Critical reviews in analytical chemistry最新文献

Lysergic acid diethylamide (LSD) remains a significant forensic and public health concern due to its widespread abuse and association with drug-facilitated crimes. Detecting LSD and its analogs in biological specimens, particularly postmortem matrices, presents analytical challenges stemming from its low dosage, rapid metabolism, and structural similarities to novel lysergamides. This review critically examines trends in validated analytical methods for LSD detection in forensic toxicology. A systematic review of literature from 1978 to 2025 was conducted using databases, such as PubMed, ScienceDirect, and Google Scholar. The analysis focused on reported methodologies for LSD and its metabolites across various matrices, including blood, urine, hair, oral fluid, and vitreous humor. Extraction techniques (LLE, SPE, DLLME) and analytical platforms (GC-MS/MS, LC-MS/MS, CE-MS) were compared, with emphasis on validation parameters, such as sensitivity, specificity, recovery, LOD, LOQ, matrix effects, and stability. The review identifies LC-MS/MS as the most sensitive and widely validated technique; however, discrepancies remain in matrix-specific validations and stability assessments. Challenges include the lack of certified reference materials for LSD analogs, matrix-dependent degradation, and limited methods for emerging sample types, such as dried blood spots (DBS). Few studies fully comply with modern forensic validation guidelines, limiting the reproducibility and admissibility of results in legal settings. This review highlights critical gaps in current forensic LSD detection protocols and underscores the need for standardized, validated methods applicable to diverse matrices. Future research should prioritize the development of rapid, eco-friendly, high-throughput methods capable of detecting LSD and its analogs at ultra-trace levels.

Micelles, formed from surfactants, offer alternative media for separation and detection techniques, addressing challenges such as sample complexity, method sensitivity and selectivity, analysis cost and time, and environmental impact. The critical micelle concentration (CMC) plays a pivotal role in micelle formation, with normal and reverse micelles being the main structures observed. Additionally, niosomes and liposomes contribute to sample preparation methods. Polymeric micelles exhibit a core-shell configuration, allowing for modification of their properties. Micellar systems find application in various techniques, including cloud point extraction (CPE), coacervation extraction, microextraction, and supercritical fluid extraction. CPE offers environmentally friendly and cost-effective extraction, enhancing analyte solubility and detection limits. The review further discusses the applications of micellar systems in CPE, including the analysis of hazardous organic impurities and the purification of biological compounds. Metal-CPE is explored as a method for extracting organically chelated metals. The utilization of micellar systems in sample preparation showcases their potential in improving analytical methodologies.

Alkaloids represent one of the most significant classes of compounds of natural and synthetic origin due to their pronounced biological activity and broad applications in pharmacy, medicine, and toxicology. Their determination in complex matrices such as biological fluids or pharmaceutical formulations necessitates the use of highly sensitive, selective, and reliable analytical techniques. Among these, electrochemical methods, particularly voltammetry, provide rapid, cost-effective, and precise detection. This review systematically evaluates voltammetric approaches for the determination of alkaloids and their metabolites, with a particular focus on studies published over the last two decades (2005-2025). More than 90 publications have been critically analyzed, covering voltammetric methodologies applied to 26 alkaloids and their metabolites, and highlighting key trends in electrode selection, experimental conditions, and strategies to enhance analytical performance. Special emphasis is placed on linking the electrochemical behavior of alkaloids to their chemical structure and functional group affiliation, providing insights into reaction mechanisms and detection sensitivity. The review also incorporates illustrative schemes of representative alkaloids and their electrochemical transformations, demonstrating practical applications for pharmaceutical analysis, food safety, and forensic monitoring. By critically assessing the strengths and limitations of current methodologies, this work offers a valuable resource for researchers and professionals in electroanalytical chemistry, pharmaceutical sciences, biomedicine, and toxicology, supporting the development of more selective and efficient voltammetric techniques for alkaloid investigation.

The integration of Artificial Intelligence (AI) into High-Performance Liquid Chromatography (HPLC) method development marks a paradigm shift from empirical and interpretive frameworks toward adaptive, data-driven optimization. This critical review dissects the technological evolution from traditional Design of Experiments (DoE) and retention modeling to AI-powered platforms employing machine learning (ML), deep learning (DL), and reinforcement learning (RL). While AI offers unmatched capabilities in predicting retention times, optimizing gradient conditions, and enabling real-time control, its adoption remains fragmented due to critical challenges in model interpretability, regulatory validation, and data standardization. A key insight is the persistent mischaracterization of deterministic simulators (e.g., DryLab^®, AutoChrom^™) as AI tools, which obfuscates the conceptual boundaries between mechanistic modeling and data-driven learning. Furthermore, black-box models-though powerful-suffer from poor explainability, limiting their acceptance in GxP-regulated environments. The review emphasizes the need for hybrid frameworks that merge mechanistic transparency with AI adaptability, and highlights gaps in training dataset diversity, feature engineering, and lifecycle-based model validation. Emerging trends such as explainable AI (XAI), closed-loop reinforcement learning, digital twins, and federated learning are discussed as pivotal enablers of next-generation autonomous analytical platforms. Ultimately, this review establishes that AI is not merely a computational enhancement, but a strategic imperative for scalable, reproducible, and intelligent HPLC workflows. However, its transformative potential can only be realized through ethical deployment, domain-aligned design, and interdisciplinary collaboration that aligns innovation with regulatory trust and operational relevance.

Early diagnosis is crucial not only for the treatment of diseases, but also for improving personal life quality. For these reasons, different methods and technologies are being developed for diagnosis of diseases. Sensor technologies have a very important place among these technologies. Electrochemical sensors are widely used in the diagnosis of diseases by biomarker detection due to their advantages such as being cheaper than alternative methods, simple design, sensitive and accurate detection capacity, portable and suitable for point-of-care use. Furthermore, it is perfectly suited to the analysis of pharmaceutical compounds. In recent years, sensitivity and stability of sensors have been increased by modification of sensors with different nano-bio materials. Nanosheets are one of these nanomaterials. This review highlights the structural properties and advantages of different types of nanosheets and their potential as promising tools for biomedical applications in biomarker and drug analysis.

The extensive use of organophosphorus pesticides (OPs) in agriculture has significantly contributed to enhanced crop yield and pest control. However, their persistence and high toxicity have raised serious environmental and public health concerns, including neurological disorders, endocrine disruption, and long-term ecological damage. This necessitates the development of rapid, highly sensitive, and cost-effective detection methods for monitoring OP residues in food, water, and soil. In recent years, gold nanoparticles (AuNPs) have gained considerable attention as smart sensing platforms for OP detection, owing to their remarkable optical properties, tunable surface chemistry, and excellent biocompatibility. This review highlights recent progress in AuNPs-based colorimetric and fluorometric sensors specifically tailored for detecting a broad range of OPs. The fundamental detection mechanisms, such as enzyme inhibition, aptamer binding, and aggregation-induced plasmonic shifts are thoroughly discussed to provide insights into sensor design strategies. By integrating nanotechnology with environmental and food safety frameworks, AuNP-based smart sensors represent a transformative approach for real-time, user-friendly detection of OPs. The innovations summarized in this review aim to support the development of accessible analytical tools that can be used by both professionals and non-specialists, ultimately contributing to safer agricultural practices and improved public health outcomes.

Outlining biological processes and disease mechanisms requires a real-time understanding of cellular metabolism. Mass spectrometry (MS) and Nuclear magnetic resonance (NMR) serve as potent analytical methods for metabolomics, leveraging their advantages in the identification and quantification of metabolites. In this review we have discussed MS and NMR based techniques, such as matrix-assisted laser desorption/ionization (MALDI), secondary ionization mass spectrometry (SIMS), desorption electrospray ionization (DESI), direct analysis in real time (DART), and nano electrospray ionization (nano ESI), to develop and implement a MS based technique in its live state for single cell and NMR strategies for thorough, real-time analysis of cell metabolism at bulk cellular level. Single-cell metabolomic investigations using mass spectrometry is a valuable tool for understanding cellular heterogeneity and cell-to-cell variation. However, they can reveal hidden processes and heterogeneity across cells that are often missed by bulk cell analysis. Overcoming the inherently low sensitivity of NMR is crucial for omics studies. This review examines hyperpolarization techniques, including dynamic nuclear polarization (DNP), parahydrogen-induced polarization, sample amplification by reversible exchange (SABER), high-resolution magic angle spinning (HRMAS) NMR, and in vivo magnetic resonance spectroscopy (MRS) for the analysis of live bulk cells. Our discussion encompasses the technological platforms and recent applications of these techniques, utilizing both NMR and MS, along with an overview of metabolomics data analysis tools, such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA).

Glyburide (glibenclamide), a second-generation sulfonylurea, remains a cornerstone in the management of type 2 diabetes mellitus. However, its narrow therapeutic index and typically low plasma concentrations demand highly sensitive and reliable quantification methods for applications spanning pharmacokinetics, therapeutic drug monitoring, clinical diagnostics, pharmaceutical quality control, and environmental surveillance. This review provides a critical overview of four decades of methodological progress in glyburide analysis, synthesizing evidence from major scientific databases, including Scopus, Web of Science, ScienceDirect, PubMed, and Google Scholar. High-performance liquid chromatography (HPLC) and liquid chromatography-tandem mass spectrometry (LC-MS/MS) have emerged as the gold-standard approaches, offering exceptional sensitivity, specificity, and reproducibility across diverse matrices. In contrast, spectrophotometric methods provide cost-effective and straightforward options for routine analyses, while electrochemical techniques, particularly voltammetry with nanotextured electrodes, demonstrate remarkable sensitivity, sustainability, and applicability to trace detection. Capillary electrophoresis, though less commonly adopted, offers distinct benefits in resolution and solvent economy. Critical challenges are highlighted, including the quantification of glyburide at clinically relevant levels (2-400 ng/mL in plasma, with LOD as low as 0.038 ng/mL and LOQ 1.5 ng/mL), as well as overcoming matrix interferences and stability issues. Rigorous method validation remains essential to ensure accuracy and clinical utility. Finally, this review identifies key future directions, emphasizing the integration of green analytical chemistry principles, miniaturized platforms, and nanostructured sensing technologies to advance selective, sensitive, and environmentally sustainable glyburide analysis. These innovations hold the potential to transform glyburide monitoring from laboratory-based methods to real-world applications, including point-of-care testing and environmental health monitoring.

The selective and sensitive detection of organic pollutants in complex matrices continues to be a major challenge in environmental, biological, and food analysis. Molecularly imprinted polymers (MIPs) have emerged as robust and highly selective sorbent materials with the capability to recognize and extract target analytes at trace levels, even in the presence of coexisting interferents. This review article highlights the integration of MIPs with gas chromatography-mass spectrometry (GC-MS) as a powerful analytical strategy for the selective pre-concentration and determination of organic contaminants. The MIP-GC-MS platform has been successfully applied in diverse matrices: for instance, bisphenol A and nonylphenol in river water, diclofenac and carbamazepine in human urine, and organochlorine pesticides in fruit and vegetable samples. These applications demonstrate that MIP-based extraction not only enhances selectivity but also minimizes matrix effects, enabling detection limits in the low ng·L^-1 to ng·g^-1 range. Furthermore, advances in MIP design, including green imprinting approaches and nanostructured composites, have broadened their applicability, improving sensitivity, reproducibility, and reusability. Overall, the synergistic combination of MIPs and GC-MS offers a reliable and sustainable solution for trace-level monitoring of organic pollutants, supporting environmental protection, food safety, and public health.