Rapid prediction of the aroma type of plain yogurts via electronic nose combined with machine learning approaches

Abstract

The unique aroma attributes of yogurts are associated with their quality and consumer acceptance. However, the aroma types of plain yogurts and methods for quickly identifying them have not been systematically established. This study used quantitative descriptive analysis (QDA) to label the aromas of commercial plain yogurts into four primary types, namely fermented, cheesy, milky, and fruity, for the first time. Electronic nose (e-nose) data were rapidly collected from the headspace of plain yogurt samples, and the aroma types classification models were established using linear discriminant analysis (LDA), logistic regression (LR), support vector machine (SVM), and decision trees (DT). Among these models, the SVM-based model displayed the best classification performance, with accuracy and F1 values at 0.933. Furthermore, this study revealed that the LR classification model, based on refined smaller-size e-nose datasets, improved the accuracy and F1 values to 0.867 and 0.872, respectively, compared to the original LR model using the complete dataset, thus demonstrating the applicability of model simplification. This work provides a theoretical basis for rapid plain yogurt aroma type identification by combining e-nose data with machine learning approaches.