Conventional Detection Research Articles

Anomaly Sign Detection for Automatic Ticket Gates by the Histogram Limitation Method

It is crucial to appropriately maintain automatic ticket gates (ATGs) to keep transportation operating smoothly in urban areas. Although the average failure rate of new ATGs is extremely low, continuous operation for many years might lead to unstable performance due to deterioration, and the need for periodic maintenance to avoid fatal faults might halt operations for extended periods. To detect anomalies at an early stage, “anomaly signs” can be utilized to flag ATGs for maintenance by service engineers before anomalies occur. In addition, to minimize the cost of ATG monitoring, the necessary computing resources should be minimized, which means using only light-weight statistical methods rather than deep learning or machine learning. In this paper, we focus on the automatic separation modules inside ATGs that separate multiple tickets by complicated mechatronic controls because this module is the major cause of maintenance calls from station attendants. We propose a simple anomaly sign detection, called the histogram limitation method (HLM). We evaluated the anomaly sign scores over time with maintenance timing and compared them with the conventional fast unsupervised anomaly detection method, Histogram-Based Outlier Score (HBOS) widely used in various domains. The experimental results using real field ATG monitoring data show that HLM successfully detected anomaly signs before a maintenance call was necessary, which is better performance compared with HBOS. Despite being a simple modification based on HBOS, HLM also provides anomaly sign scores that agree adequately with assessments by maintenance service engineers.

Second harmonic generation based on graphene hyperstructure for higher resolution and performance on top of achieving fundamental wave detection in theory

In this paper, an innovative one-dimensional graphene hyperstructure (GHS) is proposed, allowing for the concurrent detection of multiple physical parameters in both the fundamental and second harmonic generation. The sensing characteristics of GHS pertaining to magnetic field strength (B), incident electromagnetic wave angle (θ), and graphene thickness (dgt) are systematically investigated. Moreover, through the incorporation of second harmonic generation alongside fundamental detection, higher resolution and performance are achieved. The findings indicate an expansion of the measurement range for B, θ, and dgt, from 0.3∼0.5 T, 35∼55°, and 1∼6 layers to 0.3∼1 T, 35∼65°, and 1∼10 layers, providing increased flexibility and adjustability. Additionally, by leveraging nonlinear effects and widening the Fabry-Perot cavity width, this structure effectively enhances the quality factor (Q) from 2.94 × 102 to 1.95 × 105, resulting in a substantial improvement in sensing performance. This development holds tremendous promise in surpassing the diffraction limit and addressing high-Q value sensing requirements. In comparison to conventional detectors, the GHS not only enhances detection efficiency but also harbors the potential for multiple physical quantities detection. This forward-looking research is pivotal in its successful resolution of detector performance limitations, ushering in novel possibilities across diverse domains.

χ (2)-induced artifact overwhelming the third-order signal in 2D Raman-THz spectroscopy of non-centrosymmetric materials.

Through comprehensive data analysis, we demonstrate that a χ(2)-induced artifact, arising from imperfect balancing in the conventional electro-optic sampling detection scheme, contributes significantly to the measured signal in 2D Raman-THz spectroscopy of non-centrosymmetric materials. The artifact is a product of two 1D responses, overwhelming the desired 2D response. We confirm that by analyzing the 2D Raman-THz response of an x-cut beta barium borate crystal. We furthermore show that this artifact can be effectively suppressed by implementing a special detection scheme. We successfully isolate the desired third-order 2D Raman-THz response, revealing a distinct cross-peak feature, whose frequency position suggests the coupling between two crystal phonons.

High-throughput DART-MS/MS for quantification of carboxymethyl lysine and carboxyethyl lysine in beef

Carboxymethyl lysine (CML) and carboxyethyl lysine (CEL), commonly found in heat-processed foods, pose considerable health risks. Conventional detection methods for these compounds are often slow and inefficient. This study presents a high-throughput and effective method utilizing Direct Analysis in Real-Time ionization coupled with triple quadrupole tandem mass spectrometry (DART-MS/MS) for quantifying CML and CEL in beef. The method demonstrated strong linearity between concentration and peak area across a range of 1–50 μg/mL, with detection and quantification limits of 0.15 μg/g and 0.6 μg/g, respectively. Remarkably, it enables the analysis of eight samples in just two minutes per run. Additionally, the use of buckwheat extract as an antioxidant during yak beef cooking resulted in a 7.72 % reduction in CML and a 1.46 % reduction in CEL. This study provides a robust and rapid method for detecting CML and CEL in food products, contributing to the improvement of food safety.

Long afterglow nanoprobes labeled image enhancement using deep learning in rapid and sensitive lateral flow immunoassay

Point-of-care testing (POCT) is currently the predominant method of in vitro diagnostics, with lateral flow immunoassay(LFIA) being commonly utilized in POCT. The performance of the probe in LFIA will directly affect the results of the detection. Therefore, the selection of probes is particularly important. However, the current commercially produced probes generally suffer from short luminescence lifetime, high detection cost, and poor anti-interference capability. Meanwhile, the traditional detection system has difficulties in detecting weakly positive samples. These ultimately lead to the problem of poor detection sensitivity. In order to address the above issues, we have synthesized an ultralong organic phosphorescence nanoprobe (UOPN). The UOPNs exhibit a long afterglow phosphorescence signal of around 300 ms after being excited, which can be used to eliminate endogenous interference. Meanwhile, focusing on the problem of poor detection of weakly positive samples in conventional detection systems, and the weak inherent signal of UOPNs, resulting in a low signal-to-noise ratio, we have developed a detection system that combines image processing and neural network-driven analysis for long afterglow image analysis. Through the analysis of the long afterglow images of serum amyloid A (SAA) in human blood, a detection limit of 0.01 μg/mL and a linear range of 0.1–251 μg/mL were achieved. The whole analysis was completed within 5 minutes. After pre-processing with the denoising algorithm, the system demonstrated an impressive classification accuracy of 99.24 %, validating the reliability of the detection system. This approach has further enhanced the sensitivity of immunoassay, contributing to the advancement of point-of-care testing.

Myelographic Techniques for the Localization of CSF-Venous Fistulas: Updates in 2024.

CSF-venous fistulas (CVFs) are a common cause of spontaneous intracranial hypotension. Despite their relatively frequent occurrence, they can be exceedingly difficult to detect on imaging. Since the initial description of CVFs in 2014, the recognition and diagnosis of this type of CSF leak has continually increased. As a result of multi-institutional efforts, a wide spectrum of imaging modalities and specialized techniques for CVF detection is now available. It is important for radiologists to be familiar with the multitude of available techniques, because each has unique advantages and drawbacks. In this article, we review the spectrum of imaging modalities available for the detection of CVFs, explain the advantages and disadvantages of each, provide typical imaging examples, and discuss provocative maneuvers that may improve the conspicuity of CVFs. Discussed modalities include conventional CT myelography, dynamic myelography, digital subtraction myelography, conebeam CT myelography, decubitus CT myelography by using conventional energy-integrating detector scanners, decubitus photon counting CT myelography, and intrathecal gadolinium MR myelography. Additional topics to be discussed include optimal patient positioning, respiratory techniques, and intrathecal pressure augmentation.

RCLNet: an effective anomaly-based intrusion detection for securing the IoMT system.

The Internet of Medical Things (IoMT) has revolutionized healthcare with remote patient monitoring and real-time diagnosis, but securing patient data remains a critical challenge due to sophisticated cyber threats and the sensitivity of medical information. Traditional machine learning methods struggle to capture the complex patterns in IoMT data, and conventional intrusion detection systems often fail to identify unknown attacks, leading to high false positive rates and compromised patient data security. To address these issues, we propose RCLNet, an effective Anomaly-based Intrusion Detection System (A-IDS) for IoMT. RCLNet employs a multi-faceted approach, including Random Forest (RF) for feature selection, the integration of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models to enhance pattern recognition, and a Self-Adaptive Attention Layer Mechanism (SAALM) designed specifically for the unique challenges of IoMT. Additionally, RCLNet utilizes focal loss (FL) to manage imbalanced data distributions, a common challenge in IoMT datasets. Evaluation using the WUSTL-EHMS-2020 healthcare dataset demonstrates that RCLNet outperforms recent state-of-the-art methods, achieving a remarkable accuracy of 99.78%, highlighting its potential to significantly improve the security and confidentiality of patient data in IoMT healthcare systems.

Multiplicative noise induced bistability and stochastic resonance

Abstract Stochastic resonance is a well established phenomenon, which proves relevant for a wide range of applications, of broad trans-disciplinary breath. Consider a one dimensional bistable stochastic system, characterized by a deterministic double well potential and shaken by an additive noise source. When subject to an external periodic drive, and for a proper choice of the noise strength, the system swings regularly between the two existing deterministic fixed points, with just one switch for each oscillation of the imposed forcing term. This resonant condition can be exploited to unravel weak periodic signals, otherwise inaccessible to conventional detectors. Here, we will set to revisit the stochastic resonance concept by operating in a modified framework where bistability is induced by the nonlinear nature of the multiplicative noise. A candidate model is in particular introduced which fulfils the above requirements while allowing for analytical progress to be made. Working with reference to this case study, we elaborate on the conditions for the onset of the generalized stochastic resonance mechanism. As a byproduct of the analysis, a novel resonant regime is also identified which displays no lower bound for the frequencies that can be resolved, at variance with the traditional setting.

The Evolution of Illicit-Drug Detection: From Conventional Approaches to Cutting-Edge Immunosensors-A Comprehensive Review.

The increasing use of illicit drugs has become a major global concern. Illicit drugs interact with the brain and the body altering an individual's mood and behavior. As the substance-of-abuse (SOA) crisis continues to spread across the world, in order to reduce trafficking and unlawful activity, it is important to use point-of-care devices like biosensors. Currently, there are certain conventional detection methods, which include gas chromatography (GC), mass spectrometry (MS), surface ionization, surface-enhanced Raman spectroscopy (SERS), surface plasmon resonance (SPR), electrochemiluminescence (ECL), high-performance liquid chromatography (HPLC), etc., for the detection of abused drugs. These methods have the advantage of high accuracy and sensitivity but are generally laborious, expensive, and require trained operators, along with high sample requirements, and they are not suitable for on-site drug detection scenarios. As a result, there is an urgent need for point-of-care technologies for a variety of drugs that can replace conventional techniques, such as a biosensor, specifically an immunosensor. An immunosensor is an analytical device that integrates an antibody-based recognition element with a transducer to detect specific molecules (antigens). In an immunosensor, the highly selective antigen-antibody interaction is used to identify and quantify the target analyte. The binding event between the antibody and antigen is converted by the transducer into a measurable signal, such as electrical, optical, or electrochemical, which corresponds to the presence and concentration of the analyte in the sample. This paper provides a comprehensive overview of various illicit drugs, the conventional methods employed for their detection, and the advantages of immunosensors over conventional techniques. It highlights the critical need for on-site detection and explores emerging point-of-care testing methods. The paper also outlines future research goals in this field, emphasizing the potential of advanced technologies to enhance the accuracy, efficiency, and convenience of drug detection.

Photon-counting detector computed tomography in cardiac imaging.

Photon-counting detector computed tomography (PCD-CT) has emerged as arevolutionary technology in CT imaging. PCD-CT offers significant advancements over conventional energy-integrating detector CT, including increased spatial resolution, artefact reduction and inherent spectral imaging capabilities. In cardiac imaging, PCD-CT can offer amore accurate assessment of coronary artery disease, plaque characterisation and the in-stent lumen. Additionally, it might improve the visualisation of myocardial fibrosis through qualitative late enhancement imaging and quantitative extracellular volume measurements. The use of PCD-CT in cardiac imaging holds significant potential, positioning itself as avaluable modality that could serve as aone-stop-shop by integrating both angiography and tissue characterisation into asingle examination. Despite its potential, large-scale clinical trials, standardisation of protocols and cost-effectiveness considerations are required for its broader integration into clinical practice. This narrative review provides an overview of the current literature on PCD-CT regarding the possibilities and limitations of cardiac imaging.

Leveraging AI Algorithms to Combat Financial Fraud in the United States Healthcare Sector

Financial fraud is a major problem in the healthcare industry because it causes large financial losses and compromises the integrity and trust of healthcare systems. The intricacy and sophistication of contemporary fraudulent operations make conventional fraud detection techniques which rely on manual audits and rule-based systems increasingly inadequate. AI algorithms have become a viable way to improve financial fraud detection and prevention. Hence, this paper examines how AI algorithms can be used to detect and stop fraud in the healthcare industry, emphasizing how these algorithms could revolutionize fraud control procedures. This study suggests that AI algorithms greatly improve the identification of financial fraud in the healthcare industry by spotting intricate patterns and abnormalities frequently overlooked by already existing techniques. Machine learning models have proven to be highly accurate in predicting fraudulent claims and transactions. However, while AI provides numerous opportunities to improve fraud detection skills, its effective application necessitates resolving important issues, including ethical considerations, data governance, and model interpretability.

A-367 Development of a Multimodal Point-of-Care Diagnostic Platform to Perform Rapid, Accurate, and Cost-Effective Molecular, Immunochemistry, Clinical Chemistry, and Cytometry Tests

Abstract Background Access to point-of-care (POC) testing can make a significant impact on the quality of care. However, because POC diagnostic platforms available today are expensive and only perform one type of test, clinicians are unable to implement all the POC tests they wish to perform. Fluxergy is developing a multimodal POC diagnostic platform that consolidates diverse diagnostic modalities into a single, compact platform to minimize the cost of POC testing and standardize testing workflow for the most common clinical chemistry, hematology, immunochemistry and molecular tests. This study describes feasibility studies performed to benchmark these assay modalities on the Fluxergy Analyzer. Methods Fluxergy Analyzer: The Fluxergy Analyzer integrates conventional detection systems—colorimetric, fluorometric, and electrochemical—into a singular POC platform. This unique design expands -omics testing capabilities, including molecular, immunochemistry, clinical chemistry, and hematology within a single instrument. Benchmarking Feasibility Studies: Molecular benchmarking was performed using a multiplex respiratory panel on contrived samples in UTM/VTM. Immunochemistry benchmarking was performed using a procalcitonin assay on spiked sodium citrate plasma samples. Cytometry benchmarking was performed using a white blood cell count (WBC) assay and testing contrived samples whole blood samples. Results For molecular testing, the Fluxergy Analyzer was able to accurately detect flu A, flu B, SARS-CoV-2, and RSV with agreements with the Luminex ARIES® Flu A/B & RSV Assay and Cepheid Xpert® SARS-CoV-2 test results. For immunochemistry testing, the Fluxergy Analyzer accurate quantified PCT levels at 0.3, 0.6, 1.25 and 2.5 ng/mL. For hematology testing, the Fluxergy Analyzer demonstrated a strong correlation across the reportable range against the standard of care HemoCue analyzer. For clinical chemistry testing, the Fluxergy Analyzer demonstrated a strong correlation against the Siemen’s epoc® Blood Analysis System. Conclusions The Fluxergy Analyzer is uniquely suited to offer a comprehensive menu of the most common POC tests on a single, cost-effective platform.

Rapid and selective quantitative colourimetric analysis of nitrite in water using a S-Nitrosothiol based method

This study introduces a novel S-nitrosothiol based method for the rapid and highly selective detection of nitrite in complex water matrices. Sodium 3-mercapto-1-propanesulfonate forms a distinctive pink S-nitrosothiol compound upon interaction with nitrite in acidic media, allowing both visual and quantitative detection. Various factors affecting the absorbance of the final product were investigated, including pH, reaction time, acid type, and sodium 3-mercapto-1-propanesulfonate concentration. UV–Vis spectrophotometric analysis demonstrated an excellent linear correlation (R2 = 0.99) across a broad detection range (0.05 to 80 mmol l-1), while showing no interference from common ions such as nitrate or dissolved organic matter, a limitation frequently observed in conventional UV-based nitrite detection methods. The assay was further adapted into a pellet form to simplify field use, operating effectively at room temperature with a low detection limit (1.4 ppm). The S-nitrosothiol based method represents a safer and more environmentally friendly option for nitrite detection and shows a promising potential as a valuable addition to both field and laboratory water testing kits for nitrite analysis.

A comprehensive bearing prognosis framework based on piecewise function stacking convolution auto-encoder and XGBoost algorithm

Abstract Accurately forecasting the remaining useful life (RUL) stands as a pivotal and formidable task within the realm of prognostics and health management. However, there is limited research that considers integrating early fault diagnosis during the bearing’s lifecycle with the prediction of its RUL. In this article, a comprehensive bearing prognosis framework based on piecewise function stacking convolution auto-encoder (AE) and XGBoost algorithm is proposed. To achieve this, an unsupervised piecewise function-based deep stacked convolutional AE was designed to construct the health indicator (HI) of the bearing for reducing the dependency on prior knowledge and furnishing a dynamic foundation for predicting RUL. The 4σ criterion based on HI’s increment was proposed for determining the fault occurrence time (FOT) of the bearing’s operational process. Subsequently, an XGBoost algorithm model was utilized to predict the RUL of faulty bearings. The efficacy of the bearing prognosis framework was validated by two real bearing test datasets. Results indicate the employed criterion of construct HI can flexibly adjust to various operational conditions and accurately pinpoint the bearing’s FOT. Furthermore, the findings demonstrate the proposed bearing prognosis framework achieves superior performance compared with several conventional anomaly detection methods.

Monitoring water quality parameters of freshwater aquaculture ponds using UAV-based multispectral images

Monitoring water quality is crucial for water exchange, precise feeding, and quality control of water products in freshwater aquaculture. In light of the issue of spatial heterogeneity in freshwater aquaculture pond waters and the constraints of conventional sensor detection techniques and traditional machine learning models. In this study, UAV multispectral images were combined with four machine learning algorithms (Ridge, XGBoost, CatBoost, RF) and the Stacking model to model the estimation of Chlorophyll a (Chl-a) and Turbidity and map their spatial distribution. The findings indicate that, in contrast to machine learning models, the Stacking model of water quality parameter performs better with higher accuracy. Meanwhile,for Chl-a and Turbidity the optimal sub-model combination in the Stacking model varies, with the most effective estimation model for Chl-a concentration identified as RF-XGB-Ridge (R2 = 0.84, RMSE=1.882 µg/L, MAE=3.433 µg/L and Slope = 0.791). As to Turbidity, the RF-CAB-Ridge model demonstrates superior performance, with macro-averaged precision (macro-p) of 93.3 %, macro-averaged recall (macro-R) of 88.8 %, macro-averaged F1-score (macro-F1) of 0.895, and Kappa coefficient of 0.813. Furthermore, the results of the joint analyses, which included measured samples and management measures at the test site, demonstrated that the spatial distribution maps of Chl-a and Turbidity were in alignment with the current status of water quality at the test site. This consistency was observed across both temporal and spatial scales. The results demonstrate that the integration of UAV multispectral images with the Stacking model can enhance the precision of water quality parameter models, facilitates the examination of the spatial and temporal distribution of water quality parameters and the underlying influencing factors, and advances the capability for dynamic monitoring of water quality parameters in freshwater aquaculture regions. Concurrently, it offers fundamental theoretical and methodological assistance for the precise regulation of water quality in freshwater aquaculture ponds and the formulation of optimal production management strategies.

Partial Discharge Detection Technique Based on Full Convolutional Network with Channel-wise Convolution and Channel Mixing

Abstract The occurrence of partial discharge (PD) serves as an indication of power equipment failure, which, if not given due attention, may result in severe power accidents. Traditionally, the detection of PD involves applying various pre-processing techniques to the original signal and extracting specialized signal features that are subsequently classified using a classifier. The process is intricate and demands extensive expertise. One drawback of the conventional detection approach lies in its failure to consider the information conveyed between the phases of a three-phase electrical signal during the process of identifying PD in medium voltage three-phase cables. The present study proposes a novel method for detecting PDs, which is based on channel-wise convolution and channel mixing(C2CM) using full convolutional networks. Firstly, a set of pulses for each phase of a three-phase signal is extracted and combined to form a three-phase pulse set, serving as the input to the network. Subsequently, we introduce a FCN-C2CM network designed specifically for PD detection, capable of learning both the time-domain features of the input signal and the phase features of the three-phase signal, thereby enhancing recognition accuracy. We evaluated our approach using online testing tools on publicly available datasets, with experimental results demonstrating its superiority over existing.

Arc bubble edge detection method based on deep transfer learning in underwater wet welding.

The stability of arc bubble is a crucial indicator of underwater wet welding process. However, limited research exists on detecting arc bubble edges in such environments, and traditional algorithms often produce blurry and discontinuous results. To address these challenges, we propose a novel arc bubble edge detection method based on deep transfer learning for processing underwater wet welding images. The proposed method integrates two training stages: pre-training and fine-tuning. In the pre-training stage, a large source domain dataset is used to train VGG16 as a feature extractor. In the fine-tuning stage, we introduce the Attention-Scale-Semantics (ASS) model, which consists of a Convolutional Block Attention Module (CBAM), a Scale Fusion Module (SCM) and a Semantic Fusion Module (SEM). The ASS model is further trained on a small target domain dataset specific to underwater wet welding to fine-tune the model parameters. The CBAM can adaptively weight the feature maps, focusing on more crucial features to better capture edge information. The SCM training method maximizes feature utilization and simplifies training by combining multi-scale features. Additionally, the skip structure of SEM effectively mitigates semantic loss in the high-level network, enhancing the accuracy of edge detection. On the BSDS500 dataset and a self-constructed underwater wet welding dataset, the ASS model was evaluated against conventional edge detection models-Richer Convolutional Features (RCF), Fully Convolutional Network (FCN), and UNet-as well as state-of-the-art models LDC and TEED. In terms of Mean Absolute Error (MAE), accuracy, and other evaluation metrics, the ASS model consistently outperforms these models, demonstrating edge detection capabilities that are both effective and stable in detecting arc bubble edges in underwater wet welding images.

Design and Development of Lightning Detection System Utilizing Slow Atmospheric Electric Field Waveform at Legoland Malaysia

Conventional lightning localization and detection techniques, including Magnetic Direction Finder (MDF), Time of Arrival (TOA), Interferometer (ITF), and Distance of Arrival (DOA), predominantly rely on fast atmospheric electric fields, magnetic fields, and very high frequency (VHF) signals. This paper pioneers a novel approach by delving into the analysis of the slow atmospheric electric field, aiming to develop a waveform analysis utilizing this field to estimate the distance and radius of lightning occurrences at LEGOLAND Malaysia Resort. The newly implemented lightning detection system at LEGOLAND leverages a straightforward and cost-effective setup, incorporating a capacitive antenna, slow and fast atmospheric electric field sensors, and dedicated data analysis software. The system's efficacy and accuracy have undergone a rigorous comparison with LEGOLAND's existing online lightning detection service. Achieving accurate data necessitates proper grounding and isolation from electrical noise, as signal interference from power lines, towers, or machinery can potentially trigger false signals. This research has contributed detailed documentation on the analysis of slow atmospheric electric field data, encompassing waveform patterns and key characteristics. This documentation is expected to serve as a valuable resource for future research endeavors and the continuous refinement of lightning detection systems. The comparative evaluation between the novel system and the current online service at LEGOLAND Malaysia Resort has shed light on the efficiency and capabilities of the newly introduced methodology.

A virtual anti-scatter grid for multi-energy photon counting detector systems

Photon-counting (PC) systems are the next technological generation of medical computed tomography (CT) imaging and is being worked on by all major system providers. CT devices that are based on PC detectors enable multi-energy data collection. The information-content of this data can be used to obtain more detailed patient data, which improves the quality of reconstructions, compared to conventional detector systems. However, PC CT systems are subject to radiation scatter as just as any other imaging systems is. Conventionally anti-scatter grids (ASG) are used to reduce the scatter effect. These are however an imperfect solution, especially for PC detectors. In this work, a software-based scatter correction method, thus a virtual ASG is proposed. The method is tailoring a new statistical model in the measurement space and combining it with the statistical inversion method called Markov chain Monte Carlo (MCMC). The method can recover the measurement data from dense projections. We present the method on simulated data of a single photon emission computed tomography (SPECT) problem for which only under-sampled data is available. However, our approach can in principle be generalised to CT, PC-CT, Positron emission tomography (PET), radiotherapy, or even digital radiography problems. The results show that the proposed model performs similarly as physical ASGs and for cases where ASGs are not possible. The model further offers a significant improvement in the quality of the reconstruction image compared to the image reconstruction from original under-sampled data.

Laser-induced CdS/TiO2/graphene dual photoanodes for ratiometric self-powered photoelectrochemical sensor: an innovative approach for aflatoxin B1 detection.

A ratiometric self-powered photoelectrochemical sensor based on laser direct writing technology was constructed to address the problem that the conventional single-signal detection mode was susceptible to the influence of instrumentation and environmental factors, which interfered with the detection results. Laser-induced CdS/TiO2/Graphene was prepared as dual photoanodes (PA1 and PA2), which were controlled by multiplexed switches to form a photocatalytic fuel cell with Pt cathode. By modifying the aptamer of aflatoxin B1 (AFB1) on the photoanode surface, the target was specifically captured to the electrode surface to form a biological complex, which increased the steric hindrance and affected the electron transfer, thus reducing the output signal of the sensor. Targets with different concentrations were incubated on the surface of PA1, and targets with fixed concentrations were incubated on the surface of PA2. Under the control of the multiplex switch, the output signals of the two photoanodes were recorded, and the ratio of these two signals was used as the basis for the quantitative detection of AFB1. The sensor output was linearly increasing with the logarithm of AFB1 concentration from 1.0 to 150ngmL-1 and the detection limit was 0.0974ngmL-1. Additionally, this method had good stability, fast response, and good selectivity to real samples, providing an effective method for food safety monitoring.