Rapid Detection Research Articles

A novel signal amplified sandwich aptamer-based visual lateral flow assay based on self-assembly bifunctional nucleic acid for rapid and sensitive detection of CKMB

At present, rapid and sensitive detection of disease biomarkers is still the focus of early clinical diagnosis. As a rapid response to point-of-care testing (POCT), lateral flow assay (LFA) has been given serious attention. Unfortunately, due to the sensitivity limitations, LFA, especially AuNPs-based LFA, was difficult to play an important role in the detection of trace biomarkers. The combination of different signal amplification methods increases the complexity of operation and use, or prolongs the detection time, which sacrifices the advantages of fast and convenient of LFA. Hence, a novel sandwich aptamer-based visual LFA based on bifunctional nucleic acid (bFNA-LFA) was proposed for rapid and sensitive detection of creatine kinase MB (CKMB) in this study. Based on hybridization chain reaction (HCR) and nucleic acid hybridization, a bifunctional nucleic acid with target recognition and signal amplification was prepared, which effectively improved the sensitivity of CKMB detection without increasing the reaction time and depending on the detecting equipment. Under optimal reaction conditions, the limit of detection of 7.92 ng/mL was observed, which was 20 times lower than that of sandwich aptamer-based AuNPs LFA in the previous study. The proposed bFNA-LFA assay expanded a new idea in improving the sensitivity of LFA, and provided a reference for the subsequent application of nucleic acid nanomaterials in the detection of protein biomarkers.

Soybean Saponin Content Detection Based on Spectral and Image Information Combination

Soybean saponin is a natural antioxidant and is anti-inflammatory. Hyperspectral analysis technology was applied to detect soybean saponin content rapidly and nondestructively in this paper. Firstly, spectral preprocessing methods were studied, and standard normal variable (SNV) was used to remove noise information. Secondly, a two-step hybrid variable selection approach based on synergy interval partial least squares (SiPLS) and iteratively retains informative variables (IRIV) was proposed to extract characteristic variables. Then, the ensemble learning model was constructed by back propagation neural network (BPNN), deep forest (DF), partial least squares regression (PLSR), and extreme gradient boosting (EXG). Finally, image information was combined into spectral data to improve model accuracy. The prediction coefficient of determination (R2) of the final model reached 0.9216. It can provide rapid, nondestructive, and accurate detection technology of soybean saponin content. A combination of spectral and image information will provide a new idea for application of hyperspectral.

UAV and Deep Learning: Detection of selected riparian species along the Ganga River

Abstract. Environmental protection and sustainable natural resource management are being recognised worldwide as essential goals to safe guard human health and well-being. Riparian zones, that face the highest decline in freshwater biodiversity, are of prime conservation priority because they are essential for regulating climate, preserving aquatic-terrestrial biodiversity, maintaining ground water recharge and restoring rivers. In today's fast-paced data-driven environment, artificial intelligence (AI) is the precise answer to a wide range of problems including biodiversity conservation and wildlife management. Leveraging advancements like Uncrewed/Unmanned Aerial Vehicles (UAVs) and AI has resulted innovative strides in wildlife conservation. This study utilised UAV imagery to record high-resolution data of aquatic habitat and species along the Ganga River and employed deep learning algorithms to analyse the data. Through extensive field surveys in the Hastinapur Wildlife Sanctuary, 7,025 photos representing a variety of environments, including 20,000 annotated samples of aquatic animals such as turtles and gharials were generated. Vision based computing capabilities such as pattern recognition model were developed to identify these species. To enrich and enhance the dataset and model, we used different image pre-processing techniques. Slight rotation (±5 degrees), minor cropping (up to 10%), and adjustments in brightness, saturation, and shear (±15%) were applied. Controlled blur (up to 0.5%) and exposure modifications (±5%) were also implemented on the image dataset to improve accuracy. Three Convolutional Neural Network (CNN) architectures, single-stage detectors named YOLO v7, YOLO v8, and Roboflow 3.0, were used for detecting the select species. Results show YOLO v8 excels, achieving mean average precision (mAP) of 98.8% for gharial and 92.2% for turtle detection, with a rapid average detection time of 0.308 seconds per frame at 3200 × 3200 resolution. Additionally, our model demonstrates real-time species detection capability through innovative frame sampling techniques with UAVs. This methodology provides promising technique to collect scientific data on IUCN red listed turtles and critically endangered gharials, allowing detection, monitoring, and real time counting with minimal intrusion. In conclusion, the fusion of UAVs and deep learning promises to revolutionize habitat monitoring, aiding conservation decision-making.

AgroFarmIQ: Advanced Crop Monitoring and Yield Prediction

In response to the imperatives of modern agriculture, our project presents a comprehensive precision agriculture solution centered around a multifunctional robocar. This autonomous vehicle integrates IoT sensors for data-driven crop management, employing machine learning for monitoring and predicting yields while optimizing resource utilization. Automation features include automated obstacle avoidance, periodic image capture, and automatic water spraying for real-time data collection and management, empowering farmers with actionable insights for informed decision-making. Additionally, a web-based platform utilizing Streamlit framework facilitates rapid and accurate crop disease detection using a meticulously trained VGG19 model from uploaded images. By embracing advanced technology, our solution aims to enhance agricultural productivity and pave the way for a sustainable future. Keyword: Precision agriculture, Robocar, IoT sensors, Machine learning, Crop management, Automated farming, Data-driven agriculture, Resource optimization, Real-time monitoring, Sustainable farming

Accurate analysis of coal calorific value using NIRS-XRF: Utilizing RF classification and PLSR subtype modeling

Rapid and precise measurement of the calorific value of coal is crucial for coal chemical enterprises. However, due to the wide variety of coal sources and the diverse types of coal, the matrix effect significantly reduces the accuracy of spectroscopic rapid detection techniques for coal quality. To address this issue, this study conducts research on a coal calorific value detection method based on the combined use of near-infrared spectroscopy (NIRS) and X-ray fluorescence spectroscopy (XRF), exploring the feasibility of identifying coal types first and then predicting calorific value based on corresponding subtype models. In the aspect of spectral preprocessing, fusion of the dual spectra is achieved using techniques such as truncation, smoothing, Standard Normal Variate (SNV), and normalization. For coal type identification, the identification performance of five machine learning algorithms is compared, and the random forest (RF) algorithm is used to improve the identification accuracy to 98.43 %. In terms of prediction modeling, a comparison is made between traditional holistic models based on Partial Least Squares Regression (PLSR) and subtype models built according to coal types, revealing that the latter demonstrating stronger generalization ability and robustness. Accordingly, a precise prediction strategy for coal calorific value based on the combined use of NIRS and XRF in complex coal types is proposed: first, the RF algorithm is used to automatically identify coal types based on the fusion spectrum of NIRS and XRF, and then the calorific value of the corresponding coal type is accurately predicted using the PLSR subtype model selected according to the identification results. Industrial test results indicate that the average absolute error (AAE), root mean square error of prediction (RMSEP), average relative error (ARE), and standard deviation (SD) for coal calorific value prediction are 0.20 MJ/kg, 0.24 MJ/kg, 0.79 %, and 0.04 MJ/kg, respectively, meeting the requirements of enterprises. The research findings provide a practical technical method for timely and accurate acquisition of coal calorific value data, addressing the interference problem of matrix effects in complex coal type scenarios and holding broad application prospects in the coal-based energy industries.

Multiple-component covalent organic frameworks for simultaneous extraction and determination of multitarget pollutants in sea foods

Persistent organic pollutants (POPs), such as perfluoroalkyl and polyfluoroalkyl substances (PFASs), polychlorinated biphenyls (PCBs), and bisphenols (BPs), have been raising global concerns due to their toxic effects on environment and human health. The monitoring of residues of POPs in seafood is crucial for assessing the accumulation of these contaminants in the study area and mitigating potential risks to human health. However, the diversity and complexity of POPs in seafood present significant challenges for their simultaneous detection. Here, a novel multi-component fluoro-functionalized covalent organic framework (OH-F-COF) was designed as SPE adsorbent for simultaneous extraction POPs. On this basis, the recognition and adsorption mechanisms were investigated by molecular simulation. Due to multiple interactions and large specific surface area, OH-F-COF displayed satisfactory coextraction performance for PFASs, PCBs, and BPs. Under optimized conditions, the OH-F-COF sorbent was employed in a strategy of simultaneous extraction and stepwise elution (SESE), in combination with HPLC−MS/MS and GC−MS method, to effectively determined POPs in seafood collected from coastal areas of China. The method obtained low detection limits for BPs (0.0037 −0.0089 ng/g), PFASs (0.0038 −0.0207 ng/g), and PCBs (0.2308 −0.2499 ng/g), respectively. This approach provided new research ideas for analyzing and controlling multitarget POPs in seafood. Environmental implicationsPersistent organic pollutants (POPs), such as perfluoroalkyl and polyfluoroalkyl substances (PFASs), polychlorinated biphenyls (PCBs), and bisphenols (BPs), have caused serious hazards to human health and ecosystems. Hence, there is a need to develop a quantitative method that can rapidly detect POPs in environmental and food samples. Herein, a novel multi-component fluorine-functionalized covalent organic skeletons (OH-F-COF) were prepared at room temperature, and served as adsorbent for POPs. The SESE-SPE strategy combined with chromatographic techniques was used to achieve a rapid detection of POPs in sea foods from the coastal provinces of China. This method provides a valuable tool for analyzing POPs in environmental and food samples.

Metal-organic frameworks constructed with base-acid mixed ligands and its potential as dual-functional fluorescent probe toward Fe3+/Al3+ ions

By virtue of mixed ligand strategy (2,5-Bis(1H-pyrazole-4-ylmethylene)-cyclopentanone and 2,5-dihydroxyterephthalic acid / terephthalic acid), four new metal–organic frameworks (MOFs) were fabricated and characterized. When 2,5-dihydroxyterephthalic acid acted as auxiliary ligand, MOFs 1–3 show 1D chain structure. 4 containing auxiliary ligand terephthalic acid (without − OH group) displays a 2D layer structure. Notably, 1 exhibits the significant selectivity toward Fe3+ and Al3+ ions, whereas the fluorescence intensity of 4 changed slightly in the presence of various metal ions, indicating that uncoordinated –OH in 1 plays an important role in sensing process. Moreover, it is revealed that 1 could act as dual-functional fluorescent probe toward Fe3+ and Al3+ with selectivity, sensitivity and reusability, and it could be prepared as fluorescent test papers for rapid on-site detection.

Customized carbon dots as a fluorescent sensor for detecting paraquat through enhanced excited state binding energy

A novel fluorescent sensor utilizing customized carbon dots (CDs) has been developed for the rapid and efficient detection of the herbicide paraquat. The 4,4′-diamino-2,2′-stilbenedisulfonic acid (DSD acid) functionalized CDs (DSD-CDs) were synthesized by a hydrothermal method using citric acid and L-cysteine as precursors, followed by amidation with DSD acid. The resultant DSD-CDs exhibited bright blue fluorescence upon UV excitation. The fluorescence of the functionalized carbon dots was effectively quenched by paraquat, following a static quenching mechanism as confirmed by fluorescence lifetime measurements. Interestingly, the mechanism study revealed that the excited-state of the DSD-CDs could interact effectively with paraquat, resulting in stronger fluorescence quenching compared to the ground-state. The sensor showed high sensitivity, selectivity, and stability, with a wide linear range of 0.01 ∼ 2 mg/L (R2 = 0.997) and a low detection limit of 0.003 mg/L. The developed fluorescent sensor demonstrated exceptional potential for paraquat detection in actual samples, encompassing blood, urine, soybean, and river water, with recovery rates exceeding 83 % and relative standard deviations (RSD) below 10 %. This underscores its practical utility in environmental protection and clinical testing applications.

Rapid and accurate detection of Dendrobium officinale adulterated with lower-price species using NMR characteristic markers integrated with artificial neural network

Rapid and accurate detection of Dendrobium officinale adulterated with lower-price species using NMR characteristic markers integrated with artificial neural network

A novel laser-EMAT ultrasonic longitudinal wave resonance method for wall thickness measurement at high temperatures

In this paper we propose a novel ultrasonic longitudinal wave resonance method for measuring the thickness of metal walls using a laser-electromagnetic acoustic transducer (Laser-EMAT). The method is based on the surface constraint mechanism (SCM) of the material and is expected to be capable of accurately detecting local thinning of metal walls in a non-contact manner and at high temperatures. Based on finite element analysis of laser-EMAT ultrasonic resonance measurement of aluminum alloy thickness, we investigated the effects of such key factors as SCM, irradiation parameters of laser source, and the size of EMAT receiving coil on the accuracy of thickness measurement (resonance frequency position) and on the amplitude of the resonance wave. Both numerical simulations and experiments are conducted to demonstrate that the measurement accuracy of the proposed method is not affected by SCM, irradiation laser source parameters, and EMAT receiving coil size, and that accurate detection of stepped aluminum plates with thickness thinning from 3.0 mm to 0.5 mm is achieved. Furthermore, we were able to perform rapid detection of aluminum thin plate thickness at 500 °C temperature with an EMAT lift-off of 5.0 mm and achieved a relative experimental error as small as 3.40 %. The results obtained in this study showed that the proposed method performed well in non-contact measurement of metal thinning in harsh environment of high temperature.

A Novel Near-Infrared Fluorescent Probe for the Detection of Endogenous Peroxynitrite (ONOO-) in Living Cells.

In this study, we present a novel near-infrared (NIR) fluorescent probe Nile-ONO designed for the selective and sensitive detection of ONOO-. The probe Nile-ONO employed Nile red as the fluorophore, with diphenylphosphinate serving as the reaction site. In the presence of ONOO-, the probe Nile-ONO exhibits remarkable fluorescence enhancement at 659nm, with a response time of less than 20min and a low detection limit of 0.32 µM. Importantly, MTT assays demonstrate low cytotoxicity in living cells. Furthermore, Nile-ONO has excellent imaging capabilities for endogenous ONOO-. Overall, this work introduces a valuable new method for the rapid detection of ONOO- in biological systems.

A rapid solid-phase extraction purification combined with UPLC[sbnd]MS/MS used for simultaneous determination of eight tetracyclines and three metabolite in chicken and pork

A method for rapid detection and identification of 8 tetracyclines (TCs) and 3 metabolite residues in chicken and pork was established and verified by combining rapid solid-phase extraction (SPE) technology with ultrahigh-performance liquid chromatography-tandem mass spectrometry (UPLCMS/MS). After a sample was extracted, it was purified with a PRiME HLB cartridge, and the cartridge did not need to be activated, balanced, or eluted. The filtrate was dried under nitrogen, redissolved, and subsequently analyzed via UPLCMS/MS. The limits of detection for chicken and pork were 0.05–0.48 μg/kg and 0.05–0.52 μg/kg, respectively. The limits of quantification were 0.16–1.61 μg/kg and 0.16–1.75 μg/kg, respectively. The recoveries were 85.15%–107.50% and 84.21%–106.37%, respectively. The established method was suitable for qualitative and quantitative analyses of TCs and their metabolite residues in chicken and pork and has been used for the detection of TC in chicken and pork samples from local markets.

An enzymatic recombinase amplification assay combined with CRISPR-Cas12a for the rapid detection of acute hepatopancreatic necrosis disease in shrimp Penaeus vannamei

An enzymatic recombinase amplification assay combined with CRISPR-Cas12a for the rapid detection of acute hepatopancreatic necrosis disease in shrimp Penaeus vannamei

Design of a Wireless Monitoring System for Vibration Characteristics of the Wheeled Tractor at Idle Speeds

To enhance the precision and efficiency of the tractor-failure-rate and equipment-quality inspection, the present study introduces a wireless rapid detection method for assessing tractor quality. For this study, which was based on the symmetrical structural characteristics of tractors, we designed a magnetic suction accelerometer. The test system was composed of a wireless router, a magnetic suction accelerometer sensor, a data-acquisition terminal, and other components. This test system aimed to test the equipment quality of the tractor at idle speed before leaving the factory. The experiment found that the vibration characteristics of the tractor had a symmetrical pattern on the left and right sides of the front and rear axle at idle. When the idle speed of the tractor was 800 r/min and 1000 r/min, the predominant vibration direction of both sides of the front axle of the tractor was the Y direction, while the predominant vibration direction of the rear axle was the Z direction. The experimental results showed that the proposed wireless rapid detection method of tractor quality and the designed acceleration sensor had good testing accuracy. The present study could provide a novel rapid detection method for the failure detection of power machinery in the agricultural field and for inspection before leaving the factory. The implementation of the method could improve the detection efficiency, and reduce the detection cost and the incidence of failure during actual use.

Detecting Smell/Gas-Source Direction Using Output Voltage Characteristics of a CMOS Smell Sensor

Various organisms, such as dogs and moths, can locate their prey and mates by sensing their smells. Following this manner, if an engineering device with the capability to detect a smell or gas source is realized, it can have a wide range of potential applications, such as searching for landmines, locating gas leaks, and rapid detection of fire. A previous study on the estimation of smell and gas-flow direction successfully detected the smell/gas-source direction in low-wind-velocity environments using a semiconductor gas sensor array. However, some problems are generally associated with the use of semiconductor gas sensors due to the use of heaters. This study aimed to detect the location of a smell/gas source using an integrated CMOS smell sensor array, which operates at room temperature without a heater. The experiment showed that under ideal conditions, the order of gas responses and concentration gradient of the gas enabled the estimation of the direction of the smell/gas-source location on one side of the sensor.

Use of Artificial Intelligence With Deep Learning Approaches for the Follow-up of Infrarenal Endovascular Aortic Repair.

Endoleaks represent one of the main complications after endovascular aortic repair (EVAR) and can lead to increased re-intervention rates and secondary rupture. Serial lifelong surveillance is required and traditionally involves cross-sectional imaging with manual axial measurements. Artificial intelligence (AI)-based imaging analysis has been developed and may provide a more precise and faster assessment. This study aims to evaluate the ability of an AI-based software to assess post-EVAR morphological changes over time, detect endoleaks, and associate them with EVAR-related adverse events. Patients who underwent EVAR at a tertiary hospital from January 2017 to March 2020 with at least 2 follow-up computed tomography angiography (CTA) were analyzed using PRAEVAorta 2 (Nurea). The software was compared to the ground truth provided by human experts using Sensitivity (Se), Specificity (Sp), Negative Predictive Value (NPV), and Positive Predictive Value (PPV). Endovascular aortic repair-related adverse events were defined as aneurysm-related death, rupture, endoleak, limb occlusion, and EVAR-related re-interventions. Fifty-six patients were included with a median imaging follow-up of 27 months (interquartile range [IQR]: 20-40). There were no significant differences overtime in the evolution of maximum aneurysm diameters (55.62 mm [IQR: 52.33-59.25] vs 54.34 mm [IQR: 46.13-59.47]; p=0.2162) or volumes (130.4 cm3 [IQR: 113.8-171.7] vs 125.4 cm3 [IQR: 96.3-169.1]; p=0.1131) despite a -13.47% decrease in the volume of thrombus (p=0.0216). PRAEVAorta achieved a Se of 89.47% (95% confidence interval [CI]: 80.58 to 94.57), a Sp of 91.25% (95% CI: 83.02 to 95.70), a PPV of 90.67% (95% CI: 81.97 to 95.41), and an NPV of 90.12% (95% CI: 81.70 to 94.91) in detecting endoleaks. Endovascular aortic repair-related adverse events were associated with global volume modifications with an area under the curve (AUC) of 0.7806 vs 0.7277 for maximum diameter. The same trend was observed for endoleaks (AUC of 0.7086 vs 0.6711). The AI-based software PRAEVAorta enabled a detailed anatomic characterization of aortic remodeling post-EVAR and showed its potential interest for automatic detection of endoleaks during follow-up. The association of aortic aneurysmal volume with EVAR-related adverse events and endoleaks was more robust compared with maximum diameter. The integration of PRAEVAorta AI software into clinical practice promises a transformative shift in post-EVAR surveillance. By offering precise and rapid detection of endoleaks and comprehensive anatomic assessments, clinicians can expect enhanced diagnostic accuracy and streamlined patient management. This innovation reduces reliance on manual measurements, potentially reducing interpretation errors and shortening evaluation times. Ultimately, PRAEVAorta's capabilities hold the potential to optimize patient care, leading to more timely interventions and improved outcomes in endovascular aortic repair.

A Wax Interface-Enabled One-Pot Multiplexed Nucleic Acid Testing Platform for Rapid and Sensitive Detection of Viruses and Variants.

High-quality, low-cost, and rapid detection is essential for the society to reopen the economy during the critical period of transition from Coronavirus Disease 2019 (COVID-19) pandemic response to pandemic control. In addition to performing sustainable and target-driven tracking of SARS-CoV-2, conducting comprehensive surveillance of variants and multiple respiratory pathogens is also critical due to the frequency of reinfections, mutation immune escape, and the growing prevalence of the cocirculation of multiple viruses. By utilizing a 0.05 cents wax interface, a Stable Interface assisted Multiplex Pathogenesis Locating Estimation in Onepot (SIMPLEone) using nested RPA and CRISPR/Cas12a enzymatic reporting system is successfully developed. This smartphone-based SIMPLEone system achieves highly sensitive one-pot detection of SARS-CoV-2 and its variants, or multiple respiratory viruses, in 40 min. A total of 89 clinical samples, 14 environmental samples, and 20 cat swab samples are analyzed by SIMPLEone, demonstrating its excellent sensitivity (3-6 copies/reaction for non-extraction detection of swab and 100-150 copies/mL for RNA extraction-based assay), accuracy (>97.7%), and specificity (100%). Furthermore, a high percentage (44.2%) of co-infection cases are detected in SARS-CoV-2-infected patients using SIMPLEone's multiplex detection capability.

Development of a multiplex one-tube loop-mediated isothermal amplification method for rapidly simultaneous detection of the mecA and nuc genes in methicillin-resistant Staphylococcus aureus

This study developed a multiplex one-tube loop-mediated isothermal amplification method using fluorescent dye-labeled DNA probes (mfLAMP) for the rapid simultaneous detection of the nuc (S. aureus-specific marker) and mecA genes (PBP2a-specific marker) in methicillin-resistant Staphylococcus aureus (MRSA). In the mfLAMP method, a graphene oxide (GO) was used as an effective adsorbent to eliminate free fluorescent dye-labeled DNA probes (mecAf-probe: Cy5-ssDNA-Biotin and nucf-probe: FAM-ssDNA-Biotin) present in negative results. The samples were then measured using a developed program that enable simultaneous detection of fluorescein (FAM) and Cyanine5 (Cy5). Green fluorescence represents the detection of the nuc gene, red fluorescence indicates the detection of the mecA gene, and yellow fluorescence signifies the detection of MRSA. The mfLAMP was specific to MRSA and could detect 10°CFU/mL of MRSA in standard buffer and artificially inoculated irrigation water. The recovery rate from contaminated irrigation water ranged from 100.41 to 109.85 %. This study is the first to report the mfLAMP using GO, which could be point-of-care testing for the rapid and simultaneous detection of MRSA's nuc and mecA genes. The results demonstrate that the mfLAMP will be a novel tool for simultaneously detecting more than two target genes or bacteria within 1 h without an enrichment step.

Development and evaluation of time-resolved rapid immunofluorescence test for detection of TSOL18 specific antibody in porcine cysticercosis infections

BackgroundPorcine cysticercosis, a serious zoonotic parasitic disease, is caused by the larvae of Taenia solium and has been acknowledged by the World Organization for Animal Health. The current detection methods of Cysticercus cellulosae cannot meet the needs of large-scale and rapid detection in the field. We hypothesized that the immunofluorescence chromatography test strip (ICS) for detecting Cysticercus cellulosae, according to optimization of a series of reaction systems was conducted, and sensitivity, specificity, and stability testing, and was finally compared with ELISA. This method utilizes Eu3+-labeled time-resolved fluorescent microspheres (TRFM) coupled with TSOL18 antigen to detect TSOL18 antibodies in infected pig sera.ResultsICS and autopsy have highly consistent diagnostic results (n = 133), as determined by Cohen’s κ analysis (κ = 0.925). And the results showed that the proposed ICS are high sensitivity (0.9459) with specificity (0.9792). The ICS was unable to detect positive samples of other parasites. It can be stored for at least six months at 4℃.ConclusionsIn summary, we established a TRFM-ICS method with higher sensitivity and specificity than indirect ELISA. Results obtained from serum samples can be read within 10 min, indicating a rapid, user-friendly test suitable for large-scale field detection.

In situ synthesized highly sensitive AANS@ZIF-8@GZEF constructed flexible SERS sensor for CPS determination

Aiming to meet the demand for rapid detection of chlorpyrifos (CPS), a surface-enhanced Raman scattering (SERS) sensor based on a gluten/zein electrospun film (GZEF) was fabricated via electrospraying. The SERS performance was namely improved through metal nanoparticles selection and optimization of loading amounts. Further construction of the AANS@ZIF-8@GZEF sensor was achieved through in-situ growth. Owning to the porous structure of ZIF-8, the trapping ability and enrichment effect of the SERS sensor were enhanced, resulting in a superior SERS enhancement effect of 1.58 times. Moreover, the CPS detection under various concentrations was carried out using the AANS@ZIF-8@GZEF sensor. The concentration-logarithm peak intensity equations were fitted, and the limit of detection (LOD) of CPS was calculated to be 0.981 μg/L. Finally, the detection of CPS residues on the peel of apples was performed using the AANS@ZIF-8@GZEF sensor, and the results showed that the overall recovery rate ranged between 91.18% and 96.83%, with all relative standard deviations being less than 5%. Great potential applications in the rapid and sensitive detection of chlorpyrifos in food are expected for the AANS@ZIF-8@GZEF sensor constructed in this study.