Key aroma components of 33 fragrant peanut oils with different aroma types were screened by combined using flavoromics and machine learning. A total of 108 volatile compounds were identified and 100 kinds of them were accurately quantified, and 38 compounds out of them were with odorant activity value ≥1. The 33 peanut oils presented varied intensity of 'fresh peanuts', 'roasted nut', 'burnt', 'over-burnt', 'sweet', 'peanut butter-like', 'puffed food' and 'exotic flavor', and could be classified into four aroma types, namely raw, light, thick and salty. Partial least squares regression analysis, random forest and classification regression tree revealed that 2-acetyl pyrazine had a negative effect on 'fresh peanuts' and could distinguish raw flavor samples well; 2-methylbutanal and 4-vinylguaiacol were key compounds of 'roasted nut' and had significant differences (P < 0.0001) in thick and raw flavor samples; furfural contributed to the 'puffed food' as well as key compound of salty flavor.
Keywords: Flavoromics; Key aroma compounds; Machine learning; Peanut oil; Sensory characteristic.
© 2023 The Author(s).