Measurement of the colour of white and rosé wines in visual tasting conditions

Gamasa, Carlos Sáenz; Hernández, B.; Santiago, Juan Vicente de; Alberdi, C.; Alfonso, Santiago; Diñeiro, J. M.

doi:10.1007/s00217-009-1050-z

Cited by 17 publications

(17 citation statements)

References 20 publications

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“…table 1 summarises the CIELAB colour coordinates of each wine sample at t = 20 days as well as the results of the ANoVA and tukey tests performed to evaluate the differences among samples. Colour coordinate values and measurement errors were in the range of values reported in our previous work (Saenz Gamasa et al, 2009). Considering L* a* and b*, there are significant differences (p < 0.05) among all wine samples in at least one colour coordinate.…”

Section: Colour Measurementssupporting

confidence: 55%

“…For red wines, measurements in the rim are preferred (Hernández et al, 2008). For rosé and white wines, the best results are obtained in the centre region (Saenz Gamasa et al, 2009). In the present study, measurements accordingly were performed in the centre region.…”

Section: Colour Measurementsmentioning

confidence: 90%

“…Under such conditions, instrumental colour measurements were able to predict the colour classification into colour categories determined by wine tasters. Classification scores better than 85% were obtained for red wines (Hernandez, Saenz et al, 2009) and even better scores for white and rosé wines (Saenz Gamasa et al, 2009). red, rosé and white wines are types of wine with different colour and spectral characteristics and thus the success of the measurement method relies on the careful replication of the above conditions (Hernández et al, 2008).…”

Section: Colour Evolution Of Rosé Wines After Bottlingmentioning

confidence: 99%

“…on the contrary, when measurements are performed in visual tasting conditions, both coordinates a* and b* contribute to the change in hue values. (Hernández et al, 2008;Saenz Gamasa et al, 2009).…”

Section: Colour Evolution Of Rosé Wines After Bottlingmentioning

confidence: 99%

“…Furthermore, red and rosé wines have very different colours and CIELAB is known to be only approximately uniform. Experiments for wine colour classification into colour categories do not provide colour discrimination thresholds, since wines classified in the same colour category may be distinguished by colour if shown simultaneously (Hernandez et al, 2009;Saenz Gamasa et al, 2009). recently, colour discrimination data from different sources have been reviewed in terms of present colour difference formulae and common colour spaces providing an average threshold value ΔE* = 2.3±1.3 (Mahy et al, 1994).…”

Section: Figurementioning

confidence: 99%

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Colour Evolution of Rosé Wines after Bottling

Hernández

Sáenz

Alberdi

et al. 2016

SAJEV

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This research reports on the colour evolution of six rosé wines during sixteen months of storage in the bottle. Colour changes were determined in terms of CIELAB colour parameters and in terms of the common colour categories used in visual assessment. The colour measurement method reproduces the visual assessment conditions during wine tasting with respect to wine sampler, illuminating source, observing background and sample-observer geometry. CIELAB L*, a*, b*, C* and h ab colour coordinates were determined at seven different times (t = 0, 20, 80, 153, 217, 300 and 473 days). The time evolution of colour coordinate values was studied using models related to linear, quadratic and exponential rise to a maximum. Adjusted R 2 , average standard error and CIELAB ΔE* colour difference were used to compare models and evaluate their performance. For each colour coordinate, the accuracy of model predictions was similar to the standard deviation associated with a single measurement. An average ΔE* = 0.92 with a 90 percentile value ΔE* 90% = 1.50 was obtained between measured and predicted colour. These values are smaller than human colour discrimination thresholds. The classification into colour categories at different times depends on the wine sample. It was found that all wines take three to four months to change from raspberry to strawberry colour and seven to eight months to reach the redcurrant category. Only two wines had developed salmon colour by the end of the experiment.

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Section: Colour Measurementssupporting

confidence: 55%

Section: Colour Measurementsmentioning

confidence: 90%

Section: Colour Evolution Of Rosé Wines After Bottlingmentioning

confidence: 99%

Section: Colour Evolution Of Rosé Wines After Bottlingmentioning

confidence: 99%

Section: Figurementioning

confidence: 99%

See 3 more Smart Citations

Colour Evolution of Rosé Wines after Bottling

Hernández

Sáenz

Alberdi

et al. 2016

SAJEV

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Colour profile analysis of Port wines by various instrumental and visual methods

et al. 2019

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BACKGROUND Wine colour is an important quality parameter, being the first sensorial attribute evaluated during wine tasting. The perception of wine colour can be different depending on many factors, including the depth of the sample under observation. The main objectives of the present study were to measure the colour of Port wines using CIE L*, a*, b* parameters at different depths with various instrumental techniques (spectrophotometry and colorimetry), and to compare the obtained results with the sensory (visual) perception of colour samples. RESULTS Representative profiles of lightness (L*), hue (H*) and chroma (C*) at different depths were obtained using Port wine samples from various categories and ages. In general, relatively good correlations between the colorimetric and spectrophotometric methods were obtained for the L* and H* parameters. The results of the sensory tests also showed good correlations between the visually assessed hue scores and the colorimetric measurements of the H* parameter, particularly at the lower depths tested (up to 4.0 mm). CONCLUSIONS Overall, the results indicate that the colorimetric method can be used for estimating wine colour parameters, providing useful information about the colour profile of wines at different depths. © 2019 Society of Chemical Industry

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