Rapid Detection Method Research Articles

A lightweight ground crack rapid detection method based on semantic enhancement

To address the issue of detecting complex-shaped cracks that rely on manual, which may result in high costs and low efficiency, this paper proposed a lightweight ground crack rapid detection method based on semantic enhancement. Firstly, the introduction of the Context Guided Block module enhanced the YOLOv8 backbone network, improving its feature extraction capability. Next, the incorporation of GSConv and VoV-GSCSP was introduced to construct a lightweight yet efficient neck network, facilitating the effective fusion of information from multiple feature maps. Finally, the detection head achieved more precise target localization by optimizing the probability around the labels. The proposed method was validated through experiments on the public dataset RDD-2022. The experimental results demonstrate that our method effectively detects cracks. Compared to YOLOv8, the model parameters have been reduced by 73.5 %, while accuracy, F1 score, and FPS have improved by 6.6 %, 4.3 %, and 116, respectively. Therefore, our proposed method is more lightweight and holds significant application value.

Detection of Carbapenemase Production in Klebsiella pneumoniae Isolates by Rapid Carbapenemase Detection Method in an OXA-48 Endemic Region

Background: Carbapenem resistance is one of the major global public health threats caused by the overuse or misuse of carbapenems, which are often the last resort in treating multidrug-resistant infections. Today, timely detection of carbapenemase-producing organisms is critical for infection control. Therefore, rapid and accurate detection of carbapenemases using a cost-effective method with high sensitivity and specificity is essential to guide appropriate treatment and prevent further spread. Objectives: This study aimed to evaluate the sensitivity of the newly developed rapid carbapenemase detection method (rCDM) with isolates whose resistance genes were determined by molecular methods and to compare it with the modified carbapenem inactivation method (mCIM), whose diagnostic performance characteristics are well-defined. Methods: A total of 130 Klebsiella pneumoniae isolates (65 carbapenem-susceptible, 65 carbapenem-resistant) from various clinical specimens were included in the study. Identification and antibiotic susceptibility testing of the isolates was performed using the BD Phoenix™ automated system. Phenotypic carbapenemase detection methods, mCIM and rCDM, were studied for all carbapenem-sensitive and carbapenem-resistant isolates with known resistance genes. Results: Among carbapenem-resistant K. pneumoniae isolates, blaOXA-48 was the most frequently detected resistance gene (78%). Using PCR results as the reference method, the sensitivity of rCDM was 100%. When rCDM and mCIM results were compared, both phenotypic methods were 100% compatible, with no statistically significant difference observed. Conclusions: Rapid carbapenemase detection method is a rapid and reliable phenotypic carbapenemase detection method with high sensitivity. Its ease of use and interpretation make it suitable for use in laboratories with limited resources. Additionally, its excellent performance in detecting common carbapenemase types and high consistency with mCIM support its utility in various laboratory settings without requiring additional materials or technical infrastructure.

Combination of Capture-SELEX and Post-SELEX for procymidone-specific aptamer selection and broad-specificity aptamer discovery, and development of aptamer-based lateral flow assay

BackgroundDue to its wide application, procymidone has become one of the pesticides with high detection rates in supervision and sampling. Therefore, it is necessary to establish a rapid and efficient method for the detection of procymidone. However, an important bottleneck restricting the development of rapid detection methods of procymidone is that its specific recognition elements are rarely reported. In this work, Capture-SELEX and post-SELEX were used in aptamer screening, and the obtained aptamers were used to construct an aptamer-based lateral flow assay (LFA). ResultsFirstly, a specific aptamer Seq15 was obtained for procymidone by Capture-SELEX, and its dissociation constant (Kd) was 24.22 nM. Secondly, post-SELEX was used to analyze and modify Seq15 to improve its performance, and the Kd of the truncated sequence Seq15-2 was 21.28 nM. In addition to this, the broad-specificity aptamer Seq17-1 was obtained via post-SELEX. Seq17-1 could broadly recognize dicarboximide fungicides (procymidone, iprodione, chlozolinate, dimethachlon and vinclozolin) and their metabolic derivative (3,5-dichloroaniline). Finally, the specific aptamer-based LFA of procymidone was constructed, and the limit of detection (LOD) was 0.79 ng/mL. Meanwhile, the LODs of dicarboximide fungicides and their metabolic derivative were 0.62, 0.64, 0.71, 0.69, 0.64 and 0.66 ng/mL, respectively. The above LFAs were highly specific and stable, and had been successfully used for the detection of vegetable samples. SignificanceUnder the combination of Capture-SELEX and Post-SELEX, this study not only provides specific recognition elements for rapid detection of procymidone, but also provides new ideas for the discovery of broad-specificity aptamers. Combining broad-specificity primary detection and single-specificity quantification, a composite aptamer-based LFA detection platform has been developed, which significantly improves detection efficiency.

A New Dual Fluorescence Method for Rapid Detection of Infectious Bronchitis Virus at Constant Temperature.

Infectious bronchitis virus (IBV) causes infectious bronchitis in chicken, an acute, highly contagious respiratory infection. Because of genetic mutations and recombination, IBV forms many subtypes, which makes it difficult to treat the disease and apply commercial vaccines. Therefore, to detect IBV in time and stop the virus from spreading, a novel and convenient IBV detection technology based on reverse transcription recombinase-aided amplification (RT-RAA) was established in this study. According to the S1 gene of IBV CH I-V and Mass genotypes and S1 gene of IBV CH VI genotype, a set of optimal primers were designed and selected to establish a real-time dual fluorescence RT-RAA method. The lowest detection line was 10 copies/μL of RNA molecules and the method exhibited no cross-reactivity with avian reticuloendotheliosis virus (REV), infectious bursal disease virus (IBDV), avian leukosis virus (ALV), Newcastle disease virus (NDV), chicken infectious anemia virus (CIAV), infectious laryngotracheitis virus (ILTV), Marek's disease virus (MDV), and H9N2 avian influenza virus (H9N2), demonstrating high specificity. When compared to qPCR detection results, our method achieved a sensitivity of 96.67%, a specificity of 90%, and a Kappa value of 0.87 for the IBV CH I-V and Mass genotypes, and achieved a sensitivity of 100%, a specificity of 97.73%, and a Kappa value of 0.91 for the IBV CH VI genotype.

Rapid Mold Detection in Chinese Herbal Medicine Using Enhanced Deep Learning Technology.

Mold contamination poses a significant challenge in the processing and storage of Chinese herbal medicines (CHM), leading to quality degradation and reduced efficacy. To address this issue, we propose a rapid and accurate detection method for molds in CHM, with a specific focus on Atractylodes macrocephala, using electronic nose (e-nose) technology. The proposed method introduces an eccentric temporal convolutional network (ETCN) model, which effectively captures temporal and spatial information from the e-nose data, enabling efficient and precise mold detection in CHM. In our approach, we employ the stochastic resonance (SR) technique to eliminate noise from the raw e-nose data. By comprehensively analyzing data from eight sensors, the SR-enhanced ETCN (SR-ETCN) method achieves an impressive accuracy of 94.3%, outperforming seven other comparative models that use only the response time of 7.0 seconds before the rise phase. The experimental results showcase the ETCN model's accuracy and efficiency, providing a reliable solution for mold detection in Chinese herbal medicine. This study contributes significantly to expediting the assessment of herbal medicine quality, thereby helping to ensure the safety and efficacy of traditional medicinal practices.

Establishment and Validation of a Multiplex PCR Detection System for the Identification of Six Common Edible Meat Components.

To establish a rapid, accurate, and sensitive multiplex PCR detection method for the simultaneous identification of the six common edible meats (beef, lamp, chicken, pork, goose, duck), and to evaluate its application value in meat adulteration identification. Based on complete mitochondrial genomic sequences of six species in the GenBank database, DNA sequences (cattle：16S rRNA; sheep：COX-1; chickens：Cytb; pig：COX-1; goose：NADH2; duck：16S rRNA) with intra-species conservation and inter-species specificity were screened, and species-specific primers were designed to construct a multiplex PCR detection system that can simultaneously detect the meat of six common species. The species specificity, sensitivity and reproducibility of the system were studied, and the simulated mixture sample detection was performed. This study successfully constructed a multiplex PCR detection system that can detect the meats of six common species simultaneously. The system was not effective in DNA amplification of non-target species. When the DNA template sizes were 0.062 5-2 ng/μL, the amplified products of all six species could be detected. The duck component was still detected when the mixing ratio of duck and beef was as low as 0.5%. This study constructs and establishes a multiplex PCR detection system with strong specificity, high sensitivity, and good reproducibility. It can accurately identify the components of animal origin in common edible meats and provide a simple and practical method for identifying adulteration of common edible meats and meat products in China.

Layer-By-Layer Designed Spark-Type AuCuPt Alloy with Robust Broadband Absorption to Enhance Sensitivity in Flexible Detection of Estriol by a Lateral Flow Immunoassay.

Excessive intake of estrogen poses significant health risks to the human body; hence, there is a necessity to develop rapid detection methods to monitor its levels of addition. Gold nanoparticles (AuNPs), commonly utilized as colorimetric signal labels, find extensive application in lateral flow immunoassay (LFIA). However, the detection sensitivity of traditional AuNPs-LFIA is typically constrained by low molar extinction coefficients and reliance on a single signal. Herein, in this work, unique spark-type AuCuPt nanoflowers modified with tannic acid (AuCuPt@TA) were precisely designed by reasonable layer-by-layer element composition and green modification. The obtained AuCuPt displays robust broadband absorption spanning the visible to near-infrared spectrum, showcasing a notable molar extinction coefficient of 2.38 × 1012 M-1 cm-1 and a photothermal conversion efficiency of 48.5%. Based on this, selecting estriol (E3) as a model analyte, colorimetric/photothermal dual-signal LFIA (CLFIA and PLFIA) was developed. Limits of detection (LOD) of the CLFIA and PLFIA were achieved at 0.033 ng mL-1 and 0.021 ng mL-1, respectively, which represent a 9.3- and 14.6-fold improvement compared to the visual LOD of AuNPs-LFIA. Moreover, the application feasibility of the immunoassay was further evaluated in the milk and pork with satisfactory recoveries ranging from 86.21% to 117.91%. Thus, this work has enhanced the performance of LFIA for E3 detection and exhibited enormous potential for other sensing platform construction.

A pretreatment-free and eco-friendly rapid detection for mycotoxins in edible oils based on magnetic separation technique

Pretreatment steps of current rapid detection methods for mycotoxins in edible oils not only restrict detection efficiency, but also produce organic waste liquid to pollute environment. In this work, a pretreatment-free and eco-friendly rapid detection method for edible oil is established. This proposed method does not require pretreatment operation, and automated quantitative detection could be achieved by directly adding oil samples. According to polarity of target molecules, the content of surfactant in reaction solutions could be adjusted to achieve the quantitative detection of AFB1 in peanut oil and ZEN in corn oil. The recoveries are between 96.5%–110.7% with standard deviation <10.4%, and the limit of detection is 0.17 μg/kg for AFB1 and 4.91 μg/kg for ZEN. This method realizes full automation of the whole chain detection, i.e. sample in-result out, and is suitable for the on-site detection of batches of edible oils samples.

A facile fabricated electrochemical sensor based on dual-shell Sn4CoO9.3 microspheres for simultaneous detection of Pb2+ and Hg2+

The presence of heavy metal ions poses significant health hazards to humans, necessitating an urgent demand for a rapid and efficient detection method. Electrochemical sensing offers a promising approach that enables high sensitivity and selectivity in detecting heavy metal ions. In this study, a novel bimetallic oxide, Sn4CoO9.3, with dual shells and multiple pores was developed by a simple template-free method. The unique dual-shell structure and multiple pores greatly enhance its adsorption capacity for heavy metal ions, while the mesoporous pores facilitate electrolyte and ion diffusion. A sensor based on Sn4CoO9.3/CS-ERGO with the excellent electroactive properties and outstanding absorption capacity was fabricated for the simultaneous detection of different heavy metal ions (Pb2+ and Hg2+). By harnessing the benefits of Sn4CoO9.3 and graphene sheets, the sensors that were created exhibited exceptional sensitivity, stability, and minimal interference when detecting Pb2+ and Hg2+ in real samples. These results suggest promising prospects for utilizing the sensor in environmental water quality monitoring applications.

Inclusion of Anti-COVID-19 Related Research Highlights into the Classroom: Why, What and How?

Bringing recent research highlights into the classroom has drawn an intense amount of interest because it can show students what are being discovered, especially those anti-COVID-19 works. Therefore, we aim to study the feasibility and share the thoughts on the relevant innovation in education during the pandemic of COVID-19. To do so, we first examine participants’ (consisting of bachelor, master and PhD students at different universities) attitudes of towards the inclusion of anti-COVID-19 related research highlights into the classroom via the survey questionnaires. It is found that students mostly preferred such an innovative move. We then feature multiple anti-COVID-19 research highlights, ranging from the anti-fog materials, new inhalation treatment approach, antiviral 2D materials, efficiency enhancement of mask materials, rapid detection methods to vaccine development. Next, we elaborate the class design such as contents, objectives, difficulty level and learning outcomes with two specific examples. Lastly, suggestions for better implementation of including anti-COVID-19 related research highlights into the classroom are provided. The findings of this study can help universities and teachers to further improve the class design, thereby creating strong anti-COVID-19 related theory-practice linkages for students.

Study on Rapid Non-Destructive Detection Method of Corn Freshness Based on Hyperspectral Imaging Technology.

(1) Background: To achieve the rapid, non-destructive detection of corn freshness and staleness for better use in the storage, processing and utilization of corn. (2) Methods: In this study, three varieties of corn were subjected to accelerated aging treatment to study the trend in fatty acid values of corn. The study focused on the use of hyperspectral imaging technology to collect information from corn samples with different aging levels. Spectral data were preprocessed by a convolutional smoothing derivative method (SG, SG1, SG2), derivative method (D1, D2), multiple scattering correction (MSC), and standard normal transform (SNV); the characteristic wavelengths were extracted by the competitive adaptive reweighting method (CARS) and successive projection algorithm (SPA); a neural network (BP) and random forest (RF) were utilized to establish a prediction model for the quantification of fatty acid values of corn. And, the distribution of fatty acid values was visualized based on fatty acid values under the corresponding optimal prediction model. (3) Results: With the prolongation of the aging time, all three varieties of corn showed an overall increasing trend. The fatty acid value of corn can be used as the most important index for characterizing the degree of aging of corn. SG2-SPA-RF was the quantitative prediction model for optimal fatty acid values of corn. The model extracted 31 wavelengths, only 12.11% of the total number of wavelengths, where the coefficient of determination RP2 of the test set was 0.9655 and the root mean square error (RMSE) was 3.6255. (4) Conclusions: This study can provide a reliable and effective new method for the rapid non-destructive testing of corn freshness.

Surface-Imprinted Polysiloxane with Recognition Ability Based on an ITO Layer for Rapid Detection of Fusarium oxysporum f. sp. cubense by the Naked Eye.

Direct observation by the naked eye of fluorescence-stained microbes adsorbed on surface imprinted polymers (SIPs) is highly challenging and limited by speed, accuracy and the semiquantitative nature of the method. In this study, we tested for the presence of spores of Fusarium oxysporum f. sp. cubense race 4 (Foc4), which cause severe banana Fusarium wilt disease and reduces the area of banana plants. This kind of spore can become dormant in soil, which means that the detection of secreted molecules (molecular imprinting) in soil may be inaccurate; detection methods such as polymerase chain reaction (PCR) and Raman spectroscopy are more accurate but time-consuming and inconvenient. Therefore, a semiquantitative and rapid SIP detection method for Foc4 was proposed. Based on the ITO conductive layer, a reusable and naked-eye-detectable Foc4-PDMS SIP film was prepared with a site density of approximately 9000 mm-2. Adsorption experiments showed that when the Foc4 spore concentration was between 104 to 107 CFU/mL, the number of Foc4 spores adsorbed and the fluorescence intensity were strongly correlated with the concentration and could be fully distinguished by the naked eye after fluorescence staining. Adsorption tests on other microbes showed that the SIP film completely recognized only the Foc series. All the results were highly consistent with the naked-eye observations after fluorescence staining, and the results of the Foc4-infected soil experiment were also close to the ideal situation. Taken together, these results showed that Foc4-PDMS SIPs have the ability to rapidly and semiquantitatively detect the concentration of Foc in soil, which can provide good support for banana cultivation. This method also has potential applications in the detection of other fungal diseases.

Establishment and evaluation of a qPCR method for the detection of pfmdr1 mutations in Plasmodium falciparum, the causal agent of fatal malaria

Drug resistance surveillance is a major integral part of malaria control programs. Molecular methods play a pivotal role in drug resistance detection and related molecular research. This study aimed to develop a rapid and accurate detection method for drug resistance of Plasmodium falciparum (P. falciparum). A quantitative real-time PCR (qPCR) assay has been developed that identifies the mutation at locus A256T in the P.falciparum multi-drug resistance(pfmdr1) gene producing amino acid change at position 86. The results of 198 samples detected by qPCR were consistent with nested PCR and sequencing, giving an accuracy of 94.3%. The sensitivity, specificity, positive and negative predictive value of qPCR were 85.7%, 97.6%, 90.0% and 96.4%, respectively. The results of qPCR are basically consistent with the nested PCR, which is expected to replace the nested PCR as a new molecular biological method for drug resistance detection, providing reliable technical support for global malaria prevention and control.

Colorimetric detection of neomycin sulfate in serum based on ultra-small gold nanoparticles with peroxidase-like activity

Neomycin sulfate (NEO) is a kind of aminoglycoside antibiotics. Because of its strong ototoxicity, nephrotoxicity and other side effects, its content in the body should be strictly monitored during use. In this paper, a rapid colorimetric detection method for NEO based on ultrasmall polyvinylpyrrolidone modified gold nanoparticles (PVP/Au NPs) with peroxidase-like activity was developed. Firstly, ultra small PVP/Au NPs with weak peroxidase-like activity were synthetized. When they were mixed with NEO, strong hydrogen bonds were formed between NEO and PVP, resulting in the aggregation of PVP/Au NPs, and the aggregated PVP/Au NPs showed stronger peroxidase-like activity. Therefore, rapid colorimetric detection of NEO was achieved by utilizing the enhanced peroxidase-like activity mechanism caused by the aggregation of ultra small PVP/Au NPs. The naked eye detection limit of this method is 50 nM. Within the range of 1 nM-300 nM, there was a good linear relationship between NEO concentration and the change in absorbance intensity of PVP/Au NPs-H2O2-TMB solution at 652 nm, with the regression curve of y = 0.0045x + 0.0525 (R2 = 0.998), and the detection limit is 1 nM. In addition, this method was successfully applied to the detection of NEO in mouse serum. The recoveries were 104.4 % −107.6 % compared with HPLC assay results, indicating that this method for NEO detection based on PVP/Au NPs has great potential in actual detection of NEO in serum.

A novel electrochemical sensor based on Pb0.95Ti0.05O-decorated nitrogen self-doped biochar for highly sensitive and selective detection of carvacrol

Carvacrol has widely used in the field of medicine and sterilization. Thus, developing reliable, efficient, and rapid method for the carvacrol detection is meaningful. In this study, we fabricated a carvacrol sensor based on Pb0.95Ti0.05O decorated with nitrogen self-doped biochar (Pb0.95Ti0.05O/NC), which was synthesized by hydrothermal and carbonization of soybean root nodules, respectively. The prepared sensor exhibited better electrochemical performances than that of PbO, Pb0.95Ti0.05O, NC. The fabricated Pb0.95Ti0.05O/NC sensor exhibited a wide detection range from 0.1 to 190 µM and a low detection limit of 0.054 µM. The excellent electrochemical performances were attributed to the increased electron density and electron mobility after the introduction of Ti doping and nitrogen doped carbon. Mechanistic studies revealed that the electrochemical oxidation of carvacrol involves one electron and was limited by adsorption process. The sensor demonstrated good stability, anti-interference, reproducibility, and good repeatability. Moreover, the sensor displayed excellent recovery rates ranging from 92.7% to 102.9% when applied to the detection of carvacrol in real samples, making it a promising candidate for practical applications in the sensitive detection of carvacrol.

A rapid on-site visualization platform based on RPA coupled with CRISPR-Cas12a for the detection of genetically modified papaya ‘Huanong No.1’

The Papaya ringspot virus (PRSV)-resistant genetically modified (GM) papaya ‘Huanong No.1’ has been certified as safe for consumption and widely planted in China for about 18 years. To protect consumers' rights and facilitate government supervision and monitoring, it is necessary to establish a simple, rapid, and specific detection method for ‘Huanong No.1’. Herein, we developed a platform based on recombinase polymerase amplification (RPA) coupled with CRISPR-Cas12a for the detection of ‘Huanong No.1’. The RPA-CRISPR-Cas12a platform was found to have high specificity, with amplification signals only present in ‘Huanong No.1’. Additionally, the platform was highly sensitive, with a limit of detection (LOD) of approximately 20 copies. The detection process was fast and could be completed in less than 1 h. This novel platform enables the rapid on-site visualization detection of ‘Huanong No.1’, eliminating dependence on laboratory conditions and specialized instruments, and can serve as a technical reference for the rapid detection of other GM plants.

PACRAT: pathogen detection with aptamer-observed cascaded recombinase polymerase amplification-in vitro transcription.

The SARS-CoV-2 pandemic underscored the need for early, rapid, and widespread pathogen detection tests that are readily accessible. Many existing rapid isothermal detection methods use the recombinase polymerase amplification (RPA), which exhibits polymerase chain reaction (PCR)-like sensitivity, specificity, and even higher speed. However, coupling RPA to other enzymatic reactions has proven difficult. For the first time, we demonstrate that with tuning of buffer conditions and optimization of reagent concentrations, RPA can be cascaded into an in vitro transcription reaction, enabling detection using fluorescent aptamers in a one-pot reaction. We show that this reaction, which we term PACRAT (pathogen detection with aptamer-observed cascaded recombinase polymerase amplification-in vitro transcription) can be used to detect SARS-CoV-2 RNA with single-copy detection limits, Escherichia coli with single-cell detection limits, and 10-min detection times. Further demonstrating the utility of our one-pot, cascaded amplification system, we show PACRAT can be used for multiplexed detection of the pathogens SARS-CoV-2 and E. coli, along with multiplexed detection of two variants of SARS-CoV-2.

Mango Leaves (Mangifera indica)-Derived Highly Florescent Green Graphene Quantum Dot Nanoprobes for Enhanced On-Off Dual Detection of Cholesterol and Fe2+ Ions Based on Molecular Logic Operation.

In the present study, we have engineered a molecular logic gate system employing both Fe2+ ions and cholesterol as bioanalytes for innovative detection strategies. We utilized a green-synthesis method employing the mango leaves extract to create fluorescent graphene quantum dots termed "mGQDs". Through techniques like HR-TEM, i.e., high-resolution transmission electron microscopy, Raman spectroscopy, and XPS, i.e., X-ray photoelectron spectroscopy, the successful formation of mGQDs was confirmed. The photoluminescence (PL) characteristics of mGQDs were investigated for potential applications in metal ion detection, specifically Fe2+ traces in water, by using fluorescence techniques. Under 425 nm excitation, mGQDs exhibited emission bands at 495 and 677 nm in their PL spectrum. Fe2+-induced notable quenching of mGQDs' PL intensity decreased by 97% with 2.5 μM Fe2+ ions; however, adding 20 mM cholesterol resulted in a 92% recovery. Detection limits were established through a linear Stern-Volmer (S-V) plot at room temperature, yielding values of 4.07 μM for Fe2+ ions and 1.8 mM for cholesterol. Moreover, mGQDs demonstrated biocompatibility, aqueous solubility, and nontoxicity, facilitating the creation of a rapid nonenzymatic cholesterol detection method. Selectivity and detection studies underscored mGQDs' reliability in cholesterol level monitoring. Additionally, a molecular logic gate system employing Fe2+ metal ions and cholesterol as a bioanalyte was established for detection purposes. Overall, this research introduces an ecofriendly approach to craft mGQDs and highlights their effectiveness in detecting metal ions and cholesterol, suggesting their potential as versatile nanomaterials for diverse analytical and biomedical applications.

Deep learning model based on molecular spectra to determine chlorpyrifos residues in corn oil

This study presents a novel molecular spectroscopy depth fusion strategy for rapid detection of residual chlorpyrifos in corn oil. Raman and Fourier transform near-infrared (FT-NIR) spectrometers were employed to collect contaminated corn oil sample spectra. The multi-spectral fusion enhances spectral features, improving data reliability and outcomes. The research employed low and mid-level fusion strategies alongside a one-dimensional convolutional neural network (1D-CNN) for modeling. The mid-level data fusion model outperformed the low-level model, achieving a relative percent deviation (RPD) of 11.6517 and a prediction coefficient of determination (RP2) of 0.9874. The results indicate that the mid-level fusion strategy is suitable for multivariate model calibration of Raman and FT-NIR, providing a rapid detection method for pesticide residues in edible oils. This study offers a method reference for fusion strategies in molecular spectroscopy and expands the application scope of deep learning in spectroscopy.

The Establishment of the Multi-Visual Loop-Mediated Isothermal Amplification Method for the Rapid Detection of Vibrio harveyi, Vibrio parahaemolyticus, and Singapore grouper iridovirus

Groupers are valuable economic fish in the southern sea area of China, but the threat of disease is becoming more and more serious. Vibrio harveyi, V. parahaemolyticus, and Singapore grouper iridovirus (SGIV) are three important pathogens that cause disease in groupers, and infection with either a single one or a mix of these pathogens poses a serious threat to the healthy development of grouper culture. To enhance the rapid diagnosis and screening in the early stages, it is necessary to develop rapid detection methods of these pathogens. To simultaneously and rapidly detect the three pathogens, in this study, we utilized the TolC of V. harveyi, DNAJ of V. parahaemolyticus, and RAD2 of SGIV as the target genes and established a triple visual loop-mediated isothermal amplification (LAMP) method. This LAMP method showed a detection time as fast as 30 min and a high sensitivity of 100 fg/μL. Moreover, this method exhibited strong specificity and no cross-reaction with seven types of Vibrio and Staphylococcus aureus, as well as five common viruses in aquatic animals. Then, the LAMP products were enzymically cut, and three characteristic strips were used to identify the pathogen species. The results of the clinical trials demonstrated that the method could accurately and specifically detect V. harveyi, V. parahaemolyticus, and SGIV in grouper tissues. In summary, this study successfully established a triple visual LAMP rapid detection method for V. harveyi, V. parahaemolyticus, and SGIV. The method offers several advantages including simple equipment, easy operation, rapid reaction, high specificity, high sensitivity, and visual results. It is suitable for the early and rapid diagnosis of groupers infected with V. harveyi, V. parahaemolyticus, and SGIV, thereby providing useful technical support for further application in the large-scale disease surveillance of aquaculture animals.