Aroma modeling and quality evaluation of spearmint (Mentha spicata subsp. spicata) using electronic nose technology coupled with artificial intelligence algorithms

The present study aimed to evaluate the capability of an electronic nose system coupled with chemometrics tools as a fast, real-time, and non-destructive technology for spearmint grading. The developed e-nose system was utilized to measure 46 samples' volatile organic components (VOCs) procured from different geographical origins. PCA was performed for original data clustering and dimensionality reduction of the aroma data. PLS-DA and fuzzy clustering were performed for differentiating the samples. Fuzzy clustering presented a more reasonable performance due to the overlapping nature of the flowering and vegetative stages clusters. An artificial neural network model was utilized as a supervised pattern recognition algorithm to map the aroma data of the samples to their quality grade based on their essential oils percentage. The developed system predicted the sample’s grade with high correlation coefficients of prediction values (Rp2 = 0.97 and RMSEp= 0.075) and acceptable sensitivity (97.7%) and specificity (86.4%).