Odor prediction of whiskies based on their molecular composition

Abstract

Aroma compositions are usually complex mixtures of odor-active compounds exhibiting diverse molecular structures. Due to chemical interactions of these compounds in the olfactory system, assessing or even predicting the olfactory quality of such mixtures is a difficult task, not only for statistical models, but even for trained assessors. Here, we combine fast automated analytical assessment tools with human sensory data of 11 experienced panelists and machine learning algorithms. Using 16 previously analyzed whisky samples (American or Scotch origin), we apply the linear classifier OWSum to distinguish the samples based on their detected molecules and to gain insights into the key molecular structure characteristics and odor descriptors for sample type. Moreover, we use OWSum and a Convolutional Neural Network (CNN) architecture to classify the five most relevant odor attributes of each sample and predict their sensory scores with promising accuracies (up to F1: 0.71, MCC: 0.68, ROCAUC: 0.78). The predictions outperform the inter-panelist agreement and thus demonstrate previously impossible data-driven sensory assessment in mixtures. Aroma compositions are usually complex mixtures of odor-active compounds exhibiting diverse molecular structures, and assessing or predicting the olfactory qualities of such mixtures is challenging. Here, fast automated analytical assessment tools are combined with the human sensory data of 11 experienced panelists and machine learning algorithms, enabling samples to be distinguished and classified based on their detected molecules, and gaining insights into key molecular structure characteristics and odor descriptors.