An Electronic Nose Analysis of the Headspace from Extra-Virgin Olive Oil–Saliva Interactions and Its Ability to Differentiate Between Individuals Based on Body Mass Index

The interaction between fatty foods and saliva in individuals of different body weights may lead to differences in the release of volatile compounds in the mouth. This study investigates the ability of an electronic nose (E-nose) to discriminate between the headspace profiles of extra-virgin olive oil (EVOO) mixed with the saliva of 55 subjects of different body mass indices (BMI). The resulting data were analysed using linear discriminant analysis (LDA) and principal component analysis (PCA) to evaluate the E-nose’s ability to discriminate between groups. W5S, W1S, W2S, and W2W sensors exhibited the greatest variation in response intensity; in particular, they highlighted differences between obese and non-obese subjects. The LDA plot demonstrated a clear separation of samples corresponding to three BMI groups, with the first and second components accounting for 61.25% and 23.97% of the variance, respectively. Overall, the percentage of correct classification in the cross-validation results was 87.3%. These results highlight the potential of an electronic nose for use as a rapid and objective tool for screening olfactory profiles associated with food matrix–saliva interaction in different BMI groups, providing valuable insight for further research on food–saliva interactions.