Portable Detection System Research Articles

A portable LED-induced fluorescence system for quantitative detection of different kinds of vegetable oil adulteration

This study developed a portable LED-induced fluorescence detection system for quantitative detection of various vegetable oil adulteration. Eight common vegetable oils and 14 different adulterated samples with adulteration concentrations ranging from 0 to 50% were prepared. Before quantitative analysis, different classification models were established for the determination of types of adulteration oil. The overall recognition accuracy was greater than 98%. Furthermore, with a simple calculation, the proposed normalized spectral ratio (NSR) preprocessing method was used to eliminate the light scattering effects in the raw fluorescence spectra. In addition, the competitive adaptive reweighted sampling (CARS) method was used to select characteristic wavelengths. The final oil adulteration quantitative analysis model was NSR_CARS+PLS. The range of correlation coefficient (Rp) and root-mean-square-error (RMSEP) for the prediction datasets were 0.9548-0.9974 and 1.0265%-5.0236%, respectively.

Perspectives in Aptasensor-Based Portable Detection for Biotoxins.

Biotoxins are pervasive in food and the environment, posing significant risk to human health. The most effective strategy to mitigate the risk arising from biotoxin exposure is through their specific and sensitive detection. Aptasensors have emerged as pivotal tools, leveraging aptamers as biorecognition elements to transduce the specificity of aptamer-target interactions into quantifiable signals for analytical applications, thereby facilitating the meticulous detection of biotoxins. When integrated with readily portable devices such as lateral flow assays (LFAs), personal glucose meters (PGMs), smartphones, and various meters measuring parameters like pH and pressure, aptasensors have significantly advanced the field of biotoxin monitoring. These commercially available devices enable precise, in situ, and real-time analysis, offering great potential for portable biotoxin detection in food and environmental matrices. This review highlights the recent progress in biotoxin monitoring using portable aptasensors, discussing both their potential applications and the challenges encountered. By addressing these impediments, we anticipate that a portable aptasensor-based detection system will open new avenues in biotoxin monitoring in the future.

An intelligent and portable fiber optic real-time fluorescence detection system for pathogenic microorganisms detection

The detection of pathogenic microorganisms poses an increasingly serious challenge to food safety. This paper proposes an intelligent fiber optic fluorescence sensing system for detection of pathogenic microorganisms. The system integrates light-emitting diodes and fiber optic sensing arrays. Compact and multi-channel detection is achieved by this kind of design. Environmental parameters such as temperature, humidity, and location are intelligently analyzed together with sampled fluorescence signal, which successfully achieved a high accuracy detection result without affected or interfered by environmental parameters. A Radial Basis Function Neural Network (RBFNN) algorithm is utilized for intelligent analysis and prediction of pathogenic microorganisms’ concentration with environmental parameters. Under different environmental parameters, the system has successfully detected African swine fever virus or salmonella in real-time with high accuracy and sensitivity. Since location of the sensing system can be determined by GPS module integrated in the system, the change of pathogenic microorganisms with different locations can be determined even during transportation. This innovative solution provides a convenient and accurate method for high accurate on-site real time sensing of pathogenic microorganisms.

Flexible Single Microwire X-Ray Detector with Ultrahigh Sensitivity for Portable Radiation Detection System.

Sensitive, flexible, and low false alarm rate X-ray detector is crucial for medical diagnosis, industrial inspection, and scientific research. However, most semiconductors for X-ray detectors are susceptible to interference from ambient light, and their high thickness hinders their application in wearable electronics. Herein, a flexible visible-blind and ultraviolet-blind X-ray detector based on Indium-doped Gallium oxide (Ga2O3:In) single microwire is prepared. Joint experiment-theory characterizations reveal that the Ga2O3:In microwire possess a high crystal quality, large band gap, and satisfactory stability, and reliability. On this basis, an extraordinary sensitivity of 5.9×105µCGyair -1cm-2 and a low detection limit of 67.4nGyairs-1 are achieved based on the prepared Ag/Ga2O3:In/Ag device, which has outstanding operation stability and excellent high temperature stability. Taking advantage of the flexible properties of the single microwire, a portable X-ray detection system is demonstrated that shows the potential to adapt to flexible and lightweight formats. The proposed X-ray detection system enables real-time monitor for X-rays, which can be displayed on the user interface. More importantly, it has excellent resistance to natural light interference, showing a low false alarm rate. This work provides a feasible method for exploring high-performance flexible integrated micro/nano X-ray detection devices.

Modular and Portable System Design for 3D Imaging of Breast Tumors Using Electrical Impedance Tomography

This paper presents a prototype of a portable and modular electrical impedance tomography (EIT) system for breast tumor detection. The proposed system uses MATLAB to generate three-dimensional representations of breast tissue. The modular architecture of the system allows for flexible customization and scalability. It consists of several interconnected modules. Each module can be easily replaced or upgraded, facilitating system maintenance and future enhancements. Testing of the prototype has shown promising results in preliminary screening based on experimental studies. Agar models were used for the experimental stage of this project. The 3D representations provide clinicians with valuable information for accurate diagnosis and treatment planning. Further research and refinement of the system is warranted to validate its performance in future clinical trials.

Localized surface plasmon resonance-enhanced photoelectrochemical immunoassay based on high-entropy yolk-shell Au@(ZnCdMnGaCu)xS

The yolk-shell structure has become a research hotspot in the field of photocatalysis in recent years due to its stability, movable yolk, and internal voids. However, yolk-shell has not been fully explored and applied in photoelectrochemical (PEC) immunoassays. In this work, a smartphone-based portable PEC immunoassay was constructed by using high-entropy yolk-shell Au@(ZnCdMnGaCu)xS as a photosensitive material, showing satisfactory detection with the assistance of localized surface plasmon resonance (LSPR). Specifically, a typical sandwich reaction introduces a detection antibody modified and labeled with gold nanoparticles (Au NPs) of alkaline phosphatase (ALP) into the system with the presence of prostate-specific antigen (PSA). Ascorbic acid (AA) content increased with increasing PSA and showed a linear enhancement of photocurrent at PSA concentrations of 0.01–50 ng mL−1 with a limit of detection of 8.94 pg mL−1. Additionally, the proposed assay technology not only provides a portable detection system for the immediate detection of tumor markers, but also offers a promising paradigm for the development of high-entropy materials.

Feasibility study on non-destructive detection of microplastic content in flour based on portable Raman spectroscopy system combined with mixed variable selection method

Microplastics, as emerging environmental pollutants, have garnered considerable attention due to their contamination of both the environment and food. Microplastics can infiltrate the human food chain through multiple pathways, potentially posing health risks to humans. Currently, non-destructive testing of microplastics in food is considered challenging. This study aims to investigate the feasibility of employing a portable Raman spectroscopy system for non-destructive detection of microplastic content (polystyrene, PS; polyethylene, PE) in flour. In this study, a portable spectrometer was used to collect flour spectra of different abundances of microplastics. To enhance the predictive performance of the partial least squares (PLS) model, a mixed variable selection strategy that combined the wavelength interval selection method (Synergy interval partial least squares, siPLS) and the wavelength point selection method (Least absolute shrinkage and selection operator, LASSO; Multiple feature-spaces ensemble by least absolute shrinkage and selection operator, MFE-LASSO) was proposed. Four regression models (PLS, siPLS, siPLS-LASSO, siPLS-MFE-LASSO) were developed and compared for detecting PS and PE content in flour. The siPLS-MFE-LASSO model exhibited the best generalization performance in the prediction set, and was considered to have the best generalization performance (PS: RP2 = 0.9889, RMSEP=0.0344 %; PE: RP2 = 0.9878, RMSEP=0.0361 %). In conclusion, this study has demonstrated the potential of using a portable Raman spectrometer in conjunction with a mixed variable selection algorithm for non-destructive detection of PS and PE content in flour, providing more possibilities for non-destructive detection of microplastic content in food.

Reproducible, Accurate, and Sensitive Food Toxin On-Site Detection with Carbon Nanotube Transistor Biosensors.

Field-effect transistor (FET) biosensors based on nanomaterials are promising in the areas of food safety and early disease diagnosis due to their ultrahigh sensitivity and rapid response. However, most academically developed FET biosensors lack real-world reproducibility and comprehensive methodological validation to meet the standards of regulatory bodies. Here, highly uniform and well-packaged semiconducting carbon nanotube (CNT) FET biosensor chips were developed and assessed for the plug-and-play sensing for the rapid and highly sensitive detection of aflatoxin B1 (AFB1) in real food samples to meet international standards. In order to meet the requirements for reproducibility and stability, a scalable residual-free passivation and packaging process was developed for CNT FET biosensors. Portable detection systems were then constructed for on-site detection. The resulting packaged chips were functionalized with nucleic aptamers to enable highly selective detection of AFB1 in food samples with a detection limit (LOD) of 0.55 fg/mL (standard) for AFB1 and cross-reactivity coefficients to interferences as low as 1.8 × 10-7 in simulated solutions. Utilizing the portable detection system, on-site real food detection was achieved with a rapid response time less than 60 s, and LOD of 0.25 pg/kg (standard) in complex corn sample matrices. Single-blind tests demonstrated the ability of the chips to detect AFB1-positive food with 100% accuracy, using a set of 30 peanut samples. Validation experiments confirmed that the detection range, stability, and repeatability met international standards. This study showcased the accuracy, reliability, and potential practical applications of CNT FET biosensor chips in areas such as food safety and rapid biomedical testing.

H2S gas sensing based on CuNi MOFs derivative with TiO2 quantum dot decoration applied for meat freshness detection

As food safety issues have been widely revealed and exposed, the pursuit of food for people has shifted from food and clothing to fresh and healthy. Currently, there are many detection systems for detecting meat freshness on the market. However, most of the problems still exist are that they cannot be monitored in real time, cannot guarantee traceless monitoring, and the cost is high. To solve these problems, this work has built a portable meat freshness detection system based on copper oxide (CuO)/nickel oxide (NiO) doped titanium oxide (TiO2) quantum dot composites and added real-time monitoring and remote monitoring and finally packaged it into an intelligent food box. Real-time monitoring of the freshness of meat can be carried out in a specific range regardless of the space. Hydrogen sulfide (H2S) has always been an essential indicator of meat decay. Therefore, in this paper, an H2S gas sensor based on CuO/NiO doped TiO2 quantum dot composites material is designed and fabricated, which can effectively and quickly identify H2S. The gas sensor exhibits excellent gas sensing characteristics for H2S with a detection limit of 100 parts per billion (ppb) and has excellent selectivity and repeatability, which because of the presence of p-p type heterojunctions in CuO/NiO. In addition, the feasibility of the system and material for analyzing the meat deterioration process is demonstrated through the test of different meat samples. It is firmly believed that this new type of sensor mentioned in this paper can make contribution to food quality monitoring.

Discrimination of VOCs with e-nose consisting of a single MEMS sensor using lightweight deep learning model based on SqueezeNet

The detection of volatile organic compounds (VOCs) has numerous applications in environmental monitoring and biomedicine. Metal oxide semiconductor (MOS) gas sensors are widely used in gas detection due to their simple structure, low fabrication cost, and fast response speed. However, MOS gas sensors have broad-spectrum responsiveness to VOCs, making selectivity a challenge. The most commonly used method to obtain selectivity is the E-nose system consisting of multiple sensors. However, the size of E-noses has increased due to the number of sensors with high power consumption and complicated signal processing. In recent years, deep learning has flourished, allowing for accurate discrimination of VOCs and concentration prediction using E-nose consisting of a single sensor. This method significantly reduces power consumption and facilitates portable applications. In this work, we synthesize SnO2-ZnO composite oxide and fabricate a E-nose consisting of a single MEMS gas sensor. The response performance of the E-nose to VOCs is stable with a high output signal-noise ratio (SNR). We design a lightweight deep learning model based on SqueezeNet and train it using transfer learning. Linear regression is used to predict the concentration of VOCs. Our work achieves accurate identification and low-error concentration prediction of VOCs, which is valuable for subsequent portable VOC detection systems.

Mass Production of Carbon Nanotube Transistor Biosensors for Point-of-Care Tests.

Low-dimensional semiconductor-based field-effect transistor (FET) biosensors are promising for label-free detection of biotargets while facing challenges in mass fabrication of devices and reliable reading of small signals. Here, we construct a reliable technology for mass production of semiconducting carbon nanotube (CNT) film and FET biosensors. High-uniformity randomly oriented CNT films were prepared through an improved immersion coating technique, and then, CNT FETs were fabricated with coefficient of performance variations within 6% on 4-in. wafers (within 9% interwafer) based on an industrial standard-level process. The CNT FET-based ion sensors demonstrated threshold voltage standard deviations within 5.1 mV at each ion concentration, enabling direct reading of the concentration information based on the drain current. By integrating bioprobes, we achieved detection of biosignals as low as 100 aM through a plug-and-play portable detection system. The reliable technology will contribute to commercial applications of CNT FET biosensors, especially in point-of-care tests.

Development and application of a low-cost and portable multi-channel spectral detection system for mutton adulteration

It is important to develop low-cost, fast and portable meat adulteration detection systems to ensure the meat authenticity and safety in complex market environments. A multi-channel spectral detection system for meat adulteration was developed in this study. The core hardware of the system mainly includes a designed spectral module and a Raspberry pi controller. The spectral module consists of three multi-channel spectral sensors and LED lamps with specific wavelengths, containing 18 channels covering a range of 410–940 nm. The software was developed based on PyQt5. After completing the construction of the system, the detection distance was discussed and determined to be 4 mm. Based on the spectral data collected by the developed system, the models for classifying pure mutton, pure pork, mutton flavour essence adulteration, colourant adulteration and adulterated mutton with pork were established and compared. Four intelligent optimisation algorithms were further used to improve the model performance. The results of the test set showed that the support vector classification (SVC) model optimised by a sparrow search algorithm (SSA) obtained the best classification performance, with an accuracy of 97.59% and a Kappa coefficient of 0.9696. After the SSA-SVC was incorporated into the sensor software, the system performance was evaluated using external validation samples. The overall accuracy of the system was 94.29%. The system took about 5.31 s to detect a sample, and the total weight of the system was 1.55 kg. Overall, the developed portable spectral system has considerable potential to rapidly and accurately discriminate adulterated mutton in the field.

A portable real-time concrete bridge damage detection system

A drone detection system can achieve real-time inspection of concrete bridge damage. However, it is difficult to deploy existing detection algorithms on a portable system due to their high computational cost. Therefore, a portable damage detection algorithm called CD-YOLOv8 is designed. CD-YOLOv8 is operated on Jetson Xavier NX to detect drone-captured bridge images in real-time. The multi-scale feature adaptive fusion module is used to reduce model parameters and improve detection speed. The spatial context pyramid and deformable convolutional structure are introduced to improve the detection accuracy of tiny concrete bridge damage. Compared with the YOLOv8n, the mAP50 of CD-YOLOv8 is increased by 3.6 %, while the model parameters and FLOPs are decreased by 0.05 M and 0.7 G, respectively. CD-YOLOv8 is also tested on the Jetson Xavier NX with a detection speed of 44 FPS, which is 15.79 % faster than the YOLOv8n. Therefore, CD-YOLOv8 has high accuracy and excellent real-time performance.

A new portable electrochemical detection sensor based on molecularly imprinted polymer-modified MOF-808/AB for the highly sensitive and selective determination of dimethoate

Detection of dimethoate (DIM) in fruits and vegetables is crucial for life health and environmental development, but there is still a lack of versatile portable organophosphorus pesticides detection instruments. In this study, a unique portable electrochemical detection system was designed for real-time detection in the field. Meanwhile, a new combination of metal–organic skeleton MOF-808 and acetylene black (AB) was reported for the first time, and an electrochemical sensor for the detection of dimethoate (DIM) was constructed by using a molecularly imprinted polymer (MIP) coated MOF-808/AB. The AB was homogeneously grown on the surface of MOF-808, which ensured the high conductivity and mechanical stability of the material. The experimental results showed that MIP/MOF-808/AB/GCE had a better detection effect on DIM. Under the optimal conditions, the linear range of DIM was 1 × 10-5 – 1 × 10-10 M with a detection limit of 43.05 pM (n = 10) using differential pulse voltammetry.

Hexavalent chromium dual water remediation and sensing based on hybrid polymer/metal-organic framework composites

The environment contains numerous chemical pollutants from natural processes and human activities, including pesticides, pharmaceuticals, and industrial chemicals. Specifically, the issue of hexavalent chromium water pollution, whether from natural or industrial sources, remains a global and unresolved threat. Hence, there is a need for efficient and prompt monitoring of Cr(VI) concentration in water to determine the potential danger and to facilitate the development of efficient remediation strategies. In this work, the metal-organic framework (MOF) UiO-66-NH2 was immobilized into poly (vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) polymeric membranes, manufactured in different morphologies, to act as Cr(VI) sorbent to develop a novel portable, easily reactivable, and reusable water filtering and sensing technology. The multifunctional membranes demonstrated an adsorption capacity of 2.32 mg/L in 300 minutes for plasma electrospun-oriented fiber membranes with 10 % w/v of MOFs. The most efficient morphology was selected and integrated into a portable system for simultaneous detection and adsorption in a short time. It shows approximately 50 % adsorption efficiency and a colorimetric sensitivity with an excellent determination coefficient R2 of 0.990, even for very low concentrations at 0.085 ppm. To the best of our knowledge, this work presents the first system that utilizes multi-structured active materials for the remediation and monitoring of an aquatic contaminant.

Development of Portable Multi-fluorescence Detection System Using Indium Tin Oxide Heater for Loop-mediated Isothermal Amplification

Development of Portable Multi-fluorescence Detection System Using Indium Tin Oxide Heater for Loop-mediated Isothermal Amplification

Reliability and Validity Study of A Portable Gait Detection System Based on Wearable Sensors

Reliability and Validity Study of A Portable Gait Detection System Based on Wearable Sensors

An enzyme-free fluorescence sensing platform based on multiplex toehold-mediated strand displacement for point-of-care testing of single nucleotide polymorphisms

Single nucleotide polymorphisms (SNPs) are biomarkers for the prediction and diagnosis of biomedical diseases, as well as important reference markers for intelligent design and breeding of animals and plants. Therefore, the development of high-sensitivity, practicability, and convenient SNPs sensing strategies and detection platforms is of great significance. In this study, we pioneered an ultra-sensitive universal sensing strategy for single base mutation detection and an enzyme-free FRET fluorescence sensing point-of-care testing (POCT) platform for SNP detection. Based on the thermodynamically regulated strand replacement mechanism, the system rationally introduced extra base mismatches and combined multiple toehold-mediated strand displacement (TMSD) reactions to achieve enzyme-free recognition of the single base mutation and double amplification of the detection signal. Finally, the biological signal of the mutant target (MT) was recognized, amplified, and transduced into a fluorescence resonance energy transfer (FRET) signal between Cy3 and Cy5 for output. Overall, the proposed biosensing strategy demonstrated high sensitivity for MT, with a detection limit as low as 0.37 fM, and could achieve detection of targets with mutant abundances as low as 0.1 %. Moreover, a portable detection system was assembled in conjunction with this sensing strategy to form a POCT platform for SNP genotyping. This POCT platform was validated to accurately identify SNPs from human cancer cells and soybean leaf genome extracts. In summary, this study will provide new insights into the design of enzyme-free biosensors for SNP detection, and will provide important technical support tools for the prevention and diagnosis of major diseases, animal and plant breeding, etc.

Design and Development of a Portable Neutron Detection System Based on a 6Li-Doped ZnS(Ag) Scintillator

In this study, the design and development of a portable neutron detection system based on a 6LiF + ZnS(Ag) scintillator was carried out. The detector system was first modeled using the Monte Carlo simulation code MCNP. As a result of the detector simulation, the materials and photomultiplier tubes (PMTs) were supplied for the system, and the material properties and thicknesses were optimized for the best scintillator, with the EJ-420 (Eljen Technology), consisting of 95% 6Li dispersed in ZnS(Ag), chosen because it has a distinctive feature with regard to high thermal neutron detection efficiency. Since this material has been shown to offer a significant advantage in favor of neutron detection in mixed radiation fields with gammas and neutrons, the pulse shape discrimination method was employed. To achieve this, a proper electronic circuit was developed to discriminate the pulses from neutrons and gammas. In the experimental step, the EJ-420 scintillator with a 50-mm diameter was optically coupled with a special fast PMT (Hamamatsu H1949-51 model). In conclusion, this study, which underlines the performed simulations for a neutron detector configuration, gives the obtained experimental results showing discrimination capability in neutron/gamma detection using a 241Am-Be neutron source. The results show that the EJ-420 is a good scintillator due to its highly enriched 6Li transmator, which results in more effective neutron measurements when a portable neutron detector design is chosen.

基于CA-YOLOv8的玉米叶病检测方法

In order to achieve efficient and accurate detection of common corn leaf diseases such as leaf blight, gray spot disease, and rust, a corn leaf disease detection method based on CA-YOLOv8 was proposed. In this method, the Coordinate Attention(CA) attention mechanism was added after the feature map output from the Neck part to enhance the feature extraction capability of the model. The experimental results showed that the precision，recall and mean average precision(mAP) of the CA-YOLOv8 model on the test set were 94.08%, 90.53% and 97.38%, respectively. Compared with the YOLOv8, YOLOv8+SE and YOLOv8+CBAM models, the mAP was improved by 2.15, 0.86 and 2.35 percentage points, respectively. Compared with Faster R-CNN, YOLOv5s, YOLOv7, and YOLOv8 models, the mAP has increased by 63.53, 29.24, 3.21, and 2.15 percentage points, respectively. The study showed that the CA-YOLOv8 model can provide a technical reference for the development of a portable intelligent corn leaf disease detection system.