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In terms of electronic nose algorithms, data pre-processing and classifier type are the two main factors affecting gas classification results. In the early stage, data pre-processing mostly takes specific information from gas-reaction waveforms as features and uses machine learning algorithms, such as K-Nearest Neighbor(KNN) and Support Vector Machine(SVM), to classify the gas data. In recent years, some research has been done on using deep learning for gas classification. The data pre-processing takes the overall process of the gas reaction as a feature map, and the classifier uses Convolutional Neural Network(CNN) architecture to classify the gases, resulting in classification accuracy being significantly higher than those of traditional machine learning algorithms. In addition, external factors such as wind speed, and distance from the gas source are also important factors affecting gas classification. The objectives of this study are as follows: 1) improving the data pre-processing method and classifier structure in deep learning for gas analysis and 2) using hybrid deep neural networks with Multilayer Perceptron (MLP) for environment compensation to improve the sensor drift problem caused by external factors. This study used one open-source gas dataset, applied three data pre-processing methods and two deep learning architectures (GasNet, SimResNet-9) for gas analysis and comparison, selected the method with the best classification accuracy and used it in Deep Neural Networks with MLP environmental compensation to promote the accuracy of classification further by learning the relationship between external factors and gas data. The proposed SimResNet-10_X_MLP was used for data training and classification in this study, achieving a 95% classification accuracy.

The practical use of electronic nose (e-nose) systems suffers from drift issue, which alters data distribution and reduces the accuracy of classification. This letter proposes a transfer learning method called concentration-based drift calibration (CDC) for calibrating the sensor drift. Based on the sensor characteristic that sensor response is correspondent to gas concentration, transfer samples were collected in the target domain for certain gas concentrations and then used for calibration with a designed concentration-based model and CDC transfer process. This method was evaluated on a complex time-varying drift dataset. The experimental results show that the proposed method for drift calibration is effective and can be used for real-world applications. Moreover, the CDC transfer process can be applied over time with data that has been previously collected to yield a more generalized model.

Ketamine severely endangers health and dwelling quality. In Taiwan, one of the common methods of taking ketamine is by smoking ketamine cigarettes. By heating up ketamine cigarettes, it produces an odor that smells similar to burning plastic. There are no effective and convenient methods for immediately detecting and identifying the smell of ketamine cigarettes. Therefore, this research proposed to develop an electronic nose system to identify the gas of ketamine cigarettes. In this study, standard operating procedures were established for collecting ketamine gas and ketamine cigarette gas at constant flow to quantitatively collect those gas samples least affected by environmental factors. After injecting the gas samples, the variation signal of sensor array was noted. By feature extraction and dimensionality reduction algorithms, the complexity of data decreased. The classification results were also analyzed through linear regression and classification algorithms, and the classification accuracy was up to 95.92%.

A portable gas-sensing system which is periodically irradiated with light from a pulsed ultraviolet light emitting diode (UV-LED) was fabricated for sensing ppb-level NO2 gas. The portable NO2 gas-sensing system includes a device manager coupled to a pulsed-UV-LED sensing module, a liquid-crystal display module and a data capture module. The sensor made of a composite iron oxide/multi-walled carbon nanotubes/tungsten oxide (Fe2O3/MWCNTs/WO3) material was produced by a one-pot polyol method followed by metal organic decomposition (MOD). Scanning electron microscopy (SEM), energy dispersive spectrometry (EDS) and X-ray diffraction (XRD) were used to analyze the structure and morphology of the fabricated films. The effects of the wavelength and duration of the irradiation on the response of the portable gas-sensing system for sensing NO2 gas were studied. This portable gas-sensing system in periodically pulsed-UV-LED operation mode exhibited high sensitivity, good linearity, good stability, high reproducibility, a low detection limit and high selectivity in the sensing of NO2 gas.

Miniature gas detection system is desirable for many applications. In this brief, we propose a fully integrated nose-on-a-chip for mobile phones and wearable devices. The chip has 36 on-chip sensors, a 36-channel adaptive interface with an integrated programmable amplifier, a four-channel frequency readout interface, one on-chip temperature sensor, a successive approximation analog-to-digital converter, a scalable learning kernel cluster, and a reduced instruction set computing core with low-voltage static random-access memory. This chip is fabricated using the TSMC 180-nm CMOS process and consumes 2.6 mW at 1 V. on-chip temperature and humidity sensors are included in this system for acquiring environmental information. An environmental compensation algorithm based on common principal components analysis is also implemented for high-accuracy odor classification. This fully integrated nose-on-a-chip provides promising results for odor detection and classification for use in mobile and wearable device applications.

Odor sensing has been applied in many fields, such as environmental monitoring, health care, and medical diagnosis. However, traditional gas-sensing systems have critical drawbacks, particularly regarding their miniaturization. An electronic nose system is a suitable alternative to traditional gas-sensing systems because the nose system employs a gas sensor array to detect and identify different odors. Accordingly, this study designed a nose-on-a-chip by using the electronic nose concept through complementary metal–oxide–semiconductor (CMOS) technology for minimizing the device size and reducing power consumption. The chip incorporates a CMOS-compatible gas sensor array along with other electronic components that are used for signal processing and data analysis. Assessment results revealed the ability of the proposed nose-on-a-chip in executing various processes such as diagnosing ventilator-associated pneumonia. Hence, the CMOS-compatible miniature gas-sensing system is a promising candidate for use in devices intended for personal use.

The operational duration of shaking tea leaves is a critical factor in the manufacture of oolong tea, this duration influences the formation of its flavor and fragrance. The current method to control the duration of fermentation relies on the olfactory sense of tea masters, they monitor the entire process through their olfactory sense, and their experience decides the duration of shaking and setting. Because of this human factor and olfactory fatigue, it is difficult to define an optimum duration of shaking and setting, an inappropriate duration of shaking and setting deteriorates the quality of the tea. In this study, we used metal-oxide-semiconductor gas sensors to establish an electronic nose (E-nose) system and tested its feasibility. This research was divided into two experiments: distinguishing samples at various stages and an on-line experiment. The samples of tea leaves at various stages exhibited large differences in the level of grassy smell. From the experience of practitioners and from previous research, the samples could be categorized into three groups: before the first shaking (BS1), before the shaking group, and after the shaking group. We input the experimental results into a linear discriminant analysis to decrease the dimensions and to classify the samples into various groups. The results show that the smell can also be categorized into three groups. After distinguishing the samples with large differences, we conducted an on-line experiment in a tea factory and tried to monitor the smell variation during the manufacturing process. The results from the E-nose were similar to those of the sense of practitioners, which means that an E-nose has the possibility to replace the sensory function of practitioners in the future.

Introduction Electronic noses are widely used in food science such as food freshness detection [1] and fruit ripeness classification [2]. Odor detection is one of the potential methods to identify the quality of coffee. Some researchers have used gas sensors to identify different stages of coffee roasting [3]. Another researcher has employed an electronic nose as a “cup test” tool [4]. In the aforementioned applications, the electronic nose serves to provide an objective solution. The quality identification of green coffee is very important during storage. The green coffee quality is easily affected by water vapor, which causes the green coffee getting mold. The coffee made from mold coffee bean induces bad taste and affects people health. Since the storage environment for green coffee is strict, there is a requirement to find a method to check the green coffee quality during the long storage. This paper presents an electronic nose system that can detect the quality of green coffee. In this study, the Arabica green coffee from different places of origin are used. These different kinds of green coffee are stored in a moist environment to observe the change of the coffee beans. The odor of the green coffee is detected by the electronic nose, and the result shows that the electronic nose has the potential of being a useful tool to discriminate whether the green coffee is suitable for drinking. Method The electronic nose system consists of a flow meter, a three-way valve, a pump and the gas sensor array, which contains 14 metal oxide sensors. The three-way valve controls the constant flow by a flow meter between baseline gas and the target sample. The sensor signals are acquired by a data acquisition card (USB-6343) and recorded with a laptop with National Instrument®LabVIEW Signal Express program. Figure.1(a) depicts the experiment setup. For the experiment sample, the origin Arabica green coffee beans from different places were prepared. The coffee beans were stored in an open and damp environment where the beans would mold over time. To sample the coffee odor, 30g green coffee beans were placed in a 60ml vial, and the vial was sealed for 30 minutes before the experiment. Dry air was used as the baseline for the system. Moreover, an empty vial was used to avoid the influence with and without vial. After the sensor array reached a steady-state, the coffee odor was pumped into the sensor chamber for 1.5 minutes that was long enough for the gas sensor array to react with the sample. To see the coffee beans’ change, the experiments were performed every several days, and the total experiment lasted for two or three weeks depending on the status of the beans. Results In this study, the quality of coffee was divided into fresh, sub-fresh and mold stages. These three stages were evaluated by the professional coffee maker based on the color and the shape of green coffee. Fig. 1(b, c, d) shows the different response curves of these three stages. In the sensor array, the resistance variation rate (ΔRs)/Rs0 is considered as features, where Rs0 represents the resistance of the sensor array in the baseline environment, and ΔRs represents the resistance difference of the sensor array in the baseline environment and the target odor. The odor patterns obtained from sensor responses were analyzed with principal component analysis (PCA), by which a distinctive profile between initial and the following days of storage was obtained. Fig.1(e) shows the PCA result. The PCA result shows a good correlation between the changing of gas composition and the storage time. Conclusions In this paper, the response curves of the green coffee samples with different storage times were obtained using a self-made electronic nose. The PCA results show that the electronic nose could distinguish the fresh green coffee, and the green coffee’s quality decay when stored in an inappropriate environment. The result also shows the electronic nose technology has the potential to meet the demands for rapid, low-cost, and nondestructive detection for the storage of green coffee. References [1] Loutfi, A.; Coradeschi, S.; Mani, G.K.; Shankar, P.; Rayappan, J.B.B. Electronic noses for food quality: A review.Food Eng. 2015, 144, 103–111 [2] Chen, L.-Y.; Wu, C.-C.; Chou, T.-I.; Chiu, S.-W.; Tang, K.-T. Development of a Dual MOS Electronic Nose/Camera System for Improving Fruit Ripeness Classification. Sensors 2018 [3] Radi, Rivai M and Purnomo MH, Study on electronic-nose-based quality monitoring system for coffee under roasting. J Circ Syst Comput 25:1650116 (2016) [4] Rodríguez, J.; Durán, C.; Reyes, A. Electronic Nose for Quality Control of Colombian Coffee through the Detection of Defects in “Cup Tests”. Sensors 2010, 1, 36 Figure 1

Introduction Electronic nose (E-nose) has many applications in gas detection and classification such as identifying toxic gases from the environment or detecting breath biomarkers for various cancer diseases. The E-noses are usually designed using an array of gas sensors and a machine learning classifier, which is comprised of various models to distinguish the gas sensor data. However, the response of the gas sensors often suffers from unpredictable and uncertain drift issues due to sensor aging, process variation, and environmental interference. From the perspective of pattern recognition, drift causes test data distribution to differ from prior data distribution and reduce the classification accuracy. To solve the above-mentioned problems, drift correction methods such as component correction based on principle component analysis (CC-PCA) and orthogonal signal correction (OSC) can be employed. The correction methods try to remove some components from the data, while the drift is a dynamic and nonlinear one that cannot be easily separated from the data. The other method can be adaption in which new labeled data collected at different time or from different devices are required to update the classifier. The adaption method has better classification capability than the correction method; however, collecting new labeled data or transferring data is a laborious job. Hence, this paper proposes a transfer learning method to adapt different distributions of data without any additional data. Since no transfer data is required, the learning method can also be named as zero-shot learning. Method Fig. 1 shows the proposed method that considers both sensor response correction and adaption. This method combines auto-encoder as well as a neural network (NN) classifier with certain restrictions. The auto-encoder comprises an encoder and a decoder made by a 64-30-20 fully-connected neural network and a 20-30-64 fully-connected neural network, respectively. The purpose of the auto-encoder is to find latent vectors that can highly represent the input data. To find a highly representative latent vector, the encoder encodes the input sample to a 20-dimension latent vector, and then this latent vector is used to reconstruct a 64-dimension vector through the decoder. By forcing the reconstructed vector close to the input data, the latent vector can extract useful information from the input data and represent the corresponding input data. However, a highly representative latent space made up of latent vectors is not equivalent to a space that is easy to be clustered into different classes. For seeking a better classifiable latent space, the performance of the classifier is taken into consideration. In this work, a 20-10-6 fully-connected neural network is adopted as a classifier. In addition, statistical measurements are introduced to determine the transfer sample. The proposed method can be divided into two main stages: (1) Training stage (2) Calibration stage. During the training stage, training data are used to train the auto-encoder and the classifier along with the objective function including reconstruction error, classification error, and statistic error. The reconstruction error is the mean square error between the reconstructed data and the input. The classification error is determined by the classifier, which would be the binary cross-entropy error between NN output and the true label. Further, the statistic error measures the sparsity of the data distribution. For the calibration stage, some unknown samples are treated as test data. First, the test data will be given as the input of the model, consequently, the output of the model is obtained in the form of latent vector and classification result. Then, the statistic error can be calculated by the latent vector and the training data distribution in latent space. According to the customized qualification, the sample is determined to be reliable or not. If it is reliable, then it is put into the transfer dataset with its classification result as the label. Finally, the transfer dataset is used to calibrate the pre-train model through utilizing the transfer dataset as the training data in the first stage. Results and Conclusion The proposed method is evaluated on an open dataset discuss in [1], which collects 13,910 samples using a 16 metal-oxide (MOX) gas sensor array over a period of 36 months. While the dataset is processed using the proposed method, attention should be paid in determining what kind of sample is reliable. If less reliable samples are used, the model might collapse. On the other hand, if the transfer dataset contains only highly reliable samples, the classifier may be stuck with a similar distribution of training data, which means it is unable to adopt the new data distribution and leads to a big hurdle. Hence, the constraints of selecting transfer data are varied with calibration epochs. This work is the first zero-shot transfer learning method applied to E-nose data. The experimental results show that the performance is improved after data transfer. Besides, the other calibration methods on this dataset usually select the transfer data which can describe the overall data distribution in the test batch. Yet, the proposed method can be more practical and general, because a similar operation can be also applied to calibrate the instrumental variation. Furthermore, the proposed method lacks human labeling after the pre-trained model. That is, it is possible to obtain a better result working together with the traditional method and collecting newly labeled transfer data once in a while. Reference [1] A. Vergara, S. Vembu, T. Ayhan, M.A. Ryan, M.L. Homer, R. Huerta, Chemical gas sensor drift compensation using classifier ensembles, Sens. Actuators B: Chem. 166 (2012) 320-329 Figure 1

Introduction Human exhaled breath contains more than 1,000 of the vast variety of volatile organic compounds (VOCs), providing valuable information about the metabolic process in human beings. The information of breath could include current state of disease, leading to great potential of noninvasive diagnosis in medical industry 1-6. The concentration of the exhaled breath VOCs varies in sub-ppm or even lower in ppb level in healthy people7. However, when disease occurs in the human body, the metabolic process becomes disbalance. Hence, the concentration profile of exhaled breath VOCs drastically increases. Recently, deaths caused by lung cancer have reached 1.6 million each year8. Early screening and diagnosing of lung cancer are big challenges in the healthcare industry. There are many technologies to detect and diagnose lung cancer, such as low-dose chest computed tomography (CT), which enhances the likelihood of early-stage tumor diagnosis9, resulting in an increase in the survival rate10. However, the aforementioned technique still suffers from a large rate of false positive due to cross reactive responses10. In addition, due to the existence of a low dose of ionizing radiation in the CT, employing this technique increases the risk of cancer. Therefore, a noninvasive technology for breath analysis is desired to diagnose lung cancer. In this paper, we report a fast screening method of the lung cancer biomarker in exhaled breath by using gas chromatography-mass spectroscopy coupled with thermal desorption (TD-GC-MS), which is one of the noninvasive technologies that is able to perform this task. In the experiments, Tenax TA material is used as absorbent to absorb the exhaled breath VOCs, thermal desorption system is used to desorb the VOCs, which are separated by the gas chromatography column and further detected by the mass spectrometer. Method A 1 Liter Tedlar bag was used to sample the exhaled breath for the lung cancer patients at the NTU hospital Hsinchu. Further, the breath sample was transferred from Tedlar bag to Tenax TA tube with a flow rate of 40 cc/min for 25 minutes. An Agilent type7890A GC system with 5975C inert MSD with a triple-axis detector along with Perkin Elmer thermal desorption system (Turbo matrix 100) was used. Breath VOCs were separated by Elite-5 MS column (30 m × 0.25 mm, film thickness 0.25μm, Perkin Elmer) while working in a constant pressure mode (10 psi). The mass spectrometer was set to scan mode. The program of column temperature was maintained at 80 for 4 min, and then increased at a rate of 15 per min, to 230 and held at 230 for 4 min11. After breath sampling, Tenax TA absorbent was used to absorb the volatile organic compound, then the Tenax TA was connected to the Perkin Elmer thermal desorption system. Results and Conclusions Gas chromatography mass Spectro meter coupled with the thermal desorption system was used in the detection of lower concentration of the volatile organic compound in the exhaled breath for lung cancer patients. The thermal desorption process was used to desorb the volatile organic compound from the Tenax TA absorbent. The desorbed samples from Tenax TA absorbent were transferred through the column. The VOCs were moved into the column by the inert gas mobile phase; then, they were separated by the stationary phase fixed into the column. The separation efficiency depended upon the gas chromatography column. The detection principle of the GC-MS was based on the mass to charge ratio (M/Z) of the ionized atom for the detection of biomarkers in the lung cancer patients. After separation in gas chromatography, VOCs were detected by the mass spectrometer. The peak area of various kind of VOCs for lung cancer have been obtained. We have found some biomarkers from lung cancer’s exhaled breath such as acetone, toluene, ethyl benzene, decane, etc. In future, we will sample the breath for the control group, after analysis by GC-MS compare the result with lung cancer patient to identify the unique biomarkers. The Figure 1 shows the profile of the peak area with respect to the various kind of VOCs for the lung cancer patient. Therefore, the TD-GC-MS is an effective technique for noninvasive diagnosis by employing exhaled breath VOCs for the health care industry. These exhaled breath biomarkers can be used to screen the early lung cancerous disease to save millions of lives worldwide. Figure 1

Potential application of a metal oxide semiconductor based electronic nose (E-Nose) as a non-destructive system for monitoring the change in volatile organic production of fruit during the maturing process. Using PCA analysis, it was possible to distinguish and to classify the different stages. The result showed that this method yielded highest average accuracies greater than 90% in classifying fruit maturity.

In this paper, we focused on the method of lung cancer identification by breath. Lung cancer had occupied the first place in the top ten leading causes of death. When lung cancer patients were diagnosed, most of the patients had lost the opportunity of cure. However, physicians determined the lung cancer cases in complicated steps. Therefore, the purpose of this breath detection system was to help physicians to quickly screen for rapid screening lung cancer. We used KNN and SVM with leave-one-out cross validation to analyze. Finally, we got good accuracy that was 84.4%.

Metal-oxide-semiconductor gas sensors are widely used for gas concentration estimation and identification because of high sensitivity, good robustness and low price. To achieve good estimated performance, the feature extraction method is crucial for gas detection. However, the feature extracted from the transient part of gas response is required for fast gas concentrations estimation. In this paper, we proposed a new transient feature extraction method to reduce the estimated time of gas concentration. The linear regression was utilized to estimate gas concentrations. The result showed the estimation error of gas concentrations was less than 28.6%, and the average completion time was reduced up to 19 times. The proposed method showed superior performance to the conventional features.

Ventilator-associated pneumonia (VAP) still lacks a rapid diagnostic strategy. This study proposes installing a nose-on-a-chip at the proximal end of an expiratory circuit of a ventilator to monitor and to detect metabolite of pneumonia in the early stage. The nose-on-a-chip was designed and fabricated in a 90-nm 1P9M CMOS technology in order to downsize the gas detection system. The chip has eight on-chip sensors, an adaptive interface, a successive approximation analog-to-digital converter (SAR ADC), a learning kernel of continuous restricted Boltzmann machine (CRBM), and a RISC-core with low-voltage SRAM. The functionality of VAP identification was verified using clinical data. In total, 76 samples infected with pneumonia (19 Klebsiella, 25 Pseudomonas aeruginosa, 16 Staphylococcus aureus, and 16 Candida) and 41 uninfected samples were collected as the experimental group and the control group, respectively. The results revealed a very high VAP identification rate at 94.06% for identifying healthy and infected patients. A 100% accuracy to identify the microorganisms of Klebsiella, Pseudomonas aeruginosa, Staphylococcus aureus, and Candida from VAP infected patients was achieved. This chip only consumes 1.27 mW at a 0.5 V supply voltage. This work provides a promising solution for the long-term unresolved rapid VAP diagnostic problem.

This paper presents a CMOS capacitance-to-digital converter (CDC) for capacitive sensors applications. Off-chip capacitive sensors are fabricated by coating sensing materials on interdigitated electrode (IDE) structure. A CDC consists of a capacitance-to-voltage front-end and 12-bit successive approximation register analog-to-digital converter (SAR-ADC) in a single chip is used for converting sensor data into digital codes. The proposed capacitance-to-voltage front-end circuit extends the dynamic range by a select logic for combinations of reference capacitances and voltages. The SAR-ADC uses a monotonic capacitor switching procedure and the input common-mode voltage gradually converges to supply voltage. This chip is fabricated with the TSMC 0.18-um 1P6M CMOS process and the core occupies area of 719 X 276 um2. The CDC achieves measurement interval of 1.5-78 pF and 8.35-bit ENOB with 220 nW consumption at a 0.7 V supply voltage and 0.25 ms measurement time.

An electronic nose (E-Nose) is one of the applications for surface acoustic wave (SAW) sensors. In this paper, we present a low-noise complementary metal–oxide–semiconductor (CMOS) readout application-specific integrated circuit (ASIC) based on an SAW sensor array for achieving a miniature E-Nose. The center frequency of the SAW sensors was measured to be approximately 114 MHz. Because of interference between the sensors, we designed a low-noise CMOS frequency readout circuit to enable the SAW sensor to obtain frequency variation. The proposed circuit was fabricated in Taiwan Semiconductor Manufacturing Company (TSMC) 0.18 μm 1P6M CMOS process technology. The total chip size was nearly 1203 × 1203 μm2. The chip was operated at a supply voltage of 1 V for a digital circuit and 1.8 V for an analog circuit. The least measurable difference between frequencies was 4 Hz. The detection limit of the system, when estimated using methanol and ethanol, was 0.1 ppm. Their linearity was in the range of 0.1 to 26,000 ppm. The power consumption levels of the analog and digital circuits were 1.742 mW and 761 μW, respectively.

Chronic obstructive pulmonary disease (COPD) still lacks a rapid diagnosis strategy. In this paper, we propose a low-power nose-on-a-chip for rapid COPD screening. This chip is designed for implementation in a personal handheld device that detects patient breath for COPD diagnosis. The chip has 36 on-chip sensors, a 36-channel adaptive interface with an integrated programmable amplifier, a four-channel frequency readout interface, one on-chip temperature sensor, a two-channel successive approximation analog-to-digital converter, a scalable learning kernel cluster, and a reduced instruction set computing core with low-voltage static random-access memory. This chip is fabricated in 90 nm CMOS and consumes 1.68 mW at 0.5 V. In simulation, the system distinguished between undiseased and diseased patients with 90.82% accuracy for a set of diseases including COPD and asthma and exhibited 92.31% accuracy for identifying patients with COPD or asthma. The system classified severity levels of COPD under four labels (normal, mild, moderate, and severe) with 92.00% accuracy. Accordingly, this work provides a promising solution for the unmet medical need of rapid COPD screening.

The bulkiness of current electronic nose (E-Nose) systems severely limits their portability. This study designed and fabricated an E-Nose signal-processing chip by using TSMC 0.18-μ m 1P6M complementary metal-oxide semiconductor technology to overcome the need to connect the device to a personal computer, which has traditionally been a major stumbling block in reducing the size of E-Nose systems. The proposed chip is based on a conductive polymer sensor array chip composed of multiwalled carbon nanotubes. The signal-processing chip comprises an interface circuit, an analog-to-digital converter, a memory module, and a microprocessor embedded with a pattern-recognition algorithm. Experimental results have verified the functionality of the proposed system, in which the E-Nose signal-processing chip successfully classified three odors, carbon tetrachloride (CCl4), chloroform (CHCl3), and 2-Butanone (MEK), demonstrating its potential for portable applications. The power consumption of this signal-processing chip was maintained at a very low 2.81 mW using a 1.8-V power supply, making it highly suitable for integration as an electronic nose system-on-chip.

This study developed an electronic-nose sensor node based on a polymer-coated surface acoustic wave (SAW) sensor array. The sensor node comprised an SAW sensor array, a frequency readout circuit, and an Octopus II wireless module. The sensor array was fabricated on a large K(2) 128° YX LiNbO3 sensing substrate. On the surface of this substrate, an interdigital transducer (IDT) was produced with a Cr/Au film as its metallic structure. A mixed-mode frequency readout application specific integrated circuit (ASIC) was fabricated using a TSMC 0.18 μm process. The ASIC output was connected to a wireless module to transmit sensor data to a base station for data storage and analysis. This sensor node is applicable for wireless sensor network (WSN) applications.

Electronic nose (E-nose) systems have become popular in food and fruit quality evaluation because of their rapid and repeatable availability and robustness. In this paper, we propose an E-nose system that has potential as a non-destructive system for monitoring variation in the volatile organic compounds produced by fruit during the maturing process. In addition to the E-nose system, we also propose a camera system to monitor the peel color of fruit as another feature for identification. By incorporating E-nose and camera systems together, we propose a non-destructive solution for fruit maturity monitoring. The dual E-nose/camera system presents the best Fisher class separability measure and shows a perfect classification of the four maturity stages of a banana: Unripe, half-ripe, fully ripe, and overripe.