Identification of Mint Scents Using a QCM Based E-Nose

Mints emit diverse scents that exert specific biological functions and are relevance for applications. The current work strives to develop electronic noses can electronically discriminate the emitted by different species of Mint as alternative conventional profiling gas chromatography. Here, 12 sensing materials including 4 metal oxide nanoparticle dispersions (AZO, ZnO, SnO2, ITO), one Metal Organic Frame Cu(BPDC), 7 polymer films, PVA, PEDOT:PSS, PFO, SB, SW, SG, PB were used functionalizing Quartz Crystal Microbalance (QCM) sensors. purpose was six economically relevant (Mentha x piperita, Mentha spicata, spicata ssp. crispa, longifolia, Agastache rugosa, Nepeta cataria). adsorption desorption datasets obtained from each modified QCM sensor processed three classification models, Principal Component Analysis (PCA), Linear Discriminant (LDA), k-Nearest Neighbor (k-NN). This allowed discriminating with accuracies 97.2% 100% 99.9% (k-NN), respectively. Prediction a repeating test measurement reached up 90.6% LDA, 85.6% k-NN. These data demonstrate this nose in reliable efficient manner.