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Copyright of Journal of Food Safety & Quality is the property of Journal of Food Safety & Quality Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Detecting environmental contaminants is crucial for protecting ecosystems and human health. While traditional carbon dot (CD) fluorescent probes are versatile, they may suffer from limitations like fluctuations in signal intensity, leading to detection inaccuracies. In contrast, ratiometric fluorescent probes, designed with internal self-calibration mechanisms, offer enhanced sensitivity and reliability. This review focuses on the design and applications of ratiometric fluorescent probes based on CDs for environmental monitoring. Our discussion covers construction strategies, ratiometric fluorescence principles, and applications in detecting various environmental contaminants, including organic pollutants, heavy metal ions, and other substances. We also explore associated advantages and challenges and provide insights into potential solutions and future research directions. [ABSTRACT FROM AUTHOR]

Copyright of Electroplating & Finishing is the property of Electroplating & Finishing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the wake of the terrorist attacks of 11 September 2001, extensive research efforts have been dedicated to the development of computational algorithms for identifying contamination sources in water distribution systems (WDSs). Previous studies have extensively relied on evolutionary optimization techniques, which require the simulation of numerous contamination scenarios in order to solve the inverse-modeling contamination source identification (CSI) problem. This study presents a novel framework for CSI in WDSs using Bayesian optimization (BO) techniques. By constructing an explicit acquisition function to balance exploration with exploitation, BO requires only a few evaluations of the objective function to converge to near-optimal solutions, enabling CSI in real-time. The presented framework couples BO with EPANET to reveal the most likely contaminant injection/intrusion scenarios by minimizing the error between simulated and measured concentrations at a given number of water quality monitoring locations. The framework was tested on two benchmark WDSs under different contamination injection scenarios, and the algorithm successfully revealed the characteristics of the contamination source(s), i.e., the location, pattern, and concentration, for all scenarios. A sensitivity analysis was conducted to evaluate the performance of the framework using various BO techniques, including two different surrogate models, Gaussian Processes (GPs) and Random Forest (RF), and three different acquisition functions, namely expected improvement (EI), probability of improvement (PI), and upper confident bound (UCB). The results revealed that BO with the RF surrogate model and UCB acquisition function produced the most efficient and reliable CSI performance. [ABSTRACT FROM AUTHOR]

The hazards of residual triazine herbicides in the environment to aquatic organisms and humans are introduced, and the principle, development history and current research status of triazine herbicide detection by algal chlorophyll fluorescence technology are described. The basic reviews the research progress made in the rapid detection of trace triazine herbicides and the analysis of their biotoxicity using algal chlorophyll fluorescence technology. Furthermore, it analyzes the ongoing efforts to improve the detection sensitivity of this technology. In conclusion, the advantages and limitations of algal chlorophyll fluorescence technology in practical applications are summarized, and the future development direction of this technology is proposed. To enhance its potential, the future focus should involve optimizing existing detection technology, identifying more sensitive chlorophyll fluorescence parameters, or developing new algal biosensors with lower detection limits and higher sensitivity based on the response characteristics of algal fluorescence to herbicides. Moreover, the aim should be to apply this technology for in situ, online, and continuous monitoring of the water environment, thereby enabling better regulation, monitoring, and risk assessment of triazine herbicides in water. [ABSTRACT FROM AUTHOR]

One of the most effective ways to minimize polluted water consumption is to arrange quality sensors properly in the water distribution networks (WDNs). In this study, the NSGA-III algorithm is developed to improve the optimal locations of sensors by balancing four conflicting objectives: (1) detection likelihood, (2) expected detection time, (3) detection redundancy, and (4) the affected nodes before detection. The research procedure proposed the dynamic variations of chlorine between defined upper and lower bounds, which were determined utilizing the Monte Carlo simulation model. For selecting a contamination matrix with the same characteristics and effects of all possible events, a heuristic method was applied. The coefficients of importance are introduced in this study for the assessment of contamination events and network nodes. The Pareto fronts for each of the two sets of conflicting objectives were computed for benchmark and real water distribution networks using the proposed simulation–optimization approach. Results indicated that sensors should be installed downstream of the network to maximize sensor detection likelihood; however, this increases detection time. For the benchmark network, maximum and minimum detection likelihoods were calculated as 92.8% and 61.1%, respectively, which corresponded to the worst detection time of 11.58 min and the best detection time of 5.06 min. So, the position of sensors regarding the two objective functions conflicts with each other. Also, the sensitivity analysis related to the number of sensors illustrated that the Pareto fronts became a more efficient tool when the number of sensors increased. The best pollution detection likelihood in the real water network increased by 18.93% and 24.66% by incrementing the number of sensors from 5 to 10 and 5 to 15, respectively. Moreover, adding more than 10 sensors to the benchmark network and more than 15 to the real system will provide little additional detection likelihood. [ABSTRACT FROM AUTHOR]

The spectroscopic and imaging performance of energy-resolved photon counting detectors, based on new sub-millimetre boron oxide encapsulated vertical Bridgman cadmium zinc telluride linear arrays, are presented in this work. The activities are in the framework of the AVATAR X project, planning the development of X-ray scanners for contaminant detection in food industry. The detectors, characterized by high spatial (250 µm) and energy (<3 keV) resolution, allow spectral X-ray imaging with interesting image quality improvements. The effects of charge sharing and energy-resolved techniques on contrast-to-noise ratio (CNR) enhancements are investigated. The benefits of a new energy-resolved X-ray imaging approach, termed window-based energy selecting, in the detection of low- and high-density contaminants are also shown. [ABSTRACT FROM AUTHOR]

Detecting environmental contaminants is crucial for protecting ecosystems and human health. While traditional carbon dot (CD) fluorescent probes are versatile, they may suffer from limitations like fluctuations in signal intensity, leading to detection inaccuracies. In contrast, ratiometric fluorescent probes, designed with internal self-calibration mechanisms, offer enhanced sensitivity and reliability. This review focuses on the design and applications of ratiometric fluorescent probes based on CDs for environmental monitoring. Our discussion covers construction strategies, ratiometric fluorescence principles, and applications in detecting various environmental contaminants, including organic pollutants, heavy metal ions, and other substances. We also explore associated advantages and challenges and provide insights into potential solutions and future research directions.

Extensive and high residue variations in enrofloxacin (ENR) exist in different aquatic products. A novel quantitative method for measuring ENR using high-performance liquid chromatography–tandem mass spectrometry was developed employing enrofloxacin-d5 (ENR-d5) and enrofloxacin-d3 (ENR-d3) as isotope surrogates. This reduced the deviation of detected values, which results from the overpass of the linear range and/or the large difference in the residue between the isotope standard and ENR, from the actual content. Furthermore, high residue levels of ENR can be directly diluted and re-calibrated by the corresponding curve with the addition of high levels of another internal surrogate without repeated sample preparation, avoiding the overflow of the instrument response. The validation results demonstrated that the method can simultaneously determine ENR residues from MQL (2 µg/kg) to 5000 × MQL (method quantification limit) with recoveries between 97.1 and 106%, and intra-precision of no more than 2.14%. This method realized a wide linear calibration range with dual deuterated isomers, which has not been previously reported in the literature. The developed method was successfully applied to the analysis of ENR in different aquatic products, with ENR residue levels varying from 108 to 4340 μg/kg and an interval of precision in the range of 0.175~6.72%. These results demonstrate that batch samples with a high variation in ENR residues (over the linear range with a single isotope standard) can be detected by the dual isotope surrogates method in a single sample preparation process. [ABSTRACT FROM AUTHOR]

One way to mitigate the risk of consumption of contaminated water in water distribution networks is optimal placement of the quality sensors. A considerable challenge in this respect is the significance of contamination at a junction. Beside the population affected and the volume of the contaminated water consumed, importance of each junction is a parameter that must be taken into account in placing the sensors. This parameter directly concerns the service provided by each junction as well as the sensitivity and social consequences of junction contamination. The present study defines a new objective function for minimizing the effect of junction contamination with respect to its importance. Using a robust approach, this study applied the NSGA-III algorithm to solve a 5-objective problem. The algorithm was tested on a hypothetical network and a benchmark network and the Pareto response was selected for each scenario based on the slope of the different points. The proposed method suggested 12, 12, and 11 sensors for the three scenarios in the hypothetical network. The results show that sensor placement by this method yielded good performance in comparison with the other solutions presented in a benchmark network. HIGHLIGHTS A NSGA-III algorithm for five-objective sensor placement based on the robust optimization models is proposed.; By employing two typical water distribution networks, the benefits of the proposed algorithm are illustrated and some critical factors are also addressed.; A new objective function describes the impact of contamination of important nodes.; Uncertainty at the point of entry and the entry time of the contamination are considered.;

The intentional presence or cross-contamination of peanuts in other nut products can result in severe health problems for consumers allergic to peanuts. It is therefore essential to identify the presence of these allergenic compounds in commercialized nut products prior to their sale. For this purpose, we assessed the performance of a visible near infrared (Vis-NIR) and a shortwave infrared (SWIR) hyperspectral imaging (HSI) systems working in the spectral regions 419–1007 nm and 842–2532 nm, respectively, to identify peanut pieces in different chopped nuts (almonds, hazelnuts and walnuts). Two strategies were evaluated to create the training and validation sets. In Strategy I, these sets were composed of spectra belonging to individual pixels, whereas in Strategy II, the mean spectrum of each individual piece of nut was used. We used partial least squares discriminant analysis (PLS-DA) to develop the classification models, and the results were assessed by means of the values obtained for the sensitivity, specificity, and non-error rate (NER) statistics. The external validation procedure showed excellent classification results, with a NER of 98.3 % and 99.8 % for the Vis-NIR and SWIR systems, respectively, for Strategy I, and 100 % for both systems when Strategy II was followed. These results, therefore, confirm the viability of using HSI technology together with multivariate classification methods to detect peanut pieces in other chopped-nut products. • Allergenic compounds identification in commercialized nut products. • HSI was evaluated for the detection of peanuts in mixed chopped nuts. • Excellent classification results were obtained for the Vis-NIR and SWIR systems. • NER up to 99.8 and 100 % were obtained following strategies I and II, respectively. [ABSTRACT FROM AUTHOR]

The market for high-performance lithium-ion (Li-ion) batteries is growing rapidly as automobiles become electrified. The presence of small metallic particles of the order of 10 μm in a battery is likely to cause failure; therefore, it is important to eliminate them. We have developed a prototype system for detecting metallic foreign matter in liquid components for Li-ion batteries using a high-temperature superconducting radio-frequency superconducting quantum interference device, which is a highly sensitive magnetic sensor. Signal waveforms of a magnetized metallic piece passing through a trench located below the superconducting quantum interference device (SQUID) were measured. We found that the waveform depended on the direction of the magnetic dipole at the time of detection and could be roughly divided into six categories to explain all the experimental results. The maximum magnitude obtained for each sample was plotted against the equivalent spherical diameter of the sample. Our results indicate that the signal intensity was proportional to the cube of the sample diameter and that particles larger than φ20 × 30 μm (equivalent spherical diameter 23 μm) were detected with signal-to-noise ratio ≥3. [ABSTRACT FROM AUTHOR]

Extensive and high residue variations in enrofloxacin (ENR) exist in different aquatic products. A novel quantitative method for measuring ENR using high-performance liquid chromatography–tandem mass spectrometry was developed employing enrofloxacin-d5 (ENR-d5) and enrofloxacin-d3 (ENR-d3) as isotope surrogates. This reduced the deviation of detected values, which results from the overpass of the linear range and/or the large difference in the residue between the isotope standard and ENR, from the actual content. Furthermore, high residue levels of ENR can be directly diluted and re-calibrated by the corresponding curve with the addition of high levels of another internal surrogate without repeated sample preparation, avoiding the overflow of the instrument response. The validation results demonstrated that the method can simultaneously determine ENR residues from MQL (2 µg/kg) to 5000 × MQL (method quantification limit) with recoveries between 97.1 and 106%, and intra-precision of no more than 2.14%. This method realized a wide linear calibration range with dual deuterated isomers, which has not been previously reported in the literature. The developed method was successfully applied to the analysis of ENR in different aquatic products, with ENR residue levels varying from 108 to 4340 μg/kg and an interval of precision in the range of 0.175~6.72%. These results demonstrate that batch samples with a high variation in ENR residues (over the linear range with a single isotope standard) can be detected by the dual isotope surrogates method in a single sample preparation process.

In the framework of indoor air monitoring, this paper proposes an Internet of Things ready solution to detect and classify contaminants. It is based on a compact and low-power integrated system including both sensing and processing capabilities. The sensing is composed of a sensor array on which electrical impedance measurements are performed through a microchip, named SENSIPLUS, while the processing phase is mainly based on Machine Learning techniques, embedded in a low power and low resources micro controller unit, for classification purposes. An extensive experimental campaign on different contaminants has been carried out and raw sensor data have been processed through a lightweight Multi Layer Perceptron for embedded implementation. More complex and computationally costly Deep Learning techniques, as Convolutional Neural Network and Long Short Term Memory, have been adopted as a reference for the validation of Multi Layer Perceptron performance. Results prove good classification capabilities, obtaining an accuracy greater than 75% in average. The obtained results, jointly with the reduced computational costs of the solution, highlight that this proposal is a proof of concept for a pervasive IoT air monitoring system.

In recent years, as number of new building getting larger, there has been an increased interest in the cleaning of exterior walls. Accordingly, there is a growing interest in automatic cleaning robots that move around the outer building façade. These robots are also required to apply different cleaning methods to remove various contaminants on the outer wall of the building. However, current surface contaminant detection systems can either detect only a single type of contaminant, or are not compact enough for installation on mobile platforms that move around the outer façade. As cleaning workers are able to distinguish various contaminants with the naked eye, we aim to solve this problem by developing a machine-vision system using convolutional neural networks (CNNs) and image processing methods. As it is a compact system that uses only a camera to take pictures and a processor to process the images, it is suitable for applications involving mobile platforms. Object-type contaminants such as avian feces are handled by the YOLOv3 module using the object-detection algorithm. Area-type contaminants such as rusty stains are processed using the color-detection module using the HSV color space, median filter, and flood fill algorithm. Particle-type contaminants such as dust are handled by the grayscale module, converting images to grayscale images and then comparing the average brightness with a reference that is provided in advance. This proposed machine vision system will detect objects, areas, and particle-type contaminants with a single image and some reference images provided in advance.

The in vitro propagation of Colocasia esculenta is limited by the presence of microbial contaminants, especially bacteria. The objective of this work was to demonstrate that the inclusion of actions for the bacterial contamination management in the in vitro propagation of C. esculenta cv. ‘INIVIT MC-2012’, from field explants, can reduce losses. Two in vitro propagation protocols were used and the incidence of bacterial contaminants in the establishment and multiplication stages was compared. One protocol modified the other with actions for the management of bacterial contamination. In each one, 20 primary rhizomes were taken from which at least two axillary buds were extracted and from each one a line was established. The number of explants contaminated with bacteria per subculture was quantified. Bacterial contamination was observed in the culture medium below or around the explants, indicating the initial explant as the primary source of contamination. With the application of the modified protocol, the percentage of losses due to bacterial contamination in all subcultures evaluated was reduced. In the in vitro establishment it did not exceed 5%. The combination of actions for the management of bacterial contamination among which are the reduction of the size of the explant, the work by lines and the visual detection of contaminants prior to the subculture of the plant material reduces the presence of bacterial contaminants in the in vitro propagation of C. esculenta cv. ‘INIVIT MC-2012’.

The sudden release of toxic contaminants that reach indoor spaces can be hazardous to building occupants. For an acutely toxic contaminant, the speed of the emergency response strongly influences the consequences to occupants. The design of a real time sensor system is made challenging both by the urgency and complex nature of the event, and by the imperfect sensors and models available to describe it. In this research, we use Bayesian modeling to combine information from multiple types of sensors to improve the characterization of a release. We discuss conceptual and algorithmic considerations for selecting and fusing information from disparate sensors. To explore system performance, we use both real tracer gas data from experiments in a three story building, along with synthetic data, including information from door position sensors. The added information from door position sensors is found to be useful for many scenarios, but not always. We discuss the physical conditions and design factors that affect these results, such as the influence of the door positions on contaminant transport. We highlight potential benefits of multisensor data fusion, challenges in realizing those benefits, and opportunities for further improvement.

Throughout the assembly, integration, and test process, molecular contamination levels of space mission hardware are monitored to meet system performance requirements. Qualitatively, reflective surfaces and witness mirrors are continuously inspected for the visible presence of molecular contaminant films. Quantitatively, periodic reflectance measurements of witness mirrors indicate changes of mirror reflectivity over time due to the accumulation of molecular contaminant films. However, both methods only consider the presence of a contaminant film and not the molecular composition. Additionally, there is a risk that hardware may appear to be "visibly clean" even with a molecular contaminant film present on critical surfaces. To address these issues, experiments were performed to quantify the maximum molecular contaminant film that could be missed in visual inspections on witness mirrors with five different contaminants present. The corresponding changes in mirror reflectivity were modeled using the program STACK to determine the impact to space mission hardware performance. The results of this study not only show the criticality in considering the chemical make-up of molecular contaminant films on system performance, but also the need to recognize and understand the limitations of traditional visual inspection techniques on detecting molecular contaminant films. [ABSTRACT FROM AUTHOR]

The in vitro propagation of Colocasia esculenta is limited by the presence of microbial contaminants, especially bacteria. The objective of this work was to demonstrate that the inclusion of actions for the bacterial contamination management in the in vitro propagation of C. esculenta cv. 'INIVIT MC-2012', from field explants, can reduce losses. Two in vitro propagation protocols were used and the incidence of bacterial contaminants in the establishment and multiplication stages was compared. One protocol modified the other with actions for the management of bacterial contamination. In each one, 20 primary rhizomes were taken from which at least two axillary buds were extracted and from each one a line was established. The number of explants contaminated with bacteria per subculture was quantified. Bacterial contamination was observed in the culture medium below or around the explants, indicating the initial explant as the primary source of contamination. With the application of the modified protocol, the percentage of losses due to bacterial contamination in all subcultures evaluated was reduced. In the in vitro establishment it did not exceed 5%. The combination of actions for the management of bacterial contamination among which are the reduction of the size of the explant, the work by lines and the visual detection of contaminants prior to the subculture of the plant material reduces the presence of bacterial contaminants in the in vitro propagation of C. esculenta cv. 'INIVIT MC-2012'. [ABSTRACT FROM AUTHOR]

The evolution of food safety practices is crucial in addressing the challenges posed by a growing global population and increasingly complex food supply chains. Traditional methods are often labor-intensive, time-consuming, and susceptible to human error. This chapter explores the transformative potential of integrating microfluidics into smart food safety protocols. Microfluidics, involving the manipulation of small fluid volumes within microscale channels, offers a sophisticated platform for developing miniaturized devices capable of complex tasks. Combined with sensors, actuators, big data analytics, artificial intelligence, and the Internet of Things, smart microfluidic systems enable real-time data acquisition, analysis, and decision-making. These systems enhance control, automation, and adaptability, making them ideal for detecting contaminants, pathogens, and chemical residues in food products. The chapter covers the fundamentals of microfluidics, its integration with smart technologies, and its applications in food safety, addressing the challenges and future directions in this field., (Copyright © 2024. Published by Elsevier Inc.)

Highlights • Investigated the effect of imperfect sensors on the optimal sensor placement when compared to perfect sensor networks. • Exploring many different objectives together in the imperfect sensor setting. • Designed a greedy algorithm which proved to find good sensor placements in reasonable time for networks of 10,000 nodes. Abstract Water Distribution Networks (WDNs) are often susceptible to either accidental or deliberate contamination which can lead to poisoned water, many fatalities and large economic consequences. In order to protect against these intrusions or attacks, an efficient sensor network with a limited number of sensors should be placed in a WDN. In this paper, we focus on optimal sensor placements by introducing two greedy-based algorithms in which the imperfection of sensors and multiple objectives can be taken into account. The algorithms were tested using a medium scale urban WDN. It is shown that our algorithms are able to find sensor placements in reasonable time and that its solutions are close to optimal. Furthermore, relaxing the often used assumption that sensors work perfectly results in different sensor placements than were found before, indicating the importance to take sensor imperfection into account when placing sensors. [ABSTRACT FROM AUTHOR]

The automatic preservation of the indoor air quality (IAQ) is an important task of the intelligent building design in order to ensure the health and safety of the occupants. The IAQ, however, is often compromised by various airborne contaminants that penetrate the indoor environment as a result of accidents or planned attacks. In this paper, we provide the detailed analysis, implementation, and evaluation of a distributed methodology for detecting and isolating multiple contaminant events in large-scale buildings. Specifically, we consider the building as a collection of interconnected subsystems, and we design a contaminant event monitoring software agent for each subsystem. Each monitoring agent aims to detect the contaminant and isolate the zone where the contaminant source is located, while it is allowed to exchange information with its neighboring agents. For configuring the subsystems, we implement both exact and heuristic partitioning solutions. A main contribution of this paper is the investigation of the impact of the partitioning solution on the performance of the distributed contaminant detection and isolation (CDI) scheme with respect to the detectability and isolability of the contaminant sources. The performance of the proposed distributed CDI methodology is demonstrated using the models of real building case studies created on CONTAM. 1 CONTAM is a multizone simulation program developed by the U.S. National Institute of Standards and Technology. [ABSTRACT FROM AUTHOR]

The present study investigates the suitability of a machine vision-based method to detect deviations in the Shannon entropy (SE) of a European seabass (Dicentrarchus labrax) biological system fed with different selenium:mercury (Se:Hg) molar ratios. Four groups of fish were fed during 14 days with commercial feed (control) and with the same feed spiked with 0.5, 5 and 10 mg of MeHg per kg, giving Se:Hg molar ratios of 29.5 (control-C1); 6.6, 0.8 and 0.4 (C2, C3 and C4). The basal SE of C1 and C2 (Se:Hg > 1) tended to increase during the experimental period, while that of C3 and C4 (Se:Hg < 1) tended to decrease. In addition, the differences in the SE of the four systems in response to a stochastic event minus that of the respective basal states were less pronounced in the systems fed with Se:Hg molar ratios lower than one (C3 and C4). These results indicate that the SE may be a suitable indicator for the prediction of seafood safety and fish health (i.e., the Se:Hg molar ratio and not the Hg concentration alone) prior to the displaying of pathological symptoms. We hope that this work can serve as a first step for further investigations to confirm and validate the present results prior to their potential implementation in practical settings. [ABSTRACT FROM AUTHOR]

During slaughtering and further processing, chicken carcasses are inevitably contaminated by microbial pathogen contaminants. Due to food safety concerns, many countries implement a zero-tolerance policy that forbids the placement of visibly contaminated carcasses in ice-water chiller tanks during processing. Manual detection of contaminants is labor consuming and imprecise. Here, a successive projections algorithm (SPA)-multivariable linear regression (MLR) classifier based on an optimal performance threshold was developed for automatic detection of contaminants on chicken carcasses. Hyperspectral images were obtained using a hyperspectral imaging system. A regression model of the classifier was established by MLR based on twelve characteristic wavelengths (505, 537, 561, 562, 564, 575, 604, 627, 656, 665, 670, and 689 nm) selected by SPA , and the optimal threshold T = 1 was obtained from the receiver operating characteristic (ROC) analysis. The SPA-MLR classifier provided the best detection results when compared with the SPA-partial least squares (PLS) regression classifier and the SPA-least squares supported vector machine (LS-SVM) classifier. The true positive rate (TPR) of 100% and the false positive rate (FPR) of 0.392% indicate that the SPA-MLR classifier can utilize spatial and spectral information to effectively detect contaminants on chicken carcasses. [ABSTRACT FROM AUTHOR]

We propose a contaminant detection methodology suitable for robotic automation, which is able to not only locate the source(s) of the contaminant but also estimate its intensity in an environment that is allowed to evolve over both space and time. The essential idea is to flexibly model the contaminant field surface nonlinearly via radial basis functions and to utilize basic notions from the statistical design of experiments concerning optimal placement of observations in order to make incremental decisions about robot movements. Algorithms are presented for determining such movements and the subsequent collection of measurements in three different cases corresponding to different modes of spatio-temporal evolution. The result is an iterative scheme that gradually locates the peaks (sources), as well as the entire contaminant surface. The performance of the method is assessed through simulations from known surfaces. Theoretical issues concerning convergence of parameter estimates in a multiple robots scenario are examined. The method can accommodate measurement noise and does not rely on surface gradient information. [ABSTRACT FROM PUBLISHER]

Methylmercury (MeHg) is an environmental contaminant of increasing relevance as a seafood safety hazard that affects the health and welfare of fish. Non-invasive, on-line methodologies to monitor and evaluate the behavior of a fish system in aquaculture may make the identification of altered systems feasible--for example, due to the presence of agents that compromise their welfare and wholesomeness--and find a place in the implementation of Hazard Analysis and Critical Control Points and Fish Welfare Assurance Systems. The Shannon entropy (SE) of a European seabass (Dicentrarchus labrax) system has been shown to differentiate MeHg-treated from non-treated fish, the former displaying a lower SE value than the latter. However, little is known about the initial evolution of the system after removal of the toxicant. To help to cover this gap, the present work aims at providing information about the evolution of the SE of a European seabass system during a recuperation period of 11 days following a two-week treatment with 4 μg· MeHg/L. The results indicate that the SE of the system did not show a recovery trend during the examined period, displaying erratic responses with daily fluctuations and lacking a tendency to reach the initial SE values. [ABSTRACT FROM AUTHOR]

Water distribution networks (WDN) are vulnerable to either intentional or accidental contamination. In order to protect against such intrusions, effective and efficient online monitoring systems are needed. Due to cost and maintenance reasons, it is not possible to locate sensors at each and every potential intrusion point. In this work, we design minimal sensor networks which satisfy the two important properties of observability (ability to detect an intrusion) and identifiability (ability to identify the point of intrusion). Based on the hydraulic analysis of the network, a bipartite graph is constructed between intrusion points and the corresponding nodes that can potentially be affected by the contaminant. The problem of sensor network design is converted to a minimum set cover problem on the bipartite graph, and is solved using a greedy heuristic algorithm. The proposed method is illustrated using a medium scale urban WDN. [ABSTRACT FROM AUTHOR]

Food safety in the production of fresh produce for human consumption is a worldwide issue and needs to be addressed to decrease foodborne illnesses and resulting costs. Hyperspectral fluorescence imaging coupled with multivariate image analysis techniques for detection of fecal contaminates on spinach leaves ( Spinacia oleracea) was evaluated. Violet fluorescence excitation was provided at 405 nm and light emission was recorded from 464 to 800 nm. Partial least square discriminant analysis and wavelength ratio methods were compared for detection accuracy for fecal contamination. Fluorescence emission profiles of spinach leaves were monitored over a 27 days storage period; peak emission blue-shifts were observed over the storage period accompanying a color change from green to green-yellow-brown hue. The PLSDA model developed correctly detected fecal contamination on 100 % of relatively fresh green spinach leaves used in this investigation, which also had soil contamination. The PLSDA model had 19 % false positives for non-fresh post storage leaves. A wavelength ratio technique using four wavebands (680, 688, 703 and 723 nm) was successful in identifying 100 % of fecal contaminates on both fresh and non-fresh leaves. An on-line fluorescence imaging inspection system for fecal contaminant detection has potential to allow fresh produce producers to reduce foodborne illnesses and prevent against the associated economic losses. [ABSTRACT FROM AUTHOR]

Mutual interference issues between heavy metal ions tremendously affect the detection reliability and accuracy in water quality analysis, especially the serious interference of Cu(II) on the detection of As(III) is greatly hard to overcome, which needs to be solved urgently. Herein, iron single-atom catalysts with different coordination structures of FeN 2 C 2 and FeN 3 P are constructed to selectively catalyze the detection of As(III) in the coexistence of Cu(II). FeN 3 P achieves a high sensitivity of 3.90 µA ppb−1 toward As(III) in NH 4 Cl/NH 3 ·H 2 O electrolyte (pH 8.0), completely avoiding Cu(II)-interference. Moreover, the turnover frequency (TOF) of FeN 3 P is an order of magnitude higher than that of FeN 2 C 2. X-ray absorption fine structure (XAFS) spectroscopy and density functional theory (DFT) calculations demonstrate that an As-O bond of H 3 AsO 3 is broken by the strong affinities between both P and O atoms and Fe and As atoms, and H 3 AsO 3 are preferentially reduced by FeN 3 P during adsorptive process. Meanwhile, the low reaction energy barrier of the rate-determined step for As(III) reduction over FeN 3 P also accelerates the deposition of As(III) and enhances its response signals. The free-Cu(II) are difficult to adsorb on FeN 3 P and do not compete with As(III) for Fe active sites, which contributes to the excellent anti-Cu(II) interference capability. [Display omitted] • Catalytic behavior of Fe single-atoms for As(III) were regulated by N, C, and P atoms. • FeN 3 P showed a high sensitivity of 3.90 µA ppb−1, which was not affected by Cu(II). • Strong affinities of P and Fe atoms with O and As atoms broke an As-O bond of H 3 AsO 3. • Low energy barriers and fast reaction rate on FeN 3 P enhanced As(III) current signals. • Cu(II) did not compete for active sites of FeN 3 P or change their interaction way. [ABSTRACT FROM AUTHOR]

In the last few years, the traceability and labeling of processed food and feeds have gained increasing importance due to the impact that mislabeling and product fraud may have on human/animal health or on the quality of final products, such as milk, cheese, and meat, as a consequence of animal dietary. The presence of contaminants or possible frauds due to the use of alternative plant materials in food and feeds can greatly impact the economy; therefore, they are becoming important targets for product certification by competent institutional services. This is especially relevant when complex matrixes are considered, in which the visual identification of the different components is quite difficult or even impossible. Despite the existence of mandatory traceability requirements for the analysis of feed/food composition addressed by European Community regulations, the labels do not always provide a sufficient guarantee about the ingredients and additive composition of those products. In this sense, the development of new methodologies that aim to assess the traceability of feed and food complex matrixes is crucial. In this chapter, a general protocol is presented for the establishment of quantitative real-time PCR-based techniques based on TaqMan assays applied to feed/food traceability, with a special focus on applications in the areas of food and feed security (e.g., for the detection of plant species involved in allergenic reactions), fraud detection (e.g., genetically modified organisms), and certification (e.g., protected denomination of origin)., (© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

In this approach, a method, utilizing the data series from multivariate parameters to detect contaminant events in a quick time, is proposed. Eight parameters: pH, turbidity, conductivity, temperature, oxidation-reduction potential, UV-254, nitrate-nitrogen and phosphate, are used in this research and the most commonly used herbicide, glyphosate, is selected as the test contaminant. Variations of all parameters are recorded in real time at different concentrations. The results show that the proposed method could detect a glyphosate contamination 1 minute after the introduction of contaminant at the concentration of 2 mg/l using responses from online water quality sensors. It was also noticed that the patterns of sensor correlative responses were contaminant dependent and the magnitude was related to contaminant concentration. [ABSTRACT FROM AUTHOR]

Highlights: [•] Feasibility of application of ultra-low field (ULF) NMR/MRI was studied. [•] ULF-NMR/MRI system utilized HTS-rf-SQUID and permanent magnet of 1.1T. [•] Magnetic contaminants in water were successfully detected by NMR measurements. [•] Non-magnetic contaminants in water were distinguished by 1D-MRI measurements. [ABSTRACT FROM AUTHOR]

Accurate detection and identification of natural or intentional contamination events in a drinking water pipe is critical to drinking water supply security and health risk management. To use conventional water quality sensors for the purpose, we have explored a real-time event adaptive detection, identification and warning (READiw) methodology and examined it using pilot-scale pipe flow experiments of 11 chemical and biological contaminants each at three concentration levels. The tested contaminants include pesticide and herbicides (aldicarb, glyphosate and dicamba), alkaloids (nicotine and colchicine), E. coli in terrific broth, biological growth media (nutrient broth, terrific broth, tryptic soy broth), and inorganic chemical compounds (mercuric chloride and potassium ferricyanide). First, through adaptive transformation of the sensor outputs, contaminant signals were enhanced and background noise was reduced in time-series plots leading to detection and identification of all simulated contamination events. The improved sensor detection threshold was 0.1% of the background for pH and oxidation-reduction potential (ORP), 0.9% for free chlorine, 1.6% for total chlorine, and 0.9% for chloride. Second, the relative changes calculated from adaptively transformed residual chlorine measurements were quantitatively related to contaminant-chlorine reactivity in drinking water. We have shown that based on these kinetic and chemical differences, the tested contaminants were distinguishable in forensic discrimination diagrams made of adaptively transformed sensor measurements. [Copyright &y& Elsevier]

Approximately 1.4 billion m3 of fluid tailings produced from oil sands mining operations are currently being held in Alberta, Canada and pose a significant risk to the environment if not properly treated and managed. The ability to quantify levels of toxic compounds, such as naphthenic acids (NAs) and phenol, accurately and rapidly in the produced oil sands process-affected water (OSPW) is required to ensure the protection of the surrounding aquatic environment. In this paper, fluorescence techniques are investigated to rapidly quantify NAs and phenol concentrations in natural surface waters. Machine learning approaches were applied to identify relevant spectral features to improve detection accuracy in the presence of background interference from organic matter in natural waters. NAs were relatively easy to detect by all methods, however deep convolutional neural networks (CNN) resulted in optimized performance for phenol with mean absolute errors of 1.78 – 1.81 mg/L and 4.68–5.41 µg/L, respectively. Visualization of spectral areas of importance revealed that deep CNNs utilized logical areas of the fluorescence spectra associated with NAs and phenol signals. Results suggest machine learning approaches to interpreting fluorescence data can accurately predict individual toxic components of OSPW in natural waters at environmentally relevant concentrations. [Display omitted] • Rapid quantification of toxic compounds from oil sands tailings was investigated. • Convolutional neural networks improved fluorescence-based quantification. • Increasing network depth improved CNN performance, despite a small sample size. • An occlusion method helped identify the spectral regions used by the networks. • The method could provide rapid assessment of oil sands impacts on natural waters. [ABSTRACT FROM AUTHOR]

In an effort to address the scarcity of chemical sensors that can record precise, reproducible, and sensitive changes in humidity, this work reports a reversible system based on a coordinatively unsaturated, square-planar platinum(II) salt, [Pt(tpy)Cl]ClO 4 (tpy = 2,2′6′,2″-terpyridine). The sensing methodology relies on humidity induced vapochromic/vapoluminescent behavior of the salt; the anhydrous form is yellow in color and demonstrates orange-yellow luminescence indicating weak intermolecular Pt•••Pt interactions. Exposure to water vapor changes the salt color to dark red and the luminescence to red; this is triggered by incorporation of water molecules in the crystal lattice. This reorganizes the crystal packing, and extends Pt•••Pt interactions, as verified by crystal structure. The conversion between the fully hydrated and the fully dehydrated end products are spectroscopically reversible, demonstrating recyclability across cycles. However, the vapoluminescence response trajectory of the dehydrated form to water vapor sorption does not exactly reverse trace the desorption profile of the hydrated form but shows a hysteresis effect, demonstrating the vapochromic journey to be equally important as the destination. This methodology serves as the basis of humidity sensing for both powder as well as crystalline samples. The proposed sensor demonstrates a large linear operational range of humidity sensing (10–80% for crystals) and a limit of detection of 3.3%. The crystals also demonstrate an ability to sense water vapor in the presence of interfering organic vapors. The work presents a new simple, economic and scalable method for humidity sensing. [Display omitted] • Reversible humidity sensing by a linear, photochromic, luminescent Pt(II) complex. • Vapoluminescence path for vapor sorption and desorption has a hysteresis effect, showing the vapochromic journey to be vital. • Large linear range of humidity sensing (10–80% for crystals; 30–60% for powders). • A limit of detection of 3.3% and a limit of quantification of 10% for humidity sensing. • Demonstrated ability to sense water vapor in the presence of competing or interfering species (e.g. organic vapors). [ABSTRACT FROM AUTHOR]