Relevance of the Concentrations and Sizes of Oligomeric Red Wine Pigments to the Color Intensity of Commercial Red Wines (2024)

Keywords: anthocyanins, chromatographic fingerprints, polymeric pigments, proanthocyanidins, tannins

Table 1. Summary of the Commercial Red WinesUtilized in the Present Study (n = 317)f.

Red Wines

Some of the 317 wine sampleswere collectedby the Natural Chemistry Research Group (n = 45)and some were provided by Alko Inc. (n = 272), aFinnish national alcoholic beverage retailing company. Aliquots ofthe red wines were sampled from freshly opened bottles and they werestored at −80 °C.

Semiquantitative Analyses

The UPLC–MS/MS systemconsisted of a Waters Acquity ultra-performance liquid chromatograph(UPLC; Waters Corporation, Milford, MA), which was coupled to a Xevotriple quadrupole mass spectrometer (Waters Corporation, Milford,MA). The UPLC system consisted of a binary solvent manager, a samplemanager, a column oven, and a diode array detector. The column wasan Acquity UPLC BEH Phenyl column (100 × 2.1 mm i.d., 1.7 μm;Waters Corporation, Wexford, Ireland). The concentrations of pigmentgroups 1–9 (Figure 1) were semiquantified using the UPLC–MS/MSmethod of Laitila et al.¹² The compoundgroups were detected by the quantitative transitions of the group-specificMRM methods. The chromatogram areas were transformed into relativeconcentrations with calibration curves, which were prepared from asingle reference wine, a JP Chenet Merlot 2015. In other words, theconcentrations were reported as percentages of the concentrationsin the reference wine. This was done to take into account the nonlinearresponse in some of the compound groups. Refer to Laitila et al.¹² for details. A diluted external standard wine,an Alamos Tempranillo 2015, was analyzed after every 10 injectionsto monitor and account for the natural fluctuation in the performanceof the MS/MS system. The responses of malvidin glycosides, carboxypyranomalvidins,phenylpyranomalvidins, PA–Mv⁺ adducts, and PA–methylmethine–Mv⁺ adducts were monitored in the external standard and theirresponses were used to calculate a correction coefficient to correctthe raw responses of pigment groups 1–9 in the actual samples. Carboxypyranomalvidins, B-type vitisins,and methylpyranomalvidins were corrected with the correction coefficientcalculated from the responses of the carboxypyranomalvidins and allthree pinotin groups (5–7) were correctedbased on the responses of the phenylpyranomalvidins. The responsesin the external standard wine at the time of the analysis of the calibrationcurves were used as reference points, to which the areas of the pigmentgroups in the external standards during the quantitative analyseswere compared to obtain the correction coefficient. The concentrationsof the oligomeric pigments were calculated from the summed total chromatogramareas of the SOAs, MOAs, and LOAs. The LOA-% of the oligomeric pigmentswere calculated as ratios between the areas of the LOAs and the totalsummed chromatogram areas (Figure 3). The wines were analyzed as such after filtrationby a 0.2 μm PTFE filter. Other instrumentational details, operatingparameters, and methodological details are described in Laitila etal.¹²

Color Measurements

The absorbance of each red winewas measured as such at 415, 520, and 620 nm with a 96-well platereader (Multiskan Ascent, Thermo Fisher, Waltham). The absorbanceswere measured in duplicate and 125 μL of wine was pipetted toeach well. The intensity of the color was defined as the sum of thethree individual absorbances.^13,14 Typically, 420 nm isused as one of the detection wavelengths but, because of instrumentationallimitations, 415 nm was used in this study.

Statistical Analyses

All statistical analyses wereperformed with R (version 3.5.3) in Rstudio integrated developmentenvironment (version 1.2.1335).^15,16 Partial least-squaresregression (PLSR) models were utilized to study the connections betweenthe pigment groups (predictors) and the intensity of the color (response).The predictors and the response were log-transformed prior to modelfitting to meet the assumption of the linear correlations and thevariables were autoscaled by subtracting the means from the variablesand dividing them by their standard deviations. The variables werealso log-transformed for the correlation analysis. The PLSR analyseswere performed with the “plsdepot”package in R.¹⁷ The optimal number of latentvariables was chosen based on the predicted residual sums of squares(PRESS) and the residual sums of squares (RSS) as well as the coefficientof determination (R²) and the cross-validated R² (Q²). The normal QQ plot of the y-residuals and the scatterplot of the y-residuals and the predicted valueswere visually inspected to ensure that the residuals were symmetricallydistributed and homoscedastic. Separate PLSR models were made forthe whole data set and for the 1-year-old wines to test whether thecolor was determined by the same features in the whole set as wellas in the youngest of commercial wines.

Color Intensity in the Whole Wine Set

First, the concentrationsof compound groups 1–9 and the LOA-%of groups 8 and 9 were introduced into thePLSR model as predictors utilizing the whole wine set (n = 317). Three latent variables were chosen for the model as theyprovided a good balance between model complexity and the explanatorypower of the model (Figure S2). The thirdlatent variable was included because its addition still markedly reducedthe residual sums of squares. The PLSR model consisting of three latentvariables explained 64.4–93.8% of the original predictors (Table S1). The first latent variable explained73.4% of the variation in the color intensity, the second latent variableexplained 8.1%, and the third explained 1.5%, adding up to a totalof 83.0%. The Q² of the three-componentmodel was 0.819. The y-residuals were homoscedasticand they were symmetrically distributed.

Based on the regressioncoefficients of the three-component model, the concentrations of thecarboxypyranomalvidins, PA–Mv⁺ adducts, and PA–methylmethine–Mv⁺ adducts, and the LOA-% of the PA–Mv⁺ andPA–methylmethine–Mv⁺ adducts were the mostimportant variables in explaining the color intensity in the wholewine set (Figure 4).The malvidin glycosides and all three pinotin-type malvidin derivatives 5–7 had practically no important rolein explaining the color intensity, whereas the B-type vitisins andmethylpyranomalvidins had moderate correlation to the color intensity.The dimeric PA–Mv⁺-type adducts consisting of catechinand malvidin glucoside units have been shown to be similar in manyaspects to their precursor, the malvidin glucoside. The dimer hassimilar pH-dependent kinetic and thermodynamic properties as malvidinglucoside (i.e., they are mainly in colorless hemiacetal forms inthe typical pH of red wines) and they are equally susceptible to bleachingby SO₂ (a chemical commonly used in winemaking).^8,20 The catechin moiety in the dimer only causes a bathochromic shift(17 nm) in the absorption maximum of the red-colored flavylium cationform compared to malvidin glucoside.^8,20 This has ledto the conclusion that the transformation of malvidin glycosides intoPA–Mv⁺ adducts would not be as impactful on thewine color as transformations of malvidin glycosides into other typesof monomeric and oligomeric pigments.³ Thesignificance of an observed correlation between the dimeric PA–Mv⁺-type catechin–anthocyanin adducts and color intensitywas even dismissed in a previous study because of the similar physicochemicalproperties of the directly linked dimers and anthocyanins.²¹ Our method, however, detects not only the dimericadducts but rather a much bigger portion of the PA–Mv⁺ adducts consisting of numerous individual compounds with varyingdegrees of oligomerization.¹² Our resultsshowed that the concentration of the PA–Mv⁺ adductshad a significant connection to the color intensity (Figure 4). The dimeric PA–methylmethine–Mv⁺-type adducts consisting of catechin and malvidin glucosideunits, on the other hand, already have features that suggest thatthese types of pigments should be relevant to the wine color. Largerpercentages of the dimers are in colored forms in wine pH comparedto malvidin glucoside and the dimers are more protected against bleachingby SO₂.⁹ As a downside, thedimers are relatively unstable at wine pH because of acid-catalyzedcleavage of the methylmethine linkages.²² It is not currently known how well large PA–methylmethine–Mv⁺ oligomers resist depolymerization but, nonetheless, the concentrationof the PA–methylmethine–Mv⁺ adducts had asignificant connection to the color intensity as well (Figure 4).

The carboxypyranomalvidins were the most important monomeric compoundgroup in the PLSR model explaining the color intensity in the wholewine set (Figure 4).The monomeric adducts derived from malvidin glycosides, which we semiquantifiedin this study, have rather similar chromatic features in the pH rangeof red wines as they display either a yellow (3) or anorange–red color (2, 4–7) and they are mainly in colored form.^4,23 However,the concentrations of the carboxypyranomalvidins have been found tobe higher in commercial wines than the concentrations of many othertypes of monomeric malvidin derivatives.²⁴⁻²⁶ The UPLC–MS/MSmethod yields proportional information about the concentrations,¹² meaning we cannot verify if the concentrationsof the carboxypyranomalvidins were indeed higher in our wine set aswell compared to other types of monomeric pigments. However, as thechemical properties of the monomeric adducts of the malvidin glycosidesare relatively similar in the typical red wine pH, the presumablyhigher concentrations might be the reason why the carboxypyranomalvidinsstood out as the most important monomeric compound group (Figure 4).

The importanceof the PA–Mv⁺ and PA–methylmethine–Mv⁺ adducts could be partially related to their concentrationsin wines as well. The summed concentrations of only a few dimers belongingto pigment groups 8 and 9 have been estimatedto be comparable to the concentrations of many monomeric adducts ofmalvidin glycosides.²⁶ However, these smallestpossible oligomers only comprise a small portion of the whole adductcomposition¹² and the true total concentrationsof the two oligomeric compound groups are likely to be much higherthan the concentrations of the dimers alone. These observations backedup our previous reasoning: while it is important to study and knowthe thermodynamic and chromatic properties of the red wine pigments,their contribution to the color intensity cannot be deduced only fromthe properties measured in isolated conditions.

While the majorityof the variation in the color intensity wasexplained by the first latent variable, which mainly described theconcentrations (Figure 4A and Table S1), the correlation biplotof the PLSR model clearly showed how the LOA-% of the PA–Mv⁺ and PA–methylmethine–Mv⁺ adductsexplained a unique and significant portion of the variation in thecolor intensity (Figures 4A and S2). Previously, with a morelimited wine set, we noted that there was a strong negative correlationbetween the SOA-% and LOA-% of the oligomeric adducts¹² and now these correlations were confirmed with a much biggerwine set (n = 317). The correlation coefficientsbetween the SOA-% and the LOA-% were −0.95 and −0.98for the PA–Mv⁺ and PA–methylmethine–Mv⁺ adducts, respectively. Similarly, the correlation coefficientsbetween the MOA-% and LOA-% were −0.54 and −0.76. Theseresults supported our earlier argument about the LOA-% being suitableto be used as a metric of the degree of oligomerization because wineswith high proportions of LOAs were associated with lower proportionsof SOAs and MOAs. Alternatively, if a large portion of the concentrationwas produced by the LOAs, then, subsequently, a smaller portion wasproduced by the SOAs and MOAs. Now, as the LOA-% of the oligomericpigments had a positive connection to the color intensity, the chemicalinterpretation of the results was that an increase in the averagedegree of oligomerization increased the color intensity as well. Inthe PA–methylmethine–Mv⁺ and PA–Mv⁺ adducts, the PA moieties themselves do not absorb visiblelight, meaning that they cannot directly increase the color intensityas the degree of oligomerization increases. However, they might affectthe properties of the chromophores through intramolecular copigmentationor by protecting the chromophores from the nucleophilic attack ofwater (or SO₂), thereby reducing the formation of the colorlesshemiacetals. The latter mechanism might be especially important forthe PA–Mv⁺ adducts because of the restraints ofthe direct, less flexible linkage between the Mv and PA moieties,which likely causes the similarities in the thermodynamic and chromaticproperties of PA–Mv⁺-type dimers and malvidin glycosides.^8,20 Previously, the degree of oligomerization has been shown to havean effect on the chromatic properties of oligomeric pigments consistingof pyranomalvidin glucoside and catechin units.¹¹

Color Intensity in the 1-Year-Old CommercialWines

The 1-year-old wines (n = 78) werestudied separatelyto find out whether the color intensity was explained by the samefeatures in the youngest of commercial wines as in the whole wineset. The concentrations of pigment groups 1–9 and the LOA-% of groups 8 and 9 were introduced into the PLSR model as predictors and then two latentvariables were chosen for the model as they provided a good balancebetween model complexity and explanatory power of the model (Figure S3). The inclusion of additional latentvariables would have started to level and decrease the Q², implying of overfitting. The PLSR model consistingof two latent variables explained 23.2–88.1% of the originalpredictors (Table S2). The first latentvariable explained 84.5% of the variation in the color intensity andthe second latent variable explained 4.1%, adding up to a total of88.5%. The Q² of the two-component modelwas 0.862. The y-residuals were homoscedastic andthey were symmetrically distributed.

Similarly to the wholewine set, the concentrations of the monomeric carboxypyranomalvidinsand the oligomeric PA–Mv⁺ and PA–methylmethine–Mv⁺ adducts and the LOA-% of the PA–Mv⁺ adductshad a major role in explaining the color intensity (Figure 5A,B). Additionally, the B-typevitisins were more impactful on the color intensity in the young commercialwines than they were in the whole wine set. On the contrary, the LOA-%of the PA–methylmethine–Mv⁺ adducts did nothave a significant connection to the color intensity in young winesand, again, neither did the pinotin-type malvidin derivatives 5–7. Overall, the color intensity wasexplained slightly better in the 1-year-old wines than it was in thewhole wine set (Figures 4C and 5C). The B-type vitisins have been shownto have a similar evolutionary aging trend as the anthocyanins inred wines. Namely, the concentrations of B-type vitisins diminishas red wines age.^21,27 This evolutionary trend couldbe one reason why the B-type vitisins had a bigger impact on the colorintensity in the 1-year-old wines compared to the whole wine set.Their concentration might be high enough in the young wines to havean impact on the color but, as the wines age, the contribution ofB-type vitisins decreases as well, along with their concentrations.The lesser importance of the LOA-% of the PA–methylmethine–Mv⁺ adducts might imply that there is an evolutionary trend inthe composition of the PA–methylmethine–Mv⁺ adducts as well, which becomes more relevant to the color intensityas wines age. The LOA-% of the PA–Mv⁺ adducts wasstill a significant predictor, which meant that already in the youngcommercial wines, the degree of oligomerization of the PA–Mv⁺ affected the color intensity. Interestingly, in the 1-year-oldwines, the information that the LOA-% provided about the color intensitywas not as exclusive and unique as it was in the whole wine set andthe LOA-% was more correlated with the other predictors (Figure 5A). Even though themalvidin glycosides were more important to the color intensity inthe 1-year-old wines than they were in the whole wine set, other pigmentgroups derived from the malvidin glycosides were still more impactfulon the color (Figure 5A,B). Anthocyanins are often described to be the main contributorsto the color in young red wines.^22,28,29 On the contrary, our results suggested that in theyoungest commercial wines in the present wine set, the anthocyaninderivatives, mainly carboxypyranomalvidins, B-type vitisins, and theoligomeric pigments, were the primary contributors to the color intensity.

Our findings confirmed for the first time that the PA–Mv⁺ and PA–methylmethine–Mv⁺ adducts,first hypothesized to be present in red wines nearly 50 years ago,are on a compound group level truly important for the color intensityof red wines. Besides their concentrations in wines, their sizes,i.e., degrees of oligomerization, were shown to have a positive andimportant connection to the color intensity. The sizes of the oligomericpigments explained a unique and distinctive part of the variationin the color intensity. The most important monomeric pigment groupfor the color intensity was the carboxypyranomalvidins and, overall,83% of the variation in the color intensity in all 317 commercialred wines was explained by the main pigment composition. The colorintensity was largely explained by the same pigment groups in the1-year-old wines as in the whole wine set but, additionally, the B-typevitisins were major contributors to the color intensity in the youngestof wines. Moreover, the LOA-% of the PA–methylmethine–Mv⁺ adducts did not have a significant connection to the colorintensity in the 1-year-old wines. This implied that there could besome sort of an evolutionarytrend in the composition of the PA–methylmethine–Mv⁺ adducts, which becomes relevant to the wine color in olderwines. The malvidin glycosides themselves, and the anthocyanins ingeneral, might be less important for the wine color than they aregenerally thought to be. Even in the youngest of commercial wines,their contribution to the color intensity was minor compared to theother pigment groups. We were able to explain the vast majority ofthe variation in the color intensity, but the models still left someroom for improvement. Red wines contain more pigment types than wereanalyzed in this study and their accurate analyses in the future couldfurther improve our understanding of the color of red wines.

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Supplementary Materials