Among the pattern recognition techniques used in electronic noses, principal component analysis (PCA) is one of the most widely applied methods. However, PCA eliminates the correlation between variables—often corresponding to different sensors in an electronic nose array—which contradicts the overlapping sensing characteristics inherent in such sensor arrays. This limitation makes it challenging to accurately classify multi-component samples. In this study, the Wilks criterion is incorporated into the PCA framework, addressing the issue of selecting the optimal principal components for alcohol identification. This approach successfully enables the accurate classification of three distinct types of wine, moving beyond the traditional PCA method. Furthermore, the research highlights that the selection of principal axes in multi-component classification is not solely based on the variance contribution rate of each principal component. Instead, additional considerations are necessary, offering valuable insights for future studies in electronic nose applications. This work provides a more nuanced understanding of how to enhance the performance of PCA in complex recognition tasks, contributing to the development of more reliable and effective sensor-based systems.
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