A Highly Selective Biosensor Based on Peptide Directly Derived from the HarmOBP7 Aldehyde Binding Site

Biosensors for Odor Detection: A Review

Animals can easily detect hundreds of thousands of odors in the environment with high sensitivity and selectivity. With the progress of biological olfactory research, scientists have extracted multiple biomaterials and integrated them with different transducers thus generating numerous biosensors. Those biosensors inherit the sensing ability of living organisms and present excellent detection performance. In this paper, we mainly introduce odor biosensors based on substances from animal olfactory systems. Several instances of organ/tissue-based, cell-based, and protein-based biosensors are described and compared. Furthermore, we list some other biological materials such as peptide, nanovesicle, enzyme, and aptamer that are also utilized in odor biosensors. In addition, we illustrate the further developments of odor biosensors.

Sound Identification Method for Gas and Coal Dust Explosions Based on MLP

To solve the problems of backward gas and coal dust explosion alarm technology and single monitoring means in coal mines, and to improve the accuracy of gas and coal dust explosion identification in coal mines, a sound identification method for gas and coal dust explosions based on MLP in coal mines is proposed, and the distributions of the mean value of the short-time energy, zero crossing rate, spectral centroid, spectral spread, roll-off, 16-dimensional time-frequency features, MFCC, GFCC, short-time Fourier coefficients of gas explosion sound, coal dust sound, and other underground sounds were analyzed. In order to select the most suitable feature vector to characterize the sound signal, the best feature extraction model of the Relief algorithm was established, and the cross-entropy distribution of the MLP model trained with the different numbers of feature values was analyzed. In order to further optimize the feature value selection, the recognition results of the recognition models trained with the different numbers of sound feature values were compared, and the first 35-dimensional feature values were finally determined as the feature vector to characterize the sound signal. The feature vectors are input into the MLP to establish the sound recognition model of coal mine gas and coal dust explosion. An analysis of the feature extraction, optimal feature extraction, model training, and time consumption for model recognition during the model establishment process shows that the proposed algorithm has high computational efficiency and meets the requirement of the real-time coal mine safety monitoring and alarm system. From the results of recognition experiments, the sound recognition algorithm can distinguish each kind of sound involved in the experiments more accurately. The average recognition rate, recall rate, and accuracy rate of the model can reach 95%, 95%, and 95.8%, respectively, which is obviously better than the comparison algorithm and can meet the requirements of coal mine gas and coal dust explosion sensing and alarming.

Human Olfactory Receptor Sensor for Odor Reconstitution

Among the five human senses, light, sound, and force perceived by the eye, ear, and skin, respectively are physical phenomena, and therefore can be easily measured and expressed as objective, univocal, and simple digital data with physical quantity. However, as taste and odor molecules perceived by the tongue and nose are chemical phenomena, it has been difficult to express them as objective and univocal digital data, since no reference chemicals can be defined. Therefore, while the recording, saving, transmitting to remote locations, and replaying of human visual, auditory, and tactile information as digital data in digital devices have been realized (this series of data flow is defined as DX (digital transformation) in this review), the DX of human taste and odor information is not yet in the realization stage. Particularly, since there are at least 400,000 types of odor molecules and an infinite number of complex odors that are mixtures of these molecules, it has been considered extremely difficult to realize "human olfactory DX" by converting all odors perceived by human olfaction into digital data. In this review, we discuss the current status and future prospects of the development of "human olfactory DX", which we believe can be realized by utilizing odor sensors that employ the olfactory receptors (ORs) that support human olfaction as sensing molecules (i.e., human OR sensor).

Evaluation of Linkers' Influence on Peptide-Based Piezoelectric Biosensors' Sensitivity to Aldehydes in the Gas Phase

Recent findings qualified aldehydes as potential biomarkers for disease diagnosis. One of the possibilities is to use electrochemical biosensors in point-of-care (PoC), but these need further development to overcome some limitations. Currently, the primary goal is to enhance their metrological parameters in terms of sensitivity and selectivity. Previous findings indicate that peptide OBPP4 (KLLFDSLTDLKKKMSEC-NH₂) is a promising candidate for further development of aldehyde-sensitive biosensors. To increase the affinity of a receptor layer to long-chain aldehydes, a structure stabilization of the peptide active site via the incorporation of different linkers was studied. Indeed, the incorporation of linkers improved sensitivity to and binding of aldehydes in comparison to that of the original peptide-based biosensor. The tendency to adopt disordered structures was diminished owing to the implementation of suitable linkers. Therefore, to improve the metrological characteristics of peptide-based piezoelectric biosensors, linkers were added at the C-terminus of OBPP4 peptide (KLLFDSLTDLKKKMSE-linker-C-NH₂). Those linkers consist of proteinogenic amino acids from group one: glycine, L-proline, L-serine, and non proteinogenic amino acids from group two: β-alanine, 4-aminobutyric acid, and 6-aminohexanoic acid. Linkers were evaluated with in silico studies, followed by experimental verification. All studied linkers enhanced the detection of aldehydes in the gas phase. The highest difference in frequency (60 Hz, nonanal) was observed between original peptide-based biosensors and ones based on peptides modified with the GSGSGS linker. It allowed evaluation of the limit of detection for nonanal at the level of 2 ppm, which is nine times lower than that of the original peptide. The highest sensitivity values were also obtained for the GSGSGS linker: 0.3312, 0.4281, and 0.4676 Hz/ppm for pentanal, octanal, and nonanal, respectively. An order of magnitude increase in sensitivity was observed for the six linkers used. Generally, the linker's rigidity and the number of amino acid residues are much more essential for biosensors' metrological characteristics than the amino acid sequence itself. It was found that the longer the linkers, the better the effect on docking efficiency.

Adaptive Peptide Molecule as the Promising Highly-Efficient Gas-Sensor Material: In Silico Study

Gas sensors are currently employed in various applications in fields such as medicine, ecology, and food processing, and serve as monitoring tools for the protection of human health, safety, and quality of life. Herein, we discuss a promising direction in the research and development of gas sensors based on peptides-biomolecules with high selectivity and sensitivity to various gases. Thanks to the technique developed in this work, which uses a framework based on the density-functional tight-binding theory (DFTB), the most probable adsorption centers were identified and used to describe the interaction of some analyte molecules with peptides. The DFTB method revealed that the physical adsorption of acetone, ammonium, benzene, ethanol, hexane, methanol, toluene, and trinitrotoluene had a binding energy in the range from -0.28 eV to -1.46 eV. It was found that peptides may adapt to the approaching analyte by changing their volume up to a maximum value of approx. 13%, in order to confine electron clouds around the adsorbed molecule. Based on the results obtained, the prospects for using the proposed peptide configurations in gas sensor devices are good.

Data-driven design of a multiplexed, peptide-sensitized transistor to detect breath VOC markers of COVID-19

Exhaled human breath contains a rich mixture of volatile organic compounds (VOCs) whose concentration can vary in response to disease or other stressors. Using simulated odorant-binding proteins (OBPs) and machine learning methods, we designed a multiplex of short VOC- and carbon-binding peptide probes that detect a characteristic "VOC fingerprint". Specifically, we target VOCs associated with COVID-19 in a compact, molecular sensor array that directly transduces vapor composition into multi-channel electrical signals. Rapidly synthesizable, chimeric VOC- and solid-binding peptides were derived from selected OBPs using multi-sequence alignment with protein database structures. Selective peptide binding to targeted VOCs and sensor surfaces was validated using surface plasmon resonance spectroscopy and quartz crystal microbalance. VOC sensing was demonstrated by peptide-sensitized, exposed-channel carbon nanotube transistors. The data-to-device pipeline enables the development of novel devices for non-invasive monitoring, diagnostics of diseases, and environmental exposure assessment.

Odorant-responsive biological receptors and electronic noses for volatile organic compounds with aldehyde for human health and diseases: A perspective review

Volatile organic compounds (VOCs) are gaseous chemicals found in ambient air and exhaled breath. In particular, highly reactive aldehydes are frequently found in polluted air and have been linked to various diseases. Thus, extensive studies have been carried out to elucidate disease-specific aldehydes released from the body to develop potential biomarkers for diagnostic purposes. Mammals possess innate sensory systems, such as receptors and ion channels, to detect these VOCs and maintain physiological homeostasis. Recently, electronic biosensors such as the electronic nose have been developed for disease diagnosis. This review aims to present an overview of natural sensory receptors that can detect reactive aldehydes, as well as electronic noses that have the potential to diagnose certain diseases. In this regard, this review focuses on eight aldehydes that are well-defined as biomarkers in human health and disease. It offers insights into the biological aspects and technological advances in detecting aldehyde-containing VOCs. Therefore, this review will aid in understanding the role of aldehyde-containing VOCs in human health and disease and the technological advances for improved diagnosis.

Artificial Olfactory Biohybrid System: An Evolving Sense of Smell

The olfactory system can detect and recognize tens of thousands of volatile organic compounds (VOCs) at low concentrations in complex environments. Bioelectronic nose (B-EN), which mimics olfactory systems, is becoming an emerging sensing technology for identifying VOCs with sensitivity and specificity. B-ENs integrate electronic sensors with bioreceptors and pattern recognition technologies to enable medical diagnosis, public security, environmental monitoring, and food safety. However, there is currently no commercially available B-EN on the market. Apart from the high selectivity and sensitivity necessary for volatile organic compound analysis, commercial B-ENs must overcome issues impacting sensor operation and other problems associated with odor localization. The emergence of nanotechnology has provided a novel research concept for addressing these problems. In this work, the structure and operational mechanisms of biomimetic olfactory systems are discussed, with an emphasis on the development and immobilization of materials. Various biosensor applications and current developments are reviewed. Challenges and opportunities for fulfilling the potential of artificial olfactory biohybrid systems in fundamental and practical research are investigated in greater depth.

The Use of Terrestrial and Maritime Autonomous Vehicles in Nonintrusive Object Inspection

Traditional nonintrusive object inspection methods are complex or extremely expensive to apply in certain cases, such as inspection of enormous objects, underwater or maritime inspection, an unobtrusive inspection of a crowded place, etc. With the latest advances in robotics, autonomous self-driving vehicles could be applied for this task. The present study is devoted to a review of the existing and novel technologies and methods of using autonomous self-driving vehicles for nonintrusive object inspection. Both terrestrial and maritime self-driving vehicles, their typical construction, sets of sensors, and software algorithms used for implementing self-driving motion were analyzed. The standard types of sensors used for nonintrusive object inspection in security checks at the control points, which could be successfully implemented at self-driving vehicles, along with typical areas of implementation of such vehicles, were reviewed, analyzed, and classified.

Progress in the Development of Detection Strategies Based on Olfactory and Gustatory Biomimetic Biosensors

The biomimetic olfactory and gustatory biosensing devices have broad applications in many fields, such as industry, security, and biomedicine. The development of these biosensors was inspired by the organization of biological olfactory and gustatory systems. In this review, we summarized the most recent advances in the development of detection strategies for chemical sensing based on olfactory and gustatory biomimetic biosensors. First, sensing mechanisms and principles of olfaction and gustation are briefly introduced. Then, different biomimetic sensing detection strategies are outlined based on different sensing devices functionalized with various molecular and cellular components originating from natural olfactory and gustatory systems. Thereafter, various biomimetic olfactory and gustatory biosensors are introduced in detail by classifying and summarizing the detection strategies based on different sensing devices. Finally, the future directions and challenges of biomimetic biosensing development are proposed and discussed.

Development and Assessment of Regeneration Methods for Peptide-Based QCM Biosensors in VOCs Analysis Applications

Cleaning a quartz crystal microbalance (QCM) plays a crucial role in the regeneration of its biosensors for reuse. Imprecise removal of a receptor layer from a transducer's surface can lead to unsteady operation during measurements. This article compares three approaches to regeneration of the piezoelectric transducers using the electrochemical, oxygen plasma and Piranha solution methods. Optimization of the cleaning method allowed for evaluation of the influence of cleaning on the surface of regenerated biosensors. The effectiveness of cleaning the QCM transducers with a receptor layer in the form of a peptide with the KLLFDSLTDLKKKMSEC-NH2 sequence was described. Preliminary cleaning was tested for new electrodes to check the potential impact of the cleaning on deposition and the transducer's operation parameters. The effectiveness of the cleaning was assessed via the measurement of a resonant frequency of the QCM transducers. Based on changes in the resonant frequency and the Sauerbrey equation, it was possible to evaluate the changes in mass adsorption on the transducer's surface. Moreover, the morphology of the QCM transducer's surface subjected to the selected cleaning techniques was presented with AFM imaging. The presented results confirm that each method is suitable for peptide-based biosensors cleaning. However, the most invasive seems to be the Piranha method, with the greatest decrease in performance after regeneration cycles (25% after three cycles). The presented techniques were evaluated for their efficiency with respect to a selected volatile compound, which in the future should allow reuse of the biosensors in particular applications, contributing to cost reduction and extension of the sensors' lifetime.

Recent Advances in Quartz Crystal Microbalance Biosensors Based on the Molecular Imprinting Technique for Disease-Related Biomarkers

The molecular imprinting technique is a quickly developing field of interest regarding the synthesis of artificial recognition elements that enable the specific determination of target molecule/analyte from a matrix. Recently, these smart materials can be successfully applied to biomolecule detection in biomimetic biosensors. These biosensors contain a biorecognition element (a bioreceptor) and a transducer, like their biosensor analogs. Here, the basic difference is that molecular imprinting-based biosensors use a synthetic recognition element. Molecular imprinting polymers used as the artificial recognition elements in biosensor platforms are complementary in shape, size, specific binding sites, and functionality to their template analytes. Recent progress in biomolecular recognition has supplied extra diagnostic and treatment methods for various diseases. Cost-effective, more robust, and high-throughput assays are needed for monitoring biomarkers in clinical settings. Quartz crystal microbalance (QCM) biosensors are promising tools for the real-time and quick detection of biomolecules in the past two decades A quick, simple-to-use, and cheap biomarkers detection technology based on biosensors has been developed. This critical review presents current applications in molecular imprinting-based quartz crystal microbalance biosensors for the quantification of biomarkers for disease monitoring and diagnostic results.

Application and Advances in Radiographic and Novel Technologies Used for Non-Intrusive Object Inspection

Increase in trading and travelling flows has resulted in the need for non-intrusive object inspection and identification methods. Traditional techniques proved to be effective for decades; however, with the latest advances in technology, the intruder can implement more sophisticated methods to bypass inspection points control techniques. The present study provides an overview of the existing and developing techniques for non-intrusive inspection control, current research trends, and future challenges in the field. Both traditional and developing methods, techniques, and technologies were analyzed with the use of traditional and novel sensor types. Finally, it was concluded that the improvement of non-intrusive inspection experience could be gained with the additional use of novel types of sensors (such as biosensors) combined with traditional techniques (X-ray inspection).

Graphene Bioelectronic Nose for the Detection of Odorants with Human Olfactory Receptor 2AG1

A real-time sensor for the detection of amyl butyrate (AB) utilising human olfactory receptor 2AG1 (OR2AG1), a G-protein coupled receptor (GPCR) consisting of seven transmembrane domains, immobilized onto a graphene resistor is demonstrated. Using CVD graphene as the sensor platform, allows greater potential for more sensitive detection than similar sensors based on carbon nanotubes, gold or graphene oxide platforms. A specific graphene resistor sensor was fabricated and modified via non-covalent π–π stacking of 1,5 diaminonaphthalene (DAN) onto the graphene channel, and subsequent anchoring of the OR2AG1 receptor to the DAN molecule using glutaraldehyde coupling. Binding between the target odorant, amyl butyrate, and the OR2AG1 receptor protein generated a change in resistance of the graphene resistor sensor. The functionalized graphene resistor sensors exhibited a linear sensor response between 0.1–500 pM and high selectively towards amyl butyrate, with a sensitivity as low as 500 fM, whilst control measurements using non-specific esters, produced a negligible sensor response. The approach described here provides an alternative sensing platform that can be used in bioelectronic nose applications.

Sniff Species: SURMOF-Based Sensor Array Discriminates Aromatic Plants beyond the Genus Level

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.

Viscosity Measurement Sensor: A Prototype for a Novel Medical Diagnostic Method Based on Quartz Crystal Resonator

Viscosity variation in human fluids, such as Synovial Fluid (SF) or Cerebrospinal Fluid (CSF), can be used as a diagnostic factor; however, the sample volume obtained for analysis is usually small, making it difficult to measure its viscosity. On the other hand, Quartz Crystal Resonators (QCR) have been used widely in sensing applications due to their accuracy, cost, and size. This work provides the design and validation of a new viscosity measurement system based on quartz crystal resonators for low volume fluids, leading to the development of a sensor called “ViSQCT” as a prototype for a new medical diagnostic tool. The proposed method is based on measuring the resonance frequency at the crystal’s maximum conductance point through a frequency sweep, where crystals with 10 MHz fundamental resonance frequency were used. For validation purposes, artificial fluids were developed to simulate SFs and CFs in healthy and pathological conditions as experiment phantoms. A commercial QCR based system was also used for validation since its methodology differs from ours. A conventional rotational viscometer was used as a reference for calibration purposes. ViSQCT demonstrates the capability to measure the sample’s viscosity differentiation between healthy and pathological fluid phantoms and shows that it can be used as a basis for a diagnostic method of several pathologies related to the studied biological fluids. However, some performance differences between both QCR-based systems compared to the reference system deserves further investigation.

Current and potential biotechnological applications of odorant-binding proteins

Odorant-binding proteins (OBPs) are small soluble proteins whose biological function is believed to be facilitating olfaction by assisting the transport of volatile chemicals in both vertebrate and insect sensory organs, where they are secreted. Their capability to interact with a broad range of hydrophobic compounds combined with interesting features such as being small, stable, and easy to produce and modify, makes them suitable targets for applied research in various industrial segments, including textile, cosmetic, pesticide, and pharmaceutical, as well as for military, environmental, health, and security field applications. In addition to reviewing already established biotechnological applications of OBPs, this paper also discusses their potential use in prospecting of new technologies. The development of new products for insect population management is currently the most prevailing use for OBPs, followed by biosensor technology, an area that has recently seen a significant increase in studies evaluating their incorporation into sensing devices. Finally, less typical approaches include applications in anchorage systems and analytical tools. KEY POINTS: • Odorant-binding proteins (OBPs) present desired characteristics for applied research. • OBPs are mainly used for developing new products for insect population control. • Incorporation of OBPs into chemosensory devices is a growing area of study. • Less conventional uses for OBPs include anchorage systems and analytical purposes. Graphical Abstract.

Peptides, DNA and MIPs in Gas Sensing. From the Realization of the Sensors to Sample Analysis

Detection and monitoring of volatiles is a challenging and fascinating issue in environmental analysis, agriculture and food quality, process control in industry, as well as in 'point of care' diagnostics. Gas chromatographic approaches remain the reference method for the analysis of volatile organic compounds (VOCs); however, gas sensors (GSs), with their advantages of low cost and no or very little sample preparation, have become a reality. Gas sensors can be used singularly or in array format (e.g., e-noses); coupling data output with multivariate statical treatment allows un-target analysis of samples headspace. Within this frame, the use of new binding elements as recognition/interaction elements in gas sensing is a challenging hot-topic that allowed unexpected advancement. In this review, the latest development of gas sensors and gas sensor arrays, realized using peptides, molecularly imprinted polymers and DNA is reported. This work is focused on the description of the strategies used for the GSs development, the sensing elements function, the sensors array set-up, and the application in real cases.

Bio-Inspired Strategies for Improving the Selectivity and Sensitivity of Artificial Noses: A Review

Artificial noses are broad-spectrum multisensors dedicated to the detection of volatile organic compounds (VOCs). Despite great recent progress, they still suffer from a lack of sensitivity and selectivity. We will review, in a systemic way, the biomimetic strategies for improving these performance criteria, including the design of sensing materials, their immobilization on the sensing surface, the sampling of VOCs, the choice of a transduction method, and the data processing. This reflection could help address new applications in domains where high-performance artificial noses are required such as public security and safety, environment, industry, or healthcare.

Sensitively and Selectively Detect Biothiols by Using Fluorescence Method and Resonance Light Scattering Technique Simultaneously

In this paper, we designed a new quantitative and qualitive detection method for biothiols by using fluorescence method and resonance light scattering (RLS) technique. Nitrogen doped carbon quantum dots (C/N-dots) were obtained from tartaric acid and ethylenediamine by hydrothermal method, and then their morphology and optical properties were characterized by different techniques. A detection system consisting of C/N-dots and Ag⁺ complex was established. In this system, C/N-dots possessed the photoluminescent property and the Ag⁺ complex owned the RLS property, so, by combining the two luminescent properties to achieve complementary advantages, we could detect biothiols and solve the problem of distinguishing between Cys and GSH. Additionally, we optimized detecting conditions and investigated the detection mechanism of fluorescence quenching and RLS detecting. Results showed that the analytical response of fluorescence was linear in the range 0–140 μM and the detection limit (LOD) was calculated to be 6.6 μM for Cys, and the addition of GSH had no effect on fluorescence. RLS response ranges were 0–167 μM for Cys and 0–200 μM for GSH, with LOD down to 64 nM and 74 nM, respectively. Furthermore, the probe was successfully used for detecting Cys in fetal bovine serum (FBS) samples by fluorescence method, and also, by RLS technique, the content of GSH in FBS samples was detected.