Portable Electronic System Research Articles

Design of a Portable Electronic Nose for Identification of Minced Chicken Meat Adulterated With Soybean Protein Isolate

ABSTRACTThe study aimed to develop a portable electronic nose system for detecting adulteration with soybean protein isolate (SPI) in chicken meat. The system mainly consisted of three parts: the gas sensor array, the DSP28335 control board, and the upper computer. The DSP28335 control board, developed using C language, included analog to digital converter (ADC) module, digital output (DO) module, pulse width modulation (PWM) module, controller area network (CAN) module, power module, drive circuit, and so forth. The upper computer, developed using LabVIEW, facilitated user interaction with the user by primarily handling CAN configuration and monitoring, displaying and storing sensor data, temperature and flow data, and sending and monitoring electronic nose commands. The feasibility of the proposed electronic nose for characterizing adulterated chicken meat was tested on six classes of chicken meat that had been adulterated with varied quantities of SPI. The mass fractions of SPI were 0%, 5%, 10%, 15%, 20%, and 25%, respectively. On the basis of odor data from the electronic nose, K‐nearest neighbor (KNN), linear discriminant analysis (LDA), and support vector machine (SVM) were applied to qualitatively distinguish minced chicken meat with different adulteration ratios. The results showed that the SVM model had the best recognition effect. When the best parameters (c, g) were c = 16 and g = 1, the accuracy of SVM model was 97.22% and 93.75% in the training and testing sets, respectively. These results demonstrated that the portable electronic nose designed in this paper effectively identifies minced chicken meat under various adulteration conditions, enabling rapid and nondestructive detection of chicken meat adulteration.

Design and Implementation of Portable Electronic Nose System Based on Lightweight Convolutional Neural Network

Design and Implementation of Portable Electronic Nose System Based on Lightweight Convolutional Neural Network

Development of a Smart and Ultrafast Flexible Humidity Sensor Using the 0D/2D Au/GeS Heterostructure for Human Respiration Rate Monitoring

We have developed a smart, Internet of Things (IoT)-enabled, intelligent, and flexible humidity sensor for human respiration rate monitoring by using a heterostructure of two-dimensional (2D) germanium(II) sulfide (GeS) and zero-dimensional (0D) gold nanoparticles (Au NPs). The 2D GeS was synthesized by a facile liquid-phase exfoliation process, followed by the post-functionalization of Au NPs. Furthermore, the chemical and structural integrities of the heterostructure were corroborated by microscopic and spectroscopic techniques. The as-prepared heterostructure (termed Au-GeS) exhibited a maximum sensor response of 7102% at 90% of relative humidity (RH) level compared to the pristine GeS sensor response of 2700% at the same RH level. Finally, we demonstrate the use of the Au-GeS flexible humidity sensor for human respiration rate monitoring. The sensor showed an ultrafast response and recovery times of 0.69 and 0.73 s, respectively, during exhalation and inhalation. The sensor was further integrated into a portable electronic system to facilitate IoT-enabled real-time wireless sensing at the smartphone application interface. In our study, the excellent sensor performance with a simple fabrication process offers utilization of the flexible sensor in personal health monitoring.

Tomato storage quality predicting method based on portable electronic nose system combined with WOA-SVM model

Tomato storage quality predicting method based on portable electronic nose system combined with WOA-SVM model

Three‐in‐One Portable Electronic Sensory System Based on Low‐Impedance Laser‐Induced Graphene On‐Skin Electrode Sensors for Electrophysiological Signal Monitoring

AbstractOn‐skin sensors can precisely perceive important electrophysiological signals, including electroencephalogram (EEG), electrocardiogram (ECG), and electromyogram (EMG). Despite significant advances in the development of soft materials as electrode sensors, data acquisition (DAQ) unit—another indispensable component of on‐skin electronic sensory systems—typically exhibits bulkiness or unimodal sensing, which is detrimental to the portability of the sensory system or the comprehensiveness of the perceived information. Here, a portable and multimodal DAQ unit to tackle these challenges is designed. By assembling the DAQ unit with low‐impedance (<100 Ω) laser‐induced graphene on‐skin electrode sensors, a wireless communication module, a power supply module, and a 3D printed protective shell, the completed sensory system can realize three‐in‐one monitoring of EEG, ECG, and EMG with a light weight of 22 g and a low cost of $25. Moreover, a mobile App is developed to display the perceived electrophysiological signals in real time. Human–machine interface and embedded machine learning are demonstrated using the designed sensory system, indicating its potential applications in artificial intelligence. The success of this inexpensive three‐in‐one portable electronic sensory system sheds light on design, fabrication, and commercialization of multifunctional wearable electronics with wide applications in fitness tracking, medical diagnostics, and human–machine interface.

Portable electronic nose system with elastic architecture and fault tolerance based on edge computing, ensemble learning, and sensor swarm

The portable electronic nose (E-nose) systems are suffering from the limited computing ability of microcontrollers and can only adopt simple pattern recognition algorithms. The heavy coupling between the sensors greatly limits the anti-fault ability of the system. Herein, a novel ensemble learning framework based on independent artificial neural networks (ANN) is proposed in a portable E-nose system to recognize volatile organic compounds (VOCs). Edge computing is applied to complete data processing and analysis on an ARM Cortex-M3 based microcontroller in an E-nose system. Each sensing unit can recognize VOCs by training independent ANN models. Then an ensemble learning method further organizes the diverse ANN models into an onboard sensor swarm with a significantly enhanced performance. The accuracy of the type classification can reach 81.1%, which is improved by more than 20% compared with the best individual classifier. The R2 score of concentration prediction can reach 84.1%, which is improved by more than 25% compared with the best regressor in the ensemble group. For both type identification and concentration prediction, the tolerance to the sensor faults of swarm-based E-nose is 10 and 18 times greater than that of the conventional array-based sensors, respectively. Our work has designed an elastic architecture based on sensor swarms, providing a novel avenue for the development of E-nose systems with high fault tolerance.

Classifying Beers With Memristor Neural Network Algorithm in a Portable Electronic Nose System

Quality control and counterfeit product detection have become exceedingly important due to the vertical market of beers in the global economy. China is the largest producer of beer globally and has a massive problem with counterfeit alcoholic beverages. In this research, a modular electronic nose system with 4 MOS gas sensors was designed for collecting the models from four different brands of Chinese beers. A sample delivery subsystem was fabricated to inject and clean the samples. A software-based data acquisition subsystem was programmed to record the time-dependent chemical responses in 28 different models. A back-propagation neural network based on a memristor was proposed to classify the quality of the beers. Data collected from the electronic nose system were then used to train, validate, and test the created memristor back-propagation neural network model. Over 70 tests with changes in the setup parameters, feature extraction methods, and neural network parameters were performed to analyze the classification performance of the electronic nose hardware and neural network. Samples collected from 28 experiments showed a deviation of 9% from the mean value. The memristor back-propagation network was able to classify four brands of Chinese beers, with 88.3% of classification accuracy. Because the memristor neural network algorithm is easy to fabricate in hardware, it is reasonable to design an instrument with low cost and high accuracy in the near future.

Portable electronic system for fast detection of bacteria lactase fermentation in water samples

The detection of the presence of bacteria in water is essential to determine their use and prevent environmental and health problems. Conventional techniques for the detection of certain bacteria, such as Escherichia coli, are very lengthy, require trained operators and can be subjective, since they are based on the capacity of colour discernment of the person performing the analysis. In this work, we describe a portable electronic monitoring system based on colorimetric measurements, which is capable of speeding up the detection of bacteria in comparison with the conventional techniques. Such system enhances the threshold of detection and automates the sampling of the measure. Our system is capable of decreasing the waiting and processing time of more than 60% with respect to conventional methods (48 h). In addition to the mere hardware system, we propose three different simple algorithms to recognise that bacteria are present in the sample. Furthermore, we show that there is a correlation with the number of microorganisms. Given the portability of the system, and the easy scalability, this solution paves the way to a fast and in situ determination of water quality based on a cost-effective electronic system.

Portable electronic nose system for fast gynecological-conditions diagnosis in consulting room: A case study

In this work, the analytical approach to fast diagnostics of gynecological conditions based on the screening of volatile compounds in cervical mucus using a portable electronic nose system was developed for the first time. The highly sensitive electronic nose system based on seven mass-sensitive piezo-sensors with nanostructured modifiers of electrodes was utilized for the detection of highly volatile biomolecules (amines, ketones, alcohols, aldehydes, carbonic acids) in cervical mucus samples directly in a consulting room. We were solving the task to develop a model for rapid identification of patients with gynecologic inflammation diseases according to results from gaseous sensors or a single sensor scanning of a mixture composition of volatile molecules released by mucus. Whereupon we propose the following group ranking as "conditionally healthy", "remission", and "inflammation/infection". Preliminary, by individual substances we estimated selectivity and kinetic features of sorption-desorption processes on the selected array of stabilized gaseous sensors. According to the groups of compounds the detection limit by an array of sensors is 20 ррm for ketones by acetone, 10 ppm for alcohols by ethanol, for arenas 2 ppm by toluene, 20 ppb by ammonia and for amines 5 ppb by diethylamine. For the correct interpretation of the "electronic nose" results, we used the doctor’s diagnosis, their description of the condition and the severity of the lesion, which was defined by the results of microbiological research in the laboratory. At the first stage, it was established that to ensure the reliability of solution-making during sample ranking into groups, time from the moment of biomaterial sampling to the measurement should not exceed 3–5 min. At the appointment in the doctor’s office and as part of a preventative medical check-up, there were analyzed 83 bioassays according to the developed method. To elaborate a model of gynecological status assessment, there were chosen 30 conditionally healthy patients (scheduled patient care) and the ones with deviations from the conditional norm, suggesting antibacterial treatment with various inflammation histories (active form, remission, and exacerbation). To make a decision, we utilized the full amount of data, which is achieved while measuring volatile molecules above mucus during sorption (80 sec) and spontaneous desorption (120 sec). The methods and results of experimental processing of patients’ data obtained directly in a consulting room are described below. The most informative sensors were identified applying an unsupervised model for processing the output curves in bio-sample vapours. The reduction of the number of sensors in the array to one was justified. There was proved high accuracy of mucus sample ranking by presence/ absence of gynecologic disease within mere 60 s. Sensors’ data processing was carried out using machine-learning methods without a coach but with the pseudo markup. We proposed a self-learning model for bioassay separation into three classes based on the signals from the most sensitive sensor with a classification error of no more than 10%. We conducted distribution into diagnostic groups and verification of the correct classification of new samples according to the results of microbiological analysis for microflora and key cells, according to the culture test method for infectious agents of sexually transmitted diseases (Gardnerella, Candida. alb, ureaplasmosis Ureap. Parvum, Trichomonas vaginalis). For accurate confirmation of pathogens’ presence, the polymerase chain reaction method was used. The model allows us to predict processes of remission and the presence of previous illnesses in remission, not necessarily infectious (e.g., adhesion formation). Active inflammation processes of any nature are also predicted well. Characteristics of the model for prediction of the presence of sexually transmitted infections by the signals of one sensor are sensitivity – 64%, and specificity – 86%. The capacity of an analysis based on the developed method and the model constitutes 30–35 samples per shift. The minimal lifetime of the studied sensors with bio-hydroxyapatite phase is more than 12 months. In such a case, there was neither sensor calibration nor their training on test substances required. A comparative study was conducted to validate the accuracy of using the electronic nose system versus classic cultural and polymerase chain reaction methods.

Design and development of an IoT based intelligent multi parameter screening system

The SARS-CoV-2 or shortly COVID-19, is a viral disease which causes serious lung fever and hugely impacts different body parts from mild to critical depending on tolerant immune system. As the virus multiplies through human-to-human contact, it has affected our lives in a devastating way, including the vigorous pressure on the public health system, the world economy, education sector, workplaces, and shopping malls. Viral spreading of this virus can only be prevented by early detection of positive cases and to treat infected patients as quickly as possible. As many businesses, banks, gymnasiums, and stores etc., are using temperature screening as the primary step to assess for possible COVID-19 infection. Moreover, the proper hand sanitization is the very effective method to limit the outspread of this virus. This paper proposes the design and development of a fully automated low-cost portable electronic system in the form of a robot named CovBot that can be installed in the above-mentioned places by incorporating the mechanisms to automatically detect the body temperature, store the details directly to cloud so as to get the data latter by the authorities, to control/restrict the entry, a hand sanitization dispenser unit, auto alert to refill the sanitizer, a mobile display unit etc. Whole system can be managed by a mobile application. The system is controlled using an Arduino-Uno development board. The mobile and the microcontroller system is wirelessly communicated and that to cloud is done by IoT facility. Once this system is implemented, the primary concern and the initial screening associated to COVID-19 can be fully resolved. Comparing to other systems CovBot is cost effective and can be easily installed and operated.

Quantitative analysis of fish meal freshness using an electronic nose combined with chemometric methods

Total volatile basic nitrogen (TVB-N) and acid value (AV) are the main indicators for characterizing fish meal freshness. To explore the feasibility of fish meal freshness detection based on an electronic nose system, a portable electronic nose measurement system was developed to detect the TVB-N and AV mass fraction in fish meal. The device is mainly composed of a gas acquisition and transmission module, a control and processing storage module with Raspberry Pi as the core, an ARPI600 data acquisition module, and a sensor array module. The system was used to collect the odour signals of fish meal samples with different TVB-N and AV mass fractions and the chemical test was conducted for the TVB-N and AV mass fractions in fish meal samples. The feature selection method of recursive feature elimination based on random forest (RFRFE) was used to select the sensor array features, and models of the TVB-N and AV mass fraction in fish meal were established by random forest regression (RFR) and least squares support vector machine (LSSVM) model based on the particle swarm optimization (PSO) algorithm, and the models were verified by a test sample. The experimental results show that the performance of the LSSVM regression model based on the RFRFE optimized sensor array data set is better than that of the traditional LSSVM model, The R2 and root mean square error (RMSE) between the predicted values and the real values for AV and TVB-N based on the test set are 0.8585, 0.3622 and 0.8789, 20.52, respectively. Therefore, the performance of feature selection using the RFRFE method is better. The results show that the TVB-N and AV of fish meal can be detected effectively by electronic nose technology.

Electronic system for step width estimation using programmable system-on-chip technology and time of flight cameras

This paper proposes a low-cost portable electronic system for estimating step width during the human gait cycle. This device, intended to support the Walking Stance item of the fall risk assessment test Performance Oriented Mobility Assessment (POMA), contains three electronic boards, comprising two sensing nodes and a concentrator. Each sensing node contains a force sensitive resistor (FSR) and time-of-flight camera (TOF). Each FSR is placed inside the subject’s shoe, while each TOF camera is located at the back of their foot. The FSR detects contact between heel and ground, and the TOF measures the distance to a barrier located on the right side of the walking path. Step width is calculated as the difference between the TOF measurements. After the walk is complete, the information obtained by the FSRs and TOFs is sent via a 433 MHz wireless communication to the concentrator board, which is connected to the USB port of a personal computer (PC). The proposed step width measurement system was validated with an infrared based motion capture (Vicon Corp.), giving an error equal to 11.4% ± 5.5%.

Fish meal freshness detection by GBDT based on a portable electronic nose system and HS-SPME–GC–MS

The volatile compounds of super fresh, superior fresh, general fresh, corrupt and completely corrupt fish meal were studied by headspace solid-phase microextraction–gas chromatography–mass spectrometry. Principal component analysis was used to analyze the sensor and gas chromatography-mass spectrometry data, so as to study the resolution of sensor array. A total of 198 fish meal samples with different freshness were classified by the gradient boosting decision tree method, yielding a model between the sensor array data and the freshness index, such as the acid value and the volatile base nitrogen value. The gradient boosting decision tree model shows that the correlation between the predicted acid value by electronic nose and the measured acid value is 0.90 and the correlation between the predicted volatile base nitrogen value and the measured volatile base nitrogen is 0.97. The combination of an electronic nose and a pattern recognition method based on a gas sensor can predict the acid value and volatile base nitrogen of the freshness index and can detect the freshness of fish meal.

Portable electronic device to assess the human balance using a minimum number of sensors

Objective: This paper presents the design and development of a new electronic portable device to assess the human balance of the human body during standing, using a minimal number of sensors and peripheral components. This device is aimed to evaluate human balance in environments outside of specialized laboratories, such as small clinics and therapy offices. Approach: The design is based on previous designs using three or more resistive force sensors attached to the feet, however in the present work, the sensors were attached on an adjustable platform, to fit several sizes of feet. Furthermore, all the signal acquisition, process, storage and display are executed by an embedded electronic system, thus avoiding the use of computers and external peripherals. A new method to compute the CoP using only two sensors per foot was developed and tested in a group of 50 university students, (17 women and 33 men), 26.04 ± 4.94 years. Main results: It was developed a portable electronic system to measure the trajectory of the CoP and to calculate the indexes values derived from it. The system is capable to discriminate between measuring situations (open and closed eyes), using only two sensors per foot (p < 0.0001). A comparison between the values obtained for young subjects using the proposed device, and the values reported in the literature showed a similar tendency. Significance: The results indicate that the proposed system is a good, low-cost, and easy-to-use alternative tool for researchers and clinicians interested in the evaluation of human balance, especially if the measurements must be done outside laboratories.

Design of a Portable Orthogonal Surface Acoustic Wave Sensor System for Simultaneous Sensing and Removal of Nonspecifically Bound Proteins.

One challenge for current surface acoustic wave (SAW) biosensors is reducing nonspecific adsorption. A device propagating Rayleigh and shear horizontal surface acoustic waves in orthogonal directions fabricated in ST quartz has the capability of achieving simultaneous detection and nonspecific binding (NSB) protein removal. Current measurement methods for a SAW sensor system based on this device require large-size and expensive equipment such as a vector network analyzer (VNA), signal generator, and frequency counter, which are not suitable for portable, especially point-of-care, applications. In this work, a portable platform based on a direct digital synthesizer (DDS) is investigated for the orthogonal SAW sensor, integrating signal synthesis, gain control, phase/amplitude measurement, and data processing in a small, portable electronic system. This prototype was verified for both stability and repeatability, and the results matched very well with VNA measurements. Finally, system performance in real-time sensing and NSB removal was evaluated.

Meat and Fish Freshness Assessment by a Portable and Simplified Electronic Nose System (Mastersense).

The evaluation of meat and fish quality is crucial to ensure that products are safe and meet the consumers’ expectation. The present work aims at developing a new low-cost, portable, and simplified electronic nose system, named Mastersense, to assess meat and fish freshness. Four metal oxide semiconductor sensors were selected by principal component analysis and were inserted in an “ad hoc” designed measuring chamber. The Mastersense system was used to test beef and poultry slices, and plaice and salmon fillets during their shelf life at 4 °C, from the day of packaging and beyond the expiration date. The same samples were tested for Total Viable Count, and the microbial results were used to define freshness classes to develop classification models by the K-Nearest Neighbours’ algorithm and Partial Least Square–Discriminant Analysis. All the obtained models gave global sensitivity and specificity with prediction higher than 83.3% and 84.0%, respectively. Moreover, a McNemar’s test was performed to compare the prediction ability of the two classification algorithms, which resulted in comparable values (p > 0.05). Thus, the Mastersense prototype implemented with the K-Nearest Neighbours’ model is considered the most convenient strategy to assess meat and fish freshness.

Turning the Finger into a Writing Tool.

A novel writing platform composed of a wearable sensor on the fingernail and classification algorithms is described. Findings from using this platform to translate fingertip writing into shapes, letters, and numbers on a range of surfaces are reported. The new wearable platform leverages an architecture with miniaturized electronic circuitry to precisely measure a set of forces in the longitudinal and transverse directions using multiple strain gauges. We find that the directional pressure patterns are translated from the fingertip to the fingernail. Deformation of fingernails in the longitudinal and transverse directions are detected by the fingernail sensor which sends the data wirelessly to a portable electronic system. Fingernail pressure patterns are categorized through signal processing to recognize a range of shapes, numbers, and letters, enabling fingertip writing recognition. Use of the writing platform following a short training session, shows human fingertip writing on multiple surfaces were automatically transcribed to a computer.

Design and in-house validation of a portable system for the determination of free acidity in virgin olive oil

Nutritional and healthy values are well known properties of virgin olive oil (VOO). The product quality, in terms of belonging to a specific quality grade (extra virgin, virgin, lampante), is defined by a set of chemical-physical and sensory measurements. According to the official regulation of the European Union (EU Reg. 1348/2013) the free acidity is the first parameter that has to be determined by analysts; it gives information about the quality of the olives used to produce the VOO as well as the hydrolytic state of VOO just produced and stored. The official procedure is based on an acid-base titration that needs to be carried out in a chemical laboratory.In this paper a portable battery-operated electronic system to measure olive oil free acidity is presented: the system can be used for quick “in situ” tests in a production environment (olive oil mills or packaging centers) by people without particular training. The working principle of the system is based on the creation of an emulsion between oil and a hydroalcoholic solution: the free acidity is estimated on the value of the emulsion electrical conductance.The proposed system has been calibrated and in-house validated showing good results in terms of limit of detection and quantification, precision and accuracy. Moreover, a good correlation (R2adj = 0.97) with free acidity data obtained applying the official method on 30 olive oil samples belonging to different commercial categories (extra virgin, virgin and lampante olive oil) has been evidenced.

Practical Determination of Solid Fat Content in Fats and Oils by Single-Wavelength Near-Infrared Analysis

The melting profile of solid fat content (SFC) is an important parameter for many fat-based food products, such as solid matrices, emulsions, and dispersions, since it can provide essential information on product stability, physical appearance, spreadability, mouthfeeling, particles adsorption and coalescence, and on the release of compounds in the encapsulation technology. The current standard techniques to measure SFC, namely, pulsed nuclear magnetic resonance and differential scanning calorimetry (DSC), provide accurate results but require expensive instrumentation and trained personnel. On the other hand, techniques are needed for fast, in situ measurements by generic operators along production lines to control industrial processes. In this paper, a novel technique to measure SFC in vegetable fats and oils is presented that could be implemented in the form of a low-cost, portable electronic system. The method is based on single-wavelength (835 nm) optical measurements during a thermal cycle of the sample under test. The experimental results obtained with such a method are in good/excellent agreement with those provided by DSC ( $R^{2} > 0.9$ ), thus indicating that the proposed technique could represent the basis for practical analysis in production environment, in order to evaluate the quality and the properties of liquid and solid fat food materials.

Multi-Direction Piezoelectric Energy Harvesting Techniques

With the development of portable and self-powering electronic devices, micro-electromechanical system (MEMS) and wireless sensor networks, research on piezoelectric energy harvesting techniques has been paid more and more attention. To enhance the ambient adaptability and improve the generating efficiency, the multi-directional piezoelectric energy harvesting techniques turns to be a research hotspot. The current status of the multi-directional piezoelectric energy harvesting techniques was firstly reviewed. The characteristics of existed multi-directional piezoelectric harvester were then analyzed. An improved structure of multi-directional piezoelectric harvester was finally proposed. The multi-directional piezoelectric energy harvester has a good prospect in miniaturization, more sensitive to vibration directions and better energy efficiency.