Volatile Phenols in Smoke-Exposed Pinot noir Wines - Biomarkers and Model Prediction

Supplemental Table 1  Chemical standards, target ions, internal standards (IS), and calibration curves used to quantify free and total volatile phenols.

Supplemental Table 2  Eigenvalues of each principal component (PC) and cumulative percentage of variance explained. Eigenvalues represent the total variance explained by a given PC expressed as a percentage of the total variation in the data set. The cumulative percent of variance explained sums to one because PCs are orthogonal. For example, PC 1 accounted for 74.6% of the variance. PC 1 and PC 2 explained 83.2% of the total variance of the smoke-exposed group.

Supplemental Table 3  Loading coefficients from principal component analysis (PCA) and common factor analysis. The vectors found by the PCA and factor analysis explain the dominant multivariate variances of the phenols. Principal component (PC) 1 indicates nearly even contributions of the free and total forms. Factor 1 of the factor analyses illustrates more weight in the guaiacols than in the cresols. -F, free form; -T, total of free and bound forms.

Supplemental Figure 1  The frequency distribution of free-form volatile phenols in control (panels A to E) and smoke-exposed (panels a to e) wines. The frequency represents the number of samples with ratios within the interval (5:0-5, 10:5-10, 15:10-15, etc.). For example, (a) illustrates that out of the 376 smoke-exposed red wines, 84 and 159 samples had a free guaiacol concentration between 0 and 5 μg/L and between 5 to 10 μg/L, respectively; (b) shows that 140 and 292 samples (140 + 80 + 72) samples had a 4-methyguaiacol concentration between 1 and 2 μg/L and <3 μg/L, respectively.

Supplemental Figure 2  The frequency distribution of the total (free- and bound-form) volatile phenols in control (panels A to E) and smoke-exposed (panels a to e) wines.

Supplemental Figure 3  A measure of sample adequacy (MSA) by Kaiser-Meyer-Olkin test indicates how suitable the data is for factor analysis, measuring sample adequacy for each variable in the model and for the complete model. A rule of thumb is that the samples are adequate for factor analysis if values are between 0.8 and 1. -F, free form; -T, total of free and bound forms.

• SupplementalData.pdf