Fast Detection Research Articles

High-Resolution Melting PCR as a Fast and Simple Molecular Biology-Based Method for the Identification of Hypervirulent Clostridioides difficile Strains Directly in Stool Samples

Clostridioides difficile became one of the main causes of nosocomial infections in all clinical settings worldwide, especially among patients undergoing antibiotic therapy. The incidence and severity of C. difficile infections, from mild diarrhea to life-threatening pseudomembranous colitis, correlate with the spread of the hypervirulent binary toxin (CDT)-producing strains. The use of the real-time HRM-PCR method enables the identification of hypervirulent C. difficile strains directly in the diarrheal stool samples of patients suspected of being infected with this bacterium. For this purpose, the cdtA and cdtB genes encoding CDT subunits, as well as the species-specific gluD gene, were detected to identify the presence of this bacterium in the tested samples. The sensitivity, specificity, negative predictive value (NPV) and positive predictive value (PPV) of the established method were also assessed. The obtained results were compared with the results of eazyplex® C. difficile complete test (AmplexDiagnostics GmbH) based on the LAMP method, used in standard microbiological diagnostics. The values of the assessed diagnostic parameters for the detected genes ranged from 58.82% to 98.85%. The lowest value (58.82%) was obtained for the PPV of cdtB and the highest (98.85%) for the NPV of this gene. The real-time HRM-PCR method enables fast and simple detection of the investigated genes of hypervirulent C. difficile strains and, after careful optimization, may demonstrate high potential for usefulness in routine microbiological diagnostics.

Au Nanoparticles-Trisbipyridine Ruthenium(II) Nanoaggregates as Signal-Amplifying SERS Tags for Immunoassay of cTnI.

Acute myocardial infarction (AMI) is one of the leading causes of human mortality worldwide. In the early stages of AMI, the patient's electrocardiogram (ECG) may not change, so the fast, sensitive, and accurate detection of the specific biomarker of cardiac troponin I (cTnI) is of great importance in the early diagnosis of AMI. In this work, for the first time, electrostatic nanoaggregates of negatively charged Au nanoparticles and positively charged trisbipyridine ruthenium(II) ions (i.e., (-)AuNPs|[Ru(bpy)3]2+ ENAs) as novel and signal-amplifying surface-enhanced Raman scattering (SERS) tags were synthesized in an easy and rapid (<3 min) way and applied in the highly sensitive, rapid detection of cTnI in human serum by being combined with an immunochromatographic test strip (ICTS). The synthesized (-)AuNPs|[Ru(bpy)3]2+ ENAs exhibited strong SERS activity due to the multiple Raman-active units (three bpy ligands) carried by each [Ru(bpy)3]2+ complex ion and abundant hotspots in each SERS tag. The developed (-)AuNPs|[Ru(bpy)3]2+ ENAs-based SERS-ICTS has been validated to be applicable in detection of cTnI in human serum with excellent sensing performances, such as fast testing (5 min) and a low detection limit (60 pg/mL). It is envisioned that the developed (-)AuNPs|[Ru(bpy)3]2+ ENAs-based SERS-ICTS sensor may have promising applications in point of care testing of various biomarkers in clinic. Additionally, this work may inspire the finding and the application of new types of Raman reporter molecules based on high valent metal-multi ligand coordination compounds like [Ru(bpy)3]2+.

A label-free electrochemical detection of cadmium ions using aptamer-based biosensor

Abstract Cadmium is considered one of the most toxic pollutants that can be found in water as well as in soil, which might accumulate in living organisms causing severe effects such as skeletal, cardiovascular, and respiratory diseases. Hence, it is necessary to develop methods allowing for sensitive and fast detection as well as portability. One possibility is the application of biosensors containing aptamer strands as receptor layer selective towards cadmium ions. Here, we present studies on the utilization of DNA aptamer strand for fabrication of sensing layer toward cadmium ions on gold disk macroelectrodes. It is shown that proposed aptasensor enables Cd2+ ions detection in the range from 10 to 50 nM with LOD of 9.5 nM and exhibits high selectivity towards cadmium cations with a response at least two times higher than that for interfering ions. Moreover, studies on the stability revealed that sensing layer preserved its binding properties after storage and allowed for cadmium ions detection in the range from 10 to 50 nM and the aptamer sensing layer could be regenerated and applied for another set of analysis of cadmium ions.

Adaptable Plasmonic Membrane Sensors for Fast and Reliable Detection of Trace Low-Micrometer Microplastics in Lake Water.

In freshwater environments, low-micrometer microplastics (LMMPs) have captured significant attention due to their prevalence and toxicity. Yet, rapid detection of LMMPs (1-10 μm) at the single-particle level within complex freshwater matrices remains a hurdle. We developed an adaptable plasmonic membrane sensor for fast detection of individual LMMPs in eutrophic lake waters. The plasmonic membrane sensor functions both as a membrane filter and as a sensor for LMMP collection and analysis. Among the four types of membrane sensors, polycarbonate track-etch (PCTE) membrane sensors exhibit superior imaging quality for LMMPs due to their flat and homogeneous surfaces. Besides the significantly improved imaging contrast and reduced background interferences, the Raman intensity of LMMPs is enhanced by 48% ± 25% on PCTE membrane sensors compared to unmodified membranes. The increased Raman intensities of a chemical probe with an increasing gold layer thickness and a decreasing membrane pore size suggest a surface-enhanced Raman scattering effect from the membrane sensors. The membrane sensors achieve a detection limit of 1 μg/L and an ultrafast scanning time of 0.01 s for individual LMMPs across natural eutrophic lake water. The developed membrane sensors offer an adaptable tool for the swift and reliable detection of individual LMMPs in complex environmental matrices.

Lightweight concrete crack detection based on spiking neural networks

PurposeMost existing methods for concrete crack detection are based on deep learning techniques such as convolutional neural networks. However, these models, due to their large memory footprint, high power consumption and insufficient feature extraction capabilities, face challenges in mobile applications. To address these issues, this paper proposes a lightweight spiking neural network detection model.Design/methodology/approachThis model achieves fast and accurate crack detection. Firstly, the Gabor-Spiking (GS) module preprocesses input images, extracting texture features and edge features of crack images through Gabor filter convolution modules and spiking convolution modules, respectively. Next, the multiscale residual (MR) module is designed, composed of convolutional layers and residual modules of various scales, to process the fused features and perform crack detection.FindingsExperimental results demonstrate that the model’s size can be reduced to 4.6 MB, achieving accuracy improvements to 87.3 and 96.4% on the SDNET and OCD datasets, respectively.Originality/valueThis paper proposes a lightweight spiking neural network detection model based on the GS module for edge texture feature fusion and the MR module for crack detection.

LOSS BREAKDOWN IN AXIAL TURBINES: A NEW METHOD FOR VORTEX LOSS AND WAKE DETECTION FROM 3D RANS SIMULATIONS

Abstract To enhance turbine efficiency, it is essential to mitigate the losses generated by irreversible phenomena in turbine flows, including boundary layers, shock waves, vortices, and trailing edge wakes. Fast and accurate detection of losses is crucial from the earliest design stages, where reduced-order models based on oversimplified correlations are employed. This requires a deep understanding of the physics behind loss-generating mechanisms, attainable by examining the 3D flow. While existing criteria can identify various phenomena, accurately quantifying vortex-generated losses remains challenging, as these losses frequently extend beyond the vortical structure. This paper provides a straightforward and effective approach to localize and assess vortex-related losses. The method is grounded in Zlatinov's decomposition of the entropy generation rate into a streamwise and secondary flow component. A criterion based on vortex kinematics evaluates the vortex's strength, enabling the determination of its spatial influence and contribution to overall losses. To validate the method, a post-processing code is developed to perform loss breakdown, using existing identification criteria and new techniques introduced within this work, particularly for wake detection. 3D RANS simulations on various configurations, from simple curved ducts to nozzle guide vanes, allow to gradually test and validate the computational tool. Results confirm that the highest entropy generation rates occur outside the vortical structure and show good ability to identify both the vortex shape and its area of influence in terms of losses. A significant improvement in predicting vortex losses is observed, especially for turbine blades with leakage vortices.

Development of a triplex endpoint PCR assay for the detection of SARS-CoV-2: insights on cost-efficiency and method design.

Lower respiratory tract infections, including COVID-19, have a substantial global impact, making the development of diagnostic tests crucial. The study aimed to develop a new, accurate, fast, and cost-effective PCR-based detection method for SARS-CoV-2, applicable in limited settings and capable of detecting all current variants and potential future pathogens. The study was conducted between 2020 and 2022 at the molecular biology department of Mures County Clinical Hospital, Romania. Initially, pharyngeal and nasal secretions were collected and processed using the real-time qRT-PCR method for routine COVID-19 diagnosis. Ninety-two samples were randomly selected to develop the assay, including samples from different infection periods and negative controls. Complementary DNA (cDNA) was prepared from the selected samples, and the presence and integrity of the extracted RNA were evaluated by amplifying the GAPDH housekeeping gene. Primers for three specific viral genes (N, ORF1ab, and S) were designed, and their efficiency was evaluated using endpoint PCR and sequencing. Finally, the method was optimized and implemented as a one-step triplex PCR assay for routine diagnostic use. The molecular laboratory at the Mures County Clinical Hospital (MCCH) analyzed a total of 41,818 samples between June 2020 and December 2022. Among these samples, 26.15% tested positive for SARS-CoV-2, while 70.9% were negative and 2.95% were inconclusive or invalid. Three peaks of positive tests were observed in November 2020, April 2021, and February 2022. The study selected 92 preserved RNA samples for triplex-PCR assay development, validating the primers' specificity and confirming the quality of the nucleic acids. The comparative analysis showed the efficiency and accuracy of the endpoint reverse-transcription triplex-PCR method (RT-PCR), indicating its potential as a cost-effective alternative to real-time reverse-transcription PCR (qRT-PCR) in low-income countries with limited infrastructure for COVID-19 testing. This method has the potential to facilitate large-scale diagnosis of SARS-CoV-2 infections, allowing for rapid and appropriate therapeutic management and ongoing monitoring of patients. Additionally, the method can be easily adapted for the detection of other pathogens.

Fast unfolding of communities in large networks: 15 years later

Abstract The Louvain method was proposed 15 years ago as a heuristic method for the fast detection of communities in large networks. During this period, it has emerged as one of the most popular methods for community detection: the task of partitioning vertices of a network into dense groups, usually called communities or clusters. Here, after a short introduction to the method, we give an overview of the different generalizations, modifications and improvements that have been proposed in the literature, and also survey the quality functions, beyond modularity, for which it has been implemented. Finally, we conclude with a discussion on the limitations of the method and perspectives for future research.

UFLD enhancements: New loss function and image masking

In the field of autonomous driving, lane detection plays a crucial role in ensuring safety and accuracy in navigation. This paper presents enhancements to the Ultra Fast Lane Detection (UFLD) model by introducing a new loss function that ensures smoother and more continuous lane lines, and incorporating image masking techniques to simulate real-world occlusions. These modifications improve the models robustness, particularly in scenarios with degraded lane markings. Through extensive experimentation on the TuSimple dataset, we demonstrate that the proposed enhancements lead to more accurate and realistic lane detection, especially in challenging environments.

AI-Augmented Fault Detection and Diagnosis in VLSI Circuits: A Step toward Intelligent Chip Design

Fault detection and diagnosis are critical challenges in Very Large Scale Integration (VLSI) circuits, where even minor defects can cause significant performance degradation or system failure. Traditional fault detection methods often struggle with scalability and real-time analysis as circuits grow increasingly complex. This paper proposes an AI-augmented framework that leverages machine learning algorithms and neural networks to enhance the fault detection process in VLSI circuits. The proposed model not only identifies and classifies faults with high accuracy but also provides root-cause diagnostics, significantly reducing testing time and maintenance costs. Simulation results demonstrate the effectiveness of the AI-based approach in comparison with conventional techniques, showcasing improved fault coverage, faster detection, and scalability for modern chip designs. This study serves as a step toward the integration of intelligent diagnostics into chip manufacturing, paving the way for more robust and self-healing electronic systems.

2D Fe/Co-MOF/SOX cascade reactors for fast noninvasive detection of sarcosine level in prostate cancer urine

Sarcosine plays a key role in screening for early prostate cancer. However, the several already reported peroxidase mimics immobilized with sarcosine oxidase (SOX) utilized to detect uriary sarcosine still have some limitations such as complex synthesis process, using of expensive heavy metals to mimic enzyme activity and long color development time. Herein, an inexpensive peroxidase-like 2D Fe/Co-MOF nanosheet was prepared by a simple solvent modulation method. The resultant 2D Fe/Co-MOF nanosheets have strong peroxidase activity, with its Vmax value for H2O2 of 15.3 × 10-8 M/s, being 1.76 times that of HRP. Then, using the 2D Fe/Co-MOF as a peroxidase model for anchoring natural SOX to construct 2D Fe/Co-MOF/SOX , which can act as a cascade reactor for detection of sarcosine. Considering the above properties, a platform for the detection of sarcosine was built based on a colorimetric method. Because of presence of the high ratio of Fe2+ caused by the electron transfer from Co2+ to Fe3+, large specific surface area and plentiful active sites, 2D Fe/Co-MOF/SOX with TMB (3,3′,5,5′-tetramethylbenzidine) colorimetric reagent could have fast color development and can be applied conveniently and fastly in an early screening tool for prostate cancer patients. The sarcosine could be quantified by peroxidase activity with a detection range of 1–400 μM and a limit of detection (LOD) of 0.324 μM. More importantly, the average sarcosine concentration of 21.367 μM and 1.871 μM was detected in patient’s and normal urine (n = 5), respectively, which showed an excellent screening effect and a great potential in early prostate cancer.

1D supramolecular assembly-induced emission and colorimetry toward precise onsite mercury(II) detection

Manipulation of the one-dimensional (1D) supramolecular assembly of platinum(II) terpyridyl complex is promising for achieving precise onsite mercury(II) detection in complex environments, but still challenging. Herein, by systematic molecular design of platinum(II) terpyridyl complex, chloride-mediated 1D supramolecular assembly has been successfully achieved, exhibiting not only improved recognition ability but also dual-visual signal, with turn-on red luminescence and high-contrast color change from pale-yellow to orange-red. The probe also shows excellent selectivity and anti-interference properties, fast response rate (< 1 s) and low detection limit, stretching to 20.6 fg when incorporated in a hydrogel matrix. Structure insight for the dual-visual response shows that this high detection performance derives from the ancillary ligand of -NCS, which endows the increase of ion-association ability between platinum(II) terpyridyl complex and [HgCl4]2−, leading that 1D packing mode with strengthened Pt-Pt interactions. In all, this work highlights a new strategy of 1D supramolecular assembly construction for high performance detection of heavy metal ions.

Ship detection method based on attention guidance and multi-sample decision making

Single-stage target detection methods have the characteristics of fast training speed and short detection time. However, its feature pyramid network is difficult to suppress the background and noise information of SAR ship images, and the detection head has prediction errors. To address this problem, this paper proposes a detection model based on attention guidance and multi-sample decision for synthetic aperture radar ship detection. Firstly, an attention guidance network is proposed and added to the highest level of the feature pyramid to suppress background and noise interference, thereby improving the representation ability of features. Secondly, a multi-sample decision network is proposed to participate in the prediction of target position. This network alleviates the impact of prediction errors on detection results by increasing the number of samples output in the regression branch. Finally, a novel maximum likelihood loss function is designed. This loss function constructs a maximum likelihood function using the samples output from the multi-sample decision network, which is used to standardize the training of the decision network and further improve the accuracy of target positioning. Taking the RetinaNet network model as the baseline method, compared with the baseline method and the current advanced target detection methods, this method shows the highest detection accuracy on the ship detection dataset SSDD, with AP reaching 52.8%. Compared with the baseline method, the proposed method improves the AP evaluation index 3.4% ∼ 5.7%, and the training parameter Params only increases by 2.03 M, and the frame rate FPS only decreases 0.5Iter/s.

Internal-driven DNA nanowindmill: Multi-fulcrum mediated amplified rolling nanomachine for fast and ultrasensitive electrochemical detection of tau

Inspired by the wind driving windmill rolling to generate electrical energy, we have fabricated a multi-fulcrum mediated amplified rolling nanomachine (MFN) for Tau detection with the internal drive. The MFN backbone is DNA nanowindmill (DM) self-assembled from DNA, which is initially ineffective and finally activated into an effective DM through interaction with Tau followed by the magnetic separation. The DM is introduced to the electrode interface where it continues rolling under the drive of the multi-fulcrum and signal probe to achieve signal amplification. On one hand, the modular design and DM rolling feeds the impediment of signal transduction and amplification due to spatial hindrance during protein detection. On the other hand, it endows fast response and high sensitivity. The proposed MFN electrochemical biosensor is successfully applied to detect Tau ranging from 10 pg/mL to 2 μg/mL with low detection limit of 127 fg/mL, which is much lower than the pathological state concentration of Tau ∼400 pg/mL. Furthermore, our findings proves that MFN can successfully differentiate normal individuals from Alzheimer’s disease (AD) patients based on clinical samples analysis.

Feasibility of Nondestructive Soluble Sugar Monitoring in Tomato: Quantified and Sorted through ATR-FTIR Coupled with Chemometrics

As a fast detection method, Fourier transform infrared attenuated total reflection (ATR-FTIR) spectroscopy is seldom used for monitoring soluble sugars in crops. This study aimed to demonstrate the feasibility of leveraging ATR-FTIR coupled with chemometrics to quantify and sort the contents of soluble sugar in tomatoes. Firstly, 192 tomato samples were scanned using ATR-FTIR; subsequently, a quantitative model was developed using PLSR with selected wavelength variables as inputs. Finally, a classification model was estimated through probabilistic neural network (PNN) to determine the samples. The results indicated that ATR-FTIR had successfully captured the spectra from the cellular layers of tomatoes, resulting in a robust PLSR model created by 468 selected variables with a R² value of 0.86, a RMSEP of 0.71%, a ratio of performance to relative percent deviation (RPD) of 1.87, and a ratio of prediction to interquartile range (RPIQ) of 2.1. Meanwhile, the PNN model demonstrated a high rate correct (RC) of 92.17% in identifying whether the samples with a higher soluble sugar content than the limit of detection (LOD at 2.1%). Overall, ATR-FTIR coupled with chemometrics has proven effective for non-destructive determination of soluble sugars in tomatoes, offering new insights into internal monitoring techniques for crop quality assurance.

Image segmentation for pest detection of crop leaves by improvement of regional convolutional neural network

Pest detection is important for crop cultivation. Crop leaf is the main place of pest invasion. Current technologies to detect crop pests have constraints, such as low efficiency, storage demands and limited precision. Image segmentation is a fast and efficient computer-aided detection technology. High resolution image capture solidly supports the crucial processes in discerning pests from images. Study of analytical methods help parse information in the images. In this paper, a regional convolutional neural network (R-CNN) architecture is designed in combination with the radial bisymmetric divergence (RBD) method for enhancing the efficiency of image segmentation. As a special application of RBD, the hierarchical mask (HM) is produced to endorse detection and classification of the leaf-dwelling pests, offering enhanced efficiency and reduced storage requirements. Moreover, to deal with some mislabeled data, a threshold variable is introduced to adjust a fault-tolerant mechanism into HM, to generate a novel threshold-based hierarchical mask (TbHM). Consequently, the hierarchical mask R-CNN (HM-R-CNN) model and the threshold-based hierarchical mask R-CNN (TbHM-R-CNN) model are established to detect various types of healthy and pest-invasive crop leaves to select the regional image features that are rich in pest information. Then simple linear iterative clustering (SLIC) method is incorporation to finish the image segmentation for the classification of pest invasion. The models are tuned and optimized, then validated. The most optimized modeling results are from the TbHM-R-CNN model, with the classification accuracy of 96.2%, the recall of 97.5% and the F1 score of 0.982. Additionally, the HM-R-CNN model observed appreciable results second only to the best model. These results indicate that the proposed methodologies are well-suited for training and testing a dataset of plant diseases, offering heightened accuracy in pest classification. This study revealed that the proposed methods significantly outperform the existing techniques, marking a substantial improvement over current methods.

Modular Point-of-Need Tropane Alkaloid Detection at Regulatory Levels: Combining Solid-Liquid Extraction from Buckwheat with a Paper-Immobilized Liquid-Phase Microextraction and Immuno-Detection in Interconnectable 3D-Printed Devices.

Contamination with tropane alkaloids in cereals is expected to increase globally. However, current identification tools (e.g., liquid chromatography-mass spectrometry) for tropane alkaloids are time-consuming and expensive. Furthermore, their miniaturized alternatives lack sensitivity and robustness. Therefore, there is a pressing need for inexpensive and effective screening methods. Here, an on-site applicable modular workflow for tropane alkaloid detection in buckwheat is presented. The modular workflow combines paper microfluidics and interconnectable 3D-printed sample preparation tools and was evaluated for different tropane alkaloids, including atropine and scopolamine. Furthermore, integration with an indirect competitive lateral flow immunoassay (icLFIA) for atropine detection at relevant levels was demonstrated. In the modular workflow, to minimize matrix coextraction, tropane alkaloids were extracted from the milled buckwheat cereals by a mixture of alkaline aqueous and immiscible organic solvents (extraction recoveries: 66-79%). The tropane alkaloids were subsequently concentrated with a newly developed paper-immobilized liquid-phase microextraction (PI-LPME, extraction recoveries: 34-60%, concentration factor to immobilized solution in paper: 60-108×). After the PI-LPME, with an integrated 3D-printed setup, the tropane alkaloids were directly eluted (elution recoveries: 83-93%) and detected with the icLFIA. Digital read-out of the icLFIA, by employing a hand-held reader, enabled semiquantification of atropine (IC50 = 0.56 ng mL-1 in standard solutions). The modular workflow was validated by analyzing 24 blank and spiked buckwheat cereal samples with 5 and 10 μg kg-1 atropine. A cutoff value was established with an estimated false negative rate of 1% and estimated false positive rate of 0.68%. Therefore, the modular workflow can aid in fast, inexpensive, and on-site atropine detection by nonexperts, and when integrated with a scopolamine-specific icLFIA expanded toward scopolamine detection. Moreover, the developed sample extraction and concentration method (PI-LPME) is suitable for the analysis of many other compounds with pH-dependent polarity.

Quasi-visualizable detection of deep sub-wavelength defects in patterned wafers by breaking the optical form birefringence

Abstract In integrated circuit (IC) manufacturing, fast, nondestructive, and precise detection of defects in patterned wafers, realized by bright-field microscopy, is one of the critical factors for ensuring the final performance and yields of chips. With the critical dimensions of IC nanostructures continuing to shrink, directly imaging or classifying deep-subwavelength defects by bright-field microscopy is challenging due to the well-known diffraction barrier, the weak scattering effect, and the faint correlation between the scattering cross-section and the defect morphology. Herein, we propose an optical far-field inspection method based on the form-birefringence scattering imaging of the defective nanostructure, which can identify and classify various defects without requiring optical super-resolution. The technique is built upon the principle of breaking the optical form birefringence of the original periodic nanostructures by the defect perturbation under the anisotropic illumination modes, such as the orthogonally polarized plane waves, then combined with the high-order difference of far-field images. We validated the feasibility and effectiveness of the proposed method in detecting deep subwavelength defects through rigid vector imaging modeling and optical detection experiments of various defective nanostructures based on polarization microscopy. On this basis, an intelligent classification algorithm for typical patterned defects based on a dual-channel AlexNet neural network has been proposed, stabilizing the classification accuracy of λ/16-sized defects with highly similar features at more than 90%. The strong classification capability of the two-channel network on typical patterned defects can be attributed to the high-order difference image and its transverse gradient being used as the network's input, which highlights the polarization modulation difference between different patterned defects more significantly than conventional bright-field microscopy results. This work will provide a new but easy-to-operate method for detecting and classifying deep-subwavelength defects in patterned wafers or photomasks, which thus endows current online inspection equipment with more missions in advanced IC manufacturing.

Detection Methods for Pine Wilt Disease: A Comprehensive Review.

Pine wilt disease (PWD), caused by the nematode Bursaphelenchus xylophilus, is a highly destructive forest disease that necessitates rapid and precise identification for effective management and control. This study evaluates various detection methods for PWD, including morphological diagnosis, molecular techniques, and remote sensing. While traditional methods are economical, they are limited by their inability to detect subtle or early changes and require considerable time and expertise. To overcome these challenges, this study emphasizes advanced molecular approaches such as real-time polymerase chain reaction (RT-PCR), droplet digital PCR (ddPCR), and loop-mediated isothermal amplification (LAMP) coupled with CRISPR/Cas12a, which offer fast and accurate pathogen detection. Additionally, DNA barcoding and microarrays facilitate species identification, and proteomics can provide insights into infection-specific protein signatures. The study also highlights remote sensing technologies, including satellite imagery and unmanned aerial vehicle (UAV)-based hyperspectral analysis, for their capability to monitor PWD by detecting asymptomatic diseases through changes in the spectral signatures of trees. Future research should focus on combining traditional and innovative techniques, refining visual inspection processes, developing rapid and portable diagnostic tools for field application, and exploring the potential of volatile organic compound analysis and machine learning algorithms for early disease detection. Integrating diverse methods and adopting innovative technologies are crucial to effectively control this lethal forest disease.

Fast and on-site detection of fenthion in rice using core-shell Au@Ag nanoparticles and a portable Raman spectrometer

The residue of pesticide fenthion is a common issue in food safety, and how to conveniently detect it is a challenge in the food industry. Therefore, combined with a portable Raman spectrometer, a surface enhanced Raman spectroscopy (SERS) method based on a high-performance substrate Au@AgNPs had been designed for the rapid and sensitive detection of fenthion. Au@AgNPs can be induced to generate more hot spots to improve SERS signals because of their core-shell structure. The quantitative detection of fenthion was achieved under the optimal conditions: pH 7.0, 10−3 M CaCl2, 70 % Au@AgNPs concentration, and 4 min of incubation time. The results showed that the method had a wider detection range of 0.1–100 mg/L, a low detection limit (LOD) of 0.036 mg/L, excellent anti-interference ability, and reproducibility. In addition, the method was successfully validated with the actual sample rice. Therefore, the method had the advantages of being fast, on-site, and easy to use, which is useful for promoting its application.