The importance of outlier rejection and significant explanatory variable selection for pinot noir wine soft sensor development.

Sensory attributes are essential factors in determining the quality of wines. However, it can be challenging for consumers, even experts, to differentiate and quantify wines' sensory attributes for quality control. Soft sensors based on rapid chemical analysis offer a potential solution to overcome this challenge. However, the current limitation in developing soft sensors for wines is the need for a significant number of input parameters, at least 12, necessitating costly and time-consuming analyses. While such a comprehensive approach provides high accuracy in sensory quality mapping, the expensive and time-consuming studies required do not lend themselves to the industry's routine quality control activities. In this work, Box plots, Tucker-1 plots, and Principal Component Analysis (PCA) score plots were used to deal with output data (sensory attributes) to improve the model quality. More importantly, this work has identified that the number of analyses required to fully quantify by regression models and qualify by classification models can be significantly reduced. Based on regression models, only four key chemical parameters (total flavanols, total tannins, A