搜索结果：Critical reviews in analytical chemistry

Oxazolidinones remain pivotal in treating multidrug-resistant infections, yet analytical and bioanalytical methodologies supporting their development and clinical use remain uneven across the class. This critical review consolidates recent advances spanning formulation quality control to therapeutic drug monitoring (TDM), offering an integrated cross-platform comparison of how analytical and bioanalytical methodologies shape oxazolidinone readiness for precision antimicrobial therapy. Analytical methods-from spectrophotometry and HPLC to LC-MS/MS-have progressed toward stability-indicating workflows, impurity profiling, and regulatory alignment, with emerging but inconsistent adoption of Quality by Design (QbD) and Green Analytical Chemistry (GAC). Bioanalytical approaches, dominated by LC-MS/MS with evolving micro-sampling and matrix-adapted extractions, have enabled pharmacokinetic characterization, exposure-response evaluation, and patient-specific dose optimization. Together, these methodologies reveal a functional divide: analytical assays secure drug identity and formulation robustness, whereas bioanalytical tools translate oxazolidinones into clinical decision systems. However, major gaps persist, including limited characterization of newer investigational analogues, poor harmonization across matrices and laboratories, and underutilization of computational, mechanistic, and eco-sustainable strategies. Bridging these gaps requires integrated, standardized, and patient-centered analytical frameworks to ensure that both established and emerging oxazolidinones remain effective in the era of escalating antimicrobial resistance.

Ginger (Zingiber officinale) and its derivatives are highly valued spices but are particularly vulnerable to economically driven adulteration. This critical review compiles and evaluates analytical studies between 2005 and 2024 addressing the authentication of ginger. Using PRISMA guideline, 27 original research publications were selected from the Scopus and Web of Science databases. The results demonstrate that no single analytical approach is sufficient for comprehensive authentication; instead, an integrated, multi-analytical strategy is required. Spectroscopic techniques (FTIR, FT-NIR, and NMR) combined with chemometric tools provide swift, noninvasive methods for geographic and varietal differentiation as well as adulterant identification. Chromatographic methods (HPLC and GC-MS) offer comprehensive chemical characterization that is essential for evaluating quality and processing history. Stable isotope ratio analysis offers intrinsic, source-specific fingerprints for tracing geographical origin and verifying authenticity. DNA-based approaches provide conclusive species identification but remain limited for varietal differentiation due to high genetic conservation. Emerging technologies, including hyperspectral imaging, fluorescence lifetime imaging, and deep learning-driven computer vision, exhibit strong potential for noninvasive quality assessment. Overall, this review highlights the importance of combining complementary analytical methods to effectively tackle the complex issue of ginger fraud, therefore guaranteeing product authenticity, consumer safety, and adherence to regulations in the global market.

Aerogels, a remarkable class of nanoporous materials, have garnered significant attention across various scientific and industrial domains. These ultralight substances boast an impressive array of characteristics, including extraordinarily low density, expansive surface area, robust pressure resistance, and notable chemical stability. Such unique attributes have propelled aerogels into diverse applications spanning construction, medicine, and notably analytical chemistry. In the latter field, aerogels have demonstrated remarkable versatility, finding uses in electrode modification, sample preparation, and spectroscopic analysis. The main focus of this review is to discuss the application of aerogel-based materials in analytical chemistry focusing on chromatography, electrochemistry, and spectroscopy methods. As research continues to uncover new potentials and refine existing applications, aerogels are poised to play an increasingly crucial role in advancing analytical methodologies, promising improved sensitivity, selectivity, and efficiency in chemical analyses across multiple disciplines. This review shows that aerogels have an important ranking in sample preparation. Aerogels can be easily used in electrochemistry because of their electrical conductivity and monolithic structure. The presence of a metal in the structure of aerogel makes it more suitable for electrochemical applications. The use of aerogels in spectrometric methods can enhance the analytical capabilities due to their effective light interaction, low density, and transparency.

Enantioseparation remains essential in pharmaceutical and bioanalytical sciences, yet method development continues to rely heavily on empirical column screening and experience-driven optimization. Although recent advances in miniaturized platforms, hyphenated detection systems, and machine-learning-assisted modeling have expanded the analytical toolkit available for chiral analysis, these developments are often reported in isolation, without a unifying systems-level framework. This critical review synthesizes advances in chromatographic and electrophoretic enantioseparation, spanning liquid chromatography, capillary electrophoresis, supercritical fluid chromatography, micro- and nano-scale platforms, and mass spectrometry-coupled systems. Particular emphasis is placed on the integration of mechanistic descriptors, automation strategies, closed-loop optimization, and data-driven modeling within modern chiral method development. Rather than cataloguing technologies, the review evaluates how these components collectively enhance selectivity prediction, parameter optimization, reproducibility, and sustainability. Key challenges, including data quality constraints, model interpretability, regulatory alignment, and scalability, are critically examined to distinguish mature implementations from exploratory strategies. By integrating instrumentation, control logic, and computational intelligence within a coherent analytical framework, this review provides structured guidance for transitioning from empirically driven chiral separations toward more transparent, adaptive, and validation-ready analytical systems.

Regulatory agencies often require a comprehensive analysis of drug combinations for safety and efficacy assessments before approving new pharmaceutical products. Simultaneous estimation helps demonstrate compliance with regulatory standards. The analysis of Dipeptidyl Peptidase-4 (DPP-4) inhibitors in combination is essential for evaluating the efficacy and safety of combination therapies, optimizing dosages, ensuring product quality, and understanding pharmacokinetics. A thorough analysis of DPP-4 inhibitors, when used in combination with other drugs for managing diabetes, was conducted. The review places emphasis on a range of analytical and bioanalytical methodologies, encompassing spectroscopic and chromatographic techniques, to evaluate combinations of DPP-4 inhibitors across diverse sample types. The analysis of a variety of techniques offers insights to researchers and healthcare experts involved in diabetes care, thereby making meaningful contributions to ongoing research and clinical applications in this field. This article reports that more than 70 combinations involving DPP-4 inhibitors with other drugs have been reported. The methods persist in using harmful solvents like methanol and acetonitrile. Therefore, it is advisable to explore alternative approaches for the assessment of combinations of DPP-4 inhibitors, with a specific focus on the utilization of eco-friendly, or "green" solvents.

This critical review summarizes chiral volatile organic compound (VOC) analysis principles and compound-specific isotope ratio measurements, evaluating their combined application in flavor authentication. Natural flavorings are highly susceptible to adulteration, highlighting the need for authentication strategies that capture both molecular complexity and stereochemical specificity. We discuss recent advances in VOC profiling, emphasizing enantiomeric ratios (ERs) as biosynthetically grounded authenticity markers. Extraction strategies that preserve ERs are compared, and enantioselective gas chromatography-mass spectrometry (GC-MS) workflows are outlined, together with assessments of comprehensive two-dimensional GC (GC × GC) and heart-cut multidimensional GC (MDGC) for resolving co-elution. Quantitative and fingerprinting modes, library development, quality assurance protocols, and chemometric pipelines incorporating machine learning (ML) are evaluated for classification accuracy and interpretability. Orthogonal confirmation using isotope ratio mass spectrometry (IRMS) enhances decision confidence near ER thresholds. Applications across teas, fruits, essential oils, and juices show that reproducibility depends on standardized pretreatment, verified enantioselective separation, uncertainty-aware ER reporting, and leakage-safe model validation. Modular pipelines integrating GC × GC/MDGC, interpretable ML, and IRMS-with transparent, traceable outputs aligned to labeling regulations-offer a route toward industrial implementation. We also propose an integrated VOC/ER-centered framework combining chiral GC-MS, GC-combustion/pyrolysis-IRMS, and interpretable ML, emphasizing ER fidelity, and grey-zone decision rules for authentication.

Benzocaine (BC) (ethyl 4-aminobenzoate) is an ester-type local anesthetic extensively used in topical, oral, and dermatological formulations for rapid, localized pain relief. Despite its widespread application, accurate quantification remains challenging due to poor aqueous solubility, susceptibility to hydrolysis into para-aminobenzoic acid (PABA), and the risk of methemoglobinemia associated with improper exposure. This review presents a critical and comparative evaluation of chromatographic, electrochemical, and spectroscopic methods for determining BC and its metabolites, integrating analytical performance, matrix applicability, and methodological evolution within a unified framework. Evidence was synthesized from major scientific databases, including Scopus, Web of Science, ScienceDirect, PubMed, and Google Scholar. High-performance liquid chromatography (HPLC) and ultra-performance liquid chromatography (UPLC) remain gold-standard techniques, offering high selectivity, precision, and stability-indicating capability for trace-level analysis. Electrochemical and voltammetric approaches using advanced electrodes, such as boron-doped diamond and carbon nanocomposites, provide excellent sensitivity with low detection limits and improved environmental sustainability. UV-Vis and fluorescence spectrophotometry continue to support routine quality control, particularly when combined with chemometric tools. Ongoing challenges include analyte instability and complex matrices. Future perspectives highlight nanomaterial-based sensors, hybrid analytical platforms, and artificial intelligence integration for sensitive, automated BC monitoring.

Heavy metal contamination remains a critical global environmental issue due to the persistence, bioaccumulation, and toxicity of metal ions such as Pb2+, Cd2+, Hg2+, and As³+. Although conventional analytical techniques provide high sensitivity and accuracy, they often rely on energy-intensive instrumentation, hazardous reagents, and generate considerable chemical waste, raising concerns regarding their environmental sustainability. In this context, molecularly imprinted polymer (MIP)-based electrochemical sensors have emerged as promising alternatives, offering high selectivity, operational simplicity, and compatibility with miniaturized and in situ analysis. This review critically examines the integration of Green Analytical Chemistry (GAC) principles into the design and fabrication of MIP-based electrochemical sensors for heavy metal monitoring. Particular attention is given to material selection, polymerization strategies, template removal approaches, and electrode modification techniques, with emphasis on their environmental implications. The applicability of quantitative greenness assessment tools, including the Analytical Eco-Scale, GAPI, AGREE, and AGREEMIP, is discussed in the context of sensor development workflows, highlighting both their strengths and current limitations in addressing fabrication stages, nanomaterial synthesis, and end-of-life considerations. By identifying methodological bottlenecks, particularly solvent-intensive template removal and limited reusability, this review outlines practical directions for advancing more sustainable sensor platforms. Overall, the work provides a critical framework for aligning analytical performance with environmental responsibility in next-generation MIP-based electrochemical sensing systems.

One of the important regulators of cellular iron uptake is transferrin receptor 1 (TFRC), which is closely linked to ferroptosis, an iron-dependent form of regulated cell death caused by lipid peroxidation. TFRC and ferroptosis-related biomarkers (including soluble transferrin receptor, labile iron pools, 4-hydroxynonenal, malondialdehyde, glutathione status, GPX4 expression, and oxidized species of phospholipids) can be measured in tissue, blood, and single-cell systems and are beginning to emerge as potential biomarkers in laboratory medicine in oncology. This review considers their analytical and translational applications in human malignancies, focusing on clinical chemistry, diagnostic laboratory medicine, and precision oncology. We combined data showing a relationship between TFRC expression and ferroptosis-related signatures to classify diseases, explore reported associations with prognosis, treatment response, and survival. We also considered their possible uses in stratifying risks, therapies, pharmacodynamic aspects, and longitudinal disease assessment. Emphasis is placed on laboratory variables that influence implementation, such as the selection of matrix specimens, variation in preanalytical factors, assay standardization, platform-dependent cutoff values, calibrations, and cross-platform validation. The review also discusses how digital pathology, artificial intelligence, machine learning, and multimarker analytical frameworks may support biomarker quantification and interpretation after appropriate validation. Overall, a validated analytical workflow, standardized reporting, inter-laboratory harmonization, and prospective clinical validation are required to support the clinical translation of TFRC-ferroptosis biomarker panel tests. This review offers a laboratory medicine-centered framework for understanding the potential and current limitations of TFRC and ferroptosis-related biomarkers as analytical targets in precision oncology.

The convergence of artificial intelligence and chemometrics has revolutionized multi-omics data integration, enabling unprecedented insights into complex biological systems. This critical review examines AI-driven approaches for integrating genomics, proteomics, metabolomics, and other omics layers, emphasizing developments from 2020 to 2025. We explore fundamental multi-omics challenges including batch effects, high dimensionality, and structural heterogeneity, evaluating how classical chemometric methods have evolved into sophisticated deep learning architectures. Convolutional neural networks, autoencoders, variational autoencoders, and graph neural networks demonstrate remarkable capabilities for non-linear feature extraction and data fusion. Explainable AI frameworks including SHAP and LIME address interpretability concerns critical for analytical chemistry. We review vertical and horizontal integration strategies, highlighting transformer-based attention mechanisms and biological network-informed architectures. Clinical applications in Alzheimer's disease, obesity, and cancer demonstrate 20%-30% performance improvements over traditional approaches. Emerging hyphenated techniques coupling microfluidics with mass spectrometry enable miniaturized analyses. Persistent challenges include computational scalability, overfitting mitigation, regulatory validation gaps, and interdisciplinary collaboration barriers. Future directions encompass federated learning for privacy-preserving analyses, quantum computing applications, and single-cell spatial multi-omics at subcellular resolution. This assessment provides analytical chemists with critical evaluation of available tools, benchmarking strategies, and roadmaps for advancing precision medicine and analytical applications.

Cancer remains a leading global health challenge, necessitating continuous advancements in diagnostics and therapeutics to enhance patient outcomes. According to the recent data of the American Cancer Society, an estimated 20 million new cancer cases and 9.7 million cancer deaths occurred globally in 2022, with cases projected to reach 35 million by 2050. Early and accurate diagnosis is critical for effective treatment, and the traditional methods often lack sensitivity and specificity. Advanced analytical techniques, comprising molecular and genomic diagnostics, next-generation sequencing (NGS), and artificial intelligence (AI)-driven imaging, have significantly enhanced early detection and real-time tumor monitoring. In therapeutics, precision oncology has emerged as a transformative approach, integrating immunotherapy, gene editing (CRISPR), matrix-assisted laser desorption/ionization (MALDI), CAR-T cell therapy, and nanotechnology-based delivery for targeted treatment. Dual-purpose tools like NGS, predictive modeling, and molecular profiling bridge both domains, enhancing predictive accuracy via multimodal data fusion as a paradigm-shifting analytical platform revolutionizing traditional approaches. Despite advancements, resistance and accessibility challenges entail continued innovation. These advancements, driven by the convergence of biotechnology, data science, and molecular medicine, substantiate diagnostic accuracy and therapeutic precision. This review critically examines current analytical innovations, including molecular, imaging modalities, biosensors, and therapy-guiding analytics, highlighting their integration within precision oncology frameworks.

This review provides a critical evaluation of several analytical methods developed over the last two decades for the quantification of Rocuronium (ROC), either alone or in combination, in different matrices. The existing literature describes various analytical techniques for ROC analysis, such as spectrofluorimetric, electrochemical, chromatographic, capillary electrophoretic, and mass-spectrometric. However, no published study has assessed and compared these methods in terms of their analytical performance, eco-friendliness, sustainability and applicability. This review employs six assessment tools to evaluate the alignment of the reported analytical methods with the principles of Green Analytical Chemistry (GAC) and White Analytical Chemistry (WAC). The tools are: Complex modified green analytical procedure index (ComplexMoGAPI) and Analytical GREEnness Metric (AGREE) to evaluate the greenness, Blueness Assessment Graphical Index (BAGI) to assess the applicability of the method (blueness), and Red Analytical Performance Index (RAPI) for the analytical performance (redness). Finally, the Red-Green-Blue model (RGB-12 algorithm) and Multi-Color Assessment (MA) Tool were employed, which provide a unified "whiteness score" which reflects the degree of sustainability of the method. By evaluating the strengths, limitations, eco-friendliness, applicability, innovation, performance and sustainability of the reported methods, this review establishes a novel, comprehensive framework to guide decision-making in analytical method selection and development.

Achieving food safety, quality, and authenticity need analytical strategies that can meet the challenges introduced by more complex matrices and ultra-trace contaminants. Recently, click-chemistry which has the attributes of selectivity, modularity, and rapid kinetics has emerged as powerful tool for food analytical science. This review highlights the role of click chemistry in various steps of the food analytical workflow, including sample preparation, sorbent engineering, signal amplification, and sensor-based detection. A significant focus will be placed on reaction mechanisms, improve analytical performance and real-world applicability along with a systematic overview of current limitations and associated regulatory challenges. This utilizes stable conjugation, efficient tagging, and tailored material functionalization to provide enhanced analytical selectivity, sensitivity, and reproducibility via click-enabled strategies. Such applications in sorbent design and molecular imprinting lead to higher extraction efficiency and tolerance toward matrix, while the redesign of click-mediated labeling approaches facilitates great improvements in both ionization efficiency and quantitative reliability for mass spectrometry. Moreover, the compatibility with optical and electrochemical sensors enables rapid and portable detection platforms. Notwithstanding these improvements, several hurdles remain, such as catalyst toxicity, matrix effects and interference, limits to scalability, high costs of substrates/reagents used in the sensors, and need for standardization of validation protocols. Wider implementation is further constrained by regulatory gaps and lack of acceptance for non-targeted and AI-assisted methodologies. Click-chemistry in future will be integrated with miniaturized, automatized, and data-driven systems for enable real-time, decentralized monitoring of food.

Microplastics (MPs), defined as plastic particles smaller than 5 mm, are increasingly recognized as widespread environmental contaminants occurring in aquatic, terrestrial, and atmospheric ecosystems. Their small size, diverse morphology, and complex polymer composition make accurate detection, identification, and quantification analytically challenging. This review summarizes recent advances in analytical techniques for detecting MPs in environmental samples, including microscopic, spectroscopic, mass spectrometric, thermal analytical, hyperspectral imaging, and artificial intelligence (AI)-assisted approaches. The fundamental principles, strengths, and limitations of each technique are critically evaluated in the context of environmental monitoring and analytical performance. Despite considerable progress, significant challenges remain, particularly in the reliable detection of nanoplastics (NPs), real-time in situ monitoring, and the establishment of standardized analytical protocols. Emerging strategies that integrate AI-driven spectral analysis, hyperspectral imaging, and thermogravimetric analysis show promise for improving the accuracy and throughput of MP detection. Overall, this review highlights the importance of integrating conventional analytical methods with advanced computational tools and developing high-throughput, environmentally sustainable detection strategies to improve our understanding of the environmental fate of MPs and NPs as well as support future risk assessment and policy development.

The accurate quantification of heavy metals or trace elements in biogas-producing systems supports sustainable energy pathways. At present, criteria guiding the selection of analytical techniques for heavy metal determination in biogas studies remain fragmented. This study applied the Analytical Hierarchy Process (AHP), Multi-Criteria Decision-Making Analysis (MCDA), and Simple Multi-Attribute Rating Technique (SMART) to evaluate the analytical techniques using eight defined criteria. A systematic literature review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, covering publications from the past 25 years retrieved from the Scopus database through a refined search string. The results from the integrated MCDA-AHP framework indicate that XRF-based technologies demonstrated strong overall suitability across multiple operational and analytical requirements, particularly for rapid screening and field-based monitoring applications. The four highest-ranked criteria were multi-element analysis (0.258), low detection limit (0.238), cost (0.188), and matrix tolerance (0.119). Handheld XRF achieved the highest aggregate score (72.2%) within the evaluated framework. However, ICP-based techniques remain important for applications requiring ultra-trace quantification and higher analytical sensitivity. Although ICP-OES remains widely applied, four scenario tests continued to prioritize XRF-based methods, with only scenario 4 ranking ICP-OES second. The proposed framework provides a transparent and reproducible basis for selecting analytical techniques for monitoring heavy metals in biogas systems.

Baloxavir Marboxil, a first-in-class cap-dependent endonuclease inhibitor, is a prodrug used in influenza therapy. Reliable analytical methods are essential for its quantification in pharmaceutical formulations and biological matrices to support drug development, quality control, and pharmacokinetic studies. This review evaluates current analytical approaches, and future directions. A systematic literature search identified relevant analytical studies, which were critically compared based on principles, validation performance, and applicability. UHPLC-MS/MS is considered the gold standard, offering high sensitivity (LLOQ as low as 0.1 ng/mL), strong selectivity, and simultaneous determination of baloxavir marboxil and its active metabolite. Sample preparation techniques, particularly protein precipitation and liquid-liquid extraction, significantly influence method performance, while isotopically labeled internal standards effectively minimize matrix effects. The compound shows pronounced instability under alkaline conditions, whereas oxidative, thermal, and photolytic degradation are relatively limited. Emerging spectrofluorimetric methods provide greener alternatives but with reduced sensitivity and limited bioanalytical applicability. This review compares major analytical platforms, highlights regulatory validation requirements, and discusses clinical applications including therapeutic drug monitoring. It underscores the need for standardized, high-throughput, and sustainable analytical methods to support future research and clinical implementation.

Antifungal drugs play a vital role in combating life-threatening fungal infections, yet their therapeutic effectiveness and safety critically depend on purity, stability, and reliable analytical assessment. This review comprehensively discusses impurity profiling, forced degradation studies, and bioanalytical method development of key antifungal agents within a single integrated framework - an approach not previously consolidated in the literature. The review covers major antifungal classes including azoles, polyenes, allylamines, and pyrimidine analogues. It summarizes impurity types - organic, inorganic, and residual solvents - as classified by ICH guidelines, along with their origins, formation mechanisms, and control strategies. Advanced chromatographic and spectroscopic techniques such as HPLC, LC-MS/MS, and GC-MS are highlighted for impurity detection. Forced degradation studies under ICH-recommended stress conditions are evaluated for drugs including flucytosine, terbinafine, tolnaftate, benzoic acid, and azoles. Recent advances in bioanalytical method validation using LC-MS/MS and UPLC-MS/MS for pharmacokinetic and bioequivalence studies are also outlined. The review underscores the importance of these analytical strategies in ensuring safety, efficacy, and quality control of antifungal formulations - from raw material characterization to finished product evaluation - in alignment with international pharmacopeial and ICH standards.

Hyperspectral imaging (HSI), characterized by integrated spatial-spectral acquisition, offers a powerful platform for nondestructive and fine-grained forensic evidence analysis. However, high data dimensionality, spectral mixing complexity, and limited forensic sample availability constrain traditional methods reliant on handcrafted features, necessitating more robust data-driven approaches. This review systematically examines the integration of deep learning and HSI in forensic science. We first summarize three representative paradigms: convolutional neural networks (CNNs) for spatial-spectral feature extraction, attention mechanisms for discriminative enhancement and interpretability, and generative adversarial networks (GANs) for data augmentation and nonlinear spectral unmixing. We then analyze multimodal integration strategies at the input, feature, and decision levels, elucidating their methodological rationale. Representative forensic applications-including trace fiber identification, mixed ink discrimination, soil analysis, and questioned document examination-are critically evaluated to compare the strengths and limitations of different approaches. Furthermore, key challenges are discussed, including model interpretability and judicial admissibility, computational efficiency and deployability, and barriers to data standardization and sharing. Finally, future directions, including lightweight architectures, physics-informed learning, and multimodal fusion, are proposed to advance intelligent and legally defensible forensic analytics.

Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality worldwide due to the limited sensitivity of current surveillance and diagnostic strategies for early-stage detection. Electrochemical biosensors have emerged as promising tools for HCC diagnostics owing to their high sensitivity, rapid response, low cost, and compatibility with point-of-care testing. This review provides a comprehensive overview of recent advances in electrochemical biosensors for HCC detection, focusing on key biomarkers such as alpha-fetoprotein (AFP), glypican-3 (GPC-3), AFP-L3, and circulating nucleic acids. We discuss developments in biorecognition strategies, nanomaterial-assisted signal amplification, and analytical performance of reported sensor platforms. In addition, the review critically examines the gap between laboratory-scale sensor performance particularly ultra-low detection limits and practical clinical requirements, including selectivity in complex biological matrices, reproducibility, long-term stability, and validation using clinical samples. The review also discusses how nanomaterial selection, fabrication complexity, and device variability influence the clinical translation of electrochemical biosensors. Finally, the review highlights future directions for developing clinically viable electrochemical biosensors, including multiplex biomarker detection, standardized validation with real clinical samples, scalable manufacturing approaches, and integration with artificial intelligence and digital health platforms to improve HCC monitoring.