Machine Learning in Sensory Analysis of Mead—A Case Study: Ensembles of Classifiers

TL;DR

This study explores how machine learning can classify types of mead based on sensory analysis and aroma compounds, finding that certain algorithms perform better than others.

Contribution

The study introduces a novel combination of cluster mapping and machine learning algorithms for classifying mead types based on sensory and aromatic data.

Findings

Random Forest and K-Nearest Neighbors algorithms achieved the highest accuracy in mead classification.

Decision Tree achieved the highest accuracy (0.909) among tested algorithms.

Acacia mead was easier for algorithms to identify compared to tilia or buckwheat mead.

Abstract

The aim was to explore using machine learning (including cluster mapping and k-means methods) to classify types of mead based on sensory analysis and aromatic compounds. Machine learning is a modern tool that helps with detailed analysis, especially because verifying aromatic compounds is challenging. In the first stage, a cluster map analysis was conducted, allowing for the exploratory identification of the most characteristic features of mead. Based on this, k-means clustering was performed to evaluate how well the identified sensory features align with logically consistent groups of observations. In the next stage, experiments were carried out to classify the type of mead using algorithms such as Random Forest (RF), adaptive boosting (AdaBoost), Bootstrap aggregation (Bagging), K-Nearest Neighbors (KNN), and Decision Tree (DT). The analysis revealed that the RF and KNN algorithms were the most effective in classifying mead based on sensory characteristics, achieving the highest accuracy. In contrast, the AdaBoost algorithm consistently produced the lowest accuracy results. However, the Decision Tree algorithm achieved the highest accuracy value (0.909), demonstrating its potential for precise classification based on aroma characteristics. The error matrix analysis also indicated that acacia mead was easier for the algorithms to identify than tilia or buckwheat mead. The results show the potential of combining an exploratory approach (cluster map with the k-means method) with machine learning. It is also important to focus on selecting and optimizing classification models used in practice because, as the results so far indicate, choosing the right algorithm greatly affects the success of mead identification.

Figures