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Effect of Simulated Shipping Conditions on Sensory Attributes and Volatile Composition of Commercial White and Red Wines

Anthony L. Robinson, Martha Mueller, Hildegarde Heymann, Susan E. Ebeler, Paul K. Boss, Peter S. Solomon, Robert D. Trengove
Am J Enol Vitic.  2010  61: 337-347  ; DOI: 10.5344/ajev.2010.61.3.337

Article Figures & Data

Figures

  • Figure 1
    Figure 1

    Ambient temperature monitored using Tinytag data loggers for each temperature treatment. Treatments: car trunk (TR), constant 20°C (20), cycled 20°C/40°C (CY), and constant 40°C (40).

  • Figure 2
    Figure 2

    Partial least squares analysis of white wine products. Purple circles represent volatile composition loadings (X matrix), light blue circles represent sensory attribute loadings (Y matrix), and large circles represent sample scores for factor 1 (Dim1) and factor 2 (Dim2). Sample codes listed in Table 1. Treatments: car trunk (TR), constant 20°C (20), cycled 20°C /40°C (CY), and constant 40°C (40).

  • Figure 3
    Figure 3

    Partial least squares analysis of white wine products. Purple circles represent volatile composition loadings (X matrix), light blue circles represent sensory attribute loadings (Y matrix), and large circles represent sample scores for factor 1 (Dim1) and factor 3 (Dim3). Sample codes listed in Table 1. Treatments: car trunk (TR), constant 20°C (20), cycled 20°C /40°C (CY), and constant 40°C (40).

  • Figure 4
    Figure 4

    Principal component analysis of red wine products. Sample codes listed in Table 1. Treatments: car trunk (TR), constant 20°C (20), cycled 20°C /40°C (CY), and constant 40°C (40).

Tables

  • Table 1

    Details of wines used in this study.

    Table 1
  • Table 2

    Composition of sensory reference standards used to define aroma and taste attributes for white wine study.

    Table 2
  • Table 3

    Composition of sensory reference standards used to define aroma and taste attributes for red wine study.

    Table 3
  • Table 4

    Sensory attributes found to be significant due to treatment for white and red wine products. Values represent least square means (LSM) (± SE) for four-way ANOVA. A pseudo-mixed model using the judge x treatment and judge x wine interactions as denominators was used in all cases.

    Table 4
  • Table 5

    Treatment significance values for two-way ANOVA for white and red wines. Values marked in bold italics are significant at p ≤ 0.05.

    Table 5
  • Table 6

    Regression coefficients of centered and scaled X-variables for each Y-variable from the four-component PLS model of the white wines. X-variables are ordered by descending variable influence on projection (VIP) value. Compound cluster membership determined using hierarchal cluster analysis.

    Table 6
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Effect of Simulated Shipping Conditions on Sensory Attributes and Volatile Composition of Commercial White and Red Wines
Anthony L. Robinson, Martha Mueller, Hildegarde Heymann, Susan E. Ebeler, Paul K. Boss, Peter S. Solomon, Robert D. Trengove
Am J Enol Vitic.  2010  61: 337-347  ; DOI: 10.5344/ajev.2010.61.3.337
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