Detection System Research Articles

Survey of federated learning in intrusion detection

Intrusion detection methods are crucial means to mitigate network security issues. However, the challenges posed by large-scale complex network environments include local information islands, regional privacy leaks, communication burdens, difficulties in handling heterogeneous data, and storage resource bottlenecks. Federated learning has the potential to address these challenges by leveraging widely distributed and heterogeneous data, achieving load balancing of storage and computing resources across multiple nodes, and reducing the risks of privacy leaks and bandwidth resource demands. This paper reviews the process of constructing federated learning based intrusion detection system from the perspective of intrusion detection. Specifically, it outlines six main aspects: application scenario analysis, federated learning methods, privacy and security protection, selection of classification models, data sources and client data distribution, and evaluation metrics, establishing them as key research content. Subsequently, six research topics are extracted based on these aspects. These topics include expanding application scenarios, enhancing aggregation algorithm, enhancing security, enhancing classification models, personalizing model and utilizing unlabeled data. Furthermore, the paper delves into research content related to each of these topics through in-depth investigation and analysis. Finally, the paper discusses the current challenges faced by research, and suggests promising directions for future exploration.

Determination of objective taste perception among Iranian medical sciences students during COVID‑19 pandemic in Yazd, Eastern Iran: a case-control pilot study

BackgroundTaste disorders in patients suffering from COVID-19 were popular. Some people even after recovery report residual effects of loss of smell and taste. This study aimed to investigate the taste Perception of Iranian Medical Sciences students during the COVID-19 pandemic.MethodsThe case-control study evaluated 50 Medical Sciences students with a history of COVID-19 infection, as confirmed by the Novel Coronavirus (2019-nCOV) CFX96™ Real-Time PCR Detection System. The other participants consisted of 50 volunteer students with COVID-19 negative. The taste perception was measured with 4 different concentrations of the basic tastes. The stimuli were applied to the protruded tongue. Subjects were asked to identify the researcher when they felt the taste. Data are expressed as frequency distribution and analyzed with the Chi-Square test (P < 0.05).ResultsIn this survey, 54% of participants were male and 46% were female, the mean age of participants was 22.96 ± 5 years. The results showed a significant difference in the sweet and bitter taste perception score according to the history of dietary supplement use. The bitter taste perception score declared a considerable difference since COVID-19 infection. There was no significant difference between the taste perception in the two studied groups for all 4 basic tastes according to gender, COVID-19 infection status, history of taste and smell disorders, and the elapsed time since COVID-19 infection.ConclusionOur findings could provide important insights into taste perception. The history of dietary supplementation may influence how sweetness and saltiness are perceived. There was a noticeable difference in bitter taste perception depending on the time that had elapsed since the most recent COVID-19 infection.

A Novel Invasive Weed Optimization and its Variant for the Detection of Polycystic Ovary Syndrome

Introduction: This study intends to provide a novel Invasive Weed Optimization (IWO) algorithm for the detection of Polycystic Ovary Syndrome (PCOS) from ultrasound ovarian images. PCOS is an intricate anarchy described by hyperandrogenemia and irregular menstruation. Indian women are increasingly finding reproductive disorders, namely PCOS. Methods: The women having PCOS grow more small follicles in their ovaries. The radiologists take a look into women's ovaries by use of ultrasound scanning equipment to manually count the number of follicles and their size for fertility treatment. These may lead to error diagnosis. Results: This paper proposed an automatic follicle detection system for identifying PCOS in the ovary using IWO. The performance of IWO is improved in Modified Invasive Weed Optimization (MIWO). This algorithm imitates the biological weeds' behavior. The MIWO is employed to obtain the optimal threshold by maximizing the between-class variance of the modified Otsu method. The efficiency of the proposed method has been compared with the well-known optimization technique called Particle Swarm Optimization (PSO) and with IWO. Conclusion: Experimental results proved that the MIWO finds an optimal threshold higher than that of IWO and PSO.

Online transaction fraud detection in the banking sector using machine learning techniques

In this paper Nowadays, online transactions are increasing rapidly and payment scams are also increasing. A hacker is a person who gains access to another person's financial information. Improving feature selection is important to detect transaction risk in large, multidimensional data. Fraud detection and classification performance is divided into two parts: training data and test data. During this process we have focused on data analysis and pre-processing. In a transaction’s dataset, the predictive variables influence how well a machine learning algorithm for transaction risk identification classifies and identifies fraudulent transactions. This study examines the application of machine learning techniques in improving fraud detection in the banking industry. Using advanced algorithms and historical transaction data, the model aims to identify anomalous patterns that indicate fraudulent activity. Feature engineering, ensemble methods, and anomaly detection algorithms are used to improve the accuracy and performance of fraud detection systems. reducing false positives and provides an adaptive security mechanism. By harnessing the power of machine learning, this approach aims to strengthen the banking sectors defines against sophisticated fraud schemes. This study not only contributes to the academic understanding of fraud detection, but also provides industry practitioners with a practical framework for implementing proactive measures. In navigating the complex landscape of financial security, this research serves as a guide for building agile and responsive systems that stay ahead of the threats that evolve in the ever-changing landscape of the banking industry.

Copper abietate/polyvinyl acetate composite film for enhanced humidity sensing in Chinese herbal medicine monitoring systems

Sensing materials derived from natural and biodegradable sources are increasingly being developed and applied to realize environmentally friendly technologies. In this paper, we present the synthesis, characterization, and application of a novel copper abietate/polyvinyl acetate (CuA/PVAc) composite film, specifically engineered to enhance the humidity-sensing capabilities of Chinese herbal medicine monitoring systems. The sensing material was synthesized by strategically melding copper abietate, a natural renewable material, with PVAc to exploit their combined synergistic properties for optimal humidity-sensing performance. Comprehensive characterization techniques, including inductively coupled plasma optical emission spectrometry (ICP-OES), field-emission scanning electron microscopy (FESEM), water contact angle measurements, and Fourier transform infrared (FT-IR) spectroscopy, were employed to confirm the formation of CuA/PVAc composites. These analyses confirmed the homogeneity and intended chemical composition of the composite films.The resultant CuA-1/PVAc-4 (0.4 μg/μL) composite film exhibited a good logarithmic relation (0.9988) with relative humidity (RH) over a wide range (11 %–97 %) and high sensitivity (24.55 Hz/%RH). Moreover, this study introduces a novel, eco-friendly approach by integrating the CuA/PVAc composite with a quartz crystal microbalance (QCM) sensor and a sophisticated wireless circuit that enables real-time, Wi-Fi-based humidity monitoring tailored for the preservation of Chinese herbal medicines. The implementation of this wireless humidity detection system represents a significant advancement in the application of environmentally friendly materials in sensor technology and offers a practical and scalable approach for precise environmental monitoring.

Explainable deep learning approach for advanced persistent threats (APTs) detection in cybersecurity: a review

In recent years, Advanced Persistent Threat (APT) attacks on network systems have increased through sophisticated fraud tactics. Traditional Intrusion Detection Systems (IDSs) suffer from low detection accuracy, high false-positive rates, and difficulty identifying unknown attacks such as remote-to-local (R2L) and user-to-root (U2R) attacks. This paper addresses these challenges by providing a foundational discussion of APTs and the limitations of existing detection methods. It then pivots to explore the novel integration of deep learning techniques and Explainable Artificial Intelligence (XAI) to improve APT detection. This paper aims to fill the gaps in the current research by providing a thorough analysis of how XAI methods, such as Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), can make black-box models more transparent and interpretable. The objective is to demonstrate the necessity of explainability in APT detection and propose solutions that enhance the trustworthiness and effectiveness of these models. It offers a critical analysis of existing approaches, highlights their strengths and limitations, and identifies open issues that require further research. This paper also suggests future research directions to combat evolving threats, paving the way for more effective and reliable cybersecurity solutions. Overall, this paper emphasizes the importance of explainability in enhancing the performance and trustworthiness of cybersecurity systems.

Shielding networks: enhancing intrusion detection with hybrid feature selection and stack ensemble learning

The frequent usage of computer networks and the Internet has made computer networks vulnerable to numerous attacks, highlighting the critical need to enhance the precision of security mechanisms. One of the most essential measures to safeguard networking resources and infrastructures is an intrusion detection system (IDS). IDSs are widely used to detect, identify, and track malicious threats. Although various machine learning algorithms have been used successfully in IDSs, they are still suffering from low prediction performances. One reason behind the low accuracy of IDSs is that existing network traffic datasets have high computational complexities that are mainly caused by redundant, incomplete, and irrelevant features. Furthermore, standalone classifiers exhibit restricted classification performance and typically fail to produce satisfactory outcomes when dealing with imbalanced, multi-category traffic data. To address these issues, we propose an efficient intrusion detection model, which is based on hybrid feature selection and stack ensemble learning. Our hybrid feature selection method, called MI-Boruta, combines mutual information (MI) as a filter method and the Boruta algorithm as a wrapper method to determine optimal features from our datasets. Then, we apply stacked ensemble learning by using random forest (RF), Catboost, and XGBoost algorithms as base learners with multilayer perceptron (MLP) as meta-learner. We test our intrusion detection model on two widely recognized benchmark datasets, namely UNSW-NB15 and CICIDS2017. We show that our proposed IDS outperforms existing IDSs in almost all performance criteria, including accuracy, recall, precision, F1-Score, false positive rate, true positive rate, and error rate.

A Low-Power, High-Resolution Analog Front-End Circuit for Carbon-Based SWIR Photodetector

Carbon nanotube field-effect transistors (CNT-FETs) have shown great promise in infrared image detection due to their high mobility, low cost, and compatibility with silicon-based technologies. This paper presents the design and simulation of a column-level analog front-end (AFE) circuit tailored for carbon-based short-wave infrared (SWIR) photodetectors. The AFE integrates a Capacitor Trans-impedance Amplifier (CTIA) for current-to-voltage conversion, coupled with Correlated Double Sampling (CDS) for noise reduction and operational amplifier offset suppression. A 10-bit/125 kHz Successive Approximation analog-to-digital converter (SAR ADC) completes the signal processing chain, achieving rail-to-rail input/output with minimized component count. Fabricated using 0.18 μm CMOS technology, the AFE demonstrates a high signal-to-noise ratio (SNR) of 59.27 dB and an Effective Number of Bits (ENOB) of 9.35, with a detectable current range from 500 pA to 100.5 nA and a total power consumption of 7.5 mW. These results confirm the suitability of the proposed AFE for high-precision, low-power SWIR detection systems, with potential applications in medical imaging, night vision, and autonomous driving systems.

Study of a detector prototype for detecting special nuclear materials based on differential die-away technology

Differential die-away Analysis (DDAA) technology is a powerful analytical technique to detect fissile materials such as 235U and 239Pu. It can reveal the presence of nuclear materials even when shielded or embedded in non-fissile matrices by inducing fission through externally applied pulsed neutrons. An innovative modular system that is convenient for transportation and suitable for various application scenarios is proposed. The detection sensitivity of the system, with different structures, is studied. The data acquisition hardware, along with real-time online measurement and alerting software, have been developed. The results indicate that a detection system with a bilaterally arranged array of detectors offers enhanced sensitivity, enabling rapid detection of a single nuclear fuel rod (200.4g) under heavy shielding and at standard distances.

Advanced Persistent Threat Attack Detection Systems: A Review of Approaches, Challenges, and Trends

Advanced persistent threat (APT) attacks present a significant challenge for any organization, as they are difficult to detect due to their elusive nature and characteristics. In this paper, we conduct a comprehensive literature review to investigate the various APT attack detection systems and approaches and classify them based on their threat model and detection method. Our findings reveal common obstacles in APT attack detection, such as correctly attributing anomalous behavior to APT attack activities, limited availability of public datasets and inadequate evaluation methods, challenges with detection procedures, and misinterpretation of requirements. Based on our findings, we propose a reference architecture to enhance the comparability of existing systems and provide a framework for classifying detection systems. In addition, we look in detail at the problems encountered in current evaluations and other scientific gaps, such as a neglected consideration of integrating the systems into existing security architectures and their adaptability and durability. While no one-size-fits-all solution exists for APT attack detection, this review shows that graph-based approaches hold promising potential. However, further research is required for real-world usability, considering the systems’ adaptability and explainability.

Chemiluminescence detection of kanamycin by DNA aptamer regulating peroxidase-like activity of Co3O4 nanoparticles.

Kanamycin (KAN) is widely used as a growth hormone analog and an antibacterial agent. However, abuse of this substance has resulted in the accumulation of excessive residue levels in foods of animal origin, which presents a significant risk to human health. A chemiluminescent aptasensor was constructed for the rapid quantitative detection of KAN by combining the properties of Co3O4 nanoparticles (Co3O4 NPs) nanozyme activity and DNA aptamer with high specificity. The DNA aptamer/Co3O4 NPs nanozyme regulated the chemiluminescence signal by exploiting the chemiluminescent properties of luminol oxidation by H2O2. Specific binding of KAN to the aptamer led to the formation of a steric hindrance block in the solution, which inhibited the activity of nanozyme and reduced signal intensity. The degree of signal reduction is related to the concentration of KAN. Under optimal conditions, there was good linearity between KAN concentration and chemiluminescence signal intensity in the range of 0.5-8.0 μΜ, with a detection limit of 0.26 μΜ. The detection system performed well in the presence of competing antibiotics and was virtually unaffected. The method was also suitable for the detection of KAN in milk samples with sample recoveries of 97.8%-99.1%. The chemiluminescence sensor has the advantages of low cost, specificity, and sensitivity, and does not require an external light source or modification of the nucleic acid aptamer which makes it a promising candidate for applications in the field of food detection.

An Efficient Gradient Based Modified Cuckoo Search Optimization with Dynamic Levy Flight (MCSO-DLF) Algorithm for Breast Cancer Detection

Purpose of Study: This study evaluates the Gradient-Based Dynamic Levy Flight Cuckoo Search Optimization (MCSO-DLF) for breast cancer detection and compares it to six established optimization algorithms. The goal is to determine if MCSO-DLF offers higher accuracy and computational efficiency for optimizing diagnostic models. Methodology: The study compares MCSO-DLF with PSO, GA, ACO, SA, DE, and ABC using the Breast Cancer Wisconsin dataset. The algorithms are evaluated based on accuracy, convergence speed, and efficiency. MCSO-DLF uses dynamic Levy flight and gradient-based optimization to improve solution quality. Main Findings: MCSO-DLF achieved the highest accuracy (0.9912) and fastest computation time (65.94 seconds), outperforming all other algorithms. This demonstrates its effective balance between exploration and local solution refinement. Implications: MCSO-DLF significantly improves accuracy and speed, offering potential advancements in breast cancer detection systems, leading to better patient outcomes and more efficient healthcare. Novelty of Study: This study introduces MCSO-DLF as a novel hybrid optimization method that combines gradient-based optimization with dynamic Levy flight, outperforming traditional algorithms in complex medical diagnostics.

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.

A Study on Network Anomaly Detection Using Fast Persistent Contrastive Divergence

As network technology evolves, cyberattacks are not only increasing in frequency but also becoming more sophisticated. To proactively detect and prevent these cyberattacks, researchers are developing intrusion detection systems (IDSs) leveraging machine learning and deep learning techniques. However, a significant challenge with these advanced models is the increased training time as model complexity grows, and the symmetry between performance and training time must be taken into account. To address this issue, this study proposes a fast-persistent-contrastive-divergence-based deep belief network (FPCD-DBN) that offers both high accuracy and rapid training times. This model combines the efficiency of contrastive divergence with the powerful feature extraction capabilities of deep belief networks. While traditional deep belief networks use a contrastive divergence (CD) algorithm, the FPCD algorithm improves the performance of the model by passing the results of each detection layer to the next layer. In addition, the mix of parameter updates using fast weights and continuous chains makes the model fast and accurate. The performance of the proposed FPCD-DBN model was evaluated on several benchmark datasets, including NSL-KDD, UNSW-NB15, and CIC-IDS-2017. As a result, the proposed method proved to be a viable solution as the model performed well with an accuracy of 89.4% and an F1 score of 89.7%. By achieving superior performance across multiple datasets, the approach shows great potential for enhancing network security and providing a robust defense against evolving cyber threats.

Enhancing Pap Smear Image Classification: Integrating Transfer Learning and Attention Mechanisms for Improved Detection of Cervical Abnormalities

Abstract Cervical cancer remains a major global health challenge, accounting for significant morbidity and mortality among women. Early detection through screening, such as Pap smear tests, is crucial for effective treatment and improved patient outcomes. However, traditional manual analysis of Pap smear images is labor-intensive, subject to human error, and requires extensive expertise. To address these challenges, automated approaches using deep learning techniques have been increasingly explored, offering the potential for enhanced diagnostic accuracy and efficiency. This research focuses on improving cervical cancer detection from Pap smear images using advanced deep-learning techniques. Specifically, we aim to enhance classification performance by leveraging Transfer Learning (TL) combined with an attention mechanism, supplemented by effective preprocessing techniques. Our preprocessing pipeline includes image normalization, resizing, and the application of Histogram of Oriented Gradients (HOG), all of which contribute to better feature extraction and improved model performance. The dataset used in this study is the Mendeley Liquid-Based Cytology (LBC) dataset, which provides a comprehensive collection of cervical cytology images annotated by expert cytopathologists. Initial experiments with the ResNet model on raw data yielded an accuracy of 63.95%. However, by applying our preprocessing techniques and integrating an attention mechanism, the accuracy of the ResNet model increased dramatically to 96.74%. Further, the Xception model, known for its superior feature extraction capabilities, achieved the best performance with an accuracy of 98.95%, along with high precision (0.97), recall (0.99), and F1-Score (0.98) on preprocessed data with an attention mechanism. These results underscore the effectiveness of combining preprocessing techniques, TL, and attention mechanisms to significantly enhance the performance of automated cervical cancer detection systems. Our findings demonstrate the potential of these advanced techniques to provide reliable, accurate, and efficient diagnostic tools, which could greatly benefit clinical practice and improve patient outcomes in cervical cancer screening.

Choosing the machine learning algorithm for detecting intrusions into IoT

Aim. The paper aims to improve the security of IoT devices by applying machine learning algorithms to detect attacks against IoT networks. The relevance of the goal is defined by the ever-growing number of such attacks around the world and the widespread use of IoT systems. The paper provides relevant statistical data. An analysis of the available papers showed that various methods were examined individually and were not compared to each other, so the aim of this paper that consists in identifying the most promising machine learning algorithm for detecting attacks against IoT networks is of relevance. Methods. The paper used the following machine learning methods to detect attacks against IoT networks: logistic regression, SVC, random forest, K-nearest neighbour method, k-means method, naive Bayes classifier, and variants of gradient boosting (XGBoost, AdaBoost, and CatBoost). The novelty consists in the comparison of the outputs of the supervised algorithms with the unsupervised K-means in the context of detection of attacks against IoT networks. The attack detection systems under development were trained using the UNSWNB15 dataset that contains data on nine types of attacks. The number of entries is more than 80 thousand. More than half of the entries deal with attacks. The methods were compared using a number of metrics. Results. An intrusion detection system was structurally defined and implemented. The stages of its operation include the analysis of input data and the output of final statistical data. The results show that the random forest algorithm is the best one out of those examined. The method also performs well in terms of learning speed. That means that the algorithm can be deployed and applied with the greatest success. Conclusions. This paper presents the results of comparing various machine learning algorithms in the context of IoT device intrusion detection. The accuracy and the ROC-AUC curve are used to evaluate the efficiency of the employed models. Having compared the models of the employed algorithms we found that the RandomForestClassifier model has the highest accuracy and a high AUC, which means that this algorithm is the most efficient in terms of IoT network intrusion detection. Further research will be dedicated to distinguishing between the types of attack.

Artificial intelligence detection system based on multi-sensor and wireless communication

In today’s world, Internet technology and wireless communication technology are becoming more and more mature, which has brought massive network information resources to all sectors of society. At the same time, the phenomenon of data loss caused by illegal network intrusion is becoming more and more common. Therefore, it is necessary to identify and deal with them in combination with IDS (intrusion detection system). The problem of data processing is also very important. Enterprises can build a data management system according to their own needs, and use this system to process data. With the help of science and technology, AI (Artificial Intelligence) technology has become more mature and applied in many industries. Therefore, this paper proposed to build an AI IDS, and combined the deep RL (reinforcement learning) algorithm to analyze the performance of the system. This paper tested and analyzed the system from the aspects of precision and recall. The experimental results showed that the average precision of the five data sets was 94.76%, and the average recall rate was 91.4%. From the above data, combined with the algorithm in this paper, the precision and recall of the system have been significantly improved. This paper also conducted benchmark energy consumption comparison experiments for different cloud data management systems. The results showed that in terms of loading, the benchmark energy consumption of HBase was the lowest, which was 86KJ. In terms of query, the benchmark energy consumption of GridSQL was the lowest, which was 56KJ. It can be seen that different systems have their own advantages in the benchmark energy consumption of loading and query.

High Sensitivity X-Ray Detectors with Low Degradation Based on Deuterated Halide Perovskite Single Crystals.

Methylammonium lead single crystal (MAPbI3 SC) possesses superior optoelectronic properties and low manufacturing cost, making it an ideal candidate for X-ray detection. However, the ionic migration of the perovskites usually leads to instability, dark current drift, and hysteresis of the detector, limiting their applications in well-established technologies. Here, a series of X-ray detectors of MAPbI3 SCs are reported with different degrees of deuteration (DxMAPbI3, x = 0, 0.15, 0.75, 0.99). By controlling the content of deuterium (D) in organic cations, the sensitivity, detection limits, ion migration, and resistivity of the detector can be controlled, thereby improving its performance. Due to stronger hydrogen bonds (N─D···I), the ion activation energy significantly increases to 886 meV. Consequently, the D0.99MAPbI3 SC detector shows more than five-fold enhancement, achieving a record-high mobility-lifetime (µτ) product of 5.39 × 10-2cm2V-1, with an ultrahigh sensitivity of 2.18 × 106 µC Gy-1 cm-2 under 120keV hard X-ray and a low detection limit of 4.8 nGyair s-1, as well as long-term stability. The study provides a straightforward strategy for constructing ultrasensitive X-ray detection and imaging systems based on perovskite SCs.

KRF-AD: Innovating anomaly detection with KDE-KL and random forest fusion

Anomaly detection in Intrusion Detection System (IDS) data refers to the process of identifying and flagging unusual or abnormal behavior within a network or system. In the context of IoT, anomaly detection helps in identifying any abnormal or unexpected behavior in the data generated by connected devices. Existing methods often struggle with accurately detecting anomalies amidst massive data volumes and diverse attack patterns. This paper proposes a novel approach, KDE-KL Anomaly Detection with Random Forest Integration (KRF-AD), which combines Kernel Density Estimation (KDE) and Kullback-Leibler (KL) divergence with Random Forest (RF) for effective anomaly detection. Additionally, Random Forest (RF) integration enables classification of data points as anomalies or normal based on features and anomaly scores. The combination of statistical divergence measurement and density estimation enhances the detection accuracy and robustness, contributing to more effective network security. Experimental results demonstrate that KRF-AD achieves 96% accuracy and outperforms other machine learning models in detecting anomalies, offering significant potential for enhancing network security.

A silicon-based functional self-assembled aptasensor for the detection of aflatoxin B1 by SERS sensing.

One of the most harmful contaminants found in corn and its products is aflatoxin B1 (AFB1) and thus developing reliable detection methods is of great significance to consumers and the food industry. In this research, AuMBA@Ag nanoparticles (NPs) and AgNPs deposited on a silicon wafer (Si@AgNPs) were functionalized with an aptamer and its complementary strand, respectively, and self-assembled into a SERS aptasensor, which generated strong SERS signals. AFB1 bound to the aptamer prior to the complementary chain, causing AuMBA@Ag NPs to detach from Si@AgNPs. The complex dissociated, leading to a decrease in signal intensity from the solid-phase substrate. Under optimal conditions, the linear detection range was 0.05-20.0 ng mL-1, and the detection limit was 0.039 ng mL-1. Notably, the aptasensor demonstrated a recovery rate between 92.77% and 110.13% when utilized for the detection of AFB1 in corn flour and oil, indicating its good potential for detecting AFB1 in real sample matrices. In conclusion, a quantitative and reliable specific SERS detection system for AFB1 was developed in this study with significant applicability to food safety.