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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]

The unambiguous detection and classification of volatile organic compounds (VOCs) are crucial in many fields. For using VOC‐sensing to explore the alteration and spoilage of food, very inexpensive sensors are desired. Simple colorimetric sensors seem highly attractive for these applications. Here, a label‐free, colorimetric sensor array made of metal‐organic‐framework‐based (MOF‐based) Fabry‐Pérot (FP) films is presented where the signal read‐out is performed either by their optical spectra or by pictures taken with a smartphone camera. Exposing the FP‐MOF‐films to various VOCs causes a reversible shift of the photonic pattern, where the magnitude of the shift depends on the VOC type, its concentration, and the MOF structure. The application of machine‐ learning‐ algorithms on the sensor data allows to identify the VOCs with a high classification accuracy (92% at 100 ppm). It is shown that the sensor array read‐out can also be performed with a common smartphone camera, also precisely classifying the VOC analytes. Moreover, fresh and spoiled food, like milk and meat, is distinguished by its head space. Thus, the study presents a very inexpensive platform of small colorimetric sensors that allow determining the quality, alteration, and spoilage of food, and it may contribute to realizing smart labels and intelligent packaging. [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]

Lamiaceae belong to the species-richest family of flowering plants and harbor many species that are used as herbs or in medicinal applications such as basils or mints. The evolution of this group has been driven by chemical speciation, mainly volatile organic compounds (VOCs). The commercial use of these plants is characterized by adulteration and surrogation to a large extent. Authenticating and discerning this species is thus relevant for consumer safety but usually requires cumbersome analytics, such as gas chromatography, often coupled with mass spectroscopy. Here, we demonstrate that quartz-crystal microbalance (QCM)-based electronic noses provide a very cost-efficient alternative, allowing for fast, automated discrimination of scents emitted from the leaves of different plants. To explore the range of this strategy, we used leaf material from four genera of Lamiaceae along with lemongrass, which is similarly scented but from an unrelated outgroup. To differentiate the scents from different plants unambiguously, the output of the six different SURMOF/QCM sensors was analyzed using machine learning (ML) methods together with a thorough statistical analysis. The exposure and purging of data sets (four cycles) obtained from a QCM-based, low-cost homemade portable e-Nose were analyzed using a linear discriminant analysis (LDA) classification model. Prediction accuracy with repeated test measurements reached values of up to 0%. We show that it is possible not only to discern and identify plants at the genus level but also to discriminate closely related sister clades within a genus (basil), demonstrating that an e-Nose is a powerful device that can safeguard consumer safety against dangers posed by globalized trade. [ABSTRACT FROM AUTHOR]

Reliable and inexpensive sensors for the detection and discrimination of various volatile organic compounds like different alcohols are required for many applications, for example to proof the alcohol quality and for avoiding methanol poising. Here, we develop a sensor array of films made of different types of surface-mounted metal-organic frameworks (SURMOFs) with a periodic three-dimensional structure of mesoscopic voids, referred to as inverse opal structure. The wavelength shift (or alternatively, the intensity change) of the photonic reflectance peak caused by the analyte vapor adsorption in the SURMOF was used as signal. The sensor array is applied for the detection of various pure low-carbon alcohols, which are methanol, ethanol, 1-propanol, and 1-butanol. The sensor array shows a low limit of detection (LOD) in the range of 10–60 ppm and is able to reliably discriminate the alcohols from each other. Furthermore, the sensor array is able to precisely discriminate their pure vapors and binary mixtures with methanol. We foresee such sensor array with SURMOF-based inverse opal films offer a great potential for the precise and cost-efficient detection of various analytes. • A sensor array of films with inverse-opal structures made of different surface-mounted metal-organic frameworks (SURMOFs). • The SURMOFs-inverse-opal sensor array shows very high sensitivities and very low limits of detection of ∼10–60 ppm. • The different alcohols can be classified with very high precision of more than 90 %. • The SURMOFs-inverse-opal sensor array is also able to precisely discriminate various binary mixtures. [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]