Detection Of Contamination Research Articles

Real-time SERS sensing of highly toxic seawater contaminants using plasmonic silver assembled pyramidal/nanowire heterostructures

Hierarchically oriented plasmonic nanostructures have attracted an advanced molecular sensing platform for multifaceted applications. In the present work, we have attempted to fabricate ultrasensitive and cost-effective pyramidal/nanowire hetero arrays, hosted with silver nanoparticles for the effective surface-enhanced Raman spectroscopy (SERS) assisted detection of toxic water contaminants. Three-dimensionally oriented pyramidal/nanowire hetero arrays with enhanced surface area were fabricated by combining wet etching and metal-assisted chemical etching techniques. The regulation of structural features was achieved by controlling the process parameters during fabrication. Particularly, the evolution of morphological properties of the hybrid sensor was investigated in terms of nanowire aspect ratio and further analyzed in terms of SERS properties. The hetero arrays exhibited significant enhancement in Raman signal for sensing organic pollutants. Moreover, these hetero arrays with a nanowire length of ∼1 µm exhibited the highest signal enhancement. Further, the excellent reproducibility and reusability characteristics were also demonstrated for the fabricated sensors. Different cationic and anionic organic pollutants were employed for efficacy studies which showed detection limits ranging from femtomolar to picomolar levels. Finally, the real-time sensing of organic contaminants such as aniline was also investigated from seawater with an estimated enhancement factor of 8 × 108, indicating that as-fabricated hetero arrays can perform as outstanding SERS sensors for environmental remediation applications.

Design and optimization of a colorimetric biosensor using functionalized gold nanoparticles and bi-functional linker for agriculture and food industry

This study aims to develop a highly sensitive colorimetric detection system utilizing functionalized gold nanoparticles (f-AuNPs) and bifunctional linkers (BLs) for the detection of pathogens and contaminants in food matrices. The primary objective was to explore and optimize key parameters, including reaction time, system volume and the concentration of bifunctional linkers, in order to enhance the system’s detection capabilities. The core mechanism of the bi-functional linker-based detection system is based on the aggregation of streptavidin-functionalized AuNPs (stAuNPs), which varies in accordance with the concentration of the bifunctional linkers (BLs) and reflects the quantity of effective linkers (EALs) available in the system. The process comprises the binding of the targets and BLs, a reduction in EAL concentration, an increase in the BLs concentration required for the aggregation of the stAuNPs, and a shift in the range exhibiting a visible color change (REVC). In systems without a target, all BLs induce aggregation. In contrast, in systems with a target present, some BLs are bound by the target, reducing EAL concentration and altering the optimal BLs concentration for stAuNP aggregation. The system was shown to be capable of detecting protein in PBS at concentrations as low as 2 nM, as well as Salmonella at 101 CFU/mL. In whole milk, the detection limits were 20 nM for protein and 102 CFU/mL for Salmonella. The principal conclusion is that the BLs-based system provides a robust and adaptable platform for pathogen detection, even in complex food matrices, obviating the need for preprocessing. The findings demonstrate the system’s potential for practical applications in the fields of food safety and agricultural contaminant detection, offering clear visual signals and high sensitivity.

Selective and sensitive molecularly imprinted polymer-based electrochemical sensor for detection of deltamethrin

Electrochemical sensors have a broad range of industrial applications due to their sensitivity, speed, and cost-effectiveness. These sensors enable the continuous monitoring and control of critical parameters in various industrial processes. For instance, they are essential in food safety, environmental monitoring, biomedical applications, and pharmaceutical production. In the food industry, electrochemical sensors facilitate the rapid and reliable detection of contaminants and pathogens in food products, thus enhancing product quality and consumer safety. An electrochemical sensor was developed with the molecularly imprinted polymer (MIP) technique to detect deltamethrin with high sensitivity and selectivity. The sensor was fabricated by electrodeposition of Co3O4 on indium tin oxide (ITO), followed by electropolymerization of o-phenylenediamine with deltamethrin as a template molecule. The template molecules were then removed from the modified electrode by a methanol. The MIP-based electrochemical sensor exhibited high sensitivity and selectivity towards deltamethrin. Under the optimized conditions, the LOD values for the MIP/Co3O4/ITO electrode in the first and second linear regressions were 1.53 nM for linear range of 2.82 nM to 56.5 nM and 0.34 μM for linear range of 0.25 μM to 3.98 μM. Moreover, the LOD values for the NIP/Co3O4/ITO electrode in the first and second regressions were 2.43 nM for the linear range of 3.91 nM to 65.0 nM and 726.0 nM for the linear range of 0.023 μM to 4.5 μM. The developed electrochromic pesticide sensor, being an electrochemical-based molecularly imprinted polymer (MIP) sensor incorporating electrochromic materials, enables both target-specific pesticide detection and visual pesticide identification based on color changes dependent on pesticide concentration. Consequently, this system is more advantageous compared to electrochemical-based MIP sensors, as it provides both qualitative and quantitative determinations. The qualitative assessment aims to enhance the ease of use of the sensor, thereby increasing the potential for it to become a commercially viable product by reducing the need for instrumental devices.

Rapid detection and risk assessment of soil contamination at lead smelting site based on machine learning

A general prediction model for seven heavy metals was established using the heavy metal contents of 207 soil samples measured by a portable X-ray fluorescence spectrometer (XRF) and six environmental factors as model correction coefficients. The eXtreme Gradient Boosting (XGBoost) model was used to fit the relationship between the content of heavy metals and environment characteristics to evaluate the soil ecological risk of the smelting site. The results demonstrated that the generalized prediction model developed for Pb, Cd, and As was highly accurate with fitted coefficients (R2) values of 0.911, 0.950, and 0.835, respectively. Topsoil presented the highest ecological risk, and there existed high potential ecological risk at some positions with different depths due to high mobility of Cd. Generally, the application of machine learning significantly increased the accuracy of pXRF measurements, and identified key environmental factors. The adapted potential ecological risk assessment emphasized the need to focus on Pb, Cd, and As in future site remediation efforts.

Community-Engaged Research and the Use of Open Access ToxVal/ToxRef In Vivo Databases and New Approach Methodologies (NAM) to Address Human Health Risks From Environmental Contaminants.

The paper analyzes opportunities for integrating Open access resources (Abstract Sifter, US EPA and NTP Toxicity Value and Toxicity Reference [ToxVal/ToxRefDB]) and New Approach Methodologies (NAM) integration into Community Engaged Research (CEnR). CompTox Chemicals Dashboard and Integrated Chemical Environment with in vivo ToxVal/ToxRef and NAMs (in vitro) databases are presented in three case studies to show how these resources could be used in Pilot Projects involving Community Engaged Research (CEnR) from the University of California, Davis, Environmental Health Sciences Center. Case #1 developed a novel assay methodology for testing pesticide toxicity. Case #2 involved detection of water contaminants from wildfire ash and Case #3 involved contaminants on Tribal Lands. Abstract Sifter/ToxVal/ToxRefDB regulatory data and NAMs could be used to screen/prioritize risks from exposure to metals, PAHs and PFAS from wildfire ash leached into water and to investigate activities of environmental toxins (e.g., pesticides) on Tribal lands. Open access NAMs and computational tools can apply to detection of sensitive biological activities in potential or known adverse outcome pathways to predict points of departure (POD) for comparison with regulatory values for hazard identification. Open access Systematic Empirical Evaluation of Models or biomonitoring exposures are available for human subpopulations and can be used to determine bioactivity (POD) to exposure ratio to facilitate mitigation. These resources help prioritize chemical toxicity and facilitate regulatory decisions and health protective policies that can aid stakeholders in deciding on needed research. Insights into exposure risks can aid environmental justice and health equity advocates.

Enhanced Machine-Learning Flow for Microwave-Sensing Systems for Contaminant Detection in Food

Enhanced Machine-Learning Flow for Microwave-Sensing Systems for Contaminant Detection in Food

A comprehensive review: Advancements in nanomaterials on the risk prevention, detection, and elimination of mycotoxin contamination

Mycotoxins are poison that filamentous fungi generate under specific conditions. Mycotoxins in food and feed have a detrimental effect on both human and animal health, resulting in significant financial losses for agriculture sector. Despite the continuing advancement of traditional approaches, modern research trends favor novel alternatives. Therefore, it is crucial to prevent mycotoxin contamination, which has raised concerns around the globe. Recent advancements in the management of mycotoxin contamination have been possible by the application of promising new nanomaterials. Mycotoxins have negative impacts on human health, but nanotechnology methods appear to be viable, efficient, and affordable solutions. This review elucidates information on the incidence and toxicology of mycotoxins. Nanotechnology’s potential for removal of mycotoxins is mentioned briefly. Then, attention is directed on using newly developed nanomaterials to regulate mycotoxin contamination, such as testing, production, inhibition, adsorption, and removal of mycotoxins. The issues regarding the toxicity, incidence, and management of mycotoxins are tentatively presented along with potential prospects for using nanotechnology to remove mycotoxins from food and feed.

An europium complex assembled by a flexible derivative of imidazole-4,5-dicarboxylic acid as a fluorescence turn-off chemosensor to detect Cr(VI) anions

A new europium(III) CP [Eu(HBBIDA)(H2O)]n (1) (H4BBIDA = 1,1′-(butane-1,4-diyl)bis(1H-imidazole-4,5-dicarboxylic acid) was successfully constructed by hydrothermal synthesis method and detailed characterization was performed. Crystal structure analysis reveals that CP 1 presents a 2D layered structure composed of cage-like SBUs with a TiS2 topological network. CP 1 can maintain framework integrity in an air environment below 260 °C, as well as in a certain pH range of acid-base aqueous solutions and common organic reagents, showing better thermal and chemical stability. Fluorescence measurements show that CP 1 exhibited excellent sensing activities toward CrO42− and Cr2O72− anions through fluorescence quenching, and their average quenching Ksv values of 3.99 × 103 M−1 (CrO42−) and 4.36 × 103 M−1 (Cr2O72−) and the LOD of 3.30 × 10−6 M (CrO42−) and 3.02 × 10−6 M, which makes it very suitable for the detection of hexavalent chromium anions contaminants in aqueous solutions. Furthermore, possible sensing mechanisms have been studied and discussed in detail, and the results show that static photocompetitive absorption should be the primary cause for the fluorescence quenching of the sensor.

A Highly Sensitive Chitosan-Based SERS Sensor for the Trace Detection of a Model Cationic Dye.

The rapid detection of contaminants in water resources is vital for safeguarding the environment, where the use of eco-friendly materials for water monitoring technologies has become increasingly prioritized. In this context, the role of biocomposites in the development of a SERS sensor is reported in this study. Grafted chitosan was employed as a matrix support for Ag nanoparticles (NPs) for the surface-enhanced Raman spectroscopy (SERS). Chitosan (CS) was decorated with thiol and carboxylic acid groups by incorporating S-acetyl mercaptosuccinic anhydride (SAMSA) to yield CS-SAMSA. Then, Ag NPs were immobilized onto the CS-SAMSA (Ag@CS-SAMSA) and characterized by spectral methods (IR, Raman, NIR, solid state 13C NMR with CP-MAS, XPS, and TEM). Ag@CS-SAMSA was evaluated as a substrate for SERS, where methylene blue (MB) was used as a model dye adsorbate. The Ag@CS-SAMSA sensor demonstrated a high sensitivity (with an enhancement factor ca. 108) and reusability over three cycles, with acceptable reproducibility and storage stability. The Raman imaging revealed a large SERS effect, whereas the MB detection varied from 1-100 μM. The limits of detection (LOD) and quantitation (LOQ) of the biocomposite sensor were characterized, revealing properties that rival current state-of-the-art systems. The dye adsorption profiles were studied via SERS by fitting the isotherm results with the Hill model to yield the ΔG°ads for the adsorption process. This research demonstrates a sustainable dual-function biocomposite with tailored adsorption and sensing properties suitable for potential utility in advanced water treatment technology and environmental monitoring applications.

Real-time water quality monitoring using AI-enabled sensors: Detection of contaminants and UV disinfection analysis in smart urban water systems

This study introduces a novel method for assessing water quality, employing a cutting-edge sensor system integrated with artificial intelligence (AI) technologies. Addressing the global challenge of water scarcity and pollution, the research focuses on the innovative use of spectroscopic analysis for real-time water quality monitoring. The study evaluates the effectiveness of this system in distinguishing between clean, contaminated, and UV-disinfected water samples, highlighting its precision in detecting variations in water quality. Central to the research is the deployment of advanced machine learning algorithms, including Random Forest, Support Vector Machines (SVM), and Neural Networks, to process and classify spectral data. These models demonstrate remarkable accuracy in real-time classification, underscoring the synergy between AI and environmental science in addressing critical public health issues. Significantly, the study showcases the potential of UV disinfection in water treatment, as evidenced by the spectral changes observed in disinfected water samples. This aspect of the research emphasizes the role of spectral analysis in verifying the efficacy of water treatment processes. Overall, this study paves the way for more sophisticated and accessible water quality monitoring systems, offering a promising solution to one of the most pressing environmental challenges. The integration of AI and spectral analysis in this research offers a breakthrough in ensuring a safe water supply and effective water resource management. This study utilizes advanced machine learning algorithms, including Random Forest, Support Vector Machines (SVM), and Neural Networks, for water quality assessment. These models process and classify spectral data with high precision, highlighting variations in water quality.

Feasibility of biomass-based flexible and transparent AuNPs-acetylcellulose membrane for multifarious surface-enhanced Raman spectroscopy detection

BackgroundLignocellulosic biomass-based derivatives coupled with surface-enhanced Raman spectroscopy (SERS) technology have emerged as an appealing and indispensable tool in food safety and environmental monitoring for rapidly detecting trace contaminants like pesticide residues. The membrane material, serving as a substrate, ensures both sampling flexibility and test accuracy by directing the diffusion-adsorption process of the molecules. However, the existing membrane substrates, critical for the practical application of SERS, suffer from issues such as costly, intricate fabrication procedures, or restricted detection capabilities. ResultsHerein, we present a flexible, transparent, and biodegradable cellulose acetate membrane with gold nanoparticles (AuNPs) uniformly embedded, fabricated using a simple scraping method. This membrane achieved a limit of detection (LOD) of thiram pesticide in water at 10−8 g mL−1. The unique optical transparency of the substrates allowed for in-situ detection on surfaces, with an LOD of thiram reaching 30 ng cm−2. SignificanceFurthermore, SERS substrates made from corn stover-derived cellulose acetate enable the detection of various contaminants, highlighting their cost-effectiveness and eco-friendliness because of the abundance and low environmental impact of the raw materials.

Principles and challenges of whole cell microbial biosensors in the food industry.

Whole cell microbial biosensors (WCMB) are mostly genetically modified microorganisms used to detect target molecules as indicators of biological and chemical contaminants as well as in the identification of compounds of interest in the food industry. The specificity and sensitivity of these biosensors are achieved through the design of genetic circuits that make use of genetic sequences such as promoters, terminators, genes encoding regulatory proteins or reporter proteins, among others. Despite the advances of WCMBs for their application, significant challenges are faced, such as cell stability, regulatory restrictions, and the need to optimize response times so that they can be a competitive detection tool in the market. This review explores the technological progress, potential and limitations of WCMBs in the food industry, starting by reviewing the operating principles of biosensors. The importance of selecting appropriate chassis cells and the integration of recognition elements and transducers to maximize their effectiveness in the detection of contaminants and compounds of interest in the food industry is highlighted.

Low-Volume Electrochemical Sensor Platform for Direct Detection of Paraquat in Drinking Water

Direct testing of pesticide contaminants in drinking water is a challenge. Portable and sensitive sensor platforms are desirable to test water contaminants directly at farm and consumer levels. In this study, we have demonstrated the feasibility of an electrochemical sensor for the direct detection of paraquat (PQ) in drinking water samples. An immunoassay-based sensing platform was fabricated using PQ-specific antibody immobilized on the surface of the electrochemically reduced graphene oxide (rGO) modified screen-printed carbon electrode (rGO-SPCE). Using non-faradaic electrochemical impedance spectroscopy (EIS) as a detection tool, the sensor platform demonstrated a dynamic response for PQ concentration in drinking water ranging from 0.05 ng/mL to 72.9 ng/mL (0.19 to 243.8 nM), with a coefficient of determination (r2) of 0.997 and a limit of detection of 0.05 ng/mL (0.19 nM). Percentage recovery within ±20% error was obtained, and the sensor cross-reactivity test showed a selective response against glyphosate antigen. With the flexibility to use single-frequency EIS and low sample volume, the developed sensor demonstrated testing in water samples directly without any sample pre-processing. This low-volume electroanalytical sensor platforms can be translated into portable testing tools for the detection of various water contaminants.

Early Detection of Contamination with Microplastics by Changing the Phototaxis of Freshwater Mesozooplankton to Paired Photostimulation

Early Detection of Contamination with Microplastics by Changing the Phototaxis of Freshwater Mesozooplankton to Paired Photostimulation

Recent improvements in the novel approach to the fast detection of surface contaminations

Here, we present the idea behind a novel measurement system and the recent improvements in detecting surface contaminations using a tunable, very fast three-core quantum cascade laser (QCL) system. It is developed as a potential replacement for the old established double-wheel sampling system currently used to detect surface threats caused by hazardous, non-volatile chemical substances. The novel system design is based on an optical measurement principle, allowing standoff detection and identification, thus rendering ground contact unnecessary. One of the leading military requirements for a CBRN reconnaissance vehicle is a high marching speed; hence, this new system is designed to have an ultra-short acquisition time and an adequate spectral bandwidth for measurements in the mid-infrared (MIR). This innovative approach may replace the more cumbersome indirect detection techniques. We discuss the main advantages and evaluate possible application scenarios that we expect from this setup. Active IR backscatter spectroscopy generally permits the contactless identification of hazardous chemical substances on surfaces. Here, the design was optimized around an approximate measuring distance of decimeters. Using a monoaxial setup, the autofocus is less demanding than alternative approaches and even allows for rapid measurements of rough surfaces. We built the improved measurement system based on novel QCL modules that integrate micro-opto-electro-mechanical grating scanners (MOEMS) in an external cavity. These elaborate laser modules provide almost one kilohertz (kHz) spectral scan speeds and typically cover a spectral range of 200–300 cm-1, respectively. Furthermore, such modules achieve measurement times of as short as one millisecond per spectrum. The full spectral coverage of the system is realized by coupling several of such modules.

Study on Rapid, Quantitative, and Simultaneous Detection of Drug Residues and Immunoassay in Chickens.

Different levels of residual drugs can be monitored within a relatively safe range without causing harm to human health if the appropriate dosing methodology is considered and the drug withdrawal period is controlled during poultry and livestock raising. Antimicrobials are factors that can suppress the growth of microorganisms, and antibiotic residues in livestock farming have been considered as a potential cause of antimicrobial resistance in animals and humans. Antimicrobial drug resistance is associated with the capability of a microorganism to survive the inhibitory effects of the antimicrobial components. Antibiotic residue presence in chicken is a human health concern due to its negative effects on consumer health. Neglected aspects related to the application of veterinary drugs may threaten the safety of both humans and animals, as well as their environment. The detection of chemical contaminants is essential to ensure food quality. The most important antibiotic families used in veterinary medicines are β-lactams (penicillins and cephalosporins), tetracyclines, chloramphenicols, macrolides, spectinomycin, lincosamide, sulphonamides, nitrofuranes, nitroimidazoles, trimethoprim, polymyxins, quinolones, and macrocyclics (glycopeptides, ansamycins, and aminoglycosides). Antibiotic residue presence is the main contributor to the development of antibiotic resistance, which is considered a chief concern for both human and animal health worldwide. The incorrect application and misuse of antibiotics carry the risk of the presence of residues in the edible tissues of the chicken, which can cause allergies and toxicity in hypersensitive consumers. The enforcement of the regulation of food safety depends on efficacious monitoring of antimicrobial residues in the foodstuff. In this review, we have explored the rapid detection of drug residues in broilers.

Origami-based multifunctional sensing platform for sustainable detection of hazardous magnetic materials

Anthropogenic magnetite (AM) nanoparticles have been identified in the human brain and circulatory system, potentially linked to neurodegenerative and cardiovascular diseases. Specifically, AM and other magnetic nanocontaminants from industrial emissions and brake wear are hazardous components of particulate matter. Such contamination enriches urban soils with magnetite and other magnetic nanocontaminants, which can be absorbed by plants like rice and consequently enter the human body indirectly. Developing accurate and robust AM-sensing platforms is crucial, especially in areas where magnetic contamination threatens ecosystems and human health. Innovative materials, such as magnetoactive smart materials, are essential for creating sensors with specific, wireless, and adjustable magnetic properties for efficient detection and monitoring of soil contamination. This study presents an origami-based multifunctional sensing platform for sustainable detection of magnetic environmental contamination. Utilizing paper as its substrate for low-cost AM sensing, the device incorporates two wax/NdFeB magnets, four hydrophilic channels, and a hydrophilic analysis area, enclosed by hydrophobic wax. Through comprehensive analysis techniques including energy-dispersive X-ray spectroscopy, vibrating sample magnetometry, infrared spectroscopy, and photographic color changes, the device exhibited a detection limit below 156 μg. The platform's versatility, affordability, sustainability, and capacity for multi-analysis indicate promising prospects for developing economically equitable, user-friendly, mechanically robust, and flexible magnetic contamination sensing devices. These devices eliminate the need for complex machinery while delivering rapid, accurate, and precise results tailored to diverse environmental needs, thus promoting sustainable and safe societies.

Application of miniaturized most probable number method for bacterial detection in water samples: detection of multi-drug-resistant Ralstonia insidiosa in drinking water.

The detection of bacterial contamination in drinking water is essential for monitoring the spread of foodborne diseases. We developed a simple, portable, and low-cost method of mini most probable number (mini MPN) to semi-enumerate bacterial suspension in water as a drinking water analogue. In this study, there is no significant difference between mini MPN and the standard method, technique plate count (TPC), at 10 and 100 CFU/ml Klebsiella pneumoniae suspension with a P-value of 0.28. For the ease-of-use aspect of this method, we tested several variables to prove it can be mass-applied in society. The usage of a sterile-plastic pipette, sample inoculation conducted in a biosafety cabinet (BSC), the usage of a 3-month storage medium, and incubation temperature conducted at room temperature compared to aseptic standard laboratory technique showed P-value > 0.05. In a trial for this method, we used commercialized drinking water for bacterial enumeration and characterization. We found multi-drug resistant (MDR) Ralstonia insidiosa which was resistant to at least four antimicrobial classes, including aminoglycosides, penicillins, cephalosporin, and carbapenem. Vitek 2 Compact was used for bacterial identification and antimicrobial susceptibility testing. A virulence test in Omphisa fuscidentalis larvae showed R. insidiosa strain D had a low virulence.

An integrated micromachined flexible ultrasonic-inductive sensor for pipe contaminant multiparameter detection

Pipe contaminant detection holds considerable importance within various industries, such as the aviation, maritime, medicine, and other pertinent fields. This capability is beneficial for forecasting equipment potential failures, ascertaining operational situations, timely maintenance, and lifespan prediction. However, the majority of existing methods operate offline, and the detectable parameters online are relatively singular. This constraint hampers real-time on-site detection and comprehensive assessments of equipment status. To address these challenges, this paper proposes a sensing method that integrates an ultrasonic unit and an electromagnetic inductive unit for the real-time detection of diverse contaminants and flow rates within a pipeline. The ultrasonic unit comprises a flexible transducer patch fabricated through micromachining technology, which can not only make installation easier but also focus the sound field. Moreover, the sensing unit incorporates three symmetrical solenoid coils. Through a comprehensive analysis of ultrasonic and induction signals, the proposed method can be used to effectively discriminate magnetic metal particles (e.g., iron), nonmagnetic metal particles (e.g., copper), nonmetallic particles (e.g., ceramics), and bubbles. This inclusive categorization encompasses nearly all types of contaminants that may be present in a pipeline. Furthermore, the fluid velocity can be determined through the ultrasonic Doppler frequency shift. The efficacy of the proposed detection principle has been validated by mathematical models and finite element simulations. Various contaminants with diverse velocities were systematically tested within a 14 mm diameter pipe. The experimental results demonstrate that the proposed sensor can effectively detect contaminants within the 0.5−3 mm range, accurately distinguish contaminant types, and measure flow velocity.

Contamination of Vibrio parahaemolyticus in crayfish for sale.

Crayfish (Procambarus clarkii) are economically important freshwater crustaceans. With the growth of the crayfish industry, the associated food-safety risks should be seriously considered. Although Vibrio parahaemolyticus is commonly recognized as a halophilic foodborne pathogen associated with seafood, it has been found to be a major pathogen in crayfish-associated food poisoning cases. In this study, the V. parahaemolyticus contamination level in crayfish production-sale chain was investigated using crayfish and environmental samples collected from crayfish farms and markets. Serious V. parahaemolyticus contamination (detection rate of 66%) was found in the entire crayfish production-sale chain, while the V. parahaemolyticus contamination level of the market samples was extremely high (detection rate of 92%). The V. parahaemolyticus detection rate of crayfish surface was similar to that of whole crayfish, indicating that crayfish surface was important for V. parahaemolyticus contamination. The simulation experiments of crayfish for sale being contaminated by different V. parahaemolyticus sources were performed. All the contamination sources, containing V. parahaemolyticus-positive tank, water, and crayfish, were found to be efficient to contaminate crayfish. The crayfish tank displayed the most significant contaminating role, while the water seemed to inhibit the V. parahaemolyticus contamination. The contamination extent of the crayfish increased with the number of V. parahaemolyticus cells the tank carried and the contact time of the crayfish and the tank, but decreased with the time that the crayfish were maintained in the water. It was also confirmed that the crayfish surface was more susceptible to V. parahaemolyticus contamination than the crayfish intestine. Furthermore, the adsorption of V. parahaemolyticus onto the crayfish shell was analyzed. Over 90% of the V. parahaemolyticus cells were adsorbed onto the crayfish shell in 6 h, indicating a significant adsorption effect between V. parahaemolyticus and the crayfish shell. In conclusion, within a water-free sale style, the fresh crayfish for sale in aquatic products markets uses its shell to capture V. parahaemolyticus cells from the V. parahaemolyticus-abundant environments. The V. parahaemolyticus contamination in crayfish for sale exacerbates the crayfish-associated food-safety risk. This study sheds light on V. parahaemolyticus control and prevention in crayfish industry.