Odor Sensor Research Articles

Designable peptides on graphene field-effect transistors for selective detection of odor molecules

Gas sensing based on graphene field-effect transistors (GFETs) has gained broad interest due to their high sensitivity. Further progress in gas sensing with GFETs requires to detection of various odor molecules for applications in the environmental monitoring, healthcare, food, and cosmetic industries. To develop the ubiquitous odor-sensing system, establishing an artificial sense of smell with electronic devices by mimicking olfactory receptors will be key. Although the application of olfactory receptors to GFETs is straightforward for odor sensing, synthetic molecules with a similar function to olfactory receptors would be desirable to realize the robust performance of sensing. In this work, we designed three new peptides consisting of two domains: a bio-probe to the target molecules and a molecular scaffold. These peptides were rationally designed based on a motif sequence in olfactory receptors and self-assembled into a molecular thin film on GFETs. Limonene, methyl salicylate, and menthol were employed as representative odor molecules of plant flavors to demonstrate the biosensing of odor molecules. The conductivity change of GFETs against the binding to odor molecules with various concentrations and the dynamic response revealed a distinct signature of three different peptides against individual species of the target molecules. The kinetic response of each peptide exhibited characteristic time constants in the adsorption and desorption process, also supported by the principal component analysis. Our demonstration of the graphene odor sensors with the designed peptides opens a way to establish future peptide-array sensors with multi-sequence of peptide, realizing an odor sensing system with higher selectivity.

Temperature characteristics of a thickness shear mode quartz crystal resonator bonded to a support substrate

Thickness shear mode (TSM) resonators consisting of metal films and quartz plates are widely used for sensor applications such as film thickness monitoring, force sensors, and odor sensors. However, the current sensor geometry prevents further improvements in its sensitivity and stability. Thinning the plate is necessary for high sensitivity, and advanced fabrication technologies are required for their commercialization. The solution is to use a support substrate to increase the mechanical strength, which can guide the transmittance of the electric field. Herein, we report a TSM resonator bonded to a support substrate. An AT-cut quartz resonator with a floating electrode on the top side was bonded to the support substrate. Two excitation electrodes were placed under the substrate. The support substrates evaluated in this study included borosilicate glass, Z-cut quartz crystals, and AT-cut quartz crystal plates. The quartz crystal resonator (QCR) bonded to the AT-cut quartz crystal plate and positioned at 90° to the crystallographic x-axis shows an excellent temperature coefficient of frequency of −60 ± 14 ppb/°C for a temperature range 11–40 °C. The proposed method reduces temperature sensitivity to 1/4 or less compared to that without a substrate. Furthermore, the resonator could be used as a quartz crystal microbalance. The proposed method may inspire further high-frequency QCR-based biochemical chips or various sensor applications with TSM resonators.

Characterization of Congou Black Tea by an Electronic Nose with Grey Wolf Optimization (GWO) and Chemometrics

The aroma indicator is an important factor in evaluating the quality and grade of black tea and is vital in preventing fraud and reducing financial losses. A rapid method for the characterization of Congou black tea is described using an electronic nose (EN) with grey wolf optimization (GWO) and chemometrics. First, the aroma qualities of the tea were determined by the EN for 700 samples of seven grades and converted into 10 characteristic values. The GWO algorithm was used to optimize the aroma indicators of the EN sensors. Discrimination models based upon the optimized odor sensor features and partial least squares discriminant analysis, k-nearest neighbor (KNN), extreme learning machine, and support vector machine were created to assess the quality of the samples. The results showed that the KNN model constructed with the five odor sensor response values optimized by the GWO algorithm performed best with an accuracy of 97.85%. The results demonstrate that the EN is suitable for the rapid assessment of the quality and grades of Congou black tea products.

Weeds Detection and Control in Rice Crop Using UAVs and Artificial Intelligence: A Review

Weeds are serious issues in rice farming and undesired plants that compete for water, light, space, and nutrients, reducing crop yields. Weeds competition in rice crops can result in yield failure of up to 100% if weeds are not controlled. Furthermore, weeds will raise protection costs by harbouring other pests such as diseases, insects, and nematodes that use weeds as alternate hosts. Rice fields are infested with grassy weeds, broadleaves, and sedges, among other weeds. Weeds detection is important to identify the types of weeds in rice areas and make the precise decision to determine the method of weeds control and reduce herbicide use. Chemical, biological, and mechanical weed control strategies such as manual weeding, mechanical weeding, and herbicide use are all part of the Integrated Weed Management (IWM) approach. In the rice field, however, pesticide spraying to eliminate the weed is a very common control approach. Still, this method has become ineffective due to frequently spraying with the same type of herbicide. Early detection is required to define the type of weeds in rice fields, make a precise decision on weed management methods, and prescribe the appropriate herbicide to the rice farmer. Artificial intelligence and unmanned aerial vehicles (UAVs) were largely applied to identify the weeds in rice fields and herbicide spraying to control the weeds. UAVs, such as drones, have recently shown to have a lot of potential in agriculture, such as crop health monitoring systems, assisting in planning irrigation schedules, estimating production data, and capturing weather analysis data and weed infestation. In Malaysia, UAVs are mostly utilized for nutrition and pesticide applications, particularly by smallholder farmers and industries. The integration use of artificial intelligence including unmanned aerial vehicles drones, and various sensors, hyperspectral, multispectral, and RGB (red-green-blue) cameras, thermal and odor sensor for weed early detection methods could ensure the possibility of a better outcome in managing weed problems. This paper reviews the detection and controlling of weeds using UAVs and artificial intelligence technologies in rice crop.

Retronasal aroma dynamics related to the swallowing threshold.

The state of food comminution caused by chewing is an important factor triggering the swallowing reflex. However, the impact of retronasal aroma released from comminuted food to the nose upon swallowing during food intake is poorly understood. The present study investigated the relationship between aroma concentration and swallowing threshold while chewing a standardised test food. Twenty healthy participants took part in this study. Concentration of retronasal aroma was measured over time through the nostrils using an odour sensor. The aroma concentration was measured while chewing an orange-flavoured gummy jelly until swallowing, and the number of chewing strokes was measured to determine the swallowing threshold. Next, the aroma concentration was measured while chewing the gummy jelly for 30 strokes, and 100% and 200% of swallowing threshold without swallowing. The surface area increase in the expectorated gummy jelly pieces was calculated using image analysis and defined as masticatory performance at 30 strokes. The average number of chewing strokes until the swallowing threshold was 45.1± 14.2. Higher masticatory performance was associated with a smaller number of chewing strokes at swallowing threshold, and a greater increase in surface area at the swallowing threshold. The number of chewing strokes and the aroma concentration were similar between the swallowing threshold and at the maximum aroma concentration while chewing at 200% of the swallowing threshold. The findings suggested that the swallowing threshold might be influenced by retronasal aroma concentration as well as the state of food comminution.

Objective Odor Assessment in Patients with Osmidrosis.

Background:No standards for the assessment of axillary odor intensity and the effects of therapy for osmidrosis have been established. This study presents an objective method for assessing odor severity in patients with osmidrosis and investigates the volatile odorants and skin flora.Methods:The odor intensity was measured pre- and postoperatively using an industrial odor sensor in 79 patients with osmidrosis. Cultures of the axillary skin were obtained during skin flap surgery. Volatile odorants of the patients were assessed using an odor-sensor gas chromatograph mass spectrometer, and samples collected from clothing worn by the patients before and after surgery. The skin pH of the axilla was measured before and after surgery. The locations of odorants and bacteria in the skin were observed using electron microscopy.Results:The mean patient age was 28.8 years, and the male-to-female ratio was 4:3. The odor significantly decreased from 52.6 preoperatively to 20.5 postoperatively (P < 0.001). The bacterial flora on the skin included mostly Staphylococcus. Multiple causative substances (volatile proteins) were identified on gas chromatography. The mean preoperative axillary skin pH was 6.21, which was significantly different than that of patients without osmidrosis (5.92; P < 0.01).Conclusions:An odor sensor accurately assesses odor intensity in patients with osmidrosis. The neutralization of axillary pH may promote the production of odorants by creating the optimal pH for bacterial growth. Odor sensor and pH values can be used pre- and postoperatively as objective assessment measurements for patients with osmidrosis.

Response Characteristics of Sensor Array of Fresh Meat Detection Circuit using Conducting Polymer Sensor

Consumption of meat is needed by society. The meat needed is of course fresh and quality meat. However, the quality of meat sold in the community varies greatly. Fresh and quality meat is meat that is odorless, not slimy, does not change color in certain areas. Meat that is not fresh and not of good quality is smelly meat. The odor is described as a fishy smell, a foul smell, contains sulfur and smells like ammonia. Because it is dangerous to consume rotten meat, it is necessary to have an automatic meat detection device so that it can ensure the meat is fresh and of good quality. This tool replaces the human nose with an odor sensor as a bad smell detector.

Molecular Interactions between an Enzyme and Its Inhibitor for Selective Detection of Limonene.

Researchers widely apply enzyme inhibition to chemicals such as pesticides, nerve gases, and anti-Alzheimer's drugs. However, application of enzyme inhibition to odorant sensors is less common because the corresponding reaction mechanisms have not yet been clarified in detail. In this study, we propose a new strategy for highly selective detection of odorant molecules by using an inhibitor-specific enzyme. As an example, we analyzed the selective interactions between acetylcholinesterase (AChE) and limonene─the major odorant of citrus and an AChE inhibitor─using molecular dynamics simulations. In these simulations, limonene was found to be captured at specific binding sites of AChE by modifying the binding site of acetylcholine (ACh), which induced inhibition of the catalytic activity of AChE toward ACh hydrolysis. We confirmed the simulation results by experiments using an ion-sensitive field-effect transistor, and the degree of inhibition of ACh hydrolysis depended on the limonene concentration. Accordingly, we quantitatively detected limonene at a detection limit of 5.7 μM. We furthermore distinguished the response signals to limonene from those to other odorants, such as pinene and perillic acid. Researchers will use our proposed odorant detection method for other odorant-enzyme combinations and applications of miniaturized odorant-sensing systems based on rapid testing.

IoT Based Washroom Feedback System for Quality Monitoring

Abstract: In the twenty-first century, technology advances at a rapid pace, although our primary responsibility remains cleanliness. We disseminate the message of cleanliness and hygiene in our environment. In public venues or restrooms, we must preserve hygiene or cleanliness. The government provides numerous facilities in cities to keep our surroundings clean. This approach focuses solely on how to maintain washroom hygiene. This effort educates the public about hygiene and the need of keeping the environment clean. As a result, the primary goal of this study is to create safe, sanitary, and disease-free restrooms. The authorities spend a significant amount of money and personnel to keep these public restrooms in good working order. Nonetheless, all of these efforts are in vain because there is no centralized system in place to oversee the cleanliness of public restrooms and track the quality of the cleaning provided by cleaners. As a result, a system that enables centralized monitoring of all toilets and an interface to the cleaner will be beneficial in resolving this issue. We attempted to address this problem by creating an IoT-based smart system. We've utilized sensors to detect unpleasant scents in the washroom, as well as sensors to measure water level in tanks and water leakage. Our algorithm estimates the cleanliness of toilets using sensor data. Keywords: ESP2866 NodeMCU, Ultrasonic HC-SR04 Sensor, MQ-135 Odour Sensor, Water Sensor, Google FireBase, Website for Feedback, OpenCV, Python.

Prediction of laying hen house odor concentrations using machine learning models based on small sample data

• XGBoost model served better to predict odor concentration in laying hen houses than SVR and BPNN. • XGBoost algorithm had good performance and strong learning ability to deal with the odor datasets with small-sample and non-linearity characteristics. • Gas components and environment variables directly related to gas production were the key drivers in developing odor prediction model. • The knowledge mining ability of XGBoost model on odor datasets collected from laying hen house was good. In laying hen farm, odor measurement and reduction are necessary for clean environment management and human/animal health care. However, limited quantitative data and expensive detection technologies preclude an accurate assessment of odor reduction practices. This study compared the odor prediction ability of three different machine learning models of extreme gradient boosting (XGBoost), support vector regression (SVR), and back-propagation neural networks (BPNN) based on small sample size odor datasets collected from laying hen houses to achieve timely odor monitoring and check the significant components affecting the odor concentration. The input variables were ammonia (NH 3 ) concentration, hydrogen sulfide (H 2 S) concentration, temperature, relative humidity (RH), and ventilation rate, and the output value was odor concentration. Results showed the XGBoost model had the highest prediction ability and the most accuracy with the R 2 of 0.88, followed by BPNN (R 2 = 0.75) and SVR (R 2 = 0.66). XGBoost model could be a useful tool to timely predict odor concentration with moderate accuracy. In addition, in the trained XGBoost model, NH 3 concentration was the most important factor, followed by the H 2 S concentration, temperature, RH, and ventilation rate, which indicated the gas components and environment variables related to gas production were the key drivers in training odor prediction model. This study also forecasted the influences of each gas and environmental factors on odor concentrations and verified the good knowledge mining ability of XGBoost model. The practical potentials are considerable of using XGBoost model to replace human assessors and to apply to laying hen house environmental control as the odor sensor.

Development of an odorant sensor with a cell-free synthesized olfactory receptor and a graphene field-effect transistor.

Animals sense odorants using olfactory receptors. Many trials have been conducted to develop artificial odorant sensors using olfactory receptors. However, the development has been hindered by the difficulty in obtaining olfactory receptors. In this study, we expressed an olfactory receptor, cOR52, using a wheat germ cell-free synthesis system. The functionality of the expressed cOR52 was confirmed by ligand concentration-dependent interactions with the mini-G protein. The expressed cOR52 was immobilized on a graphene field-effect transistor. The cOR52-modified graphene field-effect transistor exhibited a ligand-specific response between 100nM and 100µM. This approach seems to be applicable for other olfactory receptors. Therefore, it will be possible to develop an odorant sensor equipped with various olfactory receptors by this method.

Characterisation of Flavour Attributes in Egg White Protein Using HS-GC-IMS Combined with E-Nose and E-Tongue: Effect of High-Voltage Cold Plasma Treatment Time.

Egg white protein (EWP) is susceptible to denaturation and coagulation when exposed to high temperatures, adversely affecting its flavour, thereby influencing consumers’ decisions. Here, we employ high-voltage cold plasma (HVCP) as a novel nonthermal technique to investigate its influence on the EWP’s flavour attributes using E-nose, E-tongue, and headspace gas-chromatography-ion-mobilisation spectrometry (HS-GC-IMS) due to their rapidness and high sensitivity in identifying flavour fingerprints in foods. The EWP was investigated at 0, 60, 120, 180, 240, and 300 s of HVCP treatment time. The results revealed that HVCP significantly influences the odour and taste attributes of the EWP across all treatments, with a more significant influence at 60 and 120 s of HVCP treatment. Principal component analyses of the E-nose and E-tongue clearly distinguish the odour and taste sensors’ responses. The HS-GC-IMS analysis identified 65 volatile compounds across the treatments. The volatile compounds’ concentrations increased as the HVCP treatment time was increased from 0 to 300 s. The significant compounds contributing to EWP characterisation include heptanal, ethylbenzene, ethanol, acetic acid, nonanal, heptacosane, 5-octadecanal, decanal, p-xylene, and octanal. Thus, this study shows that HVCP could be utilised to modify and improve the EWP flavour attributes.

Palm-Sized Quadcopter for Three-Dimensional Chemical Plume Tracking

In this study, we designed and experimentally verified the placement of odor sensors and an algorithm using the aero-olfactory effect of a palm-sized quadcopter to solve the three-dimensional chemical plume tracking (3D-CPT) problem. Solving 3D-CPT is important in engineering as it helps perform rescue operations during disasters and identify sources of harmful substances. Moreover, the odor sensors must be properly located and a CPT algorithm be applied to improve the tracking performance of a chemical. However, studies regarding the use of quadcopters for solving the 3D-CPT problem are scarce, and the relationship between the odor sensor location and algorithm is debatable. Hence, we utilized particle image velocimetry, an airflow visualization technology, to evaluate the arrival direction of chemicals at different heights. The results showed that odor sensors must be placed on the upper and front surfaces of a quadcopter to monitor the chemicals three-dimensionally. Additionally, we designed a 3D surge-casting algorithm, which is an extension of the CPT strategy of a flying moth, that is, surge casting, to accommodate the proposed odor sensor placement. By conducting 3D-CPT experiments based on different heights of odor sources using the proposed system, we discovered that even in an environment with significant changes in the wind direction the CPT performance is better than that of the conventional 3D-CPT algorithm. Thus, 3D-CPT should be further improved to enable its application in unknown and cluttered environments. In this study, we improved the 3D-CPT performance of a palm-sized quadcopter by designing an appropriate sensor arrangement and algorithm balance.

Towards Digital Twins for Multi-Sensor Land and Plant Monitoring

Small and medium-sized farms struggle with increased needs for monitoring in different dimensions, even by having to respond to regulatory requirements, e.g., by the global push to reduce pesticides and to improve soil health. Moreover, with the rising needs for food production there has been an effort to shift agriculture into a new era, in which the sector needs to be modernized by using new digital technologies. The goal of our research work is to provide farmers with a reliable “digital twin”, i.e., a monitoring ecosystem that allows them to visualize multi-sensor data collected from their fields, but also to "plug in" predictive models, e.g., for plant disease prediction. The paper presents an extensible solution, designed in co-creation with stakeholders, that can use a wide range of "sensors", ranging from free satellite images, low-cost off-the-shelf sensors, to even novel technologies, such as odour sensors. The TWINSOR concept is introduced, but also a first prototype applied to the vineyard is described as a first result.

DeepSniffer: A meta-learning-based chemiresistive odor sensor for recognition and classification of aroma oils

A meta-learning algorithm, conventionally used for visual recognition, was applied to the recognition and classification of aroma oils. A printable chemiresistive sensor array was fabricated, based on composites of carbon black with various active materials. Standard aromatherapy kits with 30 types of essential oils were used as targets in an odor sensing experiment. Benefiting from the pattern recognition ability of the fabricated sensor array, a high-quality dataset was obtained with 30 aroma oil classes, in which each class had nine replicate samples. A deep metric learning model, based on a Siamese neural network and a multilayer perceptron, was used to perform the N-way k-shot meta-learning. A test accuracy of over 98.7% was obtained for 31-way 9-shot learning, on discriminating whether the input pair samples were taken from similar or dissimilar classes. The model was effective in extracting meta-features of the aroma oils; this was proved by the improved clustering effect of samples in the spaces of principal components analysis and t-distributed stochastic neighbor embedding. The 30 aroma oils were divided into two datasets according to 6-fold cross-validation: 25 aroma oil classes (plus one blank class) as seen classes for constructing 26-way 9-shot learning models and the remaining five aroma oils as unseen classes for prediction. Average accuracies of 93.5% and 93.9% were achieved for recognition of the unseen aroma oils from the seen classes and classification of the unseen aroma oils themselves, respectively, demonstrating the effectiveness of the developed sensor and model for odor recognition and classification.

Plume Dynamics Structure the Spatiotemporal Activity of Mitral/Tufted Cell Networks in the Mouse Olfactory Bulb.

Although mice locate resources using turbulent airborne odor plumes, the stochasticity and intermittency of fluctuating plumes create challenges for interpreting odor cues in natural environments. Population activity within the olfactory bulb (OB) is thought to process this complex spatial and temporal information, but how plume dynamics impact odor representation in this early stage of the mouse olfactory system is unknown. Limitations in odor detection technology have made it difficult to measure plume fluctuations while simultaneously recording from the mouse's brain. Thus, previous studies have measured OB activity following controlled odor pulses of varying profiles or frequencies, but this approach only captures a subset of features found within olfactory plumes. Adequately sampling this feature space is difficult given a lack of knowledge regarding which features the brain extracts during exposure to natural olfactory scenes. Here we measured OB responses to naturally fluctuating odor plumes using a miniature, adapted odor sensor combined with wide-field GCaMP6f signaling from the dendrites of mitral and tufted (MT) cells imaged in olfactory glomeruli of head-fixed mice. We precisely tracked plume dynamics and imaged glomerular responses to this fluctuating input, while varying flow conditions across a range of ethologically-relevant values. We found that a consistent portion of MT activity in glomeruli follows odor concentration dynamics, and the strongest responding glomeruli are the best at following fluctuations within odor plumes. Further, the reliability and average response magnitude of glomerular populations of MT cells are affected by the flow condition in which the animal samples the plume, with the fidelity of plume following by MT cells increasing in conditions of higher flow velocity where odor dynamics result in intermittent whiffs of stronger concentration. Thus, the flow environment in which an animal encounters an odor has a large-scale impact on the temporal representation of an odor plume in the OB. Additionally, across flow conditions odor dynamics are a major driver of activity in many glomerular networks. Taken together, these data demonstrate that plume dynamics structure olfactory representations in the first stage of odor processing in the mouse olfactory system.

Market Perspectives and Future Fields of Application of Odor Detection Biosensors within the Biological Transformation-A Systematic Analysis.

The technological advantages that biosensors have over conventional technical sensors for odor detection and the role they play in the biological transformation have not yet been comprehensively analyzed. However, this is necessary for assessing their suitability for specific fields of application as well as their improvement and development goals. An overview of biological basics of olfactory systems is given and different odor sensor technologies are described and classified in this paper. Specific market potentials of biosensors for odor detection are identified by applying a tailored methodology that enables the derivation and systematic comparison of both the performance profiles of biosensors as well as the requirement profiles for various application fields. Therefore, the fulfillment of defined requirements is evaluated for biosensors by means of 16 selected technical criteria in order to determine a specific performance profile. Further, a selection of application fields, namely healthcare, food industry, agriculture, cosmetics, safety applications, environmental monitoring for odor detection sensors is derived to compare the importance of the criteria for each of the fields, leading to market-specific requirement profiles. The analysis reveals that the requirement criteria considered to be the most important ones across all application fields are high specificity, high selectivity, high repeat accuracy, high resolution, high accuracy, and high sensitivity. All these criteria, except for the repeat accuracy, can potentially be better met by biosensors than by technical sensors, according to the results obtained. Therefore, biosensor technology in general has a high application potential for all the areas of application under consideration. Health and safety applications especially are considered to have high potential for biosensors due to their correspondence between requirement and performance profiles. Special attention is paid to new areas of application that require multi-sensing capability. Application scenarios for multi-sensing biosensors are therefore derived. Moreover, the role of biosensors within the biological transformation is discussed.

Real-time odor concentration and direction recognition for efficient odor source localization using a small bio-hybrid drone

• Autonomous small drone with an insect antennae-based sensor device was developed. • Drone recognized real-time odorant concentration differences in air. • Sensor enclosure that enhanced the sensor directivity was developed. • Drone with an enclosure could distinguish front–back directions in an odor plume. • Drone localized the odor source in a pseudo-open environment using a spiral-surge algorithm. Small drones with biosensor devices have great potential for detecting odorant molecules in air and can be applied to environmental and security monitoring. To realize these applications, two important factors are considered: first, development of highly sensitive, selective, and real-time odorant sensor devices, and second, construction of a highly maneuverable platform with efficient odor source localization. Previously, small drones with commercial gas sensors or biosensors based on insect antennae have been developed. However, the performance of gas sensors proved to be inadequate for real-time sensing; the flight performance of a bio-hybrid drone was limited because the yaw turn was not considered and flight tests were conducted in a wind tunnel. In this study, we developed a fully autonomous small drone with a portable electroantennogram (EAG) based on silkmoth antennae mounted on it. The EAG device was also equipped with a sensor enclosure to enhance sensor directivity. The bio-hybrid drone can recognize real-time odorant concentration differences in a pseudo-open environment (outside the wind tunnel). We also developed an enclosure for enhancing the sensor directivity of EAG. Owing to the enclosure, the drone could distinguish the front-back direction in the odor plume without a wind direction sensor. Based on these results, the drone recognized the maximum value of odorant concentration during an over 360° yaw turn and localized the odor source using the spiral-surge algorithm without any assumption of the upwind direction. This study proposes an efficient flight platform for detecting odorant molecules in air and localizing their sources.

UV-Irradiation- and Inflammation-Induced Skin Barrier Dysfunction Is Associated with the Expression of Olfactory Receptor Genes in Human Keratinocytes.

Olfactory receptors (ORs) have diverse physiological roles in various cell types, beyond their function as odorant sensors in the olfactory epithelium. These previous findings have suggested that ORs could be diagnostic markers and promising therapeutic targets in several pathological conditions. In the current study, we sought to characterize the changes in the expression of ORs in the HaCaT human keratinocytes cell line exposed to ultraviolet (UV) light or inflammation, well-recognized stimulus for skin barrier disruption. We confirmed that major olfactory signaling components, including ORs, GNAL, Ric8b, and adenylate cyclase type 3, are highly expressed in HaCaT cells. We have also demonstrated that the 12 ectopic ORs detectable in HaCaT cells are more highly expressed in UV-irradiated or inflamed conditions than in normal conditions. We further assessed the specific OR-mediated biological responses of HaCaT cells in the presence of known odorant ligands of ORs and observed that specific ligand-activated ORs downregulate skin barrier genes in HaCaT cells. This study shows the potential of OR as a marker for skin barrier abnormalities. Further research is needed to explore how OR is implicated in the development and progression of barrier dysfunction.

Determination of fish quality parameters with low cost electronic nose

In the present study, the odour changes of trout, sea bream and sea bass were measured and recorded with Arduino microprocessor compatible MQ3, MQ4, MQ5, MQ8, MQ9 and MQ135 sensors during a week of storage. The odour intensity measured by the sensors were compared with the microbiological and sensory data, and spoilage thresholds were obtained. An electronic nose box was developed, which could quickly detect the quality of 10 g of fish meat placed inside using an Arduino microprocessor. Total viable counts of all test samples defined by the developed box as “Fresh Fish” were found to be were found to be lower than the level of 3 log CFU/g. The results of the study demonstrated that the odour sensors controlled by Arduino microprocessor could be fast, easy and low-cost solutions for determining the food quality parameters.