Detection Of Chemical Contaminants Research Articles

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

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

Detection of Chemical Contaminants in Water for Irrigation Systems: A Systematic Review

Water pollution is a detrimental issue that occurs when there are bad changes in water quality parameters. It directly disrupts water usage and poses a danger to society, environment, economy, and agriculture. Water quality should be monitored to alert authorities on water pollution, so that action can be taken quickly. Improper water management pollutes rivers and lakes in Malaysia such as Klang River, Semenyih River, Kim Kim River, Slim River, and others. Various techniques are introduced to detect contaminants in water such as electronic sensors, biosensor approaches, laboratory analysis, and optical techniques. The functionality tool varies depending on the specific contaminants or parameters that are targeted, and the resources allocated. Several common contaminant types are found in polluted water including heavy metals, organic and inorganic chemicals, industrial pollutants, suspended solids, and others. The objective of the review is to study various non-optical and optical contaminant detection methods in water to identify the strengths and weaknesses of the methods. In this review, water pollution problems mainly due to the agricultural sector in several countries are discussed. Besides, conventional, and modern methods are compared in terms of parameters, complexity, and reliability. We believe that conventional methods are costly and complex, whereas modern methods are also expensive but simpler with real-time detection. Recent contaminant detection methods in water are also reviewed to study any loopholes in the latest methods. We found that the spectroscopy method based on light propagation theory is suitable and one of the promising methods for chemical contaminants detection for water quality analysis. This method offers fast analysis time, high sensitivity detection, non-invasive analysis, provides reliable data, and others. Undoubtedly, some contaminants in water can be challenging to be identified rapidly and confidently even though there are numerous tools and techniques available for water quality analysis. Thus, the review is important to compare previous methods and to improve current chemical contaminants detection analysis in terms of reliability with a minimum operating system and cost effectiveness.

Detection of chemical contaminants in heat processed meat products based on UPLC-MS/MS

Detection of chemical contaminants in heat processed meat products based on UPLC-MS/MS

Laboratory Development of an AI System for the Real-Time Monitoring of Water Quality and Detection of Anomalies Arising from Chemical Contamination

Monitoring water quality is critical for mitigating risks to human health and the environment. It is also essential for ensuring high quality water-based and water-dependent products and services. The monitoring and detection of chemical contamination are often based around a small set of parameters or substances. Conventional monitoring often involves the collection of water samples in the field and subsequent analyses in the laboratory. Such strategies are expensive, time consuming, and focused on a narrow set of potential risks. They also induce a significant time delay between a contamination event and a possible reactive measure. Here, we developed a real-time monitoring system based on Artificial Intelligence (AI) for field deployable sensors. We used data obtained from full-scan UV-spec and fluorescence sensors for validation in this study. This multi-sensor system consists of (a) anomaly detection that uses multivariate statistical methods to detect any anomalous state in an aqueous environment and (b) anomaly identification, using Machine Learning (ML) to classify the anomaly into one of the a priori known categories. For a proof of concept, we tested this methodology on a supply of municipal drinking water and a few representative organic chemical contaminants applied in a laboratory-controlled environment. The outcomes confirm the ability for the multi-sensor system to detect and identify changes in water quality due to incidences of chemical contamination. The method may be applied to numerous other areas where water quality should be measured online and in real time, such as in surface-water, urban runoff, or food and industrial process water.

Digital colorimetric analysis for estimation of iron in water with smartphone-assisted microfluidic paper-based analytical devices

ABSTRACT Microfluidic paper-based analytical devices are emerging as promising options for on-the-spot detection of chemical contaminants in water. The coupling of these devices with digital imaging technology has attracted immense interest in developing portable sensor applications. However, the potential of digital techniques still remains to be fully explored. In this work, we integrate digital imaging with microfluidic paper-based analytical devices to develop a portable colorimetric assay for iron. The experimental conditions are optimised using image analysis and the effects of imaging equipment and colorimetric analysis methods on the readout of devices are studied using numerical, graphical and statistical techniques. The experimental results are obtained for the estimation of iron contamination in water samples using a colorimetric assay based on iron (III)-thiocyanate reaction. The digital image acquisition approach using smartphone as imaging equipment is adopted for on-the-spot colorimetric data collection. Image processing techniques are employed to estimate the iron concentration in water from digital images. Greyscale colour intensity obtained from a digital image is identified as a readout of the analytical device and employed for quantitative assessment. The limit of detection for iron is estimated to be 0.26 ppm. The microfluidic approach offers good recovery and repeatability with relative standard deviation of 1.4%. The maximum percentage difference for intensities between the image analysis methods is 4.94% and that between the imaging equipment is 9.96%. The gradients of regression lines for pairwise equipment and method outputs with zero intercept range from 0.9931 to 1.0162 with high values of coefficients of determination. The correlation coefficient values for various paired comparisons (>0.99) suggest strong agreement. Statistical test results suggest no significant differences between methods and between imaging equipment (significance level 0.05). The findings suggest the efficacy of digital imaging and colorimetric analysis for paper-based analytical devices.

Detection of Hazardous Contaminants in Ground Water Resources: An Alarming Situation for Public Health in Karachi, Pakistan

Due to industrialization and over population, surface water resources are out of reach from many people so consumption of ground water is the only choice to overcome the water scarcity. Naturally, ground water is one of the significant and potable water resource but some geographical conditions and anthropogenic activities deteriorate the water quality and make it objectionable for drinking. This study was conducted to evaluate the ground water quality of Karachi, Pakistan. For this, 42 ground water samples were collected from different districts of Karachi and analyzed their physicochemical and microbiological characteristics and compared with both international (WHO) and national (SEQS) drinking water standards. Observations of the study declared that overall contamination (physicochemical and microbial) in the ground water samples of different districts of Karachi was as follow West (21%), South (20%), Central (17%), Malir (16%), Korangi (14%) and East (12%). Physical assessment of the study area declared that pH and turbidity of the ground water samples varies in the range of (6.54-7.9) and (0-1.01 NTU) which exist in the standard prescribed limit. Whereas, detection of chemical contaminants particularly TDS (457-12090 mg/L), hardness (118.8-3645 mg/L) and chloride (190-4918 mg/L) content in most of the samples were also exceed from the prescribed limit. Additionally, arsenic was abundantly present ranging from 3.52-13.63 mg/L in all collected samples of Karachi city while the concentration of cadmium (range: 0.0005-0.5012 mg/L) and lead (range: 0.201-1.817 mg/L) were also high in few samples, from the permissible limit of drinking water. Microbial contamination was also detected in which coliforms were present in the range of 0-150 CFU/100mL, which also unfit the water quality. This deteriorated ground water quality of Karachi can be improved by maintenance of proper sanitary conditions of the communities and implementation of water treatments, otherwise consumption of such water may develop serious health related consequences in the consumers.

Nanomaterials and microbes’ interactions: a contemporary overview

Use of nanomaterials in the field of science and technology includes different fields in food industry, medicine, agriculture and cosmetics. Nanoparticle-based sensors have wide range of applications in food industry for identification and detection of chemical contaminants, pathogenic bacteria, toxins and fungal toxins from food materials with high specificity and sensitivity. Nanoparticle-microbe interactions play a significant role in disease treatment in the form of antimicrobial agents. The inhibitory mechanism of nanoparticles against different bacteria and fungi includes release of metal ions that interacts with cellular components through various pathways including reactive oxygen species (ROS) generation, pore formation in cell membranes, cell wall damage, DNA damage, and cell cycle arrest and ultimately inhibits the growth of cells. Nanoparticle-based therapies are growing to study the therapeutic treatments of plant diseases and to prevent the growth of phytopathogens leading to the growing utilization of engineered nanomaterials. Hence, with this background, the present review focuses thoroughly on detailed actions and responses of nanomaterials against different bacteria and fungi as well as food sensing and storage.

多角度红外差分吸收光谱的化学污染物在线检测方法

多角度红外差分吸收光谱的化学污染物在线检测方法

Research and Simulation of Optical Measurement Model in Chemical Pollution of Water

Currently, the chemical pollution of water detection is mainly based on optical method. However, the optical detector is easily restricted by light and weather conditions. Optical measurement can be set on the target feature point, such as the laser reflection apparatus in laser measurement. This article applied the optical measurement method to research detection of water chemical pollution. Use theoretical and mathematical models of optical measurement method to perform the detection of chemical contamination in water. Optical measurement method is the trend of future development.

Measuring the Applicability of Biosensors to Detect Possible Terror Chemicals in Water Distribution Network

Instant and accurate detection of chemical contamination in a water distribution system is significant in protecting public health and water resource. Using water quality surrogates to signal a contamination occurrence offers the advantage of detecting a large number of chemicals. This article describes how the indigenous biofilm in a contaminated drinking water system may provide an effective indirect surrogate response with either turbidity or ultraviolet changes (UV254). Rotating annular bioreactors (RAB) and pipe loops were used to quantify biofilm along with turbidity and UV254 measurements. The number of biofilm cells on the polyvinyl chloride (PVC) analytical coupons was also enumerated, using automated fluorescence microscopy. Measuring the biosensing capacity, the analytical coupons were submersed into beakers with potential chemical contaminants such as aldicarb, cyanide, cycloheximide, nicotine, sodium arsenate, and sodium fluoroacetate. Resulting data indicated that turbidity was a useful surrogate for forensically monitoring chemical contaminants. Turbidity measurement can make an inexpensive biosensor for the security of a water distribution system.

TERAHERTZ-BASED DETECTORS USING COLD-ATOM OPTICS

In this paper we explore the design of microwave-based structures that can enhance the interaction of electromagnetic fields with cold-atom ensembles, leading to novel sensing modalities based on the quantum-mechanical behavior of these systems. In particular, we discuss electromagnetically-induced transparency in a single uncondensed cold-atom cloud, and a two-cloud version of a SQUID, where the clouds are BEC's and take the place of the weakly coupled superconductors. These systems are both promising candidates for use in the high-precision detection of chemical contaminants.