Odor Sensor Array Research Articles

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.

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.

Highly sensitive and selective detection of human-derived volatile organic compounds based on odorant binding proteins functionalized silicon nanowire array

In this paper, we report the real-time, ultrasensitive, reproducible detection of volatile odorant molecules emitted by human based on free-standing silicon nanowire (SiNW) array functionalized with Anopheles gambiae odorant binding protein (AgOBP). Highly responsive SiNW array was fabricated with a novel low-cost top-down fabrication approach. The free-standing and triangular cross sectional structure allowed efficient sensing of AgOBP conformational change induced by odorant binding. The resulting bioelectronic nose (B-nose) possessed high sensitivity down to several ppb, and outstanding size and functional group selectivity. The device showed reproducible and long-lasting performances. We also demonstrated the multiplex detection of various odorants with a sensor array functionalized with three different AgOBPs. The AgOBP-SiNW B-nose reported herein provides a powerful and effective platform for future development of multiplexed human body odor sensor arrays.

Array of Chemosensitive Resistors with Composites of Gas Chromatography (GC) Materials and Carbon Black for Detection and Recognition of VOCs: A Basic Study.

Mimicking the biological olfaction, large odor-sensor arrays can be used to acquire a broad range of chemical information, with a potentially high degree of redundancy, to allow for enhanced control over the sensitivity and selectivity of artificial olfaction systems. The arrays should consist of the largest possible number of individual sensing elements while being miniaturized. Chemosensitive resistors are one of the sensing platforms that have a potential to satisfy these two conditions. In this work we test viability of fabricating a 16-element chemosensitive resistor array for detection and recognition of volatile organic compounds (VOCs). The sensors were fabricated using blends of carbon black and gas chromatography (GC) stationary-phase materials preselected based on their sorption properties. Blends of the selected GC materials with carbon black particles were subsequently coated over chemosensitive resistor devices and the resulting sensors/arrays evaluated in exposure experiments against vapors of pyrrole, benzenal, nonanal, and 2-phenethylamine at 150, 300, 450, and 900 ppb. Responses of the fabricated 16-element array were stable and differed for each individual odorant sample, proving the blends of GC materials with carbon black particles can be effectively used for fabrication of large odor-sensing arrays based on chemosensitive resistors. The obtained results suggest that the proposed sensing devices could be effective in discriminating odor/vapor samples at the sub-ppm level.

Improvement of odor-recorder capability for recording dynamical change in odor

A new method of recording rapid dynamical change in odors using quartz crystal microbalance (QCM) odor sensor arrays is proposed in this study. Two identical sensor arrays were used to simultaneously measure target and blended odors so that the composition of the blended odor could be adjusted in real time to reproduce the same odor as that of the target. As a result of the increased speed of determining the target-odor recipe, odor that changes over 5 s was successfully recorded. Furthermore, ambient odors were also successfully recorded despite the rapid changes in odors due to turbulent airflows.

Fundamental study of odor recorder for multicomponent odor using recipe exploration method based on singular value decomposition

A new method for the odor recorder, electronically recording the recipe of odors or scents made up of many components, was proposed. Although apple flavors have been recorded with a mixture of five components using the odor recorder in previous work, the number of odor components should be increased to expand the applicable range of odors. Since the collinearity problem of the odor sensor array became apparent with the increase of components, a new method based on variable transformation using singular value decomposition was developed in this paper, to extract the effective subspace of the sensor outputs for recipe exploration. As a result, the sensor-array response pattern of the reproduced odor, with the eight-component recipe, almost agreed with that of the target apple flavor. Furthermore, human sensory tests revealed that the smell of the approximated odor was identical to that of the target flavor.

Odor recorder for multi-component odor using two-level quantization method

A new algorithm to electronically record an odor composed of the mixture of many components using the odor sensing system was proposed. Since the human impression on the odor mostly depends on the kinds of key components rather than the accurate mixture composition, the odor was approximated with the combination of several components. Moreover, the efficient method to explore the recipe for the odor approximation with the combination of many components was established. Then, the recipe of an apple flavor was recorded using the proposed method and was evaluated by the human sensory test. As a result, it was found that the odor approximated with up to eight components was close to the target apple flavor. Furthermore, it was confirmed that the response pattern of the QCM (quartz crystal microbalance) odor sensor array was a good indicator of the odors detected by human olfaction. The proposed method was useful to realize the odor recorder with many odor components.

Study of recording apple flavor using odor recorder with five components

The odor recorder has been developed using the odor sensing system based on the active sensing method to electronically record odors and reproduce them anytime and anywhere. The recorder developed in this paper has capability of recording the target odor, the mixture of five odor components, using the odor sensor array composed of quartz crystal microbalance sensors. The difference between the sensor response patterns of the recipe and target odors was minimized so that the recipe of the five components for the target odor was determined. In this paper, the method of selecting the most appropriate sensors to record odors was investigated based on the condition number of the sensor array sensitivity matrix. Moreover, the method of determining the parameters of the odor-recording algorithm was studied. As a result, several apple flavors composed of the five components were successfully recorded. Furthermore, the typical apple flavor composed of nine components was approximated using the mixture of the five components. The approximation result by the sensing system was successfully verified by the human sensory test.

Identification of basic indoor human behaviors using odor-sensor array

Several daily human-behaviors are identified using plural odor-sensor arrays. This study was done to investigate a survey system for the aged who lived in solitude. Human beings occur various odors in their household life. The odors can be classified into two fields, namely occur from a body and occur by a vital action in a domestic environment. The concentration of the former odor is very low. The odor that generates at preparing a meal belongs to the latter, and it is comparatively high concentration. Sleeping, having a meal and awakening which are basic human behaviors can be identified using plural odor-sensor arrays. These array outputs are characterized using 2-D, 3-D graphs are also useful. As for the results, it becomes obvious that several basic indoor human behaviors can be identified by the plot-places of the sensor-array characteristics. The characteristics are caused by sensor sensitivities and odor concentrations that depend on human behaviors. This system is useful for the invasion of privacy of the occupant.

Prediction of QCM gas sensor responses and calculation of electrostatic contribution to sensor responses using a computational chemistry method

When we construct an odor sensing system using a quartz crystal microbalance (QCM) odor sensor array, it is important to know the properties of sensing films coated on those electrodes and select the optimum combination of sensors in the array. However, they have not been characterized well enough to predict the sensor responses without any experiment. Then, we tried to predict sensor responses using a computational chemistry method. We have so far succeeded in predicting the responses using sensing films of typical liquid gas chromatography (GC) stationary phase materials to various kinds of gases and a few perfumes. In this study, we calculated the amount of sorbed gas molecules into the typical solid material of GC and the amount of sorbed perfumes into various sensing films, and it was found that this computational prediction method is useful in those cases. Next, we tried to evaluate the contribution of single interaction energy term to the sorption using this method although it cannot be revealed through the experiment. It was found from the results that the contribution of electrostatic interaction to the sorption can almost be evaluated and this calculation method is useful for knowing the properties of gases and sensing films.

Using electronic odor sensors to discriminate among oak barrel toasting levels.

Toasting changes both the quantity and the quality of the extractable substances in oak wood of barrels used for the aging of fine wines and spirits. Mastery and repeatability of toasting are vital in the production of quality barrels to be used for aging wines and spirits. Toasted wood components, which can be extracted by the wines or spirits during the aging process, are normally analyzed by maceration in standard alcohol solutions at concentrations adapted to the various products and can be used to control the intensity of the wood toasting. These kinds of analyses are accurate but time-consuming and need specialized laboratories. In this work, the feasibility of monitoring barrel toasting levels using an electronic nose with a metal oxide odor sensor array (MOS) was studied. The results of oak toasting level differentiation obtained via the MOS network were identical to those obtained by analyzing extractable compounds in liquid or gas phase as described in a previous paper. The results presented in this work at the laboratory scale could be used to implement a nondestructive monitoring system based on the analysis of headspace of barrels under industrial conditions.