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Many odors, like perfumes, are complex mixtures of chiral and achiral molecules where the cost‐efficient (enantio−)selective sensing represents a major technical challenge. Here, we present a colorimetric sensor array of surface‐mounted metal‐organic‐framework (SURMOF) films in Fabry‐Pérot (FP) cavities. The optical properties of the FP‐SURMOF films with different chiral and achiral structures are affected by the (enantio−)selective adsorption of the analytes in the SURMOF pores, resulting in different responses to the analyte molecules. The read‐out of the sensor array is performed by the digital camera of a common smartphone, where the RGB values are determined. By analyzing the sensor array data with simple machine learning algorithms, the analytes are discriminated. After demonstrating the enantioselective response for a pair of pure chiral odor molecules, we apply the sensor array to detect and discriminate a large number (16) of common commercial perfumes and eau de toilettes. While our untrained human nose is not able to discriminate all perfumes, the presented colorimetric sensor array can classify all perfumes with great classification accuracy. Moreover, the sensor array was used to identify unlabeled samples correctly. We foresee such an FP‐chiral‐SURMOF‐based sensor array as a powerful approach toward inexpensive selective odors sensing applications. [ABSTRACT FROM AUTHOR]

An optical sensor array based on photonic Fabry–Pérot films of surface-mounted metal–organic-frameworks (SURMOFs) with different homochiral structures is presented. It is used to detect and enantioselectively discriminant 3 pairs of chiral odor molecules, either pure or in binary mixtures. [ABSTRACT FROM AUTHOR]

This article discusses a colorimetric sensor array that uses metal-organic framework films to detect and classify perfume scents. The sensor system is able to detect chiral odor molecules and discriminate between different commercial perfume scents. The sensor read-out is captured using a smartphone camera and analyzed using a machine-learning algorithm. The research article provides more detailed information on the topic. [Extracted from the article]

Chirality is essential in nature and often pivotal for biological information transfer, for example, via odor messenger molecules. While the human nose can distinguish the enantiomers of many chiral odors, the technical realization by an artificial sensor or an electronic nose, e‐nose, remains a challenge. Herein, we present an array of six sensors coated with nanoporous metal–organic framework (MOF) films of different homochiral and achiral structures, working as an enantioselective e‐nose. While the achiral‐MOF‐film sensors show identical responses for both isomers of one chiral odor molecule, the responses of the homochiral MOF films differ for different enantiomers. By machine learning algorithms, the combined array data allow the stereoselective identification of all compounds, here tested for five pairs of chiral odor molecules. We foresee the chiral‐MOF‐e‐nose, able to enantioselectively detect and discriminate chiral odors, to be a powerful approach towards advanced odor sensing. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• An array of 3 QCM sensors coated with different thin films of nanoporous metal-organic frameworks (SURMOFs) is presented. • Using machine learning algorithms, the SURMOF-coated QCM-array can detect and discriminate different volatile plant oils and their mixtures with very high succes rate. • Simple and inexpensive setup of SURMOF-coated QCM sensors provides a high sensitivity for detecting different plant oil odors and their mixtures. For the sensing, detection and discrimination of odors, well-defined active layers with large specific surface areas are required. We present an array of 3 quartz crystal microbalance (QCM) sensors coated with different thin films of crystalline, highly-porous surface-mounted metal-organic frameworks (SURMOFs). The sensor array is tested for the detection and discrimination of 6 different volatile plant oil (VPO) odors as well as of their mixtures with concentrations ranging from 1 mg/L to 40 mg/L. By data analysis with machine learning algorithms, accuracies for the classification and prediction of 99.97 % for the different pure odors and of 98.4 % for their mixtures were reached. Our results show, QCM sensors coated with SURMOF films can clearly discriminate the plant oil scents and their mixtures with very high success rate at low concentrations, indicating the way towards a MOF electronic nose, MOF e-nose. [ABSTRACT FROM AUTHOR]