Traditional Analytical Techniques Research Articles

NanoMIP beacons with a co-operative binding mechanism for the all-in-one detection of methamphetamine aptamer complexes

Methamphetamine is a highly addictive stimulant with significant public health implications, necessitating the development of rapid, sensitive, and reliable detection methods. Traditional analytical techniques, though accurate, often involve complex sample preparation, expensive equipment, and lengthy analysis times. This study presents the design, synthesis, and application of nanoMIP beacons with a unique co-operative binding mechanism for the detection of methamphetamine. NanoMIP beacons selective for methamphetamine/aptamer complexes were synthesized using a solid-phase synthesis method that involved the bio-conjugation of an aptamer on the stationary phase and the incubation of methamphetamine to form a methamphetamine-aptamer complex. The resultant nanoMIP beacons were eluted off the affinity column and characterised using transmission electron microscopy. The co-operative binding mechanism, which relies on the structure-switching capability of the aptamer upon analyte binding was demonstrated through comparison of the fluorescence quenching signal of a scrambled sequence and nanoNIP controls. The fluorescence quenching assay was established using a fixed optimal concentration of aptamer and varying amounts of methamphetamine. The nanoMIP beacons showed enhanced the sensitivity (LOD = 23 ± 3.5 nM) and excellent selectivity, with a 40-fold increase in quenching for methamphetamine compared to other illicit drugs. The nanoMIP beacons demonstrated acceptable sample recoveries in both urine diluent and 50% human serum. This work provides a new strategy for the development of hybrid nanoMIP/aptamer-based sensors and provides a robust analytical tool for combating methamphetamine abuse.

Malware authorship attribution: Unmasking the culprits behind malicious software

With the digital age ushering in an unprecedented proliferation of malware, accurately attributing these malicious software variants to their original authors or affiliated groups has emerged as a crucial endeavor in cybersecurity. This study delves into the intricacies of malware authorship attribution by combining traditional analytical techniques with advanced machine learning methodologies. An integrated approach, encompassing static and dynamic analyses, yielded promising results in the challenging realm of malware attribution. Despite the encouraging outcomes, the research highlighted the multifaceted complexities involved, especially considering the sophisticated obfuscation techniques frequently employed by attackers. This paper emphasizes the merits of a holistic attribution model and underscores the importance of continuous innovation in the face of an ever-evolving threat landscape.

Recent advances in portable devices for environmental monitoring applications.

Environmental pollution remains a major societal problem, leading to serious impacts on living organisms including humans. Human activities such as civilization, urbanization, and industrialization are major causes of pollution. Imposing stricter rules helps control environmental pollutant levels, creating a need for reliable pollutant monitoring in air, water, and soil. The application of traditional analytical techniques is limited in low-resource areas because they are sophisticated, expensive, and bulky. With the development of biosensors and microfluidics technology, environmental monitoring has significantly improved the analysis time, low cost, portability, and ease of use. This review discusses the fundamentals of portable devices, including microfluidics and biosensors, for environmental control. Recently, publications reviewing microfluidics and biosensor device applications have increased more than tenfold, showing the potential of emerging novel approaches for environmental monitoring. Strategies for enzyme-, immunoassay-, and molecular-based analyte sensing are discussed based on their mechanisms and applications. Microfluidic and biosensor platforms for detecting major pollutants, including metal ions, pathogens, pesticides, and antibiotic residues, are reviewed based on their working principles, advantages, and disadvantages. Challenges and future trends for the device design and fabrication process to improve performance are discussed. Miniaturization, low cost, selectivity, sensitivity, high automation, and savings in samples and reagents make the devices ideal alternatives for in-field detection, especially in low-resource areas. However, their operation with complicated environmental samples requires further research to improve the specificity and sensitivity. Although there is a wide range of devices available for environmental applications, their implementation in real-world situations is limited. This study provides insights into existing issues that can be used as references and a comparative analysis for future studies and applications.

Determination of Arylcyclohexylamines in Biological Specimens: Sensors and Sample Pre-Treatment Approaches.

Arylcyclohexylamine (ACH) compounds represent a predominant faction within new psychoactive substances. Due to their powerful dissociative effects, they are used in recreational contexts but also in situations of drug-facilitated sexual assault, and therefore, they are a constant target of analysis by forensic experts. In recent years, their consumption has been notably high, especially the use of ketamine, presenting daily challenges for laboratories in the determination of this and other ACH analogues. This review comprises the recent strategies that forensic specialists use to identify and quantify ACH compounds in the laboratory with more traditional analytical techniques and technology, and on the point-of-care testing via sensor technology. The study focuses on analogues of phencyclidine (PCP), ketamine, and eticyclidine, highlighting the consistent need for higher sensitivity in the analysis of various samples collected from real cases and simulations of possible matrices. The review also emphasises the ongoing research to develop more sensitive, quicker, and more capable sensors.

Exploring Agro-Industrial By-Products: Phenolic Content, Antioxidant Capacity, and Phytochemical Profiling via FI-ESI-FTICR-MS Untargeted Analysis.

This study investigates agro-industrial by-products as sources of bioactive compounds, particularly focusing on phenolic compounds known for their antioxidant properties. With growing interest in natural alternatives to synthetic antioxidants due to safety concerns, this study highlights the health benefits of plant-derived phenolic compounds in food preservation and healthcare products. Traditional and advanced analytical techniques were used to obtain phytochemical profiles of various residue extracts, including espresso (SCG) and cold-brew spent coffee grounds (CBCG), pineapple peel (PP), beetroot pomace (BP), apple pomace (AP), black carrot pomace (BCP), and garlic peel (GP). Assessments of total phenolic content (TPC), total flavonoid content (TFC), and antioxidant capacity (AC) supported their revalorization. CBCG showed the highest TPC, TFC, and AC. TPC content in by-products decreased in the order CBCG > SCG > GP > BCP > PP > AP > BP, with a similar trend for TFC and AC. Phytochemical profiling via FI-ESI-FTICR-MS enabled the preliminary putative identification of a range of compounds, with polyphenols and terpenes being the most abundant. Univariate and multivariate analyses revealed key patterns among samples. Strong positive correlations (Pearson's R > 0.8) indicated significant contribution of polyphenols to antioxidant capacities. These findings highlight the potential of agro-industrial residues as natural antioxidants, advocating for their sustainable utilization.

Unraveling the Chicken Meat Volatilome with Nanostructured Sensors: Impact of Live and Dehydrated Insect Larvae Feeding.

Incorporating insect meals into poultry diets has emerged as a sustainable alternative to conventional feed sources, offering nutritional, welfare benefits, and environmental advantages. This study aims to monitor and compare volatile compounds emitted from raw poultry carcasses and subsequently from cooked chicken pieces from animals fed with different diets, including the utilization of insect-based feed ingredients. Alongside the use of traditional analytical techniques, like solid-phase microextraction combined with gas chromatography-mass spectrometry (SPME-GC-MS), to explore the changes in VOC emissions, we investigate the potential of S3+ technology. This small device, which uses an array of six metal oxide semiconductor gas sensors (MOXs), can differentiate poultry products based on their volatile profiles. By testing MOX sensors in this context, we can develop a portable, cheap, rapid, non-invasive, and non-destructive method for assessing food quality and safety. Indeed, understanding changes in volatile compounds is crucial to assessing control measures in poultry production along the entire supply chain, from the field to the fork. Linear discriminant analysis (LDA) was applied using MOX sensor readings as predictor variables and different gas classes as target variables, successfully discriminating the various samples based on their total volatile profiles. By optimizing feed composition and monitoring volatile compounds, poultry producers can enhance both the sustainability and safety of poultry production systems, contributing to a more efficient and environmentally friendly poultry industry.

Novel ferrofluid based on hydrophobic deep eutectic solvents for separation and analysis of trace estrogens in environmental water and urine samples.

A novel ferrofluid prepared from ahydrophobic deep eutectic solvent (DES) and Fe3O4@graphite composite materials was introduced as a green microextraction medium for the separation and enrichment of trace estrogens in real samples. It was found that the ferrofluid greatly improved the capacity and selectivity of target analytes, benefiting from the enrichment of both DES and Fe3O4@graphite composite materials. Using a combination of high-performance liquid chromatography-fluorescence detection (HPLC-FLD) and vortex-assisted liquid-liquid microextraction (VALLME), a new method was established for simultaneous rapid processing and accurate determination of three estrogens (estradiol [E2], estriol [E3], and ethinyl estradiol [EE2]) in environmental water and urine samples. Key parameters affecting the extraction efficiency were optimized using a single-factor approach and response surface methodology. Under optimal conditions, this method yielded a low limit of detection (1.01 ng L-1, 3.03 ng L-1, and 25.0 ng L-1 for EE2, E2, and E3, respectively), wide linear range (3-200,000ng L-1), high enrichment factors (9.81-47.2), and satisfactory recovery (73.8-129.0%). Compared with traditional analytical techniques, this method avoids the use of volatile toxic organic extraction solvents and cumbersome phase separation operations.

Advanced Analytical Techniques for Characterizing Petroleum-Derived Contaminants in the Environment

The characterization of petroleum-derived contaminants in the environment is crucial for understanding their impact on ecosystems and human health. Traditional analytical techniques such as Gas Chromatography (GC), Mass Spectrometry (MS), and High-Performance Liquid Chromatography (HPLC) have been instrumental in identifying and quantifying these contaminants. However, the complexity and diversity of petroleum-derived compounds necessitate the development and application of advanced analytical techniques for more comprehensive analysis. This paper reviews the most cutting-edge methods currently employed in environmental analysis, including Comprehensive Two-Dimensional Gas Chromatography (GC×GC), Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS), Nuclear Magnetic Resonance (NMR) Spectroscopy, and Synchrotron Radiation-Based Techniques such as X-ray Absorption Spectroscopy (XAS) and X-ray Fluorescence (XRF), as well as Laser-Induced Breakdown Spectroscopy (LIBS). Each technique's principles, capabilities, and applications are discussed, highlighting their roles in detecting and characterizing hydrocarbons, polycyclic aromatic hydrocarbons (PAHs), heavy metals, and volatile organic compounds (VOCs). Case studies demonstrate the practical applications of these advanced techniques in real-world scenarios, such as oil spill analysis and the identification of complex contaminant mixtures. The paper also addresses the advantages and limitations of these advanced techniques, considering factors like sensitivity, selectivity, complexity, and cost. Finally, future directions and emerging technologies, including nanotechnology, biosensors, and machine learning, are explored for their potential to enhance environmental monitoring and remediation efforts. This comprehensive review underscores the importance of continued innovation in analytical methods to effectively address the challenges posed by petroleum-derived contaminants in the environment.

Using Artificial Intelligence in the Forensic Science for the Analysis of Microparticles: A Systematic Review

Technological advances in different industries have a tremendous impact on various aspects of human activities, including criminal activities. More complex and well-organized crimes often leave no room for the traditional analytical techniques, and require analysis of the tiniest pieces of the evidence – microobjects. Detection and study of these pieces of information obviously are time-consuming and demands more sophisticated equipment. This systematic review estimated the current status of the application of artificial intelligence systems in analysis of a specific type of the forensic evidence such as microparticles. Analysis of 27 articles extracted according to the PRISMA guidelines confirms the rationale behind using the AI for forensic investigation mainly to achieved automation of the most laborious aspects of the evidence investigation: image processing, matching a piece of the evidence to the created database and identification of the evidence. The AI technologies assist in identification of the victim or suspect personality thorough AI-assisted analysis of the DNA and RNA from the blood, saliva, urine; time of death and place of death via AI-assisted investigation of soil and fabric traces, and specific microbiome; tracking abusive substances; identification the cause of fires. The AI mainly serves as assistant to the convenient forensic methods, such as microscopy and spectroscopy, to process a big amount of data generated by the traditional techniques or to enhance the outcomes of the traditional techniques, such as image processing. The types of the AL the most widely used in the forensic science are machine learning algorithm.

Customer satisfaction antecedents in uncertain hospitality conditions: an exploratory data mining approach

PurposeInformed by trait and self-determination theories, the present study aims to extend the knowledge regarding the link between customer satisfaction (CS) and its antecedents, including job autonomy (JA), conscientiousness, customer uncertainty (CU) and extra-role customer service (E-RCS) in the hospitality industry.Design/methodology/approachA total of 306 frontline employees were selected from the hotels in North Cyprus, Turkey. Psychometric properties, including the validity and reliability of study variables, were assessed in the first step using confirmatory factor analysis. Then, the data were analyzed utilizing machine learning methods, mainly three exploratory data mining techniques, including lasso regression, decision trees and random forest, as well as partial dependence plots to visualize the role of suggested predictors on the outcome variable.FindingsData mining analysis shows that employees who can modify their job objectives are better equipped to satisfy customers in uncertain situations (JA8). In addition, the findings reveal that employees who believe they work hard to accomplish their personal and organizational goals (CON7) while also having the freedom to decide how to approach their job (JA1) and choose the procedures to utilize (JA2) are more likely to contribute to CS. In general, CS peaked when JA was high, but conscientiousness was moderate, while CU was low.Practical implicationsThis study bridges the gap among various factors at the employee and customer individual, corporate and macro-environmental levels. Hospitality organizations can cultivate a culture of autonomy and independence by promoting open communication and offering growth and development opportunities. This approach enhances conscientious employees’ engagement, leading to exceptional customer service performance, particularly, in uncertain situations.Originality/valueFrom the methodology perspective, this work proposes an opportunity for prospective scientists to broaden the trait and self-determination theories research model by relying on the riches of exploratory techniques without the limits imposed by traditional analytical techniques. Further, this study advances the current knowledge about service agility under uncertainty by extending organizational and service management research to consumer behavior literature.

Discrimination of Brazilian green canephora coffee beans by ultraviolet–visible spectroscopy as a non-target analysis: A tool for recognizing geographical indications

Specialty green coffee beans have a higher commercial value and some of them have recently been classified in Brazil based on the indication of provenance and denomination of origin. In this context, the classification of the type of coffee bean is still a challenge, using traditional analytical techniques. Thus, alternative analytical techniques, such as ultraviolet–visible spectroscopy (UV–Vis), as non-target analysis can be applied as a quick and reliable method of coffee classification using data science, such as chemometric tools. In the present study, UV–Vis were evaluated as a new strategy for discrimination of green beans of Brazilian specialty canephora coffees with recognized geographical indications (Robusta Amazônico and Conilon from state of Espírito Santo), for the first time. Spectra obtained from the aqueous extract of 222 samples. The Principal Component Analysis (PCA) was performed and subsequently Partial Least Squares with Discriminant Analysis (PLS-DA) model developed. The PCA indicated tendency to group the samples in their respective classes, pointing to the similarities in the spectra of samples of the same origin. The PLS-DA model obtained showed figures of merit values starting at 89.3% in the test set. The VIP scores showed that the variables associated with chlorogenic acids, caffeine and chlorophyll are the most important for differentiating the studied coffees. The results obtained showed that UV–Vis fingerprint − non-targeted analysis associated with PLS-DA is appropriate for the discrimination of green beans of Brazilian specialty coffee from different origins, in a simple way, using common equipment in several laboratories.

Network intrusion detection: An optimized deep learning approach using big data analytics

Managing enormous amounts of data, such as big data, and detecting network traffic intrusions are inefficiently handled by current computing technologies. Traditional analytical techniques cannot manage the incursions in continuous internet traffic and the enormous log data of server activity, leading to many inaccurate results and a prolonged training period. As a result, this research provides an efficient deep learning-based approach to enhance the attack identification task by addressing the basic big data complexity linked to many heterogeneous security data types. This framework employs a novel feature selection method incorporatingthe Aquila Optimizer (AO) and Fuzzy Entropy Mutual Information (FEMI) algorithms to pick distinctive characteristics. Subsequently, a modified canonical correlation-based technique is applied to combine selected characteristics. Then, the intrusion identification and categorization are carried out using the optimized ResNet152V2 method. Additionally, data augmentation using Auxiliary Classifier Generative Adversarial Network (ACGAN) is performed. Finally, we used the CICDDoS2019 and ToN-IoT datasets to validate the suggested methodology. By comparing the presented approach to several baseline methods, the effectiveness of the suggested methodology is assessed using various performance measures, including F1-score, recall, precision, accuracy, confusion matrix, and ROC curve. Finally, simulation results show that the suggested strategy is superior to other existing techniques and demonstrate that it is a resilient solution for network intrusion detection.

Hub‐aware random walk graph embedding methods for classification

AbstractIn the last two decades, we are witnessing a huge increase of valuable big data structured in the form of graphs or networks. To apply traditional machine learning and data analytic techniques to such data it is necessary to transform graphs into vector‐based representations that preserve the most essential structural properties of graphs. For this purpose, a large number of graph embedding methods have been proposed in the literature. Most of them produce general‐purpose embeddings suitable for a variety of applications such as node clustering, node classification, graph visualization and link prediction. In this article, we propose two novel graph embedding algorithms based on random walks that are specifically designed for the node classification problem. Random walk sampling strategies of the proposed algorithms have been designed to pay special attention to hubs–high‐degree nodes that have the most critical role for the overall connectedness in large‐scale graphs. The proposed methods are experimentally evaluated by analyzing the classification performance of three classification algorithms trained on embeddings of real‐world networks. The obtained results indicate that our methods considerably improve the predictive power of examined classifiers compared with currently the most popular random walk method for generating general‐purpose graph embeddings (node2vec).

Studying of native gold in the course of geological exploration: methodological recommendations

The updated edition of the Methodological Recommendations shows the possibilities of application of quantitatively
 assessed features of native gold in the course of forecasting and prospecting work. The main characteristics of native
 gold are described and illustrated. Information is provided on traditional and modern analytical techniques for studying
 gold in field and laboratory conditions, including multiple structural etching. A uniform terminology is recommended
 to ensure continuity in the studying of gold and comparison of data from different researchers.

Navigating the Aerosolized Frontier: A Comprehensive Review of Bioaerosol Research Post-COVID-19

In the wake of the COVID-19 pandemic, the scientific community has been galvanized to unravel the enigmatic role of bioaerosols in the transmission of infectious agents. This literature review, anchored in the extensive Web of Science Core Collection database covering the period from 1990 to 2023, utilizes a bibliometric approach to chart the dynamic landscape of bioaerosol research. It meticulously documents the paradigm shifts and burgeoning areas of inquiry that have emerged in the aftermath of the pandemic. This review meticulously maps out the sources and detection strategies of pathogens in a variety of ecosystems. It clearly shows that impaction and filtration sampling methods, followed by colony counting and PCR-based detection techniques, were predominantly used in the scientific works within the previous three decades. It synthesizes the progress and limitations inherent in a range of models for predicting aerosol-mediated pathogen spread and provides a comparative analysis of eDNA technology and traditional analytical techniques for bioaerosols. The accuracy of these detection methods and forecasting models is paramount for the early recognition of transmission risks, which, in turn, paves the way for prompt and effective disease mitigation strategies. By providing a thorough analysis of the historical progression and current state of bioaerosol research, this review illuminates the path ahead, identifying the critical research needs that will drive the field’s advancement in the years to come.

Calcium absorption by fruit and leaves of sweet cherry trees (Prunus avium L.) by isotope labeling

In sweet cherry, foliar Calcium–based products are used to mitigate rain–cracking in fruit and to improve its firmness during storage. Information on foliar Calcium (Ca) absorption efficiency by fruit and leaves in the field is scarce. In part, this is due to the limitations of the traditional analytical techniques for assessing Ca levels in these organs. Here, we hypothesize that leaves and fruit differ in their abilities to uptake surface applications of Ca and, moreover, that their abilities are influenced by phenology. To test this hypothesis, foliar 44Ca (0.05% 44CaCl2) was used to enrich sweet cherry fruit and leaves of the combination ‘Lapins’/‘Colt’ during the 2019–2020 growing season in the Central Valley of Chile (34° 0’ S, 71° 41’ W). Six treatments were applied, represented by different timings (days after full bloom, DAFB) of isotopic labeling of either fruit or leaves. Labeling was applied at Stage I, SII and SIII of fruit development. Fruit or leaves were ‘painted’ with 44CaCl2 (0.05%, 97 atom%) using a brush. Each labeling date considered a different group of similar fruiting spurs on the two-year-old wood of individual vertical branches. Additional samples were collected to measure natural abundance of 44Ca. Labeled fruit were removed 48 h after aerial 44Ca enrichment and at harvest (87 DAFB).Uptake of 44Ca by fruit and leaves was observed throughout the period of fruit development. Fruiting spur leaves (FSL) and fruit absorbed 44CaCl2 and were highly enriched 48 h after labeling. The earlier the labeling date, the higher the 44Ca tracer recovery detected in both organs. However, FSL showed higher δ44Ca ‰, 44Ca concentrations and 44Ca contents than fruit during the whole period of fruit development. After 48 h, the aerial uptake at SI (expressed as δ44Ca ‰) was 3.4 times higher in immature FSL and 5.7 times higher in immature fruit than SIII of fruit development, which indicates that Ca sprays should start soon after fruit set. We conclude that in sweet cherry, foliar Ca is absorbed by fruit and spur leaves, but the absorption is more efficient in the early stages of fruit development and for young leaves.

Modeling of electromagnetic propagation inside jet engine environment using statistical electromagnetic approach

In this paper, the distribution of the electromagnetic field inside a complex jet engine environment is analyzed using statistical electromagnetics. The jet engine environment is an extremely complex geometry and exhibits random behavior due to the presence of moving metallic parts. This renders traditional analytical and simulation modeling techniques highly inefficient. To address this issue, two different approaches are proposed to model the propagation characteristics of the jet engine environment. The first is an innovative dynamic system approach based on dynamic system simulation which is inspired by the analysis of mechanically stirred reverberation chambers. In the dynamic system simulation the dynamic system, which is characterized by the rotation of a distinct set of blades, is primarily studied through the simulation program. A dimension scaling method is also introduced along with the dynamic system simulation to solve the complex jet engine environment. In the second approach, a novel statistical excitation method is applied to develop an equivalent model for the dynamic jet engine system. The studied jet engine is considered as a static jet engine system without blade rotation (static blades), but with a random excitation.A small signal analysis method is used to integrate the static and dynamic system parameters to generate random excitation characteristics of the static system. The extracted electric field values from the dynamic jet engine simulation environment and the static system field values from the small signal analysis have been analyzed statistically to prove the statistical equality between the two systems. The numerical results of the static system model are presented and verified through comparison with finite element method simulation packages.

On the feed-forward neural network for analyzing pantograph equations

Ordinary differential equations (ODEs) are fundamental tools for modeling and understanding a wide range of chemistry, physics, and biological phenomena. However, solving complex ODEs often presents significant challenges, necessitating advanced numerical approaches beyond traditional analytical techniques. Thus, a novel machine learning (ML)-based method for solving and analyzing ODEs is proposed in the current investigation. In this study, we utilize a feed-forward neural network (FNN) with five fully connected layers trained on data samples generated from the exact solutions of specific ODEs. To show the efficacy of our suggested method, we will conduct a thorough evaluation by comparing the anticipated solutions of the FNN with the exact solutions for some ODEs. Furthermore, we analyze the absolute error and present the loss functions for some ODE examples, providing valuable insights into the model’s performance and potential areas for further development.

Precision and efficiency of an interpolation approach to weakly singular integral equations

PurposeThis study aims to discuss the numerical solutions of weakly singular Volterra and Fredholm integral equations, which are used to model the problems like heat conduction in engineering and the electrostatic potential theory, using the modified Lagrange polynomial interpolation technique combined with the biconjugate gradient stabilized method (BiCGSTAB). The framework for the existence of the unique solutions of the integral equations is provided in the context of the Banach contraction principle and Bielecki norm.Design/methodology/approachThe authors have applied the modified Lagrange polynomial method to approximate the numerical solutions of the second kind of weakly singular Volterra and Fredholm integral equations.FindingsApproaching the interpolation of the unknown function using the aforementioned method generates an algebraic system of equations that is solved by an appropriate classical technique. Furthermore, some theorems concerning the convergence of the method and error estimation are proved. Some numerical examples are provided which attest to the application, effectiveness and reliability of the method. Compared to the Fredholm integral equations of weakly singular type, the current technique works better for the Volterra integral equations of weakly singular type. Furthermore, illustrative examples and comparisons are provided to show the approach’s validity and practicality, which demonstrates that the present method works well in contrast to the referenced method. The computations were performed by MATLAB software.Research limitations/implicationsThe convergence of these methods is dependent on the smoothness of the solution, it is challenging to find the solution and approximate it computationally in various applications modelled by integral equations of non-smooth kernels. Traditional analytical techniques, such as projection methods, do not work well in these cases since the produced linear system is unconditioned and hard to address. Also, proving the convergence and estimating error might be difficult. They are frequently also expensive to implement.Practical implicationsThere is a great need for fast, user-friendly numerical techniques for these types of equations. In addition, polynomials are the most frequently used mathematical tools because of their ease of expression, quick computation on modern computers and simple to define. As a result, they made substantial contributions for many years to the theories and analysis like approximation and numerical, respectively.Social implicationsThis work presents a useful method for handling weakly singular integral equations without involving any process of change of variables to eliminate the singularity of the solution.Originality/valueTo the best of the authors’ knowledge, the authors claim the originality and effectiveness of their work, highlighting its successful application in addressing weakly singular Volterra and Fredholm integral equations for the first time. Importantly, the approach acknowledges and preserves the possible singularity of the solution, a novel aspect yet to be explored by researchers in the field.

Advancements in Fluorescence Sensing: Carbon Quantum Dots for Acrylamide Detection in Food

Acrylamide is a hazardous chemical mainly synthesized during the thermal processing of foods representing a significant concern within the broader issue of food contaminants and their impact on public health. Acrylamide can be absorbed by the human body through dietary intake, respiration, dermal contact, and mucosa. The metabolic conversion of acrylamide into mercapturic acid metabolites and glycidamide results in several adverse and toxic effects. Therefore, this review explores the formation, toxicity, and metabolism of acrylamide. Hence, it is crucial to detect and ensure product quality via risk evaluation. Traditional analytical techniques for acrylamide detection often require expensive instrumentation and complex sample preparation, prompting the exploration of alternative, cost‐effective, sustainable methods. Here, we propose the utilization of carbon quantum dots (CQDs) synthesized through green approaches as a novel solution. CQDs display their immense potential for diverse applications due to their valuable properties such as biocompatibility, photocatalysis, and strong fluorescence. This review highlights the distinct potential of CQDs as a fluorescence probe for detecting acrylamide, showcasing their efficacy in addressing food safety concerns. In addition, various extraction and purification techniques for acrylamide such as QuEChERS, solid phase extraction, Carrez clarification, and dispersive liquid‐liquid microextraction are comprehensively reviewed. QuEChERS is regarded as a most promising technique for the extraction of acrylamide owing to its cost‐effective, rapid, and higher recovery rates.