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Chapter 7
Effect of Region on the Volatile Composition and Sensory Profiles of Malbec and Cabernet Sauvignon Wines H. Heymann,* A. L. Robinson, F. Buscema, M. E. Stoumen, E. S. King, H. Hopfer, R. B. Boulton, and S. E. Ebeler Department of Viticulture and Enology, University of California-Davis, One Shields Avenue, Davis, California 95616 *E-mail:
[email protected].
Regionality, frequently called terroir, is often used as a way to market wines from different locations. In this chapter we will discuss the chemical and sensory effects of regionality using thirty commercially made Australian Cabernet Sauvignon wines as well as forty one research lots of Californian and Argentinean Malbec wines. In both studies the volatile profiles of the wines separated the regions from one another. The separations based solely on sensory descriptive analysis data was less clear cut for the Cabernet wines and more so for the Malbec wines. When the volatile chemical and sensory data were combined separating regions was possible for both sets of wines. These studies showed that for both very well controlled research fermentations and for less controlled commercial fermentations it is possible to determine sensory and chemical regional differences for wines.
© 2015 American Chemical Society In Advances in Wine Research; Ebeler, Susan B., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 2015.
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The effect of region on the quality of wine is a very old concept as seen by the writings of Pliny and Columella (1). Around 1825 James Busby stated “… we sometimes see, under the same climate, very different qualities of wine, because the differences of soil, exposure, or culture, modify the immediate influence of this grand agent” (2). In the more modern era the concept of terroir has received a great deal of popular press and there are numerous scientific publications on terroir. The listed references are a selection of recent publications (3–6). We prefer the word ‘regionality’ to the more emotionally charged ‘terroir’ and will use that in this manuscript. We include growing conditions, microbial differences in vineyrads and wineries as well as common regional winemaking practices in our concept of regionality. We also believe that distinguishing regions by sensory and/or chemical means are useful in the elucidation of regionailty. We will describe the sensory descriptive analysis and volatile profile analyses used to describe the differences among Cabernet Sauvignon wines from different regions in Australia and among Malbec wines from different regions in Argentina and California.
Australian Cabernet Sauvignon Wines Australian wine regions are divided into Geographical Indications (GIs) and in this study thirty 2009 Australian Cabernet Sauvignon wines were selected (7, 8). Three wines were selected from each of the following ten GIs: Western Australia: Margaret River (MR), Frankland River (FR) and Mount Barker (MB); South Australia: Clare Valley (CV), Barossa Valley (BV), McLaren Vale (MV), Langhorne Creek (LC), Padthaway (PA), Coonawarra (CW) and Wrattonbully (WR). We asked the winemakers from each GI to select a wine (after 3 to 4 months) that in the winemaker’s opinion represented the best regional reflection of Cabernet Sauvignon from that specific GI. The wines were then immediately racked, and bottled (without fining or filtration) in 750 ml screw cap bottles. The volatile profile analyses were performed within four months of bottling and the sensory descriptive analyses were performed approximately ten months after the 2009 harvest. In this study we had no control over the viticultural practices and except for asking for a ‘best representation of the GI’ essentially no control over the winemaking. A sensory panel (18 subjects) was trained using the consensus method (9) and they used sixteen aroma terms (bell pepper, black berry, black pepper, butter, canned vegetable, chocolate, dried fruit, earthy, eucalyptus, floral, leather, mint, oak, red berry, smoky and vanilla) and four taste and mouthfeel terms (alcohol, astringent, bitter and sour) to describe the wines. See Robinson et al. (7) for the composition of the reference standards. The wines were evaluated in individual sensory booths (temperature 20 °C) equipped with a computer screen and mouse for data collection using FIZZ (Biosystèmes, Couternon, France) and a continuous unstructured line scale (10 cm). Clear glasses (ISO 3591:1977), containing twenty five mL aliquots of wine, covered with a plastic lid were labelled with three-digit random codes. Wines were served in triplicate over eighteen sessions with five wines per session using a modified Williams Latin Square design. All samples were expectorated and panelists had an enforced 30 sec break between samples 110 In Advances in Wine Research; Ebeler, Susan B., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 2015.
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during which time they rinsed their mouths with water and an unsalted water cracker. The volatile profiles of the wines were analyzed using a sensitive non-targeted HS-SPME GCxGC-TOFMS methodology previously described in Robinson et al. (10). Briefly, a CTC CombiPAL autosampler (CTC Analytics, Zwingen, Switzerland) with an agitator and SPME fiber conditioning station was coupled to a LECO Pegasus™ 4D GCxGC-TOFMS (LECO, St Joseph, MI, U.S.A.) was used for volatile aroma compound analysis. Samples were prepared in 20 mL amber glass headspace vials with 300 g/L sodium chloride added to 10 mL wine. Methyl nonanoate (an internal standard) and retention index probes were loaded onto the SPME fiber coating (Setkova et al, 2007a, 2007b). The sample headspace was sampled using a 2 cm DVB/CAR/PDMS 50/30 μm SPME fiber (Supelco, Bellefonte, PA, U.S.A.) for 120 min at 30 °C and desorbed in the GC inlet at 260 °C for 1 min. The primary column was a 30 m Varian FactorFour VF-5MS capillary column, 0.25 mm i.d. and 0.25 μm film thickness, with a 10 m EZ-guard column (Varian, Walnut Creek, CA, U.S.A.). This column was joined to the second column (in a secondary oven) by a SilTite mini-union (SGE, Ringwood, Vic., Australia). The second column was a 1.65 m Varian FactorFour VF-17MS capillary column, 0.10 mm i.d. and 0.20 μm film thickness of which 1.44 m was coiled in the secondary oven. TOFMS data was acquired at 100 scans/s and the TOFMS detector collected masses between 35 and 350 amu at 1800 V. ChromaTOF™ (LECO, St Joseph, MI, U.S.A.) optimized for the Pegasus™ 4D software Version 4.24 was used for interrogation and spectral deconvolution. Compound mass spectral data were compared against the NIST 2008 and Wiley 9th edition Mass Spectral Libraries. The retention index (RI) for each identified compound was compared to published RI for 5% phenyl polysilphenylene-siloxane capillary GC columns or equivalent (11, 12). The minimum similarity match was set at 600 and the first and second dimension RI deviation was set at 6 and 0.25, respectively. Peak areas were normalized against the in-fiber internal standard and exported for statistical analyses. Unless otherwise indicated all analyses were conducted using JMP (ver. 8.0.2, SAS Institute, Cary, NC, USA). Some multivariate analyses were conducted in either XLSTAT (Addinsoft, New York, NY, U.S.A.) or R-Studio ver. 0.98.507 (http://www.rstudio.com/). One-way (main effect: Product) analysis of variance (ANOVA) of the normalized peak areas was used to analyze the standard wine analyses and the volatile profile data. Significant volatile compounds were used as dependent variables in a PCA with panellipse (SensomineR, R-Studio) to determine the locations of the 10 GIs in multivariate space. A three-way ANOVA (Product, Judge and Replication) with all two-way interactions was performed for the sensory attributes using REML and a pseudo-mixed model with the mean square for Judge by Product as the denominator. An overall canonical variate analysis (CVA) was conducted using all 10 GI as the categorical factor and all significant sensory attributes from the analyses above. Additional CVAs were conducted for subsets of GIs. For these subsets the number of significant sensory attributes were determined via three-way ANOVA suing only products from the specified GIs in the subset. Multifactor analysis (MFA) using XLSTAT was used to compare the sensory and chemical data. 111 In Advances in Wine Research; Ebeler, Susan B., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 2015.
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Figure 1. Principal component analysis score plot for PC1 and PC2 of the volatile compounds for the Cabernet Sauvignon wines from the ten Geographical Indications. Ellipses indicate 95% confidence intervals. MR=Margeret River, FR=Frankland River, MB=Mount Barker, CV=Clare Valley, BV=Barossa Valley, MV=MacLaren Vale, LC=Langhorne Creek, PA=Padthaway, CW=Coonawarra, Wrattonbully=WR.
The one-way ANOVA showed that 303 of the 420 volatile compounds differed significantly (p
