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Due to rapid economic development and urbanization, water pollution has posed a significant threat to public health and social stability. Therefore, accurate and rapid monitoring of water quality becomes particularly important and significant. As an environmentally friendly, nondestructive and efficient detection technology, spectral analysis technology provides an effective tool for qualitative analysis and quantitative detection of pollutants in water environment. In this paper, the principle of UV-Vis spectroscopy and near-infrared (NIR) spectroscopy and their application in water quality detection are studied. UV-Vis and NIR spectroscopy techniques for the detection of many kinds of water quality parameters are discussed according to the classification of chemical oxygen demand, organic matter, microbial pollutants, heavy metals and emerging pollutants. The information of light source in spectroscopy measurement and the algorithms used in spectral data processing are summarized. Finally, the application tendency of spectrum technology in water pollution detection is prospected. [ABSTRACT FROM AUTHOR]

Copper nanoclusters (CuNCs) as a new type of probe for environmental contaminants are gaining increasing attention because of its low cost, superior water dispersibility, wide availability and excellent optical properties. Compared with the other probes such as quantum dots and organic dyes, CuNCs show much more potential in practical application for their excellent photostability, large Stokes shift, low toxicity and other preponderance, especially in the fields of biosensing and environmental monitoring. Recently, the template-assisted synthesis of metal nanoclusters (MNCs) has been widely studied. A variety of templates such as proteins, small thiol molecules, polymers, and DNA with different spatial configuration have been used for the preparation of MNCs so far. This review primarily described recent advances in CuNCs in terms of the synthesis of CuNCs from different templates, the methods to improve the fluorescence (FL) properties of CuNCs, as well as the basic detection mechanisms based on the FL properties or catalytic properties. Finally, to promote the practical application of CuNCs probes, the challenges and prospects of CuNCs multifunctional probes are also discussed. [ABSTRACT FROM AUTHOR]

Bioburden detection is crucial for food, water, and biopharmaceutical applications as it can directly impact public health. The objective of this study is to develop and validate an assay and protocol for detecting bioburden on solid surfaces, as well as in water, with high sensitivity and accuracy in a rapid manner. Henceforth, a resazurin‐based assay optimized for detecting bioburden has been integrated with a previously developed portable multichannel fluorometer. The microbes were isolated from solid surfaces in different laboratory settings by swabbing technique, and stream water was collected for contamination analysis. Based on the results, the assay and protocol can successfully detect bioburden as low as 20 CFU/cm2 and 10 CFU/mL present in both surface and water samples, respectively. [ABSTRACT FROM AUTHOR]

At present, the electronic nose has became a new technology for the rapid detection of pesticides. However, the technique may misidentify them for samples that have not been involved in training. Therefore, a hybrid model based on unsupervised and supervised learning was proposed for the first time in this paper. The model divided the detection process of soil pesticide residues into two steps: (1) an unsupervised machine learning method was used to identify whether the soil was contaminated with pesticides; (2) when the soil was contaminated with pesticides, a supervised classifier was further used to predict the types of pesticides in the soil. The experimental results showed that the model had a recognition accuracy of 99.3% and 99.27% for whether the soil was contaminated with pesticides and the pesticide type of the contaminated soil, respectively, with a detection time of 0.03 s. The results revealed that the proposed hybrid model can quickly and comprehensively reflect the soil information's status. [ABSTRACT FROM AUTHOR]

This paper presents a practical contamination detection system for camera lenses using image analysis with deep learning. The proposed system can detect contamination in camera digital images through contamination learning utilizing deep learning, and it aims to prevent performance degradation of intelligent vision systems due to lens contamination in cameras. This system is based on the object detection algorithm YOLO (v5n, v5s, v5m, v5l, and v5x), which is trained with 4000 images captured under different lighting and background conditions. The trained models showed that the average precision improves as the algorithm size increases, especially for YOLOv5x, which showed excellent efficiency in detecting droplet contamination within 23 ms. They also achieved an average precision (mAP@0.5) of 87.46%, recall (mAP@0.5:0.95) of 51.90%, precision of 90.28%, recall of 81.47%, and F1 score of 85.64%. As a proof of concept, we demonstrated the identification and removal of contamination on camera lenses by integrating a contamination detection system and a transparent heater-based cleaning system. The proposed system is anticipated to be applied to autonomous driving systems, public safety surveillance cameras, environmental monitoring drones, etc., to increase operational safety and reliability. [ABSTRACT FROM AUTHOR]

This paper introduces a multi-objective optimisation approach for the challenging problem of efficient sensor placement in water distribution networks for contamination detection. An important question is how to identify the minimal number of required sensors without losing the capacity to monitor the system as a whole. In this study, we adapted the NSGA-II multi-objective optimisation method by applying centrality mutation. The approach, with two objectives, namely the minimisation of Expected Time of Detection and maximisation of Detection Network Coverage (which computes the number of detected water contamination events), is tested on a moderate-sized benchmark problem (129 nodes). The resulting Pareto front shows that detection network coverage can improve dramatically by deploying only a few sensors (e.g. increase from one sensor to three sensors). However, after reaching a certain number of sensors (e.g. 20 sensors), the effectiveness of further increasing the number of sensors is not apparent. Further, the results confirm that 40–45 sensors (i.e. 31 − 35% of the total number of nodes) will be sufficient for fully monitoring the benchmark network, i.e. for detection of any contaminant intrusion event no matter where it appears in the network. HIGHLIGHTS It is possible to significantly reduce the number of undetected events by deploying only a few more sensors.; Placing sensors on 31−35% of nodes is sufficient for full monitoring of the case study network.; Maximising the opportunity to detect events prioritises the selection of nodes that have neither the highest centrality nor the lowest.; Minimising the detection time of events prioritises nodes with centrality at/close to the extremes.;

In the food and medical packaging industries, clean packaging is crucial to both customer satisfaction and hygiene. An operational Quality Assurance Department (QAD) is necessary for detecting contaminated packages. Manual examination becomes tedious and may lead to instances of contamination being missed along the production line. To address this issue, a system for contamination detection is proposed using an enhanced deep convolutional neural network (CNN) in a human–robot collaboration framework. The proposed system utilizes a CNN to identify and classify the presence of contaminants on product surfaces. A dataset is generated, and augmentation methods are applied to the dataset for nine classes such as coffee, spot, chocolate, tomato paste, jam, cream, conditioner, shaving cream, and toothpaste contaminants. The experiment was conducted using a mechatronic platform with a camera for contamination detection and a time-of-flight sensor for safe machine–environment interaction. The results of the experiment indicate that the reported system can accurately identify contamination with 99.74% mean average precision (mAP). [ABSTRACT FROM AUTHOR]

The food service industry must ensure that service facilities are free of foodborne pathogens hosted by organic residues and biofilms. Foodborne diseases put customers at risk and compromise the reputations of service providers. Fluorescence imaging, empowered by state-of-the-art artificial intelligence (AI) algorithms, can detect invisible residues. However, using AI requires large datasets that are most effective when collected from actual users, raising concerns about data privacy and possible leakage of sensitive information. In this study, we employed a decentralized privacy-preserving technology to address client data privacy issues. When federated learning (FL) is used, there is no need for data sharing across clients or data centralization on a server. We used FL and a new fluorescence imaging technology and applied two deep learning models, MobileNetv3 and DeepLabv3+, to identify and segment invisible residues on food preparation equipment and surfaces. We used FedML as our FL framework and Fedavg as the aggregation algorithm. The model achieved training and testing accuracies of 95.83% and 94.94% for classification between clean and contamination frames, respectively, and resulted in intersection over union (IoU) scores of 91.23% and 89.45% for training and testing, respectively, of segmentation of the contaminated areas. The results demonstrated that using federated learning combined with fluorescence imaging and deep learning algorithms can improve the performance of cleanliness auditing systems while assuring client data privacy. [ABSTRACT FROM AUTHOR]

Motivated by the valuable epidemiological information it reveals, wastewater surveillance has received significant attention in recent years. Furthermore, monitoring the water quality in sewer systems has been shown to provide useful information to support wastewater treatment operations. Yet, a critical need still exists for developing novel approaches for rapid and efficient source identification of chemical and biological species of interest in sewer systems. A limited number of source identification approaches have been proposed in previous literature, and the majority of these approaches employed various simplifying assumptions that limit their usage in real-life applications. In this study, a machine learning–based simulation-optimization framework was developed to determine the characteristics (i.e., concentration and loading pattern) of multiple simultaneous injection sources in sewer systems. The simulation was conducted using a surrogate model in the form of a multilayer perceptron neural network, which was trained using simulation results derived from the Storm Water Management Model (SWMM). The simulation model was then coupled with a genetic algorithm to reveal the characteristics of multiple sources that reproduce the concentration patterns observed at one or more monitoring locations in the sewer system. The proposed framework was applied to a range of injection scenarios and was able to identify the characteristics of multiple simultaneous injection sources under different conditions. The results showed that the residence time plays a significant role in the identifiability of the injection source location. The proposed framework is applicable to a wide number of source identification applications, including contamination source identification and wastewater-based epidemiology. [ABSTRACT FROM AUTHOR]

The T-cell receptor (TCR) repertoire is highly diverse among the population and plays an essential role in initiating multiple immune processes. TCR sequencing (TCR-seq) has been developed to profile the T cell repertoire. Similar to other high-throughput experiments, contamination can happen during several steps of TCR-seq, including sample collection, preparation and sequencing. Such contamination creates artifacts in the data, leading to inaccurate or even biased results. Most existing methods assume 'clean' TCR-seq data as the starting point with no ability to handle data contamination. Here, we develop a novel statistical model to systematically detect and remove contamination in TCR-seq data. We summarize the observed contamination into two sources, pairwise and cross-cohort. For both sources, we provide visualizations and summary statistics to help users assess the severity of the contamination. Incorporating prior information from 14 existing TCR-seq datasets with minimum contamination, we develop a straightforward Bayesian model to statistically identify contaminated samples. We further provide strategies for removing the impacted sequences to allow for downstream analysis, thus avoiding any need to repeat experiments. Our proposed model shows robustness in contamination detection compared with a few off-the-shelf detection methods in simulation studies. We illustrate the use of our proposed method on two TCR-seq datasets generated locally. [ABSTRACT FROM AUTHOR]

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.

Real-time detection and disinfection of foodborne pathogens are important for preventing foodborne outbreaks and for maintaining a safe environment for consumers. There are numerous methods for the disinfection of hazardous organisms, including heat treatment, chemical reaction, filtration, and irradiation. This report evaluated a portable instrument to validate its simultaneous detection and disinfection capability in typical laboratory situations. In this challenging study, three gram-negative and two gram-positive microorganisms were used. For the detection of contamination, inoculations of various concentrations were dispensed on three different surface types to estimate the performance for minimum-detectable cell concentration. Inoculations higher than 103~104 CFU/mm2 and 0.15 mm of detectable contaminant size were estimated to generate a sufficient level of fluorescence signal. The evaluation of disinfection efficacy was conducted on three distinct types of surfaces, with the energy density of UVC light (275-nm) ranging from 4.5 to 22.5 mJ/cm2 and the exposure time varying from 1 to 5 s. The study determined the optimal energy dose for each of the microorganisms species. In addition, surface characteristics may also be an important factor that results in different inactivation efficacy. These results demonstrate that the proposed portable device could serve as an in-field detection and disinfection unit in various environments, and provide a more efficient and user-friendly way of performing disinfection on large surface areas. [ABSTRACT FROM AUTHOR]

Methane is an extremely potent yet short-lived greenhouse gas and is thus recognized as a promising target for rapid climate change mitigation. About 35% of anthropogenic methane emissions are associated with livestock production, and most of these emissions are the outcome of enteric fermentation in ruminant animals. The red seaweed Asparagopsis is currently considered the most efficient feed additive to suppress methane emissions from enteric fermentation but is not currently available on commercial scale. The ongoing effort to successfully commercialize Asparagopsis requires the development of pest control frameworks which rely on the quantitative assessment of biological contamination in cultures. Here we present a low-cost readily available approach for quantifying biofouling in Asparagopsis taxiformis cultures based on microscopic examination and automated image analysis. The proposed methodology is demonstrated to estimate contamination associated with Asparagopsis biomass with error rates lower than 2% over a wide range of contamination levels and contaminating organisms, while significantly cutting down image processing time and allowing for frequent contamination quantification. [ABSTRACT FROM AUTHOR]

Abstract The decreasing cost of sequencing and concomitant augmentation of publicly available genomes have created an acute need for automated software to assess genomic contamination. During the last 6 years, 18 programs have been published, each with its own strengths and weaknesses. Deciding which tools to use becomes more and more difficult without an understanding of the underlying algorithms. We review these programs, benchmarking six of them, and present their main operating principles. This article is intended to guide researchers in the selection of appropriate tools for specific applications. Finally, we present future challenges in the developing field of contamination detection.

Precise, reliable, and speedy contamination detection and disinfection is an ongoing challenge for the food-service industry. Contamination in foodrelated services can cause foodborne illness, endangering customers and jeopardizing provider reputations. Fluorescence imaging has been shown to be capable of identifying organic residues and biofilms that can host pathogens. We use new fluorescence imaging technology, applying Xception and DeepLabv3+ deep learning algorithms to identify and segment contaminated areas in images of equipment and surfaces. Deep learning models demonstrated a 98.78% accuracy for differentiation between clean and contaminated frames on various surfaces and resulted in an intersection over union (IoU) score of 95.13% for the segmentation of contamination. The portable imaging system's intrinsic disinfection capability was evaluated on S. enterica, E. coli, and L. monocytogenes, resulting in up to 8-log reductions in under 5 s. Results showed that fluorescence imaging with deep learning algorithms could help assure safety and cleanliness in the food-service industry. [ABSTRACT FROM AUTHOR]

Precise, reliable, and speedy contamination detection and disinfection is an ongoing challenge for the food-service industry. Contamination in food-related services can cause foodborne illness, endangering customers and jeopardizing provider reputations. Fluorescence imaging has been shown to be capable of identifying organic residues and biofilms that can host pathogens. We use new fluorescence imaging technology, applying Xception and DeepLabv3+ deep learning algorithms to identify and segment contaminated areas in images of equipment and surfaces. Deep learning models demonstrated a 98.78% accuracy for differentiation between clean and contaminated frames on various surfaces and resulted in an intersection over union (IoU) score of 95.13% for the segmentation of contamination. The portable imaging system’s intrinsic disinfection capability was evaluated on S. enterica, E. coli, and L. monocytogenes, resulting in up to 8-log reductions in under 5 s. Results showed that fluorescence imaging with deep learning algorithms could help assure safety and cleanliness in the food-service industry.

The food service industry must ensure that service facilities are free of foodborne pathogens hosted by organic residues and biofilms. Foodborne diseases put customers at risk and compromise the reputations of service providers. Fluorescence imaging, empowered by state-of-the-art artificial intelligence (AI) algorithms, can detect invisible residues. However, using AI requires large datasets that are most effective when collected from actual users, raising concerns about data privacy and possible leakage of sensitive information. In this study, we employed a decentralized privacy-preserving technology to address client data privacy issues. When federated learning (FL) is used, there is no need for data sharing across clients or data centralization on a server. We used FL and a new fluorescence imaging technology and applied two deep learning models, MobileNetv3 and DeepLabv3+, to identify and segment invisible residues on food preparation equipment and surfaces. We used FedML as our FL framework and Fedavg as the aggregation algorithm. The model achieved training and testing accuracies of 95.83% and 94.94% for classification between clean and contamination frames, respectively, and resulted in intersection over union (IoU) scores of 91.23% and 89.45% for training and testing, respectively, of segmentation of the contaminated areas. The results demonstrated that using federated learning combined with fluorescence imaging and deep learning algorithms can improve the performance of cleanliness auditing systems while assuring client data privacy.

Nested PCR is a useful tool for identifying low-abundance target sequences of pathogens and avoiding false negatives. However, it carries an increased risk of cross-contamination, especially with its positive control. Here, we propose using customized synthetic oligonucleotides to detect false positives due to cross-contamination. Created with BioRender.com. [Display omitted] • Alternative nested PCR positive control to minimize false positives. • New method to detect cross-contamination in nucleic acid amplification procedures. • Customized oligonucleotide for nested PCR to detect Bartonella Henselae. [ABSTRACT FROM AUTHOR]

Contaminations frequently occur in bacterial cultures, which significantly affect the reproducibility and reliability of the results from whole-genome sequencing (WGS). Decontaminated WGS data with clean reads is the only desirable source for detecting possible variants correctly. Improvements in bioinformatics are essential to analyze the contaminated WGS dataset. Existing pipelines usually contain contamination detection, decontamination, and variant calling separately. The efficiency and results from existing pipelines fluctuate since distinctive computational models and parameters are applied. It is then promising to develop a bioinformatical tool containing functions to discriminate and remove contaminated reads and improve variant calling from clean reads. In this study, we established a Python-based pipeline named CleanSeq for automatic detection and removal of contaminating reads, analyzing possible genome variants with proper verifications via local re-alignments. The application and reproducibility are proven in either simulated, publicly available datasets or actual genome sequencing reads from our experimental evolution study in Escherichia coli. We successfully obtained decontaminated reads, called out all seven consistent mutations from the contaminated bacterial sample, and derived five colonies. Collectively, the results demonstrated that CleanSeq could effectively process the contaminated samples to achieve decontaminated reads, based on which reliable results (i.e., variant calling) could be obtained. [ABSTRACT FROM AUTHOR]

Security concerns about water distribution networks (WDNs) have led to increased interest in optimizing sensor locations in WDNs achieved through a calibrated hydraulic model. This paper presents a methodology, which consists of two stages. The first stage consists of calibration of a hydraulic model using a genetic algorithm (GA). A real-life network of one of the hydraulic zones of Nagpur city, India, is considered, which optimizes the settings of a throttled controlled valve at different timings for calibration. In this stage, a detailed case study, GA calibration model, methodology and results of calibrated models are discussed. The second stage consists of identifying optimal sensor locations using a newly developed software tool named ‘S-PLACE GA’ and its efficiency and effectiveness are discussed. It can be used for the dual purpose of routine monitoring of water quality and for early detection of contamination. The optimal locations are obtained considering two objective metrics, ‘Demand Coverage’ and ‘Time-Constrained Detection Likelihood’. These two objectives are combined into a single objective by using weights. Key features, input data required for the software and their applications on (1) BWSN network 1 and result comparison with others and (2) calibrated model of the first stage are discussed. Results showed the effectiveness of S-PLACE GA for practical applications. HIGHLIGHTS Paper highlights the calibration of hydraulic model of water distribution system (WDS) using genetic algorithm performing the modelling of throttled control valve.; The new software tool ‘S-PLACE GA’ is developed for dual purpose of routine monitoring of water quality in WDS and for early detection of contamination in case of accidental or intentional contamination simultaneously.; The new formulation is suggested considering weighted objective function comprising two objective metrics, ‘Demand Coverage’ and ‘Time-Constrained Detection Likelihood’. Application and result comparison are shown on benchmark problem.;

BackgroundA major obstacle in single-cell sequencing is sample contamination with foreign DNA. To guarantee clean genome assemblies and to prevent the introduction of contamination into public databases, considerable quality control efforts are put into post-sequencing analysis. Contamination screening generally relies on reference-based methods such as database alignment or marker gene search, which limits the set of detectable contaminants to organisms with closely related reference species. As genomic coverage in the tree of life is highly fragmented, there is an urgent need for a reference-free methodology for contaminant identification in sequence data.ResultsWe present acdc, a tool specifically developed to aid the quality control process of genomic sequence data. By combining supervised and unsupervised methods, it reliably detects both known and de novo contaminants. First, 16S rRNA gene prediction and the inclusion of ultrafast exact alignment techniques allow sequence classification using existing knowledge from databases. Second, reference-free inspection is enabled by the use of state-of-the-art machine learning techniques that include fast, non-linear dimensionality reduction of oligonucleotide signatures and subsequent clustering algorithms that automatically estimate the number of clusters. The latter also enables the removal of any contaminant, yielding a clean sample. Furthermore, given the data complexity and the ill-posedness of clustering, acdc employs bootstrapping techniques to provide statistically profound confidence values. Tested on a large number of samples from diverse sequencing projects, our software is able to quickly and accurately identify contamination. Results are displayed in an interactive user interface. Acdc can be run from the web as well as a dedicated command line application, which allows easy integration into large sequencing project analysis workflows.ConclusionsAcdc can reliably detect contamination in single-cell genome data. In addition to database-driven detection, it complements existing tools by its unsupervised techniques, which allow for the detection of de novo contaminants. Our contribution has the potential to drastically reduce the amount of resources put into these processes, particularly in the context of limited availability of reference species. As single-cell genome data continues to grow rapidly, acdc adds to the toolkit of crucial quality assurance tools.

Nowadays, the growing interest in a healthy lifestyle, to compensate for modern stressful habits, has led to an increased demand for wholesome products with quick preparation times. Fresh and ready-to-eat leafy green vegetables are generally perceived as salutary and safe, although they have been recognized as a source of food poisoning outbreaks worldwide. The reason is that these products retain much of their indigenous microflora after minimal industrial processing, and are expected to be consumed without any additional treatment by consumers. Microbiological safety requires a systematic approach that encompasses all aspects of production, processing and distribution. Nevertheless, the most common laboratory techniques used for the detection of pathogens are expensive, time consuming, need laboratory professionals and are not able to provide prompt results, required to undertake effective corrective actions. In this context, the solution proposed in this work is a novel electrochemical sensing system, able to provide real-time information on microbiological risk, which is also potentially embeddable in an industrial production line. The results showed the sensor ability to detect leafy green salad bacterial contaminations with adequate sensibility, even at a low concentration. [ABSTRACT FROM AUTHOR]

Microalgal predation is one of the imminent threats for mass algae cultivation in open ponds. Invasion of predators results in total clearance of algal biomass within 24–48 h. Detection of contamination in microalgal cultures using online spectroscopy has attracted considerable interest among researchers. Currently reported spectral markers such as hyperspectral and multispectral tools, using visible wavelengths of light, mainly detect changes in the pigment composition or degradation associated with contaminants, especially predators. Unlike monitoring of pigment composition, our work leverages the species-discriminatory potential of Fourier transform infrared (FTIR) spectroscopy in combination with chemometric tools for detection of predators. Here we report FTIR-based signature features, at 1346, 1363, and 1382 cm−1, for detection of Oxyrrhis marina–mediated grazing in cultures of Dunaliella tertiolecta. Based on a partial least square regression (PLSR) model (R2 = 0.894), the signature spectra can indicate the presence of O. marina at a concentration of 5 × 102 cells mL−1 and at least 72 h prior to the culture crash. As opposed to offline grazer monitoring tools, the potential for FTIR-based flow-through design in combination with multivariate methods could enable real-time and non invasive means of early detection of algal grazers. [ABSTRACT FROM AUTHOR]

[Display omitted] • DART-MS is a promising technology in food analysis. • DART-MS can analyze different types of samples for various analysis purposes. • Several factors need to be considered during food analysis using DART-MS. Direct analysis in real time-mass spectrometry (DART-MS) has evolved as an effective analytical technique for the rapid and accurate analysis of food samples. The current advancements of DART-MS in food analysis are described in this paper. We discussed the DART principles, which include devices, ionization mechanisms, and parameter settings. Numerous applications of DART-MS in the fields of food and food products analysis published during 2018–2023 were reviewed, including contamination detection, food authentication and traceability, and specific analyte analysis in the food matrix. Furthermore, the challenges and limitations of DART-MS, such as matrix effect, isobaric component analysis, cost considerations and accessibility, and compound selectivity and identification, were discussed as well. [ABSTRACT FROM AUTHOR]

[Display omitted] • Thymine-Hg2+-Thymine Strategy is used for the detection of plasticizers. • The aptamer gives this sensor good recognition of the target. • The electrochemical signal is the output signal that allows sensitive quantification of the target. Phthalate acid esters (PAEs), known food contaminants, can lead to reproductive problems, respiratory diseases, childhood obesity, and neuropsychological disorders. This work aims to develop an electrochemical aptamer sensor for the detection of DAP and its analogs. To achieve specific recognition ability for PAEs, we have introduced the thymidine-Hg2+-thymidine strategy for the first time in an electrochemical aptamer sensor, incorporating metal–organic frameworks (MOFs) as the carrier for the aptamer. Additionally, nano signal amplification technology was applied by immobilizing MXene on the electrode to intensify the sensor's current signal. In the presence of PAEs, these molecules specifically bound to the aptamers, resulting in competitive release of Hg2+ and aptamers connected to Hg2+, subsequently reducing current signaling. Under optimal experimental conditions, the designed sensor achieved a linear range from 1.65 × 10−5 mg/mL to 3 × 10−3 mg/mL and an extremely low detection limit of 8.94 × 10−6 mg/mL. Furthermore, it exhibited good reliability and practicality in detecting actual samples. [ABSTRACT FROM AUTHOR]

Microalgal contamination in algal culture is a serious problem hampering the cultivation process, which can result in considerable economic and time losses. With the field of microalgal biotechnology on the rise, development of new tools for monitoring the cultures is of high importance. Here we present a case study of the detection of fast-growing green algae Chlorella vulgaris (as contaminant) in a diatom Phaeodactylum tricornutum culture using various approaches. We prepared mixed cultures of C. vulgaris and P. tricornutum in different cell-to-cell ratios in the range from 1:103 to 1:107. We compared the sensitivity among microscopy, cultivation-based technique, PCR, and qPCR. The detection of C. vulgaris contamination using light microscopy failed in samples containing cell ratios <1:105. Our results confirmed PCR/qPCR to provide the most reliable and sensitive results, with detection sensitivity close to 75 cells/mL. The method was similarly sensitive in a pure C. vulgaris culture as well as in a mixed culture containing 107-times more P. tricornutum cells. A next-generation sequencing analysis revealed a positive discrimination of C. vulgaris during DNA extraction. The method of cultivation media exchange from sea water to fresh water, preferred by the Chlorella contaminant, demonstrated a presence of the contaminant with a sensitivity comparable to PCR approaches, albeit with a much longer detection time. The results suggest that a qPCR/PCR-based approach is the best choice for an early warning in the commercial culturing of microalgae. This method can be conveniently complemented with the substitution-cultivation method to test the proliferating potential of the contaminant. Key points: • PCR-based protocol developed for detection of Chlorella cells. • Synergy of various approaches shows deeper insight into a presence of contaminants. • Positive/negative discrimination occurs during DNA extraction in mixed cultures. • Newly developed assays ready to use as in diagnostics of contamination. [ABSTRACT FROM AUTHOR]

Contaminations frequently occur in bacterial cultures, which significantly affect the reproducibility and reliability of the results from whole-genome sequencing (WGS). Decontaminated WGS data with clean reads is the only desirable source for detecting possible variants correctly. Improvements in bioinformatics are essential to analyze the contaminated WGS dataset. Existing pipelines usually contain contamination detection, decontamination, and variant calling separately. The efficiency and results from existing pipelines fluctuate since distinctive computational models and parameters are applied. It is then promising to develop a bioinformatical tool containing functions to discriminate and remove contaminated reads and improve variant calling from clean reads. In this study, we established a Python-based pipeline named CleanSeq for automatic detection and removal of contaminating reads, analyzing possible genome variants with proper verifications via local re-alignments. The application and reproducibility are proven in either simulated, publicly available datasets or actual genome sequencing reads from our experimental evolution study in Escherichia coli. We successfully obtained decontaminated reads, called out all seven consistent mutations from the contaminated bacterial sample, and derived five colonies. Collectively, the results demonstrated that CleanSeq could effectively process the contaminated samples to achieve decontaminated reads, based on which reliable results (i.e., variant calling) could be obtained.

Nowadays, the growing interest in a healthy lifestyle, to compensate for modern stressful habits, has led to an increased demand for wholesome products with quick preparation times. Fresh and ready-to-eat leafy green vegetables are generally perceived as salutary and safe, although they have been recognized as a source of food poisoning outbreaks worldwide. The reason is that these products retain much of their indigenous microflora after minimal industrial processing, and are expected to be consumed without any additional treatment by consumers. Microbiological safety requires a systematic approach that encompasses all aspects of production, processing and distribution. Nevertheless, the most common laboratory techniques used for the detection of pathogens are expensive, time consuming, need laboratory professionals and are not able to provide prompt results, required to undertake effective corrective actions. In this context, the solution proposed in this work is a novel electrochemical sensing system, able to provide real-time information on microbiological risk, which is also potentially embeddable in an industrial production line. The results showed the sensor ability to detect leafy green salad bacterial contaminations with adequate sensibility, even at a low concentration.

Currently, the territory of the Republic of Poland faces a growing threat of contamination with its sources in catastrophes and technical failures in industrial plants (including nuclear power plants) and uncontrolled release of high-toxic chemicals during transport and, which cannot be excluded, terrorism. The increased level of threat resulting from, among others, those factors caused that the National Contamination Detection and Alerting Systems (KSWiA) with the Contamination Detection System of the Polish Armed Forces as the system coordinator was established in the Republic of Poland in 2006. This paper presents the outline of the aerial system of contamination detection, mainly its technical elements, based on the Unmanned Aerial Vehicle (UAV) carrying on board basic and special-purpose equipment. The elementary way of operating the system while performing tasks as well as requirements for its maintenance and handling have been proposed. Introduction of UAV systems would greatly increase the effectiveness of the Contamination Detection System of the Polish Armed Forces, but also other civilian functional subsystems of KSWSiA. There are many advantages to using them, such as no need to expose personnel to contamination and an enemy impact, high mobility, maneuverability and the capability to operate under difficult terrain conditions.

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. [Display omitted] • A complete technical route for sudden water contamination event detection. • The route consists of baseline prediction, anomaly detection and event judgment. • The event detection time is proposed as an index of timeliness. • The method can detect water contamination events accurately and timely. • An online monitoring station is used for implementation and validation. [ABSTRACT FROM AUTHOR]

Abstract Background Recently, there has been a dramatic change in the field of terahertz (THz) technology. The recent advancements in the THz radiation sector considering generation, manipulation and detection have brought revolution in this field, which enable the integration of THz sensing systems into real-world. The THz technology presents detection techniques and various issues, while providing significant opportunities for sensing food and water contamination detection. Scope and approach Many researchers around the world have exploited the potential of invaluable new applications of THz sensing ranging from surveillance, healthcare and recently for food and water contamination detection. The microbial pollution in water and food is one the crucial issues with regard to the sanitary state for drinking water and daily consumption of food. To address this risk, the detection of microbial contamination is of utmost importance, since the consumption of insanitary or unhygienic food can lead to catastrophic illness. Key findings and conclusions This paper presents a first-time review of the open literature covering the advances in the THz sensing for microbiological contamination of food and water, in addition to state-of-the-art in network architectures, applications and recent industrial developments. With unique superiority, the THz non-destructive detection technology in food inspection and water contamination detection is emerging as a new area of study. With the great progress, some important challenges and future research directions are presented within the field. Highlights • Microbial pollution is one crucial issues on sanitary state of water and food. • THz detection technologies are potential for being addressed to identify contaminants. • Review of open literature covering THz research sensing for contamination detection. • Open challenges and research directions within this area are proposed. [ABSTRACT FROM AUTHOR]

Water environment monitoring is of great importance to human health, ecosystem sustainability, and water transport. Unlike traditional water quality monitoring problems, this paper focuses on visual perception of water environment. We first introduce the development of a customized aquatic sensor node equipped with an embedded camera sensor. Based on this platform, we present an efficient and holistic contamination detection approach, which can automatically adapt to the detection of floating debris in dynamic waters or the identification of salient regions in static waters. Our approach is specifically designed based on compressed sensing theory to give full consideration to the unique challenges in water environment and the resource constraints on sensor nodes. Both laboratory and field experiments demonstrate the proposed method can fast and accurately detect various types of water pollutants and is a better choice for camera sensor-based water environment monitoring compared with other methods. [ABSTRACT FROM AUTHOR]

This paper presents a system which is able to determine dry rubber content (DRC) as well as detect contaminants in latex. The system was designed and implemented with a six-port reflectometer. An open-ended coaxial sensor was designed as a measurement probe. The constant amplitude sinusoidal wave at 1 GHz transmits from the probe tip, incidents on the test sample and reflects back to the probe. A standing electromagnetic wave is formed and converted to the electrical signal by the probe. This electric signal is digitized and fed to the algorithms programmed in the microcontroller. These algorithms were developed for classifying the latex sample and determining its DRC. The system accuracy was tested with several pure and contaminated latex samples. The contaminated samples were prepared by mixing pure latex with 10 and 15% (by volume) of cassava flour solution. One hundred and ten samples, 50 pure, 40 contaminated of 10%, and 20 contaminated of 15%, were tested. The overall detection accuracy was 91.8% across the DRC range from 24 to 55%. For DRC determination, the system was tested with 30 latex samples at 27 °C. This was compared with the standard oven method (ISO 126:1972 DRC, BS 1672:part 1:1950 and ASM D 1076–80) where the mean absolute error of DRC achieved was 0.74%. [ABSTRACT FROM AUTHOR]