Fast Detection Research Articles

A fast detection method for metering anomalies of three-phase energy meters based on sliding filter and decision tree

In the actual measurement of three-phase electricity meters, the presence of electromagnetic interference, environmental temperature, and other factors increases the noise in the measurement data, which affects the accuracy of anomaly detection. In this regard, a fast detection method for three-phase meter measurement anomalies based on sliding filters and decision trees is studied. Firstly, sliding filtering and sliding window matrix methods are used to reduce the dimensionality and filter the measurement data of three-phase electric meters. Then, a cascaded random forest is constructed using a CART decision tree, and the filtered three-phase meter measurement data is input into the cascaded random forest. The CART decision tree uses a binary method to partition the nodes. Finally, using the Gini coefficient as a measurement indicator, a cascaded structure is formed through layered stacking to output rapid detection results of outliers in three-phase electricity meter measurements. The experimental results show that this method can quickly detect measurement abnormalities of three-phase energy meters, timely detect meter flying anomalies and unexpected mutations, and improve the fault handling efficiency of three-phase energy meters.

Detection of Alcoholic EEG signal using LASSO regression with metaheuristics algorithms based LSTM and enhanced artificial neural network classification algorithms

The world has a higher count of death rates as a result of Alcohol consumption. Identification is possible because Alcoholic EEG waves have a certain behavior that is totally different compared to the non-alcoholic individual. The available approaches take longer to provide the feedback because they analyze the data manually. For this reason, in the present paper we propose a novel approach applied to detect alcoholic EEG signals automatically by using deep learning methods. Our strategy has advantages as far as fast detection is concerned; hence people can help immediately when there is a need. The potential for a significant decrease in deaths from alcohol poisoning and improvement to public health is presented by this advancement. In order to create clusters and classify the alcoholic EEG signals, this research uses a cascaded process. To begin with, an initial clustering and feature extraction is done by LASSO regression. After that, a variety of meta-heuristics algorithms like Particle Swarm Optimization (PSO), Binary Coding Harmony Search (BCHS) as well as Binary Dragonfly Algorithm (BDA) are employed for feature minimization. When this method is used, normal and alcoholic EEG signals may be differentiated using non-linear features. PSO, BCHS, and BDA features allow for estimation of statistical parameters through t-test, Friedman statistic test, Mann-Whitney U test, and Z-Score with corresponding p-values for alcoholic EEG signals. Lastly, classification is done by the use of support vector machines (SVM) (including linear, polynomial, and Gaussian kernels), random forests, artificial neural networks (ANN), enhanced artificial neural networks (EANN), and LSTM models. Results showed that LASSO regression with BDA-based EANN proposed classifier have a classification accuracy of 99.59%, indicating that our method is highly accurate at classifying alcoholic EEG signals.

Setting-Less Differential Protection of Power Transformers Based on Wavelet Transform

This paper presents a novel algorithm for power transformer differential protection based on wavelet transform (WT) and introduces new indices to distinguish internal faults from normal operating conditions and the occurrence of the inrush current. The proposed setting-less algorithm has no limits on the structure, dimension, capacity, and core type of the transformer. For this purpose, six indices based on fault detection functions extracted from WT transform are presented. Then, weighting factors for the indices by using the least squares method are calculated. In order to validate the proposed method, the approach has been evaluated on four transformers with 2 kVA, 10 kVA, 400 kVA, and 125 MVA. The success rate of fault detection in 10 kVA, and 125 MVA transformers was 100% and in 2 kVA and 400 kVA transformers was 93.33% and 94.44%, respectively. Also, the proposed algorithm has a remarkable capability in fast fault detection to protect the power transformer.

An Image-based Blind Search for Fast Radio Bursts in 88 hr of Data from the EoR0 Field, with the Murchison Widefield Array

This work is part of ongoing efforts to detect fast radio bursts using the Murchison Widefield Array (MWA) in a spectral window below 300 MHz. We used an image-based method based on the pilot study of Tingay et al., scaled up via massively parallel processing using a commercial supercomputer. We searched 87.6 hr of 2 s snapshot images, each covering 1165 square degrees of the EoR0 field, over a dispersion measure range of 170–1035 pc cm−3. The large amount of data necessitated the construction of a series of filters to classify and reject the large number of false positives. Our search was more sensitive than any previous blind search using the MWA telescope, but we report no fast radio bursts detections, a result which is consistent with the extrapolation into the low-frequency domain of the results of M. Sokolowski et al. We obtain upper bounds on the event rate ranging from <1783 sky−1 day−1 at a fluence of 392 Jy ms, to <31 sky−1 day−1 at 8400 Jy ms, for our spectral window of 167–198 MHz. Our method was shown to be computationally efficient and scalable by the two or three orders of magnitude required to seriously test the model of Sokolowski et al. Our process is especially sensitive to detections of satellites and meteor trails and may find applications in the identification of these transients. We comment on future surveys using this method, with both the MWA and the SKA.

Detection of Sulfonamide Antibiotics Using an Elastic Hydrogel Microparticles-Based Optical Biosensor.

Sulfonamide antibiotics were the first synthetic antibiotics on the market and still have a broad field of application. Their extensive usage, wrong disposal, and limited degradation technologies in wastewater treatment plants lead to high concentrations in the environment, resulting in a negative impact on ecosystems and an acceleration of antibiotic resistance. Although lab-based analytical methods allow for sulfonamide detection, comprehensive monitoring is hampered by the nonavailability of on-site, inexpensive sensing technologies. In this work, we exploit functionalized elastic hydrogel microparticles and their ability to easily deform upon specific binding with enzyme-coated surfaces to establish the groundwork of a biosensing assay for the fast and straightforward detection of sulfonamide antibiotics. The detection assay is based on sulfamethoxazole-functionalized hydrogel microparticles as sensor probes and the biomimetic interaction of sulfonamide analytes with their natural target enzyme, dihydropteroate synthase (DHPS). DHPS from S. pneumoniae was recombinantly produced by E. coli and covalently coupled on a glass biochip using a reactive maleic anhydride copolymer coating. Monodisperse poly(ethylene glycol) hydrogel microparticles of 50 μm in diameter were synthesized within a microfluidic setup, followed by the oriented coupling of a sulfamethoxazole derivative to the microparticle surface. In proof-of-concept experiments, sulfamethoxazole, as the most used sulfonamide antibiotic in medical applications, was demonstrated to be specifically detectable above a concentration of 10 μM. With its straightforward detection principle, this assay has the potential to be used for point-of-use monitoring of sulfonamide antibiotic contaminants in the environment.

A 96-Well LED Array for Multiplexed Photoelectrochemical Detection of Nucleic Acids.

Photoelectrochemical detection of nucleic acid-based cancer biomarkers offers opportunities for highly sensitive, selective, and fast quantitative detection using low-cost measurement instruments. In order to establish itself as a standard method for identifying and quantifying nucleic acids, we have developed a multiplexing strategy using LED technology for photoelectrochemical detection in 96 samples simultaneously. A dedicated setup based on the 96-well plate configuration with a custom-made 96-well LED array was developed. Subsequently, a proof-of-concept study was performed for three miRNAs that are associated with prostate cancer, i.e., miRNA-141, miRNA-145, and miRNA-375. First, measurements with photosensitizer chlorin e6 and redox reporter hydroquinone free in solution proved the proper functioning of the multiplexed detection. Second, the photoelectrochemical detection of the three miRNAs at 24 nM levels was successfully demonstrated. Thereafter, linear calibration curves (R2 > 0.9 for all analytes) were made with plasma spiked with 8-500 pM miRNA. This work presents the first system for multiplexed high-throughput photoelectrochemical detection, allowing it potentially to become a cost-effective and faster alternative to RT-qPCR and gene sequencing techniques in the future.

Comparison of targeted next-generation sequencing and metagenomic next-generation sequencing in the identification of pathogens in pneumonia after congenital heart surgery: a comparative diagnostic accuracy study

BackgroundThis study aimed to compare targeted next-generation sequencing (tNGS) with metagenomic next-generation sequencing (mNGS) for pathogen detection in infants with severe postoperative pneumonia after congenital heart surgery.MethodsWe conducted a retrospective observational study using data from the electronic medical record system of infants who developed severe pneumonia after surgery for congenital heart disease from August 2021 to August 2022. Infants were divided into tNGS and mNGS groups based on the pathogen detection methods. The primary outcome was the efficiency of pathogen detection, and the secondary outcomes were the timeliness and cost of each method.ResultsIn the study, 91 infants were included, with tNGS detecting pathogens in 84.6% (77/91) and mNGS in 81.3% (74/91) of cases (P = 0.55). No significant differences were found in sensitivity, specificity, PPA, and NPA between the two methods (P > 0.05). tNGS identified five strains with resistance genes, while mNGS detected one strain. Furthermore, tNGS had a faster detection time (12 vs. 24 h) and lower cost ($150 vs. $500) compared to mNGS.ConclusiontNGS offers similar sensitivity to mNGS but with greater efficiency and cost-effectiveness, making it a promising approach for respiratory pathogen detection.

Development of Trp-AuNPs-rGO based electrochemical active biosensing interface for dopamine detection

Neurotransmitters are essential for learning, mental alertness, blood flow, and emotions. An imbalance of neurotransmitters in the human system causes neurological disorders. An imbalance of dopamine, a neurotransmitter, can cause severe diseases such as Parkinson's disease, restless legs syndrome, depression, schizophrenia, and attention deficit hyperactivity disorder (ADHD). Dopamine detection is essential but requires high sensitivity, temporal resolution, and favorable electrochemical techniques for the sensing mechanism. The ultrasensitive and selective real-time diagnosis of dopamine depends on the fabrication of a brain-on-a-chip model. Prior to fabrication of this device, it is very essential to develop a novel metal nanomaterial that exhibits biocompatibility and fast detection and is capable of improving the quality of the device. In this respect, we prepared amino acid-reduced gold nanoparticles that were supported by reduced graphene oxide. The prepared composite has been characterized by various techniques for internal and external morphology. The electrochemical behavior was examined on a glassy carbon electrode via various electrochemical techniques by a potentiostat instrument towards the diagnosis of dopamine at a micromolar level in the presence of its interference. Finally, as expected, we found 43.59 μAμM−1cm-2 sensitivity toward DA in the linear range of 1-11 μM. Trp-AuNPS-rGO shows promising results toward the diagnosis of dopamine in the presence of its interference and proves that this nanomaterial will be very promising toward the fabrication of a brain-on chip.

Carbon dots-based dual-mode sensor for highly selective detection of nitrite in food substrates through diazo coupling reaction

As an antioxidant and preservative agent, nitrite (NO2−) plays an essential role in the food industry to maintain freshness or inhibit microbial growth. However, excessive addition of NO2− is detrimental to health, so accurate and portable detection of NO2− is critical for food quality control. Notably, the selectivity of most carbon dots (CDs)-based fluorescence sensors was not enough due to the nonspecific interaction mechanism of hydrogen bond, electrostatic interaction and inner filter effect etc. Herein, a novel fluorescence/UV–vis absorption (FL/UV–vis) dual-mode sensor was developed on basis of mC-CDs, which were prepared by simple solvothermal treatment of m-Phenylenediamine (m-PDA) and cyanidin cation (CC). The fluorescence of these mC-CDs could be selectively responded by NO2− through the specific diazo coupling reaction between NO2− and amino groups on the surface of mC-CDs, thus effectively improving the selectivity of NO2− detection. The CDs-based fluorescence sensor possessed a low detection limit of 0.091 μM and 0.143 μM for FL and UV–vis methods and the excellent linear range of 0.0–60.0 μM. Furthermore, the mC-CDs sensor was employed to detect NO2− in real samples with a recovery rate of 97.11 %–104.15 % for quantitative addition. Moreover, the smartphone-assisted fluorescence sensing platform developed could identify the subtle color changes that could not be distinguished by the naked eye, and had the advantages of fast detection speed and intelligence. More importantly, the portable solid phase sensor based on mC-CDs had been successfully applied to the specific fluorescence identification and concentration monitoring of NO2−. Accordingly, the designed sensor provided a new strategy for the highly selective and convenient sensing of NO2− in food substrates, and paved the way for the wide application of CDs-based nanomaterials in the detection of food safety.

Chlorella Vulgaris‐Inspired Versatile Theranostic Nanoparticles for Specific Recognition and Detoxification to Copper (II) In Vitro and In Vivo

AbstractSpecific recognition and detoxification for copper is the key step for the early diagnosis and treatment of various diseases such as neurodegenerative disease and Wilson's disease. Herein, a Chlorella vulgaris‐inspired versatile theranostic nanoparticles is facilely and greenly prepared by assembling quercetin into poly(beta‐cyclodextrin) (PQNPs), which is further employed in the specific recognition and detoxification for Cu2+ in vitro and in vivo. PQNPs can be used in the detection of Cu2+ in aqueous solutions with fast response time (&lt;5 s) and low detection limit (6.499 nM). The detection for Cu2+ in water and serum can be achieved by naked eye instantaneously on a PQNPs‐based paper sensor, and PQNPs can also act as a biological diagnostic agent for the Cu2+ imaging. Remarkably, PQNPs significantly enhance cell viability of the cell models induced by Cu2+ through the synergistic capacities of chelating Cu2+ and antioxidation. The detoxification of PQNPs for copper poisoning model is further ascertained in vivo, and it found that poly‐β‐cyclodextrin is capable of crossing the blood–brain barrier. Taken together, the as‐prepared versatile theranostic nanoparticles possess advantages of simple composition, significant theranostic efficacy, and novel treatment pattern, presenting an intriguing avenue to develop therapeutics for tackling abnormal copper metabolism in the clinic.

Low detection limit of uric acid based on Prussian blue-enhanced photothermal effect with microfiber knot resonator

The human serum uric acid (UA) level is a crucial indicator for diagnosing dementia in middle-aged and elderly individuals, with decreased levels being expressed in patients. Therefore, developing a high-performance sensor for online uric acid detection is of significant research interest. Herein, a microfiber knot resonator (MKR) sensor for quantitative detection of UA levels is reported. Combining polydimethylsiloxane (PDMS) with an adiabatic MKR, the specificity of UA detection in serum is improved using the photothermal effect of Prussian blue-enhanced uricase reaction. During the sensor operation, UA generates the photothermal effect under 650 nm laser irradiation, causing the PDMS film to expand with heat absorption, thereby shifting the resonance spectrum of the PDMS-MKR sensor. Experimental results demonstrate a sensitivity of 0.0559 nm/(μmol/L) within a UA concentration range of 40–120 μmol/L and a detection limit as low as 0.3578 μmol/L. The proposed sensor shows potential applications in clinical point-of-care early diagnosis and prognosis of dementia due to its specificity, fast detection speed, robustness, and high integration.

Use of high-resolution fluorescence in situ hybridization for fast and robust detection of SARS-CoV-2 RNAs

Early, rapid, and accurate diagnostic tests play critical roles not only in the identification/management of individuals infected by SARS-CoV-2, but also in fast and effective public health surveillance, containment, and response. Our aim has been to develop a fast and robust fluorescence in situ hybridization (FISH) detection method for detecting SARS-CoV-2 RNAs by using an HEK 293 T cell culture model. At various times after being transfected with SARS-CoV-2 E and N plasmids, HEK 293 T cells were fixed and then hybridized with ATTO-labeled short DNA probes (about 20 nt). At 4 h, 12 h, and 24 h after transfection, SARS-CoV-2 E and N mRNAs were clearly revealed as solid granular staining inside HEK 293 T cells at all time points. Hybridization time was also reduced to 1 h for faster detection, and the test was completed within 3 h with excellent results. In addition, we have successfully detected 3 mRNAs (E mRNA, N mRNA, and ORF1a (−) RNA) simultaneously inside the buccal cells of COVID-19 patients. Our high-resolution RNA FISH might significantly increase the accuracy and efficiency of SARS-CoV-2 detection, while significantly reducing test time. The method can be conducted on smears containing cells (e.g., from nasopharyngeal, oropharyngeal, or buccal swabs) or smears without cells (e.g., from sputum, saliva, or drinking water/wastewater) for detecting various types of RNA viruses and even DNA viruses at different timepoints of infection.

A Study on Hyperspectral Soil Moisture Content Prediction by Incorporating a Hybrid Neural Network into Stacking Ensemble Learning

The accurate prediction of soil moisture content helps to evaluate the quality of farmland. Taking the black soil in the Nanguan District of Changchun City as the research object, this paper proposes a stacking ensemble learning model integrating hybrid neural networks to address the issue that it is difficult to improve the accuracy of inversion soil moisture content by a single model. First, raw hyperspectral data are processed by removing edge noise and standardization. Then, the gray wolf optimization (GWO) algorithm is adopted to optimize a convolutional neural network (CNN), and a gated recurrent unit (GRU) and an attention mechanism are added to construct a hybrid neural network model (GWO–CNN–GRU–Attention). To estimate soil water content, the hybrid neural network model is integrated into the stacking model along with Bagging and Boosting algorithms and the feedforward neural network. Experimental results demonstrate that the GWO–CNN–GRU–Attention model proposed in this paper can better predict soil water content; the stacking method of integrating hybrid neural networks overcomes the limitations of a single model’s instability and inferior accuracy. The relative prediction deviation (RPD), root mean square error (RMSE), and coefficient of determination (R2) on the test set are 4.577, 0.227, and 0.952, respectively. The average R2 and RPD increased by 0.056 and 1.418 in comparison to the base learner algorithm. The study results lay a foundation for the fast detection of soil moisture content in black soil areas and provide a data source for intelligent irrigation in agriculture.

Target localization and defect detection of distribution insulators based on ECA‐SqueezeNet and CVAE‐GAN

AbstractInsulators, as typical equipment for distribution networks, provide good electrical insulation between live conductors and earth. Timely and accurate detection is essential for insulator detection issues. However, as the complexity of neural networks increases, the detection efficiency is often lower. Therefore, this paper proposes a fast insulator positioning and defect detection method. Firstly, for insulator target localization, the SqueezeNet network is improved using ECA attention mechanism. In addition, to address the issue of low defect detection accuracy, a joint algorithm has been proposed. The integration of convolutional variational autoencoder (CVAE) and generative adversarial network (GAN) solve their own shortcomings due to different image focus angles. The target localization accuracy reaches 94.30%, and the defect detection accuracy reaches 89.60%. It solves the problems of difficulty in locating small targets in a large field of view and inaccurate detection due to a small number of abnormal samples. This method has been tried and tested in practical distribution network systems.

Fast identification of flammable chemicals based on broad learning system

Fast identification of flammable chemicals is essential for industrial production and laboratory safety. With the continuous advancement of sensor technology, data-driven methods have become a promising tool for gas identification. However, these methods face problems such as insufficient feature learning, unstable prediction of the single classifier, and overfitting caused by insufficient data. In this work, a kernel-based BLS (KBLS) method is proposed, in which the kernel matrix is used to calculate the sample distances and map the feature nodes to the kernel space to reduce the uncertainty. In addition, KBLS uses a pseudo-inverse method to solve the weights, which greatly avoids the risk of overfitting while improves computational efficiency. To avoid the errors caused by a single classifier for specific gas samples, KBLS is used as the weak learner and combined with the AdaBoost algorithm to form an Ada-KBLS classifier to achieve fast and accurate gas identification. In the Ada-KBLS model, the sample weights obtained by the previous weak learners are used to train the following weak learners. This method improves the classification performance by paying attention to difficult and misclassified samples and integrating the classification results of multiple weak learners. In addition, a dataset containing four flammable gases is used to verify the effectiveness of the Ada-KBLS model. The initial stage of all response data is divided into different time windows as the input of the model to test the fast gas identification ability of the method. The Ada-KBLS achieves an average classification accuracy of 98.4 % in the 4 s time window, the best among all models, and the training time is only 6.22 s. The result represents a 0.4 % improvement over the second-best model, KBLS, and a 4.5 % increase compared to the 93.9 % accuracy achieved by Random Forest (RF). In addition, the precision, recall, and F1-score of ethanol gas classification reach high values of 100 %. The experimental results demonstrate the robustness and effectiveness of the proposed method in handling the task of fast detection of flammable gases, thus promoting the application of BLS and ensemble learning in gas identification.

Introduction of a general class of entropy-based control charts: The Φ-chart

We introduce a new class of Shewhart control charts, namely the f-chart. This new class is based on the cumulative paired f-divergence that generalizes both the cumulative (residual) entropy and the differential entropy. The f-chart contains several subclasses; of which one has a special case, the G-chart, which uses Gini’s mean difference as a measure of dispersion. We investigate the performance of three of the subclasses of f-charts in a showcase scenario, comparing its average run length under the Gaussian and several alternative distributions relevant to process control. We find especially the new Leik control chart to outperform classical Shewhart charts, which are based on ranks, standard deviation, or Gini’s mean difference. The results imply that monitoring a production process using f-charts results in faster detection of out-of-control processes, which can be crucial for a variety of application areas.

Local-Peak Scale-Invariant Feature Transform for Fast and Random Image Stitching.

Image stitching aims to construct a wide field of view with high spatial resolution, which cannot be achieved in a single exposure. Typically, conventional image stitching techniques, other than deep learning, require complex computation and are thus computationally expensive, especially for stitching large raw images. In this study, inspired by the multiscale feature of fluid turbulence, we developed a fast feature point detection algorithm named local-peak scale-invariant feature transform (LP-SIFT), based on the multiscale local peaks and scale-invariant feature transform method. By combining LP-SIFT and RANSAC in image stitching, the stitching speed can be improved by orders compared with the original SIFT method. Benefiting from the adjustable size of the interrogation window, the LP-SIFT algorithm demonstrates comparable or even less stitching time than the other commonly used algorithms, while achieving comparable or even better stitching results. Nine large images (over 2600 × 1600 pixels), arranged randomly without prior knowledge, can be stitched within 158.94 s. The algorithm is highly practical for applications requiring a wide field of view in diverse application scenes, e.g., terrain mapping, biological analysis, and even criminal investigation.

NiO/ZrO2 hollow microspheres derived from bimetallic Ni/Zr-MOFs for fast and sensitive detection of triethylamine

Metal oxides derived from metal-organic frameworks (MOFs) can maintain the porosity and unique structure of MOFs, giving them significant development potential in the field of gas sensing. In this study, bimetallic Ni/Zr-MOFs solid microspheres were synthesized via the solvothermal method, and NiO/ZrO2 composites with p-p heterostructure were further prepared through an annealing process. The morphology, structure, specific surface area, and pore size of NiO/ZrO2 composites were analyzed using a series of characterization techniques. The gas sensing performance study showed that the NiO/ZrO2-2 sensor exhibited higher response (32.3–100 ppm), lower detection limit (7.2 ppb), shorter response/recovery time (55 s/83 s), and excellent repeatability to triethylamine gas at the operating temperature of 240 ℃. Additionally, within a specific range (1–200 ppm), a strong linear relationship was observed between the response value and the gas concentration for the NiO/ZrO2-2 sensor (R2=0.9588). This study is expected to provide a valuable reference for designing high-performance triethylamine sensors.

基于高光谱和人工智能模型的白茶鲜叶农药残留检测

The detection of pesticide residues in white tea fresh leaves is an important step to ensure the quality safety of white tea finished products. Traditional detection methods are costly and inefficient to realize the demand for fast, low-cost, and accurate detection of pesticide residues in white tea fresh leaves. In this study, five types of white tea fresh leaf pesticide residue sample data were obtained using hyperspectral imaging technology for the high-frequency detected pesticides Glyphosate and Bifenthrin, and the SVM and 1D-CNN models were established to detect the samples after noise reduction processing and feature band screening methods. The study shows that the 1D-CNN model has better feature extraction ability, in which the SG-CARS-1D-CNN model has the highest detection accuracy, which is 94.62%, 95.12%, 94.35%, 94.95%, and 95.27% for the five type of species samples, respectively. This study provides pesticide residue detection for white tea fresh leaves based on the combination of hyperspectral data and an artificial intelligence model, which provides an intelligent, nondestructive, efficient, and high-precision pesticide residue detection model for white tea fresh leaves.

Simultaneous Electrochemical Determination of Hydroquinone, Catechol and Bisphenol A using Nickel Oxide@Reduced Graphene Oxide Nanocomposites in Water Samples

Phenolic compounds, prevalent in both human life and the natural environment, pose a significant threat to human health due to their toxic effects. Therefore, accurate determination of these compounds are crucial. Herein, we developed a new electrochemical sensor based on a NiO/C@reduced graphene oxide nanocomposite (NiO/C@rGO) to simultaneously assess three phenolic compounds: hydroquinone (HYQ), catechol (CAT), and bisphenol A (BPA). The NiO/C@rGO nanocomposite was synthesized via Ni(gallate)@GO pyrolysis and extensively characterized using various techniques. Subsequently, a glassy carbon electrode (GCE) modified with the NiO/C@rGO nanocomposite was utilized as the electrochemical sensor to simultaneously detection. The developed nanosensor (NiO/C@rGO/GCE) exhibited exceptional selectivity and a broad linear ranges for HYQ, CAT, and BPA, spanning from 0.01 to 100 μM, with impressively low detection limits. Furthermore, the NiO/C@rGO nanocomposite demonstrated remarkable stability and high reproducibility. Moreover, we successfully applied the NiO/C@rGO-based sensor to evaluate the three phenolic compounds in tap water, drinking water, and mineral water samples. The results underscore the potential of the developed electrochemical sensor as a reliable tool for the sensitive and fast detection of phenolic compounds in various water sources, addressing the critical need for safeguarding human health and the environment from their adverse effects.