Conventional Detection Methods Research Articles

An E-nose system for identification and quantification of hazardous gas mixtures using a combined strategy of CNNs and attentional mechanisms

The chemical industry generates a broad spectrum of hazardous gases, presenting significant challenges for conventional detection methods due to their diverse chemical properties and low concentration levels. E-nose systems, employing sensor arrays, offer significant potential for the determination of gas mixtures. This study presents a novel E-nose algorithm, CNN-ECA, which integrated CNNs and attention mechanisms to improve the recognition accuracy of E-nose systems. By integrating the attention mechanism module into CNN’s convolutional operations, the algorithm emphasizes critical feature information. Three hazardous gases (ammonia, methanol, and acetone) and their mixtures were chosen as target gases. CNNs were combined with various attention mechanism networks (SENet, ECA, and CBAM) to construct models, which were then employed to train and evaluate data collected from the sensor array. The results were compared with traditional network models (KNN, SVM, and CNN). Experimental findings indicated that the prediction performance of CNN models combined with attention mechanism networks surpassed that of traditional network models. Particularly, the CNN-ECA network model demonstrated the highest performance in both qualitative and quantitative analyses. This study presents a promising solution for mixed gas detection by synergizing CNN and attention mechanism networks, thereby enhancing the accuracy and reliability of mixed gas measurements. Moreover, capitalizing on the lightweight architecture of the CNN-ECA model, transfer learning techniques were employed to adapt it for deployment on the Raspberry Pi hardware platform. This facilitates the development of a real-time E-nose system for gas detection.

AI-Assisted Detection of Biomarkers by Sensors and Biosensors for Early Diagnosis and Monitoring.

The steady progress in consumer electronics, together with improvement in microflow techniques, nanotechnology, and data processing, has led to implementation of cost-effective, user-friendly portable devices, which play the role of not only gadgets but also diagnostic tools. Moreover, numerous smart devices monitor patients' health, and some of them are applied in point-of-care (PoC) tests as a reliable source of evaluation of a patient's condition. Current diagnostic practices are still based on laboratory tests, preceded by the collection of biological samples, which are then tested in clinical conditions by trained personnel with specialistic equipment. In practice, collecting passive/active physiological and behavioral data from patients in real time and feeding them to artificial intelligence (AI) models can significantly improve the decision process regarding diagnosis and treatment procedures via the omission of conventional sampling and diagnostic procedures while also excluding the role of pathologists. A combination of conventional and novel methods of digital and traditional biomarker detection with portable, autonomous, and miniaturized devices can revolutionize medical diagnostics in the coming years. This article focuses on a comparison of traditional clinical practices with modern diagnostic techniques based on AI and machine learning (ML). The presented technologies will bypass laboratories and start being commercialized, which should lead to improvement or substitution of current diagnostic tools. Their application in PoC settings or as a consumer technology accessible to every patient appears to be a real possibility. Research in this field is expected to intensify in the coming years. Technological advancements in sensors and biosensors are anticipated to enable the continuous real-time analysis of various omics fields, fostering early disease detection and intervention strategies. The integration of AI with digital health platforms would enable predictive analysis and personalized healthcare, emphasizing the importance of interdisciplinary collaboration in related scientific fields.

Melanoma Detection and Classification using Deep Learning

Melanoma is a type of carcinoma with a notably high mortality rate. Accurate diagnosis of this aggressive cancer is crucial due to its severe implications. Key diagnostic indicators include asymmetrical shape, heterogeneous color, diameter greater than 6 mm, and irregular borders, which dermatologists typically identify through visual examination. The conventional method for carcinoma detection is biopsy, involving the removal or scraping of skin samples for extensive laboratory testing. This process is both painful and time- consuming. To improve patient experience and enhance diagnostic efficiency, computer-based detection using image processing techniques and deep learning algorithms, specifically Convolutional Neural Networks (CNNs), has been developed to accurately identify melanoma. Keywords: Deep learning, CNN, Computer- based detection

A bulged-type enzyme-free recognition strategy designed for single nucleotide polymorphisms integrating with label-free electrochemical biosensor

Compared to conventional nucleic acid detection methods, label-free single nucleotide polymorphism (SNP) detection presents challenging due to the necessity of discerning single base mismatches, especially in the field of enzyme-free detection. In this study, we introduce a novel bulged-type DNA duplex probe designed to significantly amplify single-base differences. This probe is integrated with programmable DNA-based nanostructures to develop a sensitive, label-free biosensor for nonenzymatic SNP detection. The duplex probe with one bulge could selectively identify wild-typed DNA (WT) and mutant-type DNA (MT) based on a competitive strand displacement reaction mechanism. The hyperbranched HCR (HHCR) by incorporating of hairpin DNA into the DNA tetrahedron and surface-tethering on the portable screen printing electrode (SPCE) significantly favor the formation of negatively charged DNA nanostructure. We harnessed strong repulsion of DNA nanostructure towards the electroactive [Fe(CN)₆]³⁻/⁴⁻ in combination with electrochemical technique to create a label-free biosensor. This simple, enzyme-free and label-free biosensor could detect MT with a detection limit of 56 aM, even in multiple sequence backgrounds. The study served as the proof-of-concept for the integration of enzyme-free competitive mechanism and label-free strategy, which can be extended as a powerful tool to various fields.

Integrated microfluidic chip with a MIP-gated organic electrochemical transistor for continuous, selective, and label-free quantification of tumor marker

Early diagnosis of cancer is crucial to effective treatment of the patients and to higher survival rates. Tumor marker detection as an effective and noninvasive cancer diagnosis method has important clinical significance. N1, N12-diacetylspermine (DAS) is a new tumor marker. The development of its detection methods is an urgent need for medical diagnosis and human health monitoring. The conventional detection methods of DAS are labor-intensive and time-consuming approaches. In this work, a novel electrochemical detection platform was established by integrating an organic electrochemical transistor (OECT) with a microfluidic chip and applied for trace measurement of DAS. The sensing unit is a gate electrode in the OECT modified with molecularly imprinted polymers (MIPs), which can be used as an artificial receptor to realize label-free detection. The high specific recognition ability of MIPs films combined with the amplified function of an OECT yielded a highly sensitive and selective electrochemical detection chip. The integrated microfluidic channel controlledthe exact amount of solution to be delivered and provided real-time detection of DAS. The experimental results indicated that changes in channel current was proportional to DAS concentrations from 1 nM to 10 μM, with the detection limits of 0.8 nM. It is the first demonstration of the integration of a microfluidic system with a MIP-gated OECT for real-time tumor marker detection. The study results of this work helped to develop Point-Of-Care (POC) tools for the early diagnosis of lung cancer, colon cancer and breast cancer.

Blood CO Status Classification Using UV-VIS Spectroscopy and PSO-optimized 1D-CNN Model

Rapid and effective blood carbon monoxide (CO) assessment is of great importance, especially in estimating CO-related morbidity and instituting effective preventive measures. The conventional detection methods using CO breath analysis lack sensitivity, while collecting biological fluid samples for CO level measurement is prone to external contamination and expensive for frequent use. This study proposes a one-dimensional convolutional neural network (1D-CNN) consisting of three stacked biconvolutional layers for binary classification of blood CO status using the diffuse reflectance spectroscopy technique. Iterative particle swarm optimization (PSO) has efficiently found the best network parameters to learn important features from the reflectance spectroscopy data. The findings showed good testing accuracy, specificity, and precision of 92.9%, 90%, and 89.7%, respectively, and a high sensitivity of 96.3% in determining abnormal blood CO among smokers using the proposed CNN network. Comparisons with eight existing machine learning and deep learning models revealed the proposed method’s effectiveness in classifying blood CO status while reducing computing time by 8–13 folds. The findings of this work provide new insights that are valuable for researchers in neural network design automation, healthcare management, and skin-related research, specifically for application in nondestructive evaluation and clinical decision-making.

ML-Based Online Transaction Fraud Detection

This paper focuses on fraud detection and the measures necessary to fully automate this process, which has become crucial for banks. With fraud on the rise, it poses significant threats and can result in substantial damages to financial institutions. Transaction data presents unique challenges for fraud detection due to the lack of short-term processing capabilities. The primary objective is to conduct a feasibility study on the selected fraud detection methods. Using various models, we aim to test each transaction individually and proceed accordingly.Initially, we define the detection task, outlining the dataset attributes, metric choices, and techniques to manage unbalanced datasets. This analysis helps identify underlying patterns within the dataset, such as how cardholders' purchasing habits may evolve over time and how fraudsters may adapt their tactics. We then explore various methods used to derive sequential features from credit card transactions. Financial fraud, the practice of gaining financial benefits through deceitful and unlawful means, has become a significant threat to businesses and organizations. Despite numerous efforts to combat financial fraud, it continues to inflict substantial economic and societal harm, with daily losses reaching significant amounts. Traditional methods for fraud detection, introduced years ago, were predominantly manual, making them time- consuming, costly, and prone to errors. Although more research is being conducted, current approaches have not effectively reduced the financial losses associated with fraud. Conventional fraud detection methods, such as manual verifications and inspections, are inefficient, costly, and inaccurate. Keywords: Transaction data, cardholders' purchasing.

Ion coordination and chelation in Eu-MOFs matrices: Ultrafast fluorescence visual quantification monitoring of antibiotic residues

Rapid monitoring of trace antibiotics in the field in real time is essential for environment forewarning and human health. High sensitivity and real-time on-site quantitative monitoring of antibiotic residues can be accomplished by integrating portable sensors alongside fluorescent optics to construct an intelligent sensing platform that smoothly eliminates the instability of conventional detection methods. In this study, a ratiometric fluorescence sensor for the ultrasensitive detection of pefloxacin was built employing the photoinduced electron transfer (PET) mechanism from red Eu-MOFs to Mn2+-PEF complex. A visual color change results from the photoinduced electron transfer process from manganese ions to pefloxacin weakening the ligand metal charge transfer (LMCT) process in Eu-MOFs. This enables the ultrafast visible detection of pefloxacin and produces a transient shift in visual color with a detection limit as low as 15.4 nM. For the detection of pefloxacin in water, tomato, and raw pork samples, various sensing devices based on the developed fluorescent probes exhibit good practicability and accuracy. With the development of the ratiometric fluorescence sensing probe, it is now possible to quickly and quantitatively identify pefloxacin residues in the environment, offering a new method for ensuring the safety of food and people's health.

Towards a Wearable Feminine Hygiene Platform for Detection of Invasive Fungal Pathogens via Gold Nanoparticle Aggregation.

Candida albicans is an opportunistic fungus that becomes pathogenic and problematic under certain biological conditions. C. albicans may cause painful and uncomfortable symptoms, as well as deaths in immunocompromised patients. Therefore, early detection of C. albicans is essential. However, conventional detection methods are costly, slow, and inaccessible to women in remote or developing areas. To address these concerns, we have developed a wearable and discrete naked-eye detectable colorimetric platform for C. albicans detection. With some modification, this platform is designed to be directly adhered to existing feminine hygiene pads. Our platform is rapid, inexpensive, user-friendly, and disposable and only requires three steps: (i) the addition of vaginal fluid onto sample pads; (ii) the addition of gold nanoparticle gel and running buffer, and (iii) naked eye detection. Our platform is underpinned by selective thiolated aptamer-based recognition of 1,3-β-D glucan molecules-a hallmark of C. albicans cell walls. In the absence of C. albicans, wearable sample pads turn bright pink. In the presence of C. albicans, the wearable pads turn dark blue due to significant nanoparticle target-induced aggregation. We demonstrate naked-eye colorimetric detection of 4.4 × 106C. albicans cells per ml and nanoparticle stability over a pH range of 3.0-8.0. We believe that this proof-of-concept platform has the potential to have a significant impact on women's health globally.

Detection of COVID-19: A Metaheuristic-Optimized Maximally Stable Extremal Regions Approach

The challenges associated with conventional methods of COVID-19 detection have prompted the exploration of alternative approaches, including the analysis of lung X-ray images. This paper introduces a novel algorithm designed to identify abnormalities in X-ray images indicative of COVID-19 by combining the maximally stable extremal regions (MSER) method with metaheuristic algorithms. The MSER method is efficient and effective under various adverse conditions, utilizing symmetry as a key property to detect regions despite changes in scaling or lighting. However, calibrating the MSER method is challenging. Our approach transforms this calibration into an optimization task, employing metaheuristic algorithms such as Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Firefly (FF), and Genetic Algorithms (GA) to find the optimal parameters for MSER. By automating the calibration process through metaheuristic optimization, we overcome the primary disadvantage of the MSER method. This innovative combination enables precise detection of abnormal regions characteristic of COVID-19 without the need for extensive datasets of labeled training images, unlike deep learning methods. Our methodology was rigorously tested across multiple databases, and the detection quality was evaluated using various indices. The experimental results demonstrate the robust capability of our algorithm to support healthcare professionals in accurately detecting COVID-19, highlighting its significant potential and effectiveness as a practical and efficient alternative for medical diagnostics and precise image analysis.

Leak and Burst Detection in Water Distribution Network Using Logic- and Machine Learning-Based Approaches

Urban water systems worldwide are confronted with the dual challenges of dwindling water resources and deteriorating infrastructure, emphasising the critical need to minimise water losses from leakage. Conventional methods for leak and burst detection often prove inadequate, leading to prolonged leak durations and heightened maintenance costs. This study investigates the efficacy of logic- and machine learning-based approaches in early leak detection and precise location identification within water distribution networks. By integrating hardware and software technologies, including sensor technology, data analysis, and study on the logic-based and machine learning algorithms, innovative solutions are proposed to optimise water distribution efficiency and minimise losses. In this research, we focus on a case study area in the Sunbury region of Victoria, Australia, evaluating a pumping main equipped with Supervisory Control and Data Acquisition (SCADA) sensor technology. We extract hydraulic characteristics from SCADA data and develop logic-based algorithms for leak and burst detection, alongside state-of-the-art machine learning techniques. These methodologies are applied to historical data initially and will be subsequently extended to live data, enabling the real-time detection of leaks and bursts. The findings underscore the complementary nature of logic-based and machine learning approaches. While logic-based algorithms excel in capturing straightforward anomalies based on predefined conditions, they may struggle with complex or evolving patterns. Machine learning algorithms enhance detection by learning from historical data, adapting to changing conditions, and capturing intricate patterns and outliers. The comparative analysis of machine learning models highlights the superiority of the local outlier factor (LOF) in anomaly detection, leading to its selection as the final model. Furthermore, a web-based platform has been developed for leak and burst detection using a selected machine learning model. The success of machine learning models over traditional logic-based approaches underscores the effectiveness of data-driven, probabilistic methods in handling complex data patterns and variations. Leveraging statistical and probabilistic techniques, machine learning models offer adaptability and superior performance in scenarios with intricate or dynamic relationships between variables. The findings demonstrate that the proposed methodology can significantly enhance the early detection of leaks and bursts, thereby minimising water loss and associated economic costs. The implications of this study are profound for the scientific community and stakeholders, as it provides a scalable and efficient solution for water pipeline monitoring. Implementing this approach can lead to more proactive maintenance strategies, ultimately contributing to the sustainability and resilience of urban water infrastructure systems.

Clinical performance of the TrueMark™ MSI assay for microsatellite instability detection in a Chinese colorectal cancer cohort

BackgroundMicrosatellite instability (MSI) and mismatch repair (MMR) detection is valuable in assessing prognosis and treatment options. However, the conventional detection methods such as immunohistochemistry (IHC) are limited by not fully consistent results as well as a long turnaround time. TrueMark™ MSI Assay is a novel solution for MSI analysis, but lack of research support in the Chinese colorectal cancer (CRC) patients. Materials and methods60 dMMR and 60 pMMR CRC samples identified by IHC were collected and their MSI status were detected using TrueMark™ MSI assay with an expanded panel of 13 markers. The overall performance and diagnostic concordance between TrueMark™ MSI test and MMR IHC analysis were assessed and analyzed. ResultsAccording to the TrueMark™ test, 55 out of the 120 (45.8 %) CRCs were identified as MSI-high (MSI-H) with an instability at ≥ 4/13 markers. Compared with the MMR IHC analysis, an overall percent agreement of 94.2 % and a Kappa of 0.883 were achieved. For the seven inconsistent samples, tumor mutation burden analysis was performed and the results supported the diagnosis by TrueMark™ test. To confirm the robustness of the above findings, a validation was performed in an independent cohort comprising 51 consecutive CRCs. Furthermore, an optimized panel composed of NR-21, NR-24, NR-27, ABI-16, ABI-17 and ABI-20B was developed by multivariate logistic regression model, and showed 100 % agreement with the 13-marker panel for MSI detection in both the derivation and validation sets. ConclusionTrueMark™ MSI provides a fast, reliable and highly automated solution to MSI detection in Chinese CRC patients, and the new 6-marker panel we established shows promise deserving further evaluation.

Domain Adaptation-Based Deep Learning Framework for Android Malware Detection Across Diverse Distributions

This study addresses the challenge of Android malware detection, a critical issue due to the pervasive threats affecting mobile devices. As Android malware evolves, conventional detection methods struggle with novel or polymorphic malware that bypasses traditional defenses. This research leverages machine learning (ML) and deep learning (DL) techniques to overcome these limitations by adopting domain adaptation strategies that enhance model generalization across different distributions. The approach involves dividing a dataset into distinct distributions and applying domain adaptation techniques to ensure robustness and accuracy despite distribution shifts. Preliminary results demonstrate that domain adaptation significantly improves detection accuracy in target domains not represented in the training data. This paper showcases a domain adaptation-based method for Android malware detection, illustrating its potential to enhance security measures in dynamic environments. The findings suggest that integrating advanced ML and DL strategies with domain adaptation can substantially improve the efficacy of malware detection systems.

Comprehensive Survey on Fault Detection and Classification in Three-Phase Induction Motors

Electric motors have revolutionized the way of human living and resulted in the modern lifestyle. These motors often operate in corrosive and dusty places and are exposed to a variety of undesirable conditions and situations that result in the failure of the motor. The faults occurring in Induction Motors (IM) need to be detected at a proper time for avoiding losses and further consequences. A well-designed fault detection scheme not only reduces motor failure but also increases productivity and even sometimes avoids accidents. This paper presents a review of fault detection and classification techniques in three-phase induction motors (TPIM). The main theme of this paper is to revisit the conventional methods for fault detection in TPIM and compare them with recently published methods based on parameters to be sensed, and the type of fault that can be detected, with their advantages and drawbacks. Around a hundred papers are critically reviewed and studied from old and new regimes. Attention is also given to fault detection methods based on artificial intelligence (AI) and machine learning (ML). This paper concludes with brief remarks which will be very useful for new researchers who are willing to research in the domain of fault detection and classification.

Research progress of integrating electrical impedance sensors with microfluidic chips in cell detection

Cell culture is a fundamental tool for cell-based assays in biological and preclinical research. The measurements of cell culture, including cell count, viability, and metabolic activity, can reflect the conditions of cells under culture conditions. The conventional cell culture and detection methods have problems such as high consumption of reagents and samples, inability to monitor cell status in real time, and difficulty in spatiotemporally adjusting the cell microenvironment. A cell impedance sensor measures changes in the electrical impedance of cells through alternating current, enabling real-time monitoring of impedance changes caused by cell activities such as attachment, growth, proliferation, and migration. Microfluidic chips are praised for reducing complex biological processes, integrating multiple analysis modes, and achieving high automation in detection. Integrating microfluidic chips with cell impedance sensors greatly improves the capability and efficiency of cell-related analysis. This review outlines the application of microfluidic chip-based impedance sensors in 2D and 3D cell systems and summarizes the research progress in application of such sensors in research on cell growth, proliferation, viability, metabolic activity, and drug screening. Finally, this review prospects the future development trends and possible challenges, providing ideas for the development of microfluidic chips integrated with electrical impedance sensors in drug screening.

A residual autoencoder-based transformer for fault detection of multivariate processes

The complexity of high-dimensional and noisy process signals reduces the effectiveness of conventional fault detection methods in industrial processes. Based on the hypothesis that data collected from normal and faulty processes has different characteristics, unsupervised deep neural networks, e.g., autoencoders, have been widely applied in process fault detection and achieved good performance. Many variants have been proposed to improve feature learning by combining different network structures. In this paper, a new transformer model, residual autoencoder-based transformer, is proposed for process fault detection. Firstly, autoencoder and transformer are integrated for better unsupervised feature learning of process signals. Secondly, linear embedding and attention mechanisms with bias are proposed to generate effective features from process signals. Finally, residual connections are constructed between the encoder and decoder of RATransformer to address overfitting in training. Four industrial cases are used to test the performance of RATransformer for process fault detection. The results show that the fault detection rate of RATransformer is at least 1 % higher than other comparison methods. Moreover, the testing results show that the model structure improves the fault detection performance of RATransformer. The complex models like RATransformer can be used in the industrial process when sufficient normal process data is available. An end-to-end training method can be further developed to improve the applicability of RATransformer in process fault detection in the future.

Revisiting the standards of cancer detection and therapy alongside their comparison to modern methods.

In accordance with the World Health Organization data, cancer remains at the forefront of fatal diseases. An upward trend in cancer incidence and mortality has been observed globally, emphasizing that efforts in developing detection and treatment methods should continue. The diagnostic path typically begins with learning the medical history of a patient; this is followed by basic blood tests and imaging tests to indicate where cancer may be located to schedule a needle biopsy. Prompt initiation of diagnosis is crucial since delayed cancer detection entails higher costs of treatment and hospitalization. Thus, there is a need for novel cancer detection methods such as liquid biopsy, elastography, synthetic biosensors, fluorescence imaging, and reflectance confocal microscopy. Conventional therapeutic methods, although still common in clinical practice, pose many limitations and are unsatisfactory. Nowadays, there is a dynamic advancement of clinical research and the development of more precise and effective methods such as oncolytic virotherapy, exosome-based therapy, nanotechnology, dendritic cells, chimeric antigen receptors, immune checkpoint inhibitors, natural product-based therapy, tumor-treating fields, and photodynamic therapy. The present paper compares available data on conventional and modern methods of cancer detection and therapy to facilitate an understanding of this rapidly advancing field and its future directions. As evidenced, modern methods are not without drawbacks; there is still a need to develop new detection strategies and therapeutic approaches to improve sensitivity, specificity, safety, and efficacy. Nevertheless, an appropriate route has been taken, as confirmed by the approval of some modern methods by the Food and Drug Administration.

Metal ion-complexed DNA probe coupled with CRISPR/Cas12a amplification and AuNPs for sensitive colorimetric assay of metallothionein in fish

The accurate and sensitive detection of metallothionein (MT) is of great significance in the fields of biomedical, toxicological and environmental sciences. In this work, based on the high affinity interaction between MT and the heavy metal ions of Hg2+ and the significant signal amplification capability of Cas12a/crRNA enzyme as well, we report a simple and highly sensitive method for visual detection of MT, a biomarker in fish for heavy metal ion-induced water bio-pollution. The target MT molecules bind Hg2+ in the Hg2+- complexed hairpin DNA probes to unfold the hairpin structure into ssDNAs, which hybridize with the partial dsDNA duplexes via strand displacement to yield specific sequence-containing dsDNAs. Cas12a/crRNA recognizes these specific sequences to activate its enzyme activity to cyclically cleave the ssDNA linkers in the blue colored gold nanoparticle aggregates to transit their color into red to realize visual detection of MT. Owing to the signal amplification by Cas12a/crRNA, as low as 25 nM of MT can be visually detected with naked eye. In addition, our colorimetric detection method has high selectivity for MT against other interference proteins and can detect MT in the livers and kidneys of crucian carps bought from a local supermarket. Moreover, the developed assay overcomes the limitations of conventional MT detection methods in terms of complexity, high cost and low sensitivity and can therefore offer new methods for monitoring water bio-pollutions.

Markovian noise-induced delta synchronization approach for Hindmarsh–Rose model

The paper explores noise-induced synchronization in uncoupled Hindmarsh–Rose neurons, introducing two distinctive elements: the application of Markovian noise and an analysis of synchronization via unpredictability. The noise is defined as an unpredictable and continuous process with characteristics proper for stochasticity. While identical synchronization is also investigated, the primary focus is to reveal synchronization in noise intensity domains that elude conventional detection methods, through delta synchronization within the neural system. Furthermore, a stronger form of synchronization, namely complete synchronization of unpredictability, is found to emerge in the domain with identical synchronization. The research findings are substantiated by numerical outcomes assessing unpredictability and synchronization, alongside comprehensive tables displaying characteristic time sequences for synchronization.

Amplification-free and label-free rapid detection of Staphylococcus aureus using solution-gated graphene transistor-based DNA biosensor with hybridization enhancement by interface engineering

Staphylococcus aureus (S. aureus) is one of the leading causes of foodborne illnesses worldwide, posing a significant risk to food quality and safety. However, conventional detection methods often require complex amplification and labeling procedures, which are time consuming and expensive. In this study, an extremely sensitive electrochemical biosensor using a solution-gated graphene transistor (SGGT) was constructed for the amplification-free and label-free rapid detection of S. aureus by utilizing specific single-stranded DNA (ssDNA) to modify the gold (Au) sensing gate. This modified ssDNA was designed to hybridize with the target S. aureus DNA sequence. Interface engineering of the ssDNA-modified sensing gate via optimization of specific target and probe sequences was proposed to improve its hybridization efficiency. This enabled the sensitive quantitative detection of S. aureus DNA. The resulting biosensor exhibited excellent specificity in distinguishing non-complementary, 3-base/9-base DNA mismatch targets of S. aureus DNA and the specific DNA sequences of other common pathogenic bacteria. It also enabled the rapid detection of extracted target DNA sequences in solution (within 27 min) and had a low limit of detection (LoD) of 10−17 M over a wide linear detection range (10−17–10−8 M). Furthermore, the newly-developed biosensor facilitated the real-time and on-site detection of the S. aureus genome (ATCC 6538) with LoD of 10−17 M (103 CFU/mL) in a portable smart-sensing system. This suggests a promising future for the development of rapid, label-free, and amplification-free detection of foodborne illnesses using low-cost, highly sensitive SGGT DNA biosensors with appropriate functionalization and interface engineering of the sensing electrode.