Aroma Compounds in Commercial Viognier and Chardonnay Wines. In recent years, aroma extract dilution analysis (AEDA) has been a useful tool in the identification of the most potent aroma compounds in wine samples41,42 and is followed by targeted quantitation of the identified aroma compounds. However, if only one aroma attribute is of interest and it is not due to an individual (impact) compound, then AEDA, while valuable, can be inconclusive. A recent study using GC-O-MS was unsuccessful in finding an aroma-active zone corresponding to the ‘stone fruit’ aroma in Viognier and Chardonnay wines with this attribute.4 For the present study, a different strategy was applied: after a careful selection of an appropriate set of wines, predictive models were developed using multivariate analysis by relating ‘stone fruit’ sensory attributes to chemical compositional data. Therefore, targeted analyses of a wide range of known wine aroma compounds, fermentation-derived volatiles, grape-derived volatiles, and oak- and aging-related volatiles, from major components present at grams-per-liter concentrations to trace-level components present at nanograms-per-liter concentrations, were carried out on the set of 18 wines in conjunction with sensory descriptive analyses. Some of these compounds were previously found to be of importance to Viognier and Chardonnay wines in a GC-O-MS study.4 In addition, the known impact aroma compounds of peaches and apricots, several n-alkyl γ-lactones,43 were included. Extra compounds of interest were also added to the calibration sets of some analyses because of their previously reported ‘stone fruit’ or ‘fruity’ aroma descriptors: ethyl-9-decenoate, γ-methyldecalactone, 6amyl-α-pyrone, and (Z)-7-decen-5-olide. Of the 104 targeted wine aroma compounds, 79 were detected and quantified in the sample set, with 52 found in every wine and 15 others found in over half of the wines (Table 3). This data upholds the concept that many aroma compounds are common to all wines and differ only in their relative proportions.8 Table 3 lists the aroma compounds determined to be above the limit of quantitation in at least one of the wines, together with their CAS numbers; abbreviation codes; aroma detection thresholds in wines or model wines; mean concentrations in all wines; resulting odor activity values (OAV); and comparisons of the minimums, maximums, and means of the Chardonnay wines and Viognier wines. The 25 aroma compounds not quantified were either below the limit of quantitation or not detectable by the prescribed analytical

aroma and effect of alcohol (4 mL of ethanol)

a

The standards were presented in 30 mL of a young, neutral white wine unless otherwise noted.

limited number of wines (n ≤ 8) were not included in the model, nor was methional, furfurylthiol, or β-citronellol. Following an assessment of the relationships between the chemical data and the sensory data, the aroma compounds indicated to be important to the ‘apricot’ aroma in the selected wines were further evaluated, with eight addition and omission samples prepared by adding up to three specific mixtures of the aroma compounds to the control model (see Tables 4 and 5). The concentration of each of these aroma compounds was derived from the mean value calculated from its concentrations in the three wines with the highest ‘apricot’ intensity ratings in the wine sensory descriptive analysis (Table 4). Ethanol was added to each reconstitution model, including the control and blank models, to achieve the same final amount (13.3% v/v). The reconstitution models were presented as described above for the wines in the first sensory study, except that for the formal sessions, 10 samples were presented to the assessors in a complete-randomizedblock design in triplicate over two consecutive days. Statistical Analysis. For the sensory data, panel performance was assessed using Fizz, Senstools (OP&P, The Netherlands), and PanelCheck (panelcheck.com) software and included analyses of variances for the effects of sample, judge, presentation replicate, and their interactions; degrees of agreement with the panel means; and degrees of discrimination across the samples. All of the judges were found to be performing to an acceptable standard. Analysis of variance (ANOVA) was carried out using Minitab 17.1.0 (State College, PA). The effects of each judge, wine, and presentation replicate and of their interactions were calculated, the judges being treated as a fixed effect. Following ANOVA, Fisher’s least-significantdifference (LSD) value was calculated (P = 0.05). Principal component analysis (PCA) was conducted on the mean values of the significant attributes averaged over the panelists and replicates using the correlation matrix. D


141-78-6 105-37-3 97-62-1 105-54-4 7452-79-1 108-64-5 123-66-0 106-32-1 110-38-3 106-33-2 67233-91-4 103-36-6 110-19-0 624-41-9 123-92-2 142-92-7 103-45-7 78-83-1 71-36-3 137-32-6 123-51-3 111-27-3 60-12-8 64-19-7 79-09-4 79-31-2 142-62-1 124-07-2 334-48-5

106-24-1 78-70-6 106-25-2 876-17-5 98-55-5 106-22-9

23726-93-4 30364-38-6

geraniol** linalool** nerol** cis-rose oxide α-terpineol** β-citronellol

β-damascenone** 1,1,6-trimethyl-1,2-dihydronaphthalene

CAS registry number

ethyl acetate ethyl propanoate ethyl 2-methylpropanoate ethyl butanoate ethyl 2-methylbutanoate ethyl 3-methylbutanoate ethyl hexanoate ethyl octanoate ethyl decanoate ethyl dodecanoate ethyl 9-decenoate trans-ethyl cinnamate*** 2-methylpropyl acetate 2-methylbutyl acetate 3-methylbutyl acetate hexyl acetate 2-phenylethyl acetate 2-methylpropanol butanol 2-methylbutanol 3-methylbutanol hexanol 2-phenylethanol* acetic acid propanoic acid 2-methylpropanoic acid hexanoic acid octanoic acid decanoic acid

compound

E

βDam TDN

Geraniol Linalool Nerol RoseOx αTerp βCitron

EtOAc EtPr Et2MePr EtBu Et2MeBu Et3MeBu EtHex EtOct EtDec EtDodec Et9Decen EtCinn 2MePrOAc 2MeBuOAc 3MeBuOAc HexOAc 2PhEtOAc 2MePrOH BuOH 2MeBuOH 3MeBuOH HexOH 2PhEtOH AcAc PrAc 2MePrAc HexAc OctAc DecAc

code

0.05 2

30 25 700 0.2 250 700

7500 1840c 15 20 1 3 14 5 200 1500 100 1 1600d 160c 30 670c 250 40 000 150 000 65 000d 40 000 8000 14 000 200 000 20 000 2300 420 500 1000

aroma threshold (μg/L)b Fermentation Derived 76 207 206 81 510 13 30 1079 2887 638 68 10 6 43 83 1061 53 78 24 987 956 44 881 111 945 1805 17 407 306 767 848 626 5578 9131 2838 Monoterpenes 12.7 42.4 2.4 0.02 39.6 7.3 Norisoprenoids 1.28 0.11

mean (μg/L)

25.6 0.1

0.4 1.7 ≤0.1 0.1 0.2 ≤0.1

10 0.1 5 25 13 10 77 577 3.2 ≤0.1 0.1 6.3 ≤0.1 0.5 35 0.1 0.3 0.6 ≤0.1 0.7 2.8 0.2 1.2 1.5 ≤0.1 0.3 13 18 2.8

OAV

all wines

14 4

17 17 10 3 18 17

18 18 18 18 18 18 18 18 18 18 18 15 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18

n

1.75 0.15

18 63 3.6 0.03 57 8.7

82 400 206 70 537 12 27 1060 2880 676 74 11.1 2.3 52 88 1107 50 76 25 807 1016 42 989 106 758 1824 15 951 341 015 893 616 5493 9070 2774

mean (μg/L)

Table 3. Summary of the Aroma Compounds Quantified in the 18 Selected Viognier and Chardonnay Winesa

0.50 nd

1.0 2.0 nd nd 27 nd

55 190 106 30 327 4 9 681 1651 398 29 2.3 nd 6.8 13 144 3 7 13 576 631 35 230 87 010 1153 11 046 109 278 452 372 3198 4769 1745

min (μg/L)

Viognier

3.00 1.00

37 113 7.0 0.16 153 16.8

141 634 336 165 816 48 77 1377 3770 1006 169 38 8.5 80 214 2870 309 260 45 104 1353 61 990 134 123 3983 24 058 584 828 1494 767 7581 11 985 3497

max (μg/L)

0.33 0.03

1.7 2.3 nd nd 4.5 4.7

63 821 208 104 454 15 37 1119 2901 561 54 8.5 14.2 24 73 968 60 83 23 347 838 48 667 122 317 1768 20 318 238 271 759 646 5747 9254 2964

mean (μg/L)

nd nd

nd nd nd nd 3.0 2.1

31 953 187 29 242 4 14 647 1872 470 35 4.9 8.2 12 21 222 3 33 17 428 516 43 768 106 333 1380 15 232 115 679 449 352 3529 6910 2428

min (μg/L)

Chardonnay

1.00 0.20

3.0 5.0 nd nd 6.0 8.9

99 790 233 161 559 26 58 1315 3698 681 103 14.3 21.2 36 158 2277 144 143 29 437 1185 56 124 133 150 2580 27 190 409 169 989 1018 6836 11 080 3496

max (μg/L)

Journal of Agricultural and Food Chemistry Article


F

90-05-1 93-51-6 55013-32-6 39638-67-0 98-01-1 620-02-0 121-33-5 2785-89-9 123-07-9 97-53-0

505-10-2 27538-10-9 118-71-8 78-84-2 590-86-3 66-25-1 6728-26-3 928-95-0 928-96-1 18829-56-6 28664-35-9 3268-49-3

methionol homofuraneol maltol 2-methylpropanal 2 and 3-methylbutanal hexanal (E)-2-hexenal* (E)-2-hexenol* (Z)-3-hexenol** (E)-2-nonenal* sotolon methional

104-50-7 104-61-0 706-14-9 2305-05-7 27593-23-3 18679-18-0

γ-octalactone γ-nonalactone γ-decalactone* γ-dodecalactone 6-amyl-α-pyrone (Z)-6-dodeceno-γ-lactone*

guaiacol 4-methylguaiacol cis-oak lactone trans-oak lactone furfural 5-methylfurfural vanillin 4-ethylguaiacol 4-ethylphenol eugenol

7783-06-4 74-93-1 75-15-0 75-18-3 19872-52-7 51755-83-0 136954-20-6 98-02-2 100-53-8

CAS registry number

hydrogen sulfide methanethiol carbon disulfide dimethyl sulfide 4-methyl-4-mercaptopentan-2-one 3-mercaptohexanol* 3-mercaptohexyl acetate** 2-furfurylthiol benzenemethanethiol

compound

Table 3. continued

Methionol HFuraneol Maltol 2MePrAl MeBuAl HexAl Hexenal E2Hexenol Z3Hexenol Nonenal Sotolon Methional

Guaiacol 4MeG cisOak trOak Furf 5MeFurf Vanillin 4EG 4EP Eugenol

γOcta γNona γDeca γDodeca 6Amylα γDodeceno

H2S MeSH CS2 DMS 4MMP 3MH 3MHA FFT BMT

code

1000 10 11 000 6 4.6 20 4e 400e 400 0.6 5 0.5

30 30 20c 140c 14 100 16 000 65 33 440 6

700 76 10 29 150e 0.100

1.6c 3.1c 38c 10 0.0006 0.060 0.004 0.0004 0.004c

aroma threshold (μg/L)b Sulfur Volatiles 2 6 1 23 0.003 0.897 0.011 0.015 0.011 Lactones 1.0 4.1 0.8 0.4 7 0.028 Oak Volatiles 2 2 29 13 435 54 45 4 11 5 Oxidation Related 1084 195 38 35 10 8.4 0.2 13 94 1.1 20 0.6

mean (μg/L)

1.1 19.5 ≤0.1 5.8 2.1 0.4 ≤0.1 ≤0.1 0.2 1.8 3.9 1.1

≤0.1 ≤0.1 1.4 0.1 ≤0.1 ≤0.1 0.7 0.1 ≤0.1 0.9

≤0.1 0.1 0.1 ≤0.1 ≤0.1 0.3

1.2 2.0 ≤0.1 2.3 5 15 2.7 37.8 2.7

OAV

all wines

18 18 18 18 18 18 18 18 18 18 18 17

14 13 11 8 18 12 14 1 1 18

18 18 18 16 18 18

18 17 12 18 2 18 16 15 16

n

701 193 37 45 11 10 0.3 17 74 1.3 19 0.7

2 1 30 12 440 55 49 nd nd 5

1.0 4.4 0.9 0.4 7 0.034

2 6 1 20 0.0004 0.668 0.008 0.013 0.008

mean (μg/L)

261 65 12 9.1 5.1 3.3 0.1 2.3 29 0.7 5 nd

nd nd nd nd 63 nd nd nd nd 1

0.8 2.0 0.5 nd 2 0.013

1 nd nd 6 nd 0.296 nd nd nd

min (μg/L)

Viognier

2892 469 49 268 24 29 0.8 42 135 2.8 52 2.8

5 4 86 41 974 249 107 nd nd 12

1.3 6.1 1.3 2.0 25 0.057

9 10 3 80 0.004 1.509 0.014 0.046 0.014

max (μg/L)

1850 198 41 15 6.4 5.1 0.1 3.7 134 0.7 20 0.2

1 2 25 14 425 53 37 11 32 5

1.1 3.5 0.6 0.2 7 0.017

2 7 1 29 nd 1.355 0.017 0.020 0.017

mean (μg/L)

560 125 28 9.3 4.0 2.8 nd 1.4 93 0.4 12 nd

nd nd nd nd 134 nd nd nd nd 3

0.7 2.4 0.4 nd 5 0.005

1 3 nd 8 nd 0.437 0.010 0.002 0.010

min (μg/L)

Chardonnay

5929 304 53 26 13 10 0.2 9.1 204 0.9 41 0.6

2 5 110 57 915 118 99 68 191 9

1.6 4.6 0.8 0.5 11 0.031

3 17 4 50 nd 2.542 0.029 0.046 0.029

max (μg/L)

Journal of Agricultural and Food Chemistry Article


Article

Significant differences in the mean concentrations between Viognier and Chardonnay wines are indicated by *, P < 0.05; **, P < 0.01; and ***, P < 0.001. Boldface type indicates that the compound was included in the control model reconstitution. Italicized boldface type indicates that the concentration was adjusted in the control model reconstitution. OAV, odor activity value expressed as concentration/ aroma threshold; n, number of wines that the compound was detected and quantified in; min, minimum concentration in the wine variety; max, maximum concentration in the wine variety; nd, not detected. bAroma-detection threshold determined in aqueous ethanol unless indicated otherwise. The original references are cited in the Supporting Information, Table S3. cAroma detection threshold determined in white wine. dAroma detection threshold determined in beer. eAroma detection threshold determined in water.

150 000c 150 000c Acetoin 2,3-BuDiol 513-86-0 513-85-9 acetoin 2,3-butanediol

Table 4. Concentrations of Compounds That Were Included in the Reconstitution Study concentrationa (μg/L)

aroma compound Lactones (L) γ-nonalactone γ-decalactone (Z)-6-dodeceno-γ-lactone Monoterpenes (M) geraniol linalool nerol Aldehydes (A) (E)-2-hexenal benzaldehyde (E)-2-nonenal (E)-2-hexenol

5.9 1.2 0.050 27 83 5.3 0.37 206 1.8 32

a

The concentrations are the mean values determined from the concentrations of the three wines with the highest ‘apricot’ attribute intensities found in the wine descriptive sensory study.

method used (Supporting Information, Table S4). The fermentation-derived compounds butanoic acid, 2-methylbutanoic acid, 3-methylbutanoic acid, and ethyl lactate were not quantified because of interference from coeluting compounds with the same mass fragments. The furaneol peak shifted out of the MRM window for some samples, and therefore, this compound was removed from the study. The concentrations of the volatile aroma compounds measured in the Chardonnay wines were within the concentration ranges previously reported;3,11,40,44 however, the maximum concentrations of the following compounds were higher: 2-methylbutanol (1.7-fold), furfuryl thiol (18fold), 5-methyl furfural (3-fold), and methionol (5-fold), and no reported data could be found for ethyl 9-decenoate, (E)-2hexenol, maltol, 2,3-butanediol, or 6-amyl-α-pyrone. For the Viognier wines, only three studies, one that assessed a viticultural treatment and two recently completed in our laboratory, could be found with any quantitative data regarding aroma composition, and the concentrations were reported for a limited number of compounds.4,5,45 The concentrations of 15 aroma compounds, several monoterpenes, γ-lactones, and fermentation volatiles and β-damascenone, reported in those studies were within the ranges found in this study, but the concentrations of octanoic acid, decanoic acid, and 3methylbutyl acetate were higher in the viticulture-study wines. In the current study, the Viognier wines had significantly higher concentrations (P < 0.05) of geraniol, linalool, nerol, αterpineol, β-damascenone, γ-decalactone, (Z)-6-dodeceno-γlactone, (E)-2-hexenal, (E)-2-hexenol, (E)-2-nonenal, and benzaldehyde than the Chardonnay wines. In contrast, the concentrations of trans-ethyl cinnamate, 2-phenylethanol, 3mercaptohexanol, 3-mercaptohexyl acetate, and (Z)-3-hexenol were significantly higher in the Chardonnay wines. Polyfunctional thiols have recently been reported in other wine varieties beyond Sauvignon blanc, for example, Chardonnay.40 Even though the Viognier wines contained lower concentrations of 3mercaptohexanol and 3-mercaptohexyl acetate than the Chardonnay wines, all were above their aroma detection thresholds (60 and 4 ng/L, respectively) (Supporting Information, Table S3). Sensory Descriptive Analysis. The sensory descriptive panel evaluated the 18 wines for 34 appearance, aroma, and

a

nd 449 000 nd nd nd 76 000 nd nd 3 15 ≤0.1 1.6

300 312 000

2800 1 032 000

41 11 34 3 36 5 254 35 29 4 119 12 18 18 ≤0.1 9.4

Oxidation Related 92 9 Miscellaneous 200 233 000 3000c 1 BenzAl PhAAl 100-52-7 122-78-1 benzaldehyde* phenylacetaldehyde

min (μg/L) min (μg/L) OAV compound

Table 3. continued

CAS registry number

code

aroma threshold (μg/L)b

mean (μg/L)

all wines

n

mean (μg/L)

Viognier

max (μg/L)

mean (μg/L)

Chardonnay

max (μg/L)


G


Article


Table 5. Reconstitution Experiments: Groups of Aroma Compounds Added to a Wine-like Blank Model in the Reconstitution Study model blank control control control control control control control control control

+L+M+A +L +M +A +M+A +L+A +L+M +2×L+M+A

55 compounds

lactones

monoterpenes

aldehydes

− + + + + + + + + +

− − + + − − − + + ++

− − + − + − + − + +

− − + − − + + + − +

antagonistic, or perceptual interactions that might be occurring with other volatile compounds or nonvolatile components.9,13 Therefore, all of the aroma compounds and standard chemical parameters measured in the wines were used for the PLS regression to find associations with the descriptive sensory aroma ratings, similar to Liu et al.46 Partial least-squares regression analysis was used to assess the relationships between the aroma attribute ratings and the concentrations of the chemical compounds for each of the 18 wines. Figure 1 shows the scores plot and the x and y loadings for the chemical composition and sensory aroma attribute ratings. For the PLS regression, two factors were optimal, on the basis of the predicted residual sum of squares, with the first two components of the model explaining 58% of the y variance and 39% of the x variance (Figure 1). Most of the aroma attributes were located in the outer ellipses, which indicated that the data for those attributes was well explained by the model. The chemical parameters plotted in the vicinity of an attribute were associated positively with that attribute. In Figure 1, several aroma attributes were associated with the volatile compounds that would be expected from the literature. The fruity attributes ‘pineapple’ and ‘passion-fruit’ were associated with several fatty acid ethyl and acetate esters and linear fatty acids.9,11 The ‘kerosene’ attribute was associated with 1,1,6trimethyl-1,2-dihydronaphthalene (TDN), which would be expected for aged Riesling wines,44 but it is interesting to note the presence of the ‘kerosene’ attribute despite the very low concentrations of TDN in the older Viognier and Chardonnay wines. Therefore, it is likely that other compounds might contribute to ‘kerosene’, such as higher alcohols or compounds not measured in this study. ‘Nutty’ was closely related to sotolon.47 The ‘lemon’ attribute was associated with 3-mercaptohexyl acetate, which has previously been described as ‘grape-fruit’.7,8 Also, ‘flint’ and ‘vegetal’ were related to methanethiol, 2-furfurylthiol, and dimethyl sulfide.20,22 Generally, the predictions provided by the PLS model were considered very well modeled, with the coefficients of determination for the predicted (calibration) values versus the measured values for most of the attributes in the PLS model being >0.58. For example, the R2 (calibration) values for ‘pineapple’, ‘passion-fruit’, ‘lemon’, ‘vegetal’, and ‘apricot’ were 0.61, 0.73, 0.69, 0.66, and 0.73, respectively. ‘Peach’, ‘grassy’, ‘butter’, and ‘pungent’ were modeled moderately well, with coefficients of determination (calibration) of 0.54, 0.51, 0.52, and 0.42, respectively. ‘Box hedge’ and ‘lime’ were not described well by the model. Notably for this study, several lactones, γ-nonalactone, γdecalactone, and (Z)-6-dodeceno-γ-lactone, were associated

palate attributes (Table 1). Importantly, the sensory panel determined that there were two different types of ‘stone fruit’ aromas that were applicable to this set of wines. The first was described as preserved or tinned apricots, categorized as ‘apricot’, and the second was reminiscent of fresh white peaches, categorized as ‘peach’ (Table 1). From the ANOVA, the wines were rated significantly different for all of the attributes (P < 0.05). Hence, the wines did have varied intensities of ‘apricot’ and ‘peach’ aroma attributes. Details on the sensory panel intensity means for the individual attributes are provided in the Supporting Information (Table S2). All of the attributes were initially included in a PCA (data not shown). The first three PCs were important in showing the variation in the data set; PC1 and PC2 accounted for 62.5% of the variance, and PC3 accounted for a further 9.5% of the variance. The aroma (A) and palate (P) versions of similar attributes were highly correlated; for example, ‘honey’ (A) was highly correlated with ‘honey’ (P) and ‘passion-fruit’ (A) was highly correlated with ‘tropical fruit’ (P). Consequently, the PCA was recalculated with only the aroma attributes included as variables; then, PC1 and PC2 accounted for 69.6% of the variation in the data set (Supporting Information, Figure S1), and PC3 accounted for a further 7.3% of variance (data not shown). Importantly, the ‘apricot’ and ‘peach’ attributes were not significantly correlated. Thus, the sensory panel could discriminate between these specific ‘stone fruit’ descriptors. Three of the French Viognier wines (VIOG-7, -11, and -12) were rated highly in the ‘apricot’ and ‘honey’ attributes, and the Chardonnay wines, except for CHARD-4, were rated lower in the ‘apricot’ attribute than the Viognier wines, as seen along PC1 (Supporting Information, Figure S1). VIOG-2 and VIOG-9 were rated highly in ‘peach’, ‘passion-fruit’, and ‘pineapple’, and the older wines, CHARD-4, VIOG-6, and VIOG-4, drove the ‘kerosene’, ‘flint’, and ‘nutty’ vectors, as seen by their separation along PC2 (Supporting Information, Figure S1). The French Viognier wines are mostly in one quadrant whereas the Australian Viogniers were more sensorially diverse and spread across the PCA. The sensory data confirmed that the selected set of 18 wines had differing ‘stone fruit’ characters and intensities and, thus, were suitable for the purposes of this study. Relationships Between the Sensory and Chemical Data. Numerous previous wine aroma studies have used OAVs as measures of aroma compounds’ contributions to wine aromas.19,41,42 In this study, 39 compounds were found to have OAVs ≥ 0.5, calculated as the mean concentration across the set of wines, with ethyl octanoate having the highest (Table 3). However, the OAV does not account for any additive, synergic, H


Article


Figure 1. Plots for partial least-squares regression for the significant aroma attributes using the chemical compositional data for the selected 18 wines. (a) Scores plot. The Australian Viognier (VIOG) wines are shown in red, the French Viognier (VIOG) wines are shown in purple, and the Australian Chardonnay (CHARD) wines are shown in green. (b) x- and y-loadings plot. The x loadings (chemical variables) are shown in blue, and the y loadings (sensory attributes) are shown in red. The abbreviation codes of volatiles can be found in Table 3, and the abbreviation codes for the standard chemical measures are as follows: TA, titratable acidity; VA, volatile acidity (as acetic acid); EtOH, ethanol; G+F, glucose plus fructose; SG, specific gravity; OD, optical density; FSO2, free sulfur dioxide; TSO2, total sulfur dioxide.

with the ‘apricot’ sensory attribute, together with the monoterpenes linalool, geraniol, and nerol; the aldehydes (E)-2-hexenal, benzaldehyde, and (E)-2-nonenal; (E)-2-hexenol; and the oak compounds guaiacol, trans-oak lactone, and vanillin (Figure 1). 3-Mercaptohexyl acetate, trans-ethyl cinnamate, ethyl propanoate, and 2-phenylethyl acetate, located on the opposite side of the loadings plot, were negatively related to the ‘apricot’ attribute, suggesting that they had suppressing or masking effects or simply that there were less of these compounds present in the higher-rated ‘apricot’ wines. The ‘peach’ aroma attribute was associated with 2methylpropyl acetate; 3-methylbutyl acetate; ethyl decanoate; benzaldehyde; and the monoterpenes linalool, geraniol, and nerol. The γ-lactones were not important to the model for this attribute. 3-Methylbutyl acetate and 2-phenylethyl acetate have previously been related to the ‘peach’ aroma in Chardonnay.15 In this study, 2-phenylethyl acetate was not significant, but it was shown to be an important variable for ‘peach’ (Figure 2). Aroma compounds linked to wine aging, such as branched ethyl esters, TDN, and sotolon,11,37,44 were located on the opposite side of the loadings plot and were negatively related to the ‘peach’ attribute along with hydrogen sulfide, methanethiol, and 2-furfurylthiol. Previously, sulfides have been shown to suppress fruity aromas in wines.48 Figure 2 further highlights the important aroma compounds predicted to be contributors to the ‘apricot’ and ‘peach’ aroma attributes in the wines. The aroma compounds positively related to the attributes have positive regression coefficients. Both the aroma compounds with the largest weighted regression coefficients and those reported as significant on the basis of the uncertainty test (P < 0.05) are considered the most important for the model. The aroma compounds with the largest regression coefficients for the ‘apricot’ attribute (i.e., γnonalactone, γ-decalactone, (Z)-6-dodeceno-γ-lactone, geraniol,

linalool, nerol, (E)-2-hexenal, benzaldehyde, (E)-2-nonenal, (E)-2-hexenol, the oak volatiles, ethyl-9-decenoate, and acetic acid) and the ‘peach’ attribute (i.e., 2-methylpropyl acetate, 2and 3-methylbutyl acetate, ethyl octanoate, and benzaldehyde) were similar to those with higher relative concentrations in the Viognier and Chardonnay wines with high ‘stone fruit’ aromas in the previous GC-O-MS study,4 although (Z)-6-dodeceno-γlactone had not been analyzed in the previous study. The aromas often described for γ-lactones are reported as ‘peach’, ‘apricot’, and ‘coconut’ and are impact aroma compounds in fresh stone fruits.43 However, very low concentrations of the γlactones were found in the wines, and the calculated OAVs for the γ-lactones were very low, with the highest being 0.6 for (Z)6-dodeceno-γ-lactone. Consequently, individual γ-lactones were not likely to be impact aroma compounds, but combinations of these γ-lactones might act additively or synergistically with each other10 or with the other compounds implicated in this study as giving rise to the ‘apricot’ aroma attribute. Monoterpenes were also associated with the ‘apricot’ and peach’ aromas, and although additive effects have been described for them,49 they have been considered to impart only floral and citrus-like attributes to wine.44 The link between the ‘stone fruit’ aroma and monoterpenes in the present study suggests that the monoterpenes interact with other aroma compounds to cause the sensory perception of the ‘apricot’ aroma. Aroma Reconstitution, Addition, and Omission Studies. To confirm the statistical predictions given by relating the sensory data to the chemical compositional data, reconstitution, addition, and omission experiments were conducted. Preliminary reconstitution experiments, performed under similar conditions to the sensory assessments used for the wine selection, showed that sotolon at the mean concentration of the wines, 20 μg/L, dominated the aroma of the model wine system. Therefore, the concentration of this compound was I


Article


Figure 2. Weighted regression coefficients (≥0.015) from a partial least-squares regression analysis generated to relate the volatile compositions with the aroma attributes for (a) ‘apricot’ and (b) ‘peach’ for the 18 selected wines. Significant variables (P < 0.05) are shown in black. The abbreviation codes of volatiles can be found in Table 3.

decreased to provide a subtler contribution to the overall aroma of the model. Full reconstitution models, equivalent to wines with high ‘peach’ intensities and low ‘peach’ intensities, were compared by a group of experienced wine tasters in an initial sensory assessment (data not shown), but they were not clearly rated differently in ‘peach’. Therefore, the ‘peach’ models were not continued in this study. For ‘apricot’, the positively related aroma compounds highlighted in Figure 2 were allocated into groupings with similar chemistry or origins, that is, oak-derived volatiles, γ-lactones, monoterpenes, and aldehydes. The oakderived aroma compounds guaiacol, trans-oak lactone, and vanillin were trialed as additions for ‘apricot’, but there was no obvious difference to the intensity of ‘apricot’ between the samples with or without the oak compounds. However, they did make the model more complex and wine-like; therefore, they were included in the control model, each at their mean concentration found in the 18 wines. Methional, 2-furfurylthiol, and β-citronellol were not available for the preliminary assessments and were not added for the ensuing formal study. The final control model reconstitution consisted of 55 aroma compounds (Table 3, highlighted in bold) together with a range of common nonvolatile compounds and SO2. The control model, following a preliminary assessment by the expert panel, was considered to resemble white wine aroma well. The formal reconstitution study was conducted using sensory descriptive analyses. The panel evaluated the 10 models (Tables 4 and 5) and rated 14 aroma attributes (Table 2). The blank model, which had no additional volatiles, was included in the sensory analysis to confirm the minimal fruitlike aroma attributes, but it was not included in the subsequent statistical data analyses of the model samples. From the ANOVA of the reconstitution sensory data, the ‘apricot’, ‘tropical’, ‘confection/floral’, and ‘cardboard’ attribute

intensities were significantly different across the models (P < 0.05). Not unexpectedly, ‘peach’ was not significantly different. The ‘nail polish remover’ attribute was almost significant (P < 0.1) and was included in a PCA to visualize the differences among the samples. The first three components were important in showing the variation in the data set; PC1 and PC2 accounted for 68.3% of the variation in the data set (Figure 3),

Figure 3. PCA-scores and -loadings biplot of the mean aroma sensory ratings for the reconstitution experiments. CONTROL, blank model wine with the 55 volatile compounds added; +L, addition of lactones; +M, addition of monoterpenes; +A, addition of aldehydes.

and PC3 accounted for a further 16.6% of the variance, which clearly separated the ‘tropical’ attribute (loading on PC3 of −0.924, data not shown). Figure 3 shows that the models containing aldehydes, which were rated highly in ‘cardboard’ and lower in ‘confection/floral’ and ‘tropical’, separated on PC1 from the models with monoterpenes added and the control. J


Article


Figure 4. Mean ratings of the important aroma attributes in the ‘apricot’ white wine reconstitution models. The compounds in the control model are listed in Table 3. The groups of compounds added to the control model are listed in Tables 4 and 5. L, lactones; M, monoterpenes; A, aldehydes. 1 The error bars are + 2 LSD, least-significant difference (P = 0.05).

The models with monoterpenes added were all more highly rated for ‘apricot’ than the control model. However, the presence of aldehydes with the monoterpenes suppressed the ‘apricot’ attribute. Figure 4 provides a more detailed illustration of the differences among the samples and the ability to assess significant differences. The +L+M+A model was rated significantly higher in ‘apricot’ than the control but lower in ‘apricot’ than the +L+M model. Only when double the concentration of lactones was added, +2×L+M+A, was the suppressing effect of the aldehydes reversed (Figure 4). The addition of the lactone group alone (+L) did not increase the intensity of ‘apricot’ compared with the control. There were suppressing effects from most of the additions on the ‘confection/floral’ attribute, which dominated the control sample, whereas the model +L+M+A was rated lowest in ‘tropical’, indicating that there was strong suppressing effect on this attribute. The strong suppressing effect of the aldehyde group was unexpected, because the concentrations added were relatively low compared with their aroma detection thresholds (10-fold lower), with only (E)-2-nonenal being higher (1.8fold), potentially highlighting the roles of matrix components not included in this study in ameliorating the effect of aldehydes in wine. This might be partly due to the aldehyde concentrations being measured as ‘total’ concentrations rather than as ‘free’ aldehydes, that is, those not bound to SO2. Several authors have recently presented findings on the surprisingly complex interactions of aroma compounds in wine reconstitution models that result in unexpected effects and perceptual interactions, such as branched fatty acids lifting ‘red fruit’ but suppressing ‘black fruit’9 and two lactones, cis-oak lactone and 2-nonen-4-olide, and eugenol, which have ‘coconut’, ‘minty’, and ‘spicy’ aromas, respectively, being required for the ‘overripe orange’ aroma to be perceived.50 In the present study, the ‘apricot’ aroma was perceived from a specific mixture of monoterpenes with ‘floral’ and ‘citrus-like’ aromas in a winelike medium. Riesling wines can also have moderately high levels of monoterpenes (12, 18, and 54 μg/L)44 and are often described as ‘floral’, but some styles have been described as having ‘apricot’ characters.1 Therefore, these monoterpenes might also be important to ‘apricot’ aromas in Riesling wines. Whether an aroma in wine is perceived as ‘apricot’, ‘floral’, or ‘fruity’ might be dependent on the concentrations of monoterpenes and their specific ratios. Further research to investigate this is warranted. Overall, the Viognier and Chardonnay wines selected for this study had distinctive ‘apricot’ and ‘peach’ aroma attributes that

ranged in intensity, and relating the extensive quantitative chemical compositional data to the sensory descriptive scores of the wines enabled the prediction of important aroma compounds for these specific wine aromas. The reconstitution studies confirmed that the monoterpenes linalool, geraniol, and nerol, at moderate concentrations and in the presence of ubiquitous wine compounds, were the key aroma compounds for the ‘apricot’ attribute in white wine. However, the reconstitution models did not represent the sensory differences well for ‘peach’. The ‘peach’ aroma attribute was associated with a range of fermentation-derived ethyl and acetate esters, fatty acids, benzaldehyde, and monoterpenes (Figure 2). However, the association with monoterpenes was likely influenced by a few Viognier wines rated highly in both ‘apricot’ and ‘peach’, thus confounding the ‘peach’ reconstitution model, which emphasizes the importance of confirming statistical predictions with reconstitution studies.

■

ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jafc.7b05343. Standard wine compositions of the Viognier and Chardonnay wines studied, mean attribute scores for the appearance and aroma descriptors of the 18 selected wines, aroma detection thresholds of the aroma compounds quantified in the 18 selected Viognier and Chardonnay wines, aroma compounds not quantified in the 18 selected wines, and PCA-scores and -loadings biplot of the mean aroma sensory ratings for the selected 18 wines (PDF)

■

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. ORCID

Tracey E. Siebert: 0000-0002-4878-839X Markus J. Herderich: 0000-0002-4136-8886 Present Address §

A.B.: MMR Research Worldwide Ltd., 104−110 Crowmarsh Battle Barns, Preston Crowmarsh, Wallingford, Oxfordshire OX10 6SL, UK K


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This work was supported by Australian grape growers and winemakers through their investment body, Wine Australia, with matching funds from the Australian government. The Australian Wine Research Institute (AWRI) is a member of the Wine Innovation Cluster in Adelaide, South Australia. T.E.S. was additionally supported by the Australian Government Research Training Program Scholarship through the University of South Australia (UniSA) and also by the State Government of South Australia through the Premier’s Research and Industry Fund for establishing the Bordeaux−Adelaide−Geisenheim (BAG) Alliance. M.A.D.B.L. acknowledges financial support from the School of Pharmacy and Medical Science, UniSA, and P.D. acknowledges financial support from Institut des Sciences de la Vigne et du Vin (ISVV), University of Bordeaux. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We thank our colleagues from the AWRI and the ISVV, University of Bordeaux, for assisting with the wine selection. We thank the AWRI-trained panelists for their time and effort with the sensory analyses. Cory Black, formerly of the AWRI, is thanked for the synthesis of ethyl 9-decenoate. We thank Dimitra Capone for the polyfunctional thiol data; Mark Solomon for the acetaldehyde-related compound data; and Mango Parker and Amanda Agius of the AWRI and Pascaline Redon of the ISVV for technical assistance. We thank Panagiotis Stamatopoulos of the ISVV for assistance and valuable discussions. Symrise and Pyrazine Specialties are thanked for their provision of the aroma compounds. We thank ISVV for their donation of wine. We also thank Yalumba for their donation of wine, and Louisa Rose for assistance and valuable discussions. We are grateful to members of the Australian wine industry for their assistance and provision of numerous wine samples, especially the Chapel Hill Winery, Mount Majura Vineyard, and McWilliam’s Wines Group.

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ABBREVIATIONS USED

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REFERENCES

AEDA, aroma extract dilution analysis; ANOVA, analysis of variance; CW/DVB, carbowax/divinylbenzene; DVB/CAR/ PDMS, divinylbenzene/carboxen/polydimethylsiloxane; GC, gas chromatography; GC-O-MS, gas chromatography olfactometry mass spectrometry; HS, headspace; LC, liquid chromatography; LLE, liquid−liquid extraction; LSD, least significant difference; MPS, multipurpose sampler; MRM, multiple reaction monitoring; MS, mass spectrometry; OAV, odor activity value; PA, polyacrylate; SPE, solid phase extraction; SPME, solid phase microextraction; PCA, principal component analysis; PLS, partial least squares; SIDA, stableisotope dilution analysis; TDN, 1,1,6-trimethyl-1,2-dihydronaphthalene

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