Method For Detection Of Contamination Research Articles

Rapid Detection of Cleanliness on Direct Bonded Copper Substrate by Using UV Hyperspectral Imaging.

In the manufacturing process of electrical devices, ensuring the cleanliness of technical surfaces, such as direct bonded copper substrates, is crucial. An in-line monitoring system for quality checking must provide sufficiently resolved lateral data in a short time. UV hyperspectral imaging is a promising in-line method for rapid, contactless, and large-scale detection of contamination; thus, UV hyperspectral imaging (225-400 nm) was utilized to characterize the cleanliness of direct bonded copper in a non-destructive way. In total, 11 levels of cleanliness were prepared, and a total of 44 samples were measured to develop multivariate models for characterizing and predicting the cleanliness levels. The setup included a pushbroom imager, a deuterium lamp, and a conveyor belt for laterally resolved measurements of copper surfaces. A principal component analysis (PCA) model effectively differentiated among the sample types based on the first two principal components with approximately 100.0% explained variance. A partial least squares regression (PLS-R) model to determine the optimal sonication time showed reliable performance, with R2cv = 0.928 and RMSECV = 0.849. This model was able to predict the cleanliness of each pixel in a testing sample set, exemplifying a step in the manufacturing process of direct bonded copper substrates. Combined with multivariate data modeling, the in-line UV prototype system demonstrates a significant potential for further advancement towards its application in real-world, large-scale processes.

Comparative analysis of sampling and detection methods for fungal contamination on common healthcare environment surface materials.

We evaluated sampling and detection methods for fungal contamination on healthcare surface materials, comparing the efficacy of foam sponges, flocked swabs, and Replicate Organism Detection And Counting (RODAC) plates alongside culture-based quantification and quantitative polymerase chain reaction (qPCR). Findings indicate that sponge sampling and qPCR detection performed best, suggesting a foundation for future studies aiming to surveillance practices for fungi.

Flexible Biosensors for Food Pathogen Detection

AbstractFood contamination poses a significant threat to public health, the economy, and human health worldwide, occurring at any stage of the food supply chain, from farm to fork. Efficient and effective real‐time monitoring methods for the early identification and rapid detection of pathogen contamination are critical to preventing possible food safety issues. In the past decade, flexible electrochemical biosensors have rapidly expanded in the detection of foodborne pathogens, owing to their ability to function well at biological interfaces that may be soft, intrinsically curvy, irregular, or deformable. The most important features of flexible sensors are their flexibility, multifunctionality, low cost, and good compatibility with solution processing methods. These properties enable flexible sensors to be used in various applications, including smart packaging. This review focuses on the materials, structures, and fabrication methods of flexible biosensors, as well as their applications in food safety monitoring. It examines recent advances in electrochemical immunosensors and aptasensors for pathogen detection, with a focus on their key components and their application in food analysis, while critically examining their current trends, particularly in conjunction with nanomaterials such as carbon nanotubes, graphene, gold nanoparticles, Indium tin oxide, etc. Finally, current issues and prospects for the future are discussed.

Evaluation of bacterial safety approaches of platelet blood concentrates: bacterial screening and pathogen reduction.

This mini-review analyzed two approaches to screening bacterial contamination and utilizing pathogen reduction technology (PRT) for Platelet concentrates (PCs). While the culture-based method is still considered the gold standard for detecting bacterial contamination in PCs, efforts in the past two decades to minimize transfusion-transmitted bacterial infections (TTBIs) have been insufficient to eliminate this infectious threat. PRTs have emerged as a crucial tool to enhance safety and mitigate these risks. The evidence suggests that the screening strategy for bacterial contamination is more successful in ensuring PC quality, decreasing the necessity for frequent transfusions, and improving resistance to platelet transfusion. Alternatively, the PRT approach is superior regarding PC safety. However, both methods are equally effective in managing bleeding. In conclusion, PRT can become a more prevalent means of safety for PCs compared to culture-based approaches and will soon comprehensively surpass culture-based bacterial contamination detection methods.

A "Prediction - Detection - Judgment" framework for sudden water contamination event detection with online monitoring

The contamination detection technology helps in water quality management and protection in surface water. It is important to detect sudden contamination events timely from dynamic variations due to various interference factors in online water quality monitoring data. In this study, a framework named “Prediction - Detection - Judgment” is proposed with a method framework of “Time series increment - Hierarchical clustering - Bayes' theorem model”. Time to detection is used as an evaluation index of contamination detection methods, along with the probability of detection and false alarm rate. The proposed method is tested with available public data and further applied in a monitoring site of a river. Results showed that the method could detect the contamination events with a 100% probability of detection, a 17% false alarm rate and a time to detection close to 4 monitoring intervals. The proposed index time to detection evaluates the timeliness of the method, and timely detection ensures that contamination events can be responded to and dealt with in time. The site application also demonstrates the feasibility and practicability of the framework proposed in this study and its potential for extensive implementation.

Small object Lentinula Edodes logs contamination detection method based on improved YOLOv7 in edge-cloud computing

A small object Lentinus Edodes logs contamination detection method (SRW-YOLO) based on improved YOLOv7 in edge-cloud computing environment was proposed to address the problem of the difficulty in the detection of small object contaminated areas of Lentinula Edodes logs. First, the SPD (space-to-depth)-Conv was used to reconstruct the MP module to enhance the learning of effective features of Lentinula Edodes logs images and prevent the loss of small object contamination information, and improve the detection reliability of resource-limited edge devices. Meanwhile, RepVGG was introduced into the ELAN structure to improve the efficiency and accuracy of inference on the contaminated regions of Lentinula Edodes logs through structural reparameterization. This enables models to run more efficiently in mobile edge computing environments while reducing the burden on cloud computing servers. Finally, the boundary regression loss function was replaced with the WIoU (Wise-IoU) loss function, which focuses more on ordinary-quality anchor boxes and makes the model output results more accurate. In this study, the measures of Precision, Recall, and mAP@0.5 reached 97.63%, 96.43%, and 98.62%, respectively, which are 4.62%, 3.63%, and 2.31% higher compared to those for YOLOv7. Meanwhile, the SRW-YOLO model detects better compared with the current advanced one-stage object detection model, providing an efficient, accurate and practical small object detection solution in mobile edge computing environments and cloud computing scenarios.

Microplastics in the Ecosystem: A Systematic Review of the Methods for Their Detection and Removal

Currently, research on microplastics (MPs) has increased due to their rapid distribution throughout the world and their harmful effects on the ecosystem. However, a detailed description of their dispersion and the methods for both detection and removal has not been given. The objective of this research is to carry out a bibliographic review that allows for a multidisciplinary analysis of microplastic contamination and current detection and removal methods. The method used is PRISMA in which articles from reliable databases such as Scopus, Web of science, and Google Scholar were collected and analyzed to finally provide details on the physical and chemical methods for detecting MPs, in addition to presenting the technologies for their removal. As a result of the analysis, critical information was obtained from the different studies on the impact of MPs on the ecosystem and the variation in detection and removal efficiency according to the type of pretreatment and methods applied to the sample. It is concluded that this research is essential to understand the consequences that MPs have on the ecosystem and provide tools to evaluate and improve current technologies, mainly detection and removal.

Generative adversarial networks for detecting contamination events in water distribution systems using multi-parameter, multi-site water quality monitoring.

Contamination events in water distribution networks (WDNs) can have a huge impact on water supply and public health; increasingly, online water quality sensors are deployed for real-time detection of contamination events. Machine learning has been used to integrate multivariate time series water quality data at multiple stations for contamination detection; however, accurate extraction of spatial features in water quality signals remains challenging. This study proposed a contamination detection method based on generative adversarial networks (GANs). The GAN model was constructed to simultaneously consider the spatial correlation between sensor locations and temporal information of water quality indicators. The model consists of two networks-a generator and a discriminator-the outputs of which are used to measure the degree of abnormality of water quality data at each time step, referred to as the anomaly score. Bayesian sequential analysis is used to update the likelihood of event occurrence based on the anomaly scores. Alarms are then generated from the fusion of single-site and multi-site models. The proposed method was tested on a WDN for various contamination events with different characteristics. Results showed high detection performance by the proposed GAN method compared with the minimum volume ellipsoid benchmark method for various contamination amplitudes. Additionally, the GAN method achieved high accuracy for various contamination events with different amplitudes and numbers of anomalous water quality parameters, and water quality data from different sensor stations, highlighting its robustness and potential for practical application to real-time contamination events.

Contamination detection and microbiome exploration with GRIMER.

Contamination detection is a important step that should be carefully considered in early stages when designing and performing microbiome studies to avoid biased outcomes. Detecting and removing true contaminants is challenging, especially in low-biomass samples or in studies lacking proper controls. Interactive visualizations and analysis platforms are crucial to better guide this step, to help to identify and detect noisy patterns that could potentially be contamination. Additionally, external evidence, like aggregation of several contamination detection methods and the use of common contaminants reported in the literature, could help to discover and mitigate contamination. We propose GRIMER, a tool that performs automated analyses and generates a portable and interactive dashboard integrating annotation, taxonomy, and metadata. It unifies several sources of evidence to help detect contamination. GRIMER is independent of quantification methods and directly analyzes contingency tables to create an interactive and offline report. Reports can be created in seconds and are accessible for nonspecialists, providing an intuitive set of charts to explore data distribution among observations and samples and its connections with external sources. Further, we compiled and used an extensive list of possible external contaminant taxa and common contaminants with 210 genera and 627 species reported in 22 published articles. GRIMER enables visual data exploration and analysis, supporting contamination detection in microbiome studies. The tool and data presented are open source and available at https://gitlab.com/dacs-hpi/grimer.

Smart optical measurement probe for autonomously detecting nano-defects on bare semiconductor wafer surface: highly sensitive observation system using phase-contrast microscopy with a spatial light modulator

Nowadays, the yield rate of semiconductor process is strongly affected even by nanoscale defects on bare Si wafer surfaces. Therefore, a non-destructive and high-speed method for detection of nanoparticle contamination is highly demanded. In this research, a nanoparticle detection method is proposed based on the fact that the volatilization process of the volatile inert liquid behaves as an autonomous nanoparticle search probe. Dynamic phase contrast microscopy (Dynamic PCM) is proposed to detect small thickness changes of a thin liquid film with high sensitivity. The dynamic PCM is expected to have double sensitivity compared to normal PCM and it is experimentally verified. Based on previous results, 4% intensity change is expected for 10 nm particles and even over 1.5% intensity change is expected for 5 nm particles by using this proposed dynamic PCM.

Rapid Detection of Escherichia coli: Optimized Peptide-Polythiophene Interactions Help Reduce Assay Time and Improve Naked-Eye Detection.

Recent improvements in methods for rapid detection of microbial contamination in food and water samples have aided in the development of on-site and point-of-care testing. Early detection, made possible via on-site testing, can help limit the spread of food and waterborne illnesses. Recently, we reported a novel fluorescence-based Omptin-Polythiophene assay (the assay) to detect Escherichia coli in contaminated water samples. The assay targets OmpT─an E. coli outer membrane protease─and exploits the protease's ability to cleave at dibasic sites within a peptide. By combining a peptide substrate optimized for OmpT with a conjugated polythiophene reporter molecule whose optical properties vary upon interaction with the intact or cleaved peptide, we demonstrated the detection of 1-10 CFU/mL and 105 CFU/mL E. coli in 5.5 and 1 h, respectively. In comparison, most microbial detection methods that rely on culturing and plating techniques take anywhere between 8 and 24 h to report their results. Herein we report significant improvements in the assay which include reducing the assay time from an already short 1 h to a mere 10 min for detecting E. coli in highly contaminated samples and augmenting the assay with colorimetric sensing capability for naked eye discernment under normal visible light or under UV-A light. These improvements were made possible by characterizing the optical changes resulting from the interaction of the peptide with five carboxylate-functionalized polythiophene variants carrying different 3- side chain carboxylic acids and by identifying preferential peptide substrates via the screening of ten peptide sequence variants for OmpT activity.

Applications of surface-enhanced Raman spectroscopy in environmental detection.

As the human population grows, the anthropogenic impacts from various agricultural and industrial processes produce unwanted contaminants in the environment. The accurate, sensitive and rapid detection of such contaminants is vital for human health and safety. Surface-enhanced Raman spectroscopy (SERS) is a valuable analytical tool with wide applications in environmental contaminant monitoring. The aim of this review is to summarize recent advancements within SERS research as it applies to environmental detection, with a focus on research published or accessible from January 2021 through December 2021 including early-access publications. Our goal is to provide a wide breadth of information that can be used to provide background knowledge of the field, as well as inform and encourage further development of SERS techniques in protecting environmental quality and safety. Specifically, we highlight the characteristics of effective SERS nanosubstrates, and explore methods for the SERS detection of inorganic, organic, and biological contaminants including heavy metals, pharmaceuticals, plastic particles, synthetic dyes, pesticides, viruses, bacteria and mycotoxins. We also discuss the current limitations of SERS technologies in environmental detection and propose several avenues for future investigation. We encourage researchers to fill in the identified gaps so that SERS can be implemented in a real-world environment more effectively and efficiently, ultimately providing reliable and timely data to help and make science-based strategies and policies to protect environmental safety and public health.

Formalin-Fixed Paraffin-Embedded (FFPE) samples are not a beneficial replacement for frozen tissues in fetal membrane microbiota research

Formalin-Fixed Paraffin-Embedded (FFPE) tissues are routinely collected, archived, and used for clinical diagnosis, including maternal and neonatal health. Applying FFPE samples to microbiota research would be beneficial to reduce preparation, storage and costs associated with limited available frozen samples. This research aims to understand if FFPE fetal membrane samples are comparable to frozen tissues, which are the current gold standard for DNA microbiota analysis. Extracted DNA from nine matched paired patients were sequenced by Illumina sequencing of the V4 16S rRNA gene region. This included duplicate frozen amnion and chorion fetal membrane rolls or FFPE combined amniochorionic samples. Negative controls of surrounding wax blocks and DNA extraction reagents were processed alongside samples using identical methods. DNA quality and quantity was assessed by NanoDrop, agarose gel electrophoresis and Bioanalyzer. Decontam and SourceTracker were integrated into microbiota analysis to identify the presence of contaminating sources. The bacterial profile and nine genera differed between FFPE and frozen fetal membranes. There were no differences in bacterial profiles between FFPE samples and corresponding wax negative controls, with 49% of bacteria in FFPE fetal membrane samples matched to the source origin of paraffin wax, and 40% originating from DNA extraction reagent sources. FFPE samples displayed high fragmentation and low quantity of extracted DNA compared to frozen samples. The microbiota of FFPE fetal membrane samples is influenced by processing methods, with the inability to differentiate between the microbiota of the tissue sample and the surrounding wax block. Illumina sequencing results of FFPE and frozen fetal membrane samples should not be compared using the methods employed here. Variation could be influenced by limitations including storage time, DNA extraction and purification methods. To utilise FFPE fetal membrane samples in microbiota research then contamination prevention and detection methods must be included into optimised and standardised protocols, with recommendations presented here.

Research progress on photoelectrochemical sensors for contamination analysis in agricultural fields.

The establishment of rapid and efficient contamination detection methods in agricultural fields is of great significance for safeguarding agricultural production and human health, and for ensuring the healthy development of agricultural industries. As a booming analytical method, photoelectrochemical (PEC) sensors, which combine the advantages of electrochemical analysis and optical analysis, have received increasing research attention. Due to the complete separation of the excitation source (light) and the detection signal (current), the background signal of PEC sensors is greatly decreased, and their sensitivity is high. This review aims to cover the advances in PEC sensors for contamination analysis in agricultural fields over the past 5 years, including the analysis of heavy metals, antibiotics, pesticides, and toxins. The future prospects in this field are also discussed.

Recent advances in aptamer-based optical and electrochemical biosensors for detection of pesticides and veterinary drugs

Food contaminants are a threat to agriculture, the food chain, and consequently human health. Traditional analytical methods for food safety usually have limitations in achieving quick on-site detection of contaminants in large food samples. Hence, there is an urgent need to develop sensitive, simple, and reproducible methods for the on-site detection of food contaminants. Aptamer-based biosensors have attracted increasing attention owing to their superior performance, simplicity, and sensitivity. Among aptamer-based biosensors, optical and electrochemical biosensors have been constructed for the detection of pesticides and veterinary drugs, such as acetamiprid, organophosphorus pesticides, and various antibiotics, which are together the cause of serious food pollution. Food sample preparation techniques pertaining to testing contaminants in food samples by using those aptasensors are briefly discussed. Additionally, the various nanomaterials and nanocomposites used in the sensing platform are described, to show the good effect of the currently employed means of generating and amplifying sensing signals to improve sensitivity and accuracy of detection. This review provides an overview of the latest advances in the sensing principles of aptamer-based biosensors, such as fluorescent, colorimetric, and electrochemical measurement methods. Recently, using the subsistent aptamers with affinity for food contaminants, optical aptasensors play an important role in detection assays to achieve easier observation and operation with an inexpensive instrument, while electrochemical aptasensors have the advantage of being sensitive, accurate, and less time-consuming. These detection methods provide a meaningful reference for monitoring food safety. The advantages and challenges of aptamer-based biosensors for food contaminant detection are described.

Monitoring Food Contamination in Real Time Using Printable Dnazyme Sensors

Foodborne illnesses have a pronounced impact on the global community, with cases numbering into the hundreds of millions annually and associated treatments costing tens of billions of dollars. Our inability to commercially detect food contamination in real-time is at the center of this crisis, as it places emphasis on static expiry dates that lack accuracy. These predicted dates not only fail to convey contamination, but also result in the disposal of mass amounts of edible food products. When contaminants are retroactively detected, measures such as food recalls are logistically complex and have massive economic ramifications. Existing methods for real-time detection of contaminants are expensive, require sophisticated equipment or offer poor detection capabilities, thus preventing commercialization. Additionally, detection requires packaging to be opened, making it unfeasible to monitor food products on an individual basis. Here, we propose a detection system centered upon the covalent immobilization of pathogen-specific RNA-cleaving fluorescent deoxyribozymes (RFDs) onto food wraps for real-time pathogen detection. These biomolecules exhibit a significant increase in fluorescence when exposed to their respective target. By functionalizing food wrap materials through plasma treatment, suitable crosslinkers can be used to bind amine modified RFDs onto the bulk wrap material. In particular, carbon dioxide plasma paired with carbodiimide chemistry presents itself as a well-studied immobilization approach that is applicable within the proposed sensing platform. Additionally, the use of microcontact printing to pattern RFD-crosslinker solutions onto food wraps presents a strategy suitable for industry scale-up. Given the transparency of food wrap materials and the printed sensor, this platform also overcomes the current need to open packaging for pathogen detection. Ultimately, we envision a patterned RFD barcode that provides multiplex detection of common foodborne pathogens. Use of a handheld fluorescence reader would allow for inexpensive, portable detection of food contamination along the entirety of the food production pipeline.

An aptasensor for the detection of Pb2+ based on photoinduced electron transfer between a G-quadruplex-hemin complex and a fluorophore.

Due to the threat to health of heavy metal contamination, simple and rapid detection methods for heavy metals are an urgent needed in environment protection and food safety. In this work, we have developed a fluorescent aptasensor for the 'turn-off' model detection of Pb2+ . The key feature of the aptasensor is that the dye-labelled nucleic acid strand can be folded into a G-quadruplex structure in the presence of Pb2+ . This conformational change induces photoinduced electron transfer (PET) between a G-quadruplex-hemin complex and 6-carboxyrhodamine X (ROX), which results in fluorescence quenching of ROX. We systematically investigated the interaction mechanism between Pb2+ and the aptasensor and the effects of several environmental parameters were also studied. Under the optimum conditions, the proposed method exhibited a good liner relationship between the concentration of Pb2+ and fluorescence quenching efficiency in the range 25-500 nM and the limit of detection was 1.02 nM. In addition, this method also manifested good performance in spiked lettuce samples with satisfactory recoveries of 87.10-109.6%. This target-induced PET platform can be further expanded to other heavy metal analysis.

Application of Raman Spectroscopic Methods in Food Safety: A Review.

Food detection technologies play a vital role in ensuring food safety in the supply chains. Conventional food detection methods for biological, chemical, and physical contaminants are labor-intensive, expensive, time-consuming, and often alter the food samples. These limitations drive the need of the food industry for developing more practical food detection tools that can detect contaminants of all three classes. Raman spectroscopy can offer widespread food safety assessment in a non-destructive, ease-to-operate, sensitive, and rapid manner. Recent advances of Raman spectroscopic methods further improve the detection capabilities of food contaminants, which largely boosts its applications in food safety. In this review, we introduce the basic principles of Raman spectroscopy, surface-enhanced Raman spectroscopy (SERS), and micro-Raman spectroscopy and imaging; summarize the recent progress to detect biological, chemical, and physical hazards in foods; and discuss the limitations and future perspectives of Raman spectroscopic methods for food safety surveillance. This review is aimed to emphasize potential opportunities for applying Raman spectroscopic methods as a promising technique for food safety detection.

Development of a Manometric Monitoring Method for Early Detection of Air Microbiological Contamination in the Bloodstream

Atmospheric air is a microbial habitat of pathogenic bioaerosols that may pose serious risks to humans. A commonly laboratory-based approach for the diagnosis of such infections in the bloodstream is the blood culture analysis. Its clinical relevance is attributed to the fact that these infections are characterized by high rates of morbidity and mortality, requiring the need for efficient methods for rapid diagnosis. For this reason, our study aimed to develop a method of manometric monitoring for the rapid detection of viable microorganisms in blood culture vials. A methodology was developed to detect pressure variation in intra-vials through a manometric instrument that was coupled to vials of blood culture containing culture broth that allowed microbial growth. This device allowed the early detection of microbial activity based on the production or use of intra-flask gases as a result of microbial metabolic activity. The analyzed variables were the pressure as a function of time, microbial species, and culture medium. The highest pressure found in the flasks without microorganisms was 40 mmHg between 2 and 6 h, and the lowest pressure was −42 mmHg between 21 and 24 h. The variation of the internal pressure in blood culture flasks according to different groups of microorganisms as a function of time demonstrated that the fermentative gram-negative bacilli and gram-positive cocci exhibited a significant increase in relation to their respective control groups (p < 0.001). The non-fermenting gram-negative bacilli showed expected results in relation to the pressure variation in which the production of negative pressures was noticed during the period of analysis, with a significant difference with respect to their control groups (p < 0.001). The developed methodology for the early detection of microorganisms responsible for bloodstream infection was demonstrated to be effective.

A Selective Ratiometric Fluorescent Probe for No-Wash Detection of PVC Microplastic.

Microplastics (MP) are micrometric plastic particles present in drinking water, food and the environment that constitute an emerging pollutant and pose a menace to human health. Novel methods for the fast detection of these new contaminants are needed. Fluorescence-based detection exploits the use of specific probes to label the MP particles. This method can be environmentally friendly, low-cost, easily scalable but also very sensitive and specific. Here, we present the synthesis and application of a new probe based on perylene-diimide (PDI), which can be prepared in a few minutes by a one-pot reaction using a conventional microwave oven and can be used for the direct detection of MP in water without any further treatment of the sample. The green fluorescence is strongly quenched in water at neutral pH because of the formation dimers. The ability of the probe to label MP was tested for polyvinyl chloride (PVC), polyethylene (PE), polyethylene terephthalate (PET), polypropylene (PP), polystyrene (PS), poly methyl methacrylate (PMMA) and polytetrafluoroethylene (PTFE). The probe showed considerable selectivity to PVC MP, which presented an intense red emission after staining. Interestingly, the fluorescence of the MP after labeling could be detected, under excitation with a blue diode, with a conventional CMOS color camera. Good selectivity was achieved analyzing the red to green fluorescence intensity ratio. UV–Vis absorption, steady-state and time-resolved fluorescence spectroscopy, fluorescence anisotropy, fluorescence wide-field and confocal laser scanning microscopy allowed elucidating the mechanism of the staining in detail.