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Influence of Honey Varieties, Fermentation Techniques, and Production Process on Sensory Properties and Odor-Active Compounds in Meads
This study investigates the impact of key factors on the formation of odorants and sensory properties in mead. The effects of the honey type (acacia, buckwheat, linden), wort heating, and the fermentation method (commercial Saccharomyces cerevisiae yeasts, spontaneous fermentation, Galactomyces geotrichum molds) were examined. Twelve model mead batches were produced, matured for 12 months, and analyzed using gas chromatography–olfactometry (GC–O) and headspace SPME-GC/MS to identify odor-active compounds. Results confirmed that the honey type plays a significant role in sensory profiles, with distinct aroma clusters for buckwheat, acacia, and linden honey. Compounds like phenylacetic acid, 2- and 3-methylbutanal, and butanoic acid were identified as the most important odorants, correlating with sensory attributes such as honey-like, malty, and fermented aromas. Univariate and multivariate analyses, followed by correlation analysis, highlighted how production parameters affect mead aroma, providing insights to optimize sensory quality.
Machine Learning in Sensory Analysis of Mead—A Case Study: Ensembles of Classifiers
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.