Odor Sensor Research Articles

Development of Odor Sensors Based on Membrane-Type Surface Stress Sensors and Challenges for Social Implementation

Development of Odor Sensors Based on Membrane-Type Surface Stress Sensors and Challenges for Social Implementation

Non-contact identification of essential oils with IR sensors and analysis of components with an electronic nose

AbstractIn this study, the odour values of rosemary, mint, black cumin, lavender, and thyme oils were determined by an electronic nose working with an Arduino microprocessor. The components of the plants were compared with the sensor data, and the most sensitive sensors were determined by sorting according to the highest components. The study data indicated that the MQ3 sensor exhibited the highest sensitivity for thyme, black cumin, and lavender oils, which contain 63% carvacrol, 38% thymoquinone, and 36% linalool, respectively. Also, MQ7 was the most sensitive sensor for menthone (68%) in mint oil and eucalyptol (45%) in rosemary oil. In addition, a low-cost and non-contact device that works with an infrared sensor has been developed to detect the identity of the oil added into the vial. The study data showed that low-cost Arduino-based IR and odour sensors can determine the identity and component percentage of oils. The fact that the developed device can detect with 100 percent accuracy even in case of peppermint and rosemary oils, which are very similar in appearance, shows that the study data will be an inspiration for contactless determination of oil quality and type.

Identification of Beef Odors under Different Storage Day and Processing Temperature Conditions Using an Odor Sensing System.

This study used an odor sensing system with a 16-channel electrochemical sensor array to measure beef odors, aiming to distinguish odors under different storage days and processing temperatures for quality monitoring. Six storage days ranged from purchase (D0) to eight days (D8), with three temperature conditions: no heat (RT), boiling (100 °C), and frying (180 °C). Gas chromatography-mass spectrometry (GC-MS) analysis showed that odorants in the beef varied under different conditions. Compounds like acetoin and 1-hexanol changed significantly with the storage days, while pyrazines and furans were more detectable at higher temperatures. The odor sensing system data were visualized using principal component analysis (PCA) and uniform manifold approximation and projection (UMAP). PCA and unsupervised UMAP clustered beef odors by storage days but struggled with the processing temperatures. Supervised UMAP accurately clustered different temperatures and dates. Machine learning analysis using six classifiers, including support vector machine, achieved 57% accuracy for PCA-reduced data, while unsupervised UMAP reached 49.1% accuracy. Supervised UMAP significantly enhanced the classification accuracy, achieving over 99.5% with the dimensionality reduced to three or above. Results suggest that the odor sensing system can sufficiently enhance non-destructive beef quality and safety monitoring. This research advances electronic nose applications and explores data downscaling techniques, providing valuable insights for future studies.

Recent progress of e-Nose type odor sensor and its application

Recent progress of e-Nose type odor sensor and its application

Effective Dissolution of a Gaseous Odorant in Liquid Using Gas Flow

AbstractOlfactory receptors are known for their high specificity, sensitivity, and nanometer‐sized sensing element, making them ideal for in situ odorant sensor applications. However, one of the major limitations of these sensors is the requirement for an aqueous environment to maintain their function, while most odorants are poorly soluble in water. To address this issue, a microfluidic device consisting of a gas‐flow channel and a microwell is proposed that enables the effective dissolution of a gaseous odorant in an aqueous solution. Computational simulations are used to design the gas‐flow channel and the well, and the performance of the odorant dissolution is verified using the fabricated device. This study considers that convection in water induced by the gas flow enhances the dissolution of a gaseous odorant. The feasibility of the device is demonstrated for on‐site detection of a gaseous odorant. This approach facilitates the development of highly responsive and portable sensors utilizing olfactory receptors, offering a promising solution to the limitations of current odorant sensors.

Designing signal processing for robotic olfaction based on electroantennogram

ABSTRACT In this study, we experimentally evaluated a signal-processing method for the stable use of an insect antenna as an odor sensor for a robot because of its excellent sensitivity and selectivity to odors. Changes in potential in response to an odor stimulus can be measured when the electrodes are connected to both ends of an insect antenna. This is called an electroantennogram (EAG), and is a type of physiological response. Hence, we must extract only the EAG waveform using appropriate signal processing because it has a low signal-to-noise ratio. Accordingly, we applied signal processing based on an exponentially weighted moving average (EWMA), which demonstrates excellent real-time performance, to an EAG to eliminate high-frequency noise. Moreover, we experimentally investigated the sampling rate to properly measure the EAG. From the results, the EAG-driven robot with the designed signal processing demonstrated a search performance of more than 70 % , even in odor-source localization experiments with different types of moths and environments.

Quantitative identification and sensor sensitivity analysis in detection of odor volatile compounds emitted by scaly grain mites, Suidasia pontifica Oudemans.

In Thailand, the scaly grain mite, Suidasia pontifica Oudemans, impacts the feed industry by emitting a lemongrass-like odor in contaminated feed, reducing its palatability. This study focused on identifying volatile organic compounds (VOCs) associated with this odor in contaminated chicken feeds and ground maize, as well as individual mites using gas chromatography-mass spectrometry (GC-MS). We explored the relationship between VOC concentration and mite population size in chicken feed cultures, aiming to detect minimal mite presence through regression models. Additionally, we tested the sensitivity of nine electronic odor sensors in detecting these VOCs. Our results showed Z-citral and E-citral present in mite-contaminated ground maize, chicken feeds, but absent in uncontaminated samples. Mite populations in chicken feed followed a normal distribution, increasing rapidly, peaking, and then declining, a pattern mirrored in the concentrations of Z-citral and E-citral. Simple linear regression models confirmed a positive correlation between mite density and citral concentrations. Polynomial regression models indicated Z-citral is detectable at mite densities over 67 individuals per gram and E-citral over 3.89 individuals per gram. Odor sensors showed varying readings across different citral concentrations, uncontaminated, and mite-infested chicken feeds. Our study confirms that S. pontifica are responsible for the lemongrass-like odor in infested samples, due to Z-citral and E-citral. The citral concentrations increase with mite population growth and are detectable at low mite densities, suggesting their effectiveness as biomarkers for early mite infestation detection. Additionally, MQ series odor sensors detected these VOCs, indicating their potential for monitoring mite infestations in storage environments. © 2024 Society of Chemical Industry.

Enantioselective Detection of Gaseous Odorants with Peptide-Graphene Sensors Operating in Humid Environments.

Replicating the sense of smell presents an ongoing challenge in the development of biomimetic devices. Olfactory receptors exhibit remarkable discriminatory abilities, including the enantioselective detection of individual odorant molecules. Graphene has emerged as a promising material for biomimetic electronic devices due to its unique electrical properties and exceptional sensitivity. However, the efficient detection of nonpolar odor molecules using transistor-based graphene sensors in a gas phase in environmental conditions remains challenging due to high sensitivity to water vapor. This limitation has impeded the practical development of gas-phase graphene odor sensors capable of selective detection, particularly in humid environments. In this study, we address this challenge by introducing peptide-functionalized graphene sensors that effectively mitigate undesired responses to changes in humidity. Additionally, we demonstrate the significant role of humidity in facilitating the selective detection of odorant molecules by the peptides. These peptides, designed to mimic a fruit fly olfactory receptor, spontaneously assemble into a monomolecular layer on graphene, enabling precise and specific odorant detection. The developed sensors exhibit notable enantioselectivity, achieving a remarkable 35-fold signal contrast between d- and l-limonene. Furthermore, these sensors display distinct responses to various other biogenic volatile organic compounds, demonstrating their versatility as robust tools for odor detection. By acting as both a bioprobe and an electrical signal amplifier, the peptide layer represents a novel and effective strategy to achieve selective odorant detection under normal atmospheric conditions using graphene sensors. This study offers valuable insights into the development of practical odor-sensing technologies with potential applications in diverse fields.

Inspired by human olfactory system: Deep-learning-assisted portable chemo-responsive dye-based odor sensor array for the rapid sensing of shrimp and fish freshness

Colorimetric sensor arrays (CSAs) can detect the volatile organic compounds (VOCs) produced during food storage, enabling odor visualization for freshness monitoring. Herein, sixteen chemo-responsive dyes that undergo observable color changes to VOCs are absorbed on ammonium quaternized cellulose nanofibres (C-CNFs) to design a paper-based CSA. The strong ionic interactions between the dyes and C-CNFs immobilize the dyes onto the sensor without leakage and provide stable and consistent colorimetric change even at a high relative humidity (RH = 60 %). Owing to the three-dimensional porous structure of C-CNFs, the CSA demonstrates high sensitivity for detecting VOCs produced during shrimp and fish spoilage. The limits of detection for ammonia, dimethylamine, trimethylamine, cadaverine, and putrescine are 2, 21, 3, 1, and 1 ppm, respectively. By selecting dyes covering a wide range of VOCs, the color changes in the CSA can accurately differentiate fresh, less fresh, slightly spoilt, and spoilt shrimp and fish. These color changes are further digitized and used to train a convolutional neural network (CNN). The CNN method has high accuracy (99 %) for determining abovementioned freshness levels. The combination of CSA and CNN is a promising approach for fabricating a portable sensing platform for the advanced non-destructive determination of food freshness levels.

Polypyrrole-inorganic oxide hybrid synthesis and adaptability to odor sensor

Polypyrrole-inorganic oxide hybrid synthesis and adaptability to odor sensor

Phospholipid Molecular Layer that Enhances Distinction of Odors Based on Artificial Sniffing.

We propose a novel odor-sensing system based on the dynamic response of phospholipid molecular layers for artificial olfaction. Organisms obtain information about their surroundings based on multidimensional information obtained from sniffing, i.e., periodic perturbations. Semiconductor- and receptor-based odor sensors have been developed previously. However, these sensors predominantly identify odors based on one-dimensional information, which limits the type of odor molecule they can identify. Therefore, the development of odor sensors that mimic the olfactory systems of living organisms is useful to overcome this limitation. In this study, we developed a novel odor-sensing system based on the dynamics of phospholipids that responds delicately to chemical substances at room temperature using multidimensional information obtained from periodic perturbations. Odor molecules are periodically supplied to the phospholipid molecular layer as an input sample. The waveform of the surface tension of the phospholipid molecular layer changes depending on the odor molecules and serves as an output. Such characteristic responses originating from the dynamics of odor molecules on the phospholipid molecular layer can be reproduced numerically. The phospholipid molecular layer amplified the information originating from the odor molecule, and the mechanism was evaluated by using surface pressure-area isotherms. This paper offers a platform for an interface-chemistry-based artificial sniffing system as an active sensor and a novel olfactory mechanism via physicochemical responses of the receptor-independent membranes of the organism.

Widely targeted metabolomics combined with E-tongue and E-nose reveal dynamic changes of tender coconut water in responses to the infection of Ceratocystis paradoxa

Ceratocystis paradoxa is a major cause of postharvest disease in tender coconuts worldwide. We conducted a comprehensive study using widely targeted metabolomics, electronic tongue (E-tongue), and electronic nose (E-nose) analyses to investigate the impacts of C. paradoxa invasion on the quality of tender coconut water (TCW) from fresh control (FC), uninoculated (UN), skin-inoculated (SI), and deep-inoculated (DI) nuts. DI exhibited significantly higher taste indicators associated with bitterness, saltiness, astringency aftertaste, and bitter aftertaste, as well as odor sensor values related to various compounds such as long-chain alkanes, hydrides, methane, organic sulfides, etc. Invasion of C. paradoxa into the endosperm altered the flavor characteristics of TCW mainly through the modulation of carbohydrate and secondary metabolite pathways. Furthermore, significant correlations were observed between the differentially expressed flavorful metabolites and the sensor indicators of the E-nose and E-tongue. These findings offer valuable insights into understanding the impact of C. paradoxa infection on coconuts.

(Invited) CMOS-Based Multimodal Sensing

In the coming Society 5.0, information will be collected, aggregated, and utilized in a wide range of areas through the IoT, and as the concept of "Trillion Sensors" states, the importance of "sensors" as an interface between physical space and cyberspace is increasing. In the development of sensor devices to meet this demand attempts to realize the sensing of multiple items by integrating single-function sensors with high detection selectivity have been the main focus in the past. On the other hand, our research group has proposed the measurement concept of multimodal sensing, in which sensors with broad detection characteristics are used to realize the sensing of multiple items, and has developed semiconductor CMOS-based sensors to realize this concept. In this talk, we introduce ion image sensors and odor sensors as examples of CMOS-based sensors suitable for multimodal sensing, and data analysis using machine learning, which is important for this measurement concept will be mentioned and introduce an example of machine learning-based sensing. The application of these sensors to smart agriculture and other fields will also be discussed, as well as the current status and issues for the practical application of sensor systems.An ion-sensitive transistor (ISFET) can be fabricated by changing the MOSFET's gate to an ion-sensitive membrane. An ion image sensor can be constructed by arraying ion-sensitive elements that operate based on the same principle using CMOS image sensor technology. This sensor visualizes the solution's hydrogen ion concentration (pH) and enables real-time observation of ion dynamics in the solution. By changing the ion-sensitive membrane of the sensor, it is possible to image specific ions such as potassium and calcium, and by adding an enzyme-immobilized membrane, it is also possible to capture chemical substances such as lactic acid. By combining these methods, the development of a multimodal sensor that can simultaneously image multiple ions and chemical substances is in progress as an approach from the hardware side. The future approach from the software side is expected to include measurement data processing by machine learning, thereby expanding the range of substances to be measured. In addition, making this sensor extremely thin makes it possible to perform ion imaging by inserting it into the object to be measured. Functional demonstration of visualization of ion distribution in a plant stem has been performed successfully, and we are currently studying its application in the field of smart agriculture.Since the ion image sensor mentioned above captures minute changes in electric potential generated by ion-sensitive membranes, it can be operated as a gas sensor by forming a membrane on the sensor whose electric properties change depending on gas adsorption. Certain polymer membranes respond to a wide range of gases, they can be used as odor-sensitive membranes. When multiple gas- and odor-sensitive membranes with different response characteristics are formed on a CMOS sensor, gas- and odor-specific response patterns can be obtained. Machine learning is effective in analyzing these patterns, and this presentation will introduce an example of gas-type discrimination.

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).

MXene/perovskite-based bionic human odor sensor array with machine learning

Human identification is crucial in many fields. However, it is challenging to achieve instant and accurate human identification through body odors. In this work, an in-situ growth strategy was utilized to establish a gas-sensitive nanocomposite material library with MXene and perovskite. A plug-and-play bionic sensor array module based on the material library was manufactured and assembled into an instant detection platform (IDP). Machine learning (ML) algorithms were introduced to assist IDP in identifying human odors. Due to its unique Schottky Barrier structure, the material library exhibited higher performance and responded 37 % ∼ 70 % higher than initial MXene. IDP was applied to detect the odors of volunteers’ breaths and clothes with an accuracy rate of 69.2 % and 51.1 %, respectively, with the assistance of ML. In all, an instant, convenient, and accurate human identification prototype machine was fabricated, providing a general solution for more complex application scenarios.

(Invited) CMOS-Based Multimodal Sensing

Our research group has proposed the measurement concept of multimodal sensing, in which sensors with broad detection characteristics are used to realize the sensing of multiple items, and has developed semiconductor CMOS-based sensors to realize this concept. In this talk, we introduce ion image sensors and odor sensors as examples of CMOS-based sensors suitable for multimodal sensing, and data analysis using machine learning, which is important for this measurement concept will be mentioned and introduce an example of machine learning-based sensing. The application of these sensors to smart agriculture and other fields will also be discussed, as well as the current status and issues for the practical application of sensor systems.

Correlation Study between Quality and Sensory Characteristics of Kelp Paste by Aspergillus oryzae and Aspergillus niger during Fermentation.

In order to clarify the relationship between quality and sensory characteristics of kelp paste during fermentation, this study analyzed the quality and sensory characteristics of kelp paste through physicochemical indexes, nutritional components, electronic nose and electronic tongue. The results showed that with the extension of fermentation time, the contents of amino nitrogen, total acid, ammonium salt and ash increased gradually, while the pH value, moisture, fat, protein and carbohydrate decreased gradually. Short-chain alkanes such as nitrogen oxides and methane were the main causes of odor. Freshness, salinity and richness were the main indexes of kelp paste taste. Many quality indexes, such as amino nitrogen and protein, were significantly related to the odor sensor, which can better reflect the odor produced in the fermentation process of kelp paste. There was a significant correlation between quality indicators and important taste indicators such as umami, richness and salty taste, which can better reflect the taste of kelp paste during fermentation. To sum up, there was a significant correlation between the quality characteristics and sensory quality of kelp paste, so the relationship between quality characteristics and sensory characteristics in kelp paste can be clarified.

ガスセンサ・ニオイセンサに向けた酸化スズナノマテリアルの創製

Expectations for the sensing of very small amounts of gases and odors are increasing in many fields such as medicine, healthcare, and mobility. In recent years, nanomaterials with various forms such as needle-like, belt-like, sheet-like, and dendrite-like have been developed. These nanomaterials have been used to detect very small amounts of gas with a semiconductor gas sensor. In addition, detection of various gases and odor molecules has been realized using these gas sensors. In particular, in the field of ceramics, which requires high-temperature firing at several hundred degrees Celsius, the realization of “cold crystallization”, which has been a long-standing issue, has had a great impact. Nanomaterials with high surface area, unique shape, metastable crystal facets, etc. have been developed with cold crystallization of ceramics. These nanomaterials have improved the low-concentration gas detection characteristics of gas sensors and odor sensors. In this review, I would like to introduce the fabrication of new SnO2 nanomaterials and the development of gas and odor sensors using the SnO2 nanomaterials.

Repetitive Direct Comparison Method for Odor Sensing.

Olfactory sensors are one of the most anticipated applications of gas sensors. To distinguish odors-complex mixtures of gas species, it is necessary to extract sensor responses originating from the target odors. However, the responses of gas sensors tend to be affected by interfering gases with much higher concentrations than target odor molecules. To realize practical applications of olfactory sensors, extracting minute sensor responses of odors from major interfering gases is required. In this study, we propose a repetitive direct comparison (rDC) method, which can highlight the difference in odors by alternately injecting the two target odors into a gas sensor. We verified the feasibility of the rDC method on chocolates with two different flavors by using a sensor system based on membrane-type surface stress sensors (MSS). The odors of the chocolates were measured by the rDC method, and the signal-to-noise ratios (S/N) of the measurements were evaluated. The results showed that the rDC method achieved improved S/N compared to a typical measurement. The result also indicates that sensing signals could be enhanced for a specific combination of receptor materials of MSS and target odors.

An odorant receptor-derived peptide biosensor for monitoring the occurrence of Hyphantria cunea larvae by recognizing herbivore-induced plant volatile

The release of volatile β-ocimene induced by Hyphantria cunea (H. cunea) larvae in mulberry is an important olfactory cue for host localization of H. cunea adults. In the present study, we demonstrated that H. cunea adults sensitively recognized β-ocimene through a Y-tube test and single sensillum recording (SSR) experiments. Through a combination of homology alignment, tissue profile expression, and transgenic Drosophila SSR assays, HcunOR31 was identified as the specific odorant receptor for β-ocimene in H. cunea. We used transmembrane domain prediction, molecular docking, and peptide synthesis techniques to fabricate three odorant receptor-derived peptide sensors (ORP-sensors) that had the potential to monitor β-ocimene. Among these three, the best sensitivity and specificity to β-ocimene were demonstrated by the ORP3-sensor. Further experimentation revealed that the detection limit of the ORP3-sensor could be as low as one larva feeding on mulberry leaves; in addition, the sensor showed a larger resistance change with the increase in the number of larvae. These findings address the application value of insect olfactory mechanisms and provide an entry point for real-time monitoring of the early occurrence of H. cunea larvae in woodlands.