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Pesticide poisoning constantly threatens bees as they forage for resources in pesticide-treated crops. This poisoning requires thorough investigation to identify its causes, underscoring the importance of reliable pesticide detection methods for bee monitoring. Infrared spectroscopy provides reflectance data across hundreds of spectral bands (hyperspectral reflectance), presumably enabling the efficient classification of pesticide contamination in bee carcasses using artificial intelligence (AI) models, such as machine learning. In this study, bee contamination by commercial formulations of three insecticides-dimethoate (organophosphate), fipronil (phenylpyrazole), and imidacloprid (neonicotinoid)-as well as glyphosate, the most widely used herbicide globally, was detected using machine learning models. These models classified the hyperspectral reflectance profiles of the body surfaces of contaminated bees. The best-performing model, the linear discriminant analysis, achieved 98 % accuracy in discriminating contamination across species Apis mellifera, Melipona mondury, and Partamona helleri, with prediction speeds of 0.27 s. Our pioneering study introduced an effective method for discerning multiple classes of bees contaminated with pesticides using hyperspectral reflectance. An AI-driven spectral data analysis tool (https://github.com/bernardesrodrigoc/MACSS) was developed for the purpose of identifying and characterizing new samples through their spectral characteristics. This platform aids efforts to monitor and conserve bee populations and holds potential importance in environmental monitoring, agricultural research, and industrial quality control., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could to influence the results or interpretation reported in this manuscript., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Food contamination poses serious health risks, compelling the discovery of new methods to guarantee regulatory compliance and build consumer conviction. Surface Enhanced Raman Spectroscopy (SERS) has come into sight as a sophisticated approach for the ultrasensitive discovery of toxins in food and water, proposing non-destructive, quick, and precise analysis. Instantaneously, quantum dots (QDs) are astonishing nanomaterials, characterized by distinctive attributes such as quantum confinement and optical photostability. This article extends a decisive outline of SERS technology, pointing out its amalgamation with QDs and discussing numerous augmentation approaches i.e., chemical enhancement, electromagnetic enhancement, Van Hove singularities, the Brus equation, Förster resonance energy transfer, band gap energy, and quantum yield. The amalgamation of SERS with QDs commands an important promise in international food security and conservational sustainability. Nevertheless, QDs provide several compensations, they also aspect a few concerns, counting probable toxicity, stability problems, and predisposition to interference. To tackle these items, further research is required to synthesize safer, more stable QD materials and to refine protocols for practical real-world applications. While some reviews on SERS have been published recently, to our knowledge, the current review is the first one dedicated to QDs-assisted SERS in food safety., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Water distribution networks play a crucial role in ensuring a reliable water supply, yet they encounter challenges such as corrosion, scale formation, and biofilm growth due to interactions with environmental elements. Biofilms and corrosion layers are significant contaminants in water pipes, formed by complex interactions with pipe materials. As the structure of these contamination layers varies depending on the pipe material, it is essential to investigate the contamination layer for each material individually. Specifically, biofilm growth is typically investigated concerning organic sources, while the growth of humus layers is examined in relation to inorganic elements such as manganese (Mn), iron (Fe), and aluminum (Al), which are major elements and organic substances found in water pipes. Real-time imaging of recently contaminated layers can provide important insights to improve system performance by optimizing operations and cleaning processes. In this study, cast iron (7.10 ± 0.78 nm) exhibits greater surface roughness compared to PVC (5.60 ± 0.14 nm) and provides favorable conditions for biofilm formation due to its positive charge. Over a period of 425 h, the fouling layer on cast iron and PVC surfaces gradually increased in fouling thickness, porosity, roughness, and density, reaching maximum value of 29.72 ± 3.6 μm, 11.44 ± 1.1%, 41673 ± 1025.6 pixels, and 0.80 ± 0.3 fouling layer pixel/layer pixel for cast iron, and 8.15 ± 0.4 μm, 20.64 ± 0.9%, 35916.6 ± 755.7 pixels, and 0.58 ± 0.1 fouling layer pixel/layer pixel, respectively. Within the scope of the current research, CNN model demonstrates high correlation coefficients (0.98 and 0.91) in predicting biofilm thickness for cast iron and PVC. The model also presented high accuracy in predicting porosity for both materials (over 0.91 for cast iron and 0.96 for PVC). While the model accurately predicted biofilm roughness and density for cast iron (correlation coefficients 0.98 and 0.94, respectively), it had lower accuracy for PVC (correlation coefficients 0.92 for both parameters)., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

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/cm 2 and 10 CFU/mL present in both surface and water samples, respectively., (© 2024 American Institute of Chemical Engineers.)

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

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.

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., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

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 10 3 ~10 4 CFU/mm 2 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/cm 2 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.

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., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Authors. Published by Elsevier B.V. on behalf of Chinese Society for Environmental Sciences, Harbin Institute of Technology, Chinese Research Academy of Environmental Sciences.)

This study presents a new stacking ensemble model for contamination event detection using multiple water quality parameters. The stacking model consists of a number of machine learning base predictors and a meta-predictor, and it is trained using cross-validation to capture different features in multiple water quality parameters and then used for water quality predictions. For each water quality parameter, the residuals between predicted and measured data are classified to identify anomalies with thresholds derived from the sequential model-based optimization method and detection probabilities updated using Bayesian analysis. Alarms derived from individual water quality parameters are fused to enhance the anomaly signals and improve the detection accuracy. The proposed stacking-based method is evaluated using a data set of six water quality parameters from a real water distribution system with randomly simulated events. The stacking-based method could detect 2496 events out of a total 2500 events without a false alarm. The results show that the stacking method outperforms an artificial neural network (ANN) benchmark method in contamination event detection. The stacking method has a higher true positive rate, lower false positive rate and higher F1 score than the ANN method. This implies that the stacking method has great promise of detecting contamination events in the water distribution system., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier B.V.)

Contamination detection in drinking water is crucial for water utilities in terms of public health; however, current online water quality sensors can be unresponsive to various possible contaminants consisting of particulate and dissolved content or require a constant supply of reagents and sample preparation. We used a two-line test environment connected to a drinking water distribution system with flow-imaging particle counters and conventional sensors to assess their responses to the injection of contaminants into one line, including stormwater, treated wastewater, wastewater, well water, and Escherichia coli, while simultaneously measuring responses to normal water quality fluctuations in the other line. These water quality fluctuations were detected with all of the conventional sensors (except conductivity) and with 3 out of 5 of the size- and shape-derived particle classes of the flow-imaging particle counter. The flow-imaging particle counter was able to detect all of the studied contaminants, e.g. municipal wastewater at 0.001% (v/v), while the oxidation-reduction potential sensor outperformed other conventional sensors, detecting the same wastewater at 0.03% (v/v). The presence of particles less than 1 µm in size was shown to be a generic parameter for the detection of particulates present in the studied contaminants; however, they manifested a considerable response to fluctuations which led to lower relative response to contaminants in comparison to larger particles. The particle size and class distributions of contaminants were different from those of drinking water, and thus monitoring particles larger than 1 µm or specific particle classes of flow-imaging particle counter, which are substantially more abundant in contaminated water than in pure drinking water, can improve the detection of contamination events. Water utilities could optimize contamination detection by selecting water quality parameters with a minimal response to quality fluctuations and/or a high relative response to contaminants., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

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., (© 2022. The Author(s).)

Background: Real time process data facilitates timely decisions, enables better process control, and can increase quality assurance. Biological drugs (mol. Wt. ≥ 40 kDa) are manufactured using mammalian cells such as Chinese hamster ovary (CHO) cells in bioreactors and have significant risks of contamination during processing. In such processes, in-line monitoring of biomass can provide real-time cell growth profiles and indications of bioreactor health., Methods: An in-line conductivity/capacitance probe (Aber Instruments, Aberystwyth, UK) for monitoring CHO cell growth during fed batch cultures for producing an IgG1 monoclonal antibody was employed. Cell growth was measured in real-time using the capacitance probe (pF cm -1 ) while being compared with off-line measurements using a metabolic analyzer (Nova Biomedical, Waltham, MA, USA). Conductivity measurements (mS cm -1 ) detected variations in the solute concentrations in the bioreactor due to nutrient feed, bicarbonate buffer, and cellular metabolism by-products., Results and Conclusion: Abnormal increases in conductivity were found to consistently correspond to bacterial contamination, which was confirmed by orthogonal methods. The contaminated bioreactor runs exhibited sharp increases in conductivity rates hours before dissolved oxygen levels precipitously decreased due to bacterial growth. It is proposed that in-line measurement of conductivity could be employed for early detection of bacterial contaminations. The probe may be adopted in pharmaceutical aseptic aqueous liquid handling processes., (© 2021 Wiley-VCH GmbH.)

Hierarchical genotyping approaches can provide insights into the source, geography and temporal distribution of bacterial pathogens. Multiple hierarchical SNP genotyping schemes have previously been developed so that new isolates can rapidly be placed within pre-computed population structures, without the need to rebuild phylogenetic trees for the entire dataset. This classification approach has, however, seen limited uptake in routine public health settings due to analytical complexity and the lack of standardized tools that provide clear and easy ways to interpret results. The BioHansel tool was developed to provide an organism-agnostic tool for hierarchical SNP-based genotyping. The tool identifies split k-mers that distinguish predefined lineages in whole genome sequencing (WGS) data using SNP-based genotyping schemes. BioHansel uses the Aho-Corasick algorithm to type isolates from assembled genomes or raw read sequence data in a matter of seconds, with limited computational resources. This makes BioHansel ideal for use by public health agencies that rely on WGS methods for surveillance of bacterial pathogens. Genotyping results are evaluated using a quality assurance module which identifies problematic samples, such as low-quality or contaminated datasets. Using existing hierarchical SNP schemes for Mycobacterium tuberculosis and Salmonella Typhi, we compare the genotyping results obtained with the k-mer-based tools BioHansel and SKA, with those of the organism-specific tools TBProfiler and genotyphi, which use gold-standard reference-mapping approaches. We show that the genotyping results are fully concordant across these different methods, and that the k-mer-based tools are significantly faster. We also test the ability of the BioHansel quality assurance module to detect intra-lineage contamination and demonstrate that it is effective, even in populations with low genetic diversity. We demonstrate the scalability of the tool using a dataset of ~8100 S . Typhi public genomes and provide the aggregated results of geographical distributions as part of the tool's output. BioHansel is an open source Python 3 application available on PyPI and Conda repositories and as a Galaxy tool from the public Galaxy Toolshed. In a public health context, BioHansel enables rapid and high-resolution classification of bacterial pathogens with low genetic diversity.

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 1:10 3 to 1:10 7 . 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 <1:10 5 . 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 10 7 -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.

Cancer cell lines serve as invaluable model systems for cancer biology research and help in evaluating the efficacy of new therapeutic agents. However, cell line contamination and misidentification have become one of the most pressing problems affecting biomedical research. Available methods of cell line authentication suffer from limited access, time-consuming and often costly for many researchers, hence a new and cost-effective approach for cell line authentication is needed. In this regard, we developed a new method called CeL-ID for cell line authentication using genomic variants as a byproduct derived from RNA-seq data. CeL-ID was trained and tested on publicly available more than 900 RNA-seq dataset derived from the Cancer Cell Line Encyclopedia (CCLE) project; including most frequently used adult and pediatric cancer cell lines. We generated cell line specific variant profiles from RNA-seq data using our in-house pipeline followed by pair-wise variant profile comparison between cell lines using allele frequencies and depth of coverage values of the entire variant set. Comparative analysis of variant profiles revealed that they differ significantly from cell line to cell line whereas identical, synonymous and derivative cell lines share high variant identity and their allelic fractions are highly correlated, which is the basis of this cell line authentication protocol. Additionally, CeL-ID also includes a method to estimate the possible cross-contamination using a linear mixture model with any possible CCLE cells in case no perfect match was detected., Competing Interests: Competing interests The authors declare that they have no competing interests.

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.

Background: A 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., Results: We 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., Conclusions: Acdc 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.

Early warning systems are widely used to safeguard water security, but their effectiveness has raised many questions. To understand why conventional detection methods fail to identify contamination events, this study evaluates the performance of three contamination detection methods using data from a real contamination accident and two artificial datasets constructed using a widely applied contamination data construction approach. Results show that the Pearson correlation Euclidean distance (PE) based detection method performs better for real contamination incidents, while the Euclidean distance method (MED) and linear prediction filter (LPF) method are more suitable for detecting sudden spike-like variation. This analysis revealed why the conventional MED and LPF methods failed to identify existence of contamination events. The analysis also revealed that the widely used contamination data construction approach is misleading., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Early warning systems have been widely deployed to safeguard water security. Many contamination detection methods have been developed and evaluated in the past decades. Although encouraging detection performance has been obtained and reported, these evaluations mainly used artificial or laboratory data. The evaluation of detection performance with data from real contamination accidents has rarely been conducted. Implementation of contamination event methods without full assessment using field data might lead to failure of an early warning system. In this paper, the detection performance of three contamination detection methods, a Pearson correlation Euclidean distance (PE) based detection method, a multivariate Euclidean distance (MED) method and a linear prediction filter (LPF) method, was evaluated using data from a real contamination accident. Results improve understanding of the implementation of detection methods to field situations and show that all methods are prone to yielding worse detection performance when applied to data from a real contamination accident. They also revealed that the Pearson correlation Euclidean distance based method is more capable of differentiating between equipment noise and presence of contamination and has greater potential to be used in real field situations than the MED and LPF methods., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Mycoplasma contamination of cell lines is a major problem in cell culture technology. This unit presents protocols involving either the polymerase chain reaction (PCR) or fluorescent in situ hybridization (FISH) to provide independent, fast, and sensitive techniques to monitor mycoplasma contamination in laboratory cultures. Special emphasis is placed on the integration of control reactions to prevent false-negative as well as false-positive results due to reaction inhibition or contamination and background staining, respectively., (Copyright © 2014 John Wiley & Sons, Inc.)