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I wrote a blog post inspired by Jamie Goode's book "Wine Science: The Application of Science in Winemaking".
In this book, Goode argued that reductionistic approach cannot explain relationship between chemical ingredients and taste of wine. Indeed, we know not all high (alcohol) wines are excellent, although in general high wines are believed to be good. Usually taste of wine is affected by a complicated balance of many components such as sweetness, acid, tannin, density or others that are given by corresponding chemical entities.
However, I think (and probably many other data science experts agree) that it is not a limitation of reductionistic approach, but a limitation of univariate modeling. To illustrate it, I performed a series of multivariate modeling with random forest or other models on "Wine Quality" dataset of UCI Machine Learning repository.
As a result, a random forest classifier predicted tasting score of wine better than intuitive univariate modeling. At the same time, it also showed some hidden and complicated dynamics between chemical ingredients and taste of wine. I believe that modern multivariate modeling such as machine learning can reveal more complicated relationship between chemical ingredients and taste of wine.
See my blog post below for more details.