• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
• /
• 2001.06a
• /
• pp.1523-1523
• /
• 2001

The wine industry requires practical methods for objectively measuring the composition of both red wine grapes on the vine to determine optimal harvest time; and of freshly harvested grapes for efficient allocation to vinery process streams for particular red wine products, and to determine payment of contract grapegrowers. To be practical for industry application these methods must be rapid, inexpensive and accurate. In most cases this restricts the analyses available to measurement of TSS (total soluble solids, predominantly sugars) by refractometry and pH by electropotentiometry. These two parameters, however, do not provide a comprehensive compositional characterization for the purpose of winemaking. The concentration of anthocyanin pigment in red wine grapes is an accepted indicator of potential wine quality and price. However, routine analysis for total anthocyanins is not considered as a practical option by the wider wine industry because of the high cost and slow turnaround time of this multi-step wet chemical laboratory analysis. Recent work by this ${group}^{l,2}$ has established the capability of near infrared (NIR) spectroscopy to provide rapid, accurate and simultaneous measurement of total anthocyanins, TSS and pH in red wine grapes. The analyses may be carried out equally well using either research grade scanning spectrometers or much simpler reduced spectral range portable diode-array based instrumentation. We have recently expanded on this work by collecting thousands of red wine grape samples in Australia. The sample set spans two vintages (1999 and 2000), five distinct geographical winegrowing regions and three main red wine grape varieties used in Australia (Cabernet Sauvignon, Shiraz and Merlot). Homogenized grape samples were scanned in diffuse reflectance mode on a FOSE NIR Systems6500 spectrometer and subject to laboratory analysis by the traditional methods for total anthocyanins, TSS and pH. We report here an analysis of the correlations between the NIR spectra and the laboratory data using standard chemometric algorithms within The Unscrambler software package. In particular, various subsets of the total data set are considered in turn to elucidate the effects of vintage, geographical area and grape variety on the measurement of grape composition by NIR spectroscopy. The relative ability of discrete calibrations to predict within and across these differences is considered. The results are then used to propose an optimal calibration strategy for red wine grape analysis.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
• /
• 2001.06a
• /
• pp.1253-1253
• /
• 2001

The ability to accurately assess wine quality is important during the wine making process, particularly when allocating batches of wines to styles determined by consumer requirements. Grape payments are often determined by the quality category of the wine that is produced from them. Wine quality, in terms of sensory characteristics, is normally a subjective measure, performed by experienced winemakers, wine competition judges or winetasting panellists. By nature, such assessments can be biased by individual preferences and may be subject to day-to-day variation. Taste and aroma compounds are often present in concentrations below the detection limit of near infrared (NIR) spectroscopy but the more abundant organic compounds offer potential for objective quality grading by this technique. Samples were drawn from one of Australia's major wine shows and from BRL Hardy's post-vintage wine quality allocation tastings. The samples were scanned in transmission mode with a FOSS NIR Systems 6500, over the wavelength range 400-2500 ㎚. Data analysis was performed with the Vision chemometrics package. With samples from the allocation tastings, the best correlations between NIR spectra and tasting data were obtained with dry red wines. These calibrations used loadings in the wavelengths related to anthocyanins, ethanol and possibly tannins. Anthocyanins are a group of compounds responsible for colour in red wines - restricting the wavelengths to those relating to anthocyanins produced calibrations of similar accuracy to those using the full wavelength range. This was particularly marked with Merlot, a variety that tends to have relatively lower anthocyanin levels than Cabernet Sauvignon and Shiraz. For dry white wines, calibrations appeared to be more dependent on ethanol characteristics of the spectrum, implying that quality correlated with fruit maturity. The correlations between NIR spectra and sensory data obtained using the wine show samples were less significant in general. This may be related to the fact that within most classes in the show, the samples may span vintages, glowing areas and winemaking styles, even though they may be made from only one grape variety. For dry red wines, the best calibrations were obtained with a class of Pinot Noir - a variety that tends to be produced in limited areas in Australia and would represent the least matrix variation. Good correlations were obtained with a tawny port class - these wines are sweet, fortified wines, that are aged for long periods in wooden barrels. During the ageing process Maillard browning compounds are formed and the water is lost through the barrels in preference to ethanol, producing “concentrated” darkly coloured wines with high alcohol content. These calibrations indicated heaviest loadings in the water regions of the spectrum, suggesting that “concentration” of the wines was important, whilst the visible and alcohol regions of the spectrum also featured as important factors. NIR calibrations based on sensory scores will always be difficult to obtain due to variation between individual winetasters. Nevertheless, these results warrant further investigation and may provide valuable Insight into the main parameters affecting wine quality.

• Korean Journal of Food Science and Technology
• /
• v.44 no.3
• /
• pp.280-286
• /
• 2012

The aim of this study was to describe the effects of genetic factors on the chemical composition or metabolites of grapes harvested within the same region. Grapes were separated into pulp, skin, and seed, and physicochemical characteristics were compared among seven grape varieties. The sugar concentrations of the grape musts ranged from $15.17-20.93^{\circ}Bx$ with Seibel variety being highest at $20.93^{\circ}Bx$. pH ranges of grape musts were 3.46-4.02 and total acidity was highest with 1.05 in Steuben variety. Tartaric acid content was highest with 146.68, 500.10 mg/L in pulp and skin extract of Merlot variety. Malic acid content was highest in pulp extract of Seibel variety (1127.14 mg/L) and skin extract of Chardonnay variety (1720.06 mg/L). K content was highest with 379.13 and 828.01 mg/L in pulp and skin extract of Chardonnay variety. Ca content was highest in pulp extract of Kyoho variety (6.98 mg/L) and skin extract of Campbell Early variety (12.26 mg/L).

• Korean Journal of Food Science and Technology
• /
• v.35 no.5
• /
• pp.841-848
• /
• 2003

The sensory characteristics of red wine Gerbong (G), Campbell (C), Moru (M), Gerbong ＋ Moru (70 : 30, GM), Gerbong + Campbell (70 : 30, GC) and French wine (F, Carbernet Sauvignon, 1998) were evaluated. The preferences of color, flavor, taste and total evaluation were determined by a ranking test, and the organoleptic characteristics were evaluated by a quantitative descriptive analysis (QDA) method. The mean color scores of C, GM, F, GC, M and G were 4.74, 3.94, 4.67, 3.70, 2.65 and 1.47, respectively (p<0.001). The order for the mean score for flavor was GM (4.12) = M (3.94) = C (3.76) = F (3.76) ${\geq}$ GC (3.12)>G (2.29) (p<0.01), and the order for taste was F (4.75) ${\geq}$ C (4.25) ${\geq}$ GM (3.37) = GC (3.50) ${\geq}$ G (2.75) = M (2.37) (p<0.001). The total evaluation of mean scores showed G, M, C, GM, GC and F were 237, 2.44, 4.06, 3.87, 3.64 and 4.81, respectively (p<0.001). Influences of sensory characteristics on the total evaluation, in percentages, were 69.3% for taste, 3.7% for color, and 1.5% for flavor. The influences of taste, color, and flavor in red wine were 17% for sweet, acid, bitter and salty taste, 28.9% for purple and red color, and 14.4% for grape flavor. The attributes of the purple and red colors showed a positive correlation with grape flavor, oak flavor, grape taste, and floral tastes, but a negative correlation with $SO_2$, flavor. The attribute of sweet taste showed a positive correlation with grape flavorand floral flavor, but a negative correlation with bitter and astringency tastes, according to Pearsons correlation analysis (p<0.01).