Identification of Mint Scents Using a QCM Based E-Nose

Salih Okur,Zejun Zhang,Peter Nick,Uli Lemmer,Mohammed Sarheed,Robert Huber,Lars Heinke,Christof Wöll,Adnan Kanbar

doi:10.3390/chemosensors9020031

Salih Okur, Zejun Zhang + Show 7 more

Open Access

https://doi.org/10.3390/chemosensors9020031

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Journal: Chemosensors	Publication Date: Feb 4, 2021
Citations: 29	License type: CC BY 4.0

Affiliation: Karlsruhe Institute of Technology

Abstract

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

Highlights

As sessile organisms, plants have to rely on chemistry to cope with their biotic environment
All the sensors exhibit a maximal response upon exposure to the scent of Mentha spicata (MS), and the lowest response upon exposure to the scent of Agastache rugosa (AR)
The adsorption and desorption datasets obtained from each modified Quartz Crystal Microbalance (QCM) sensor were used for three different classification models (PCA, Linear Discriminant Analysis (LDA), k-Nearest Neighbor Analysis (k-NN))

Summary

Introduction

Plants have to rely on chemistry to cope with their biotic environment. As a result, they have evolved an elaborate and proficient secondary metabolism. An estimated 100,000 of compounds specific for plants have already been identified [1]. These compounds include volatile compounds that are signals with the function to steer the interaction of neighboring plants or the interaction with other organisms, such as insects or microorganisms. In response to an attack by caterpillars, tomato plants warn their neighbors by emitting (Z)-3-hexenol, such that these neighbors can already synthetize defense compounds prior to being attacked [2]. A specific subset of plant volatiles act to inhibit growth or development of their competitors, a phenomenon termed as allelopathy [3,4]

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