Research Progress Reports
Figure 1. PCA loading (A) and score (B) plots of aromatic compounds of 18 Pinot noir wines.
to their variability. As stated earlier, this statistical technique is a way of analysing complex datasets where many parameters are measured, in this case concentrations of 47 different compounds. From a statistical point of view, this means that the data exists in 47 dimensions, but in these diagrams we are looking at only two of the most important dimensions. From Figure 1A and 1B the first two Principal Components only explain 42.7% of the variation: This simply goes to show
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how difficult it is to pin down differences in wines to only a few parameters. Nevertheless, Figure 1B demonstrates that the first two PCs can begin to differentiate the two categories of wines, as the premium wines are predominantly found to the left of the graph and the affordable wines on the right. We can use Figure 1A to find out which chemical compounds are responsible for this: it seems that a premium wine has concentrations of unbranched aromatic compounds and
NZ WINEGROWER OCTOBER/NOVEMBER 2019
cinnamic acid derivative esters, while affordable wines have higher concentrations of long-chain alcohols, acetates, and branched aromatic compounds. So, what does this all really mean? Remember that this is only a snapshot in two dimensions of all the differences of the wines involved. This snapshot, while not explaining the entirety of what defines a premium or affordable wine, does present a compelling story of aromatic compounds that are important for defining
these categories. Studies like these and others examining other attributes, such as tannin and phenolic content, when combined with machine learning techniques for analysis of the large datasets can begin to paint the complete picture of the attributes associated with wine quality. While these studies are the initial steps in understanding the attributes associated with quality, they are the ones that must be taken first to achieve the end goal. nzwine.com