Critical reviews in analytical chemistry最新文献

Zolpidem (ZLP), a non-benzodiazepine hypnotic of the imidazopyridine class, is widely prescribed for the short-term management of insomnia owing to its rapid onset of action and minimal residual effects. However, its extensive metabolism, low plasma concentration, and instability in pharmaceutical formulations, biological matrices, and environmental samples create significant analytical challenges. This review provides a comprehensive overview of analytical strategies for the accurate, sensitive, and selective quantification of ZLP and its metabolites across various matrices. Articles were retrieved from major scientific databases, including Scopus, Web of Science, PubMed, ScienceDirect, and Google Scholar. Chromatographic methods such as HPLC, liquid chromatographic-tandem mass spectrometry (LC-MS/MS), and ultra-high-performance liquid chromatography (UHPLC)-MS/MS demonstrate high sensitivity, precision, and selectivity, while spectrophotometric and electrochemical approaches provide faster, simpler, and more economical alternatives suitable for quality control and routine screening. While LC-MS/MS and UHPLC-MS/MS offer superior sensitivity and selectivity for trace-level analysis, their high cost and operational complexity limit routine use, whereas HPLC-UV, spectroscopic, and electrochemical methods remain valuable for quality control and screening despite lower sensitivity. The incorporation of nanomaterials, molecularly imprinted polymers (MIPs), and biosensor-based systems has markedly enhanced analytical sensitivity and selectivity and enabled miniaturization. Additionally, adopting green analytical chemistry (GAC) principles, eco-friendly solvents, and microextraction techniques has improved method sustainability and environmental compatibility. Looking ahead, the integration of artificial intelligence (AI), machine learning (ML), and lab-on-a-chip (LOC) technologies is expected to enhance ZLP determination by enabling automation, real-time monitoring, and predictive analysis. These innovative, eco-conscious approaches will yield robust, intelligent, and sustainable platforms for ZLP detection in pharmaceutical, biological, and environmental samples.

Lateral flow assays (LFAs), as miniaturized paper-based sensing platforms, have introduced simple, effective, and point-of-care testing (POCT) devices for detecting various targets, including contaminants, pathogens, antibiotic residues, and pesticide residues, in complex food matrices. The principle of operation in these assays relies on a signal reporting label, which plays a crucial role in improving the sensitivity of the detection approach. However, conventional LFA signal labels often exhibit insufficient sensitivity when detecting biomarkers at low concentrations. The integration of aggregation-induced luminescence (AIE) materials into the structure of LFAs has enabled sensitive and selective analytical methods for a variety of assay applications. The increase in luminescence intensity in aggregated states is accompanied by a high signal-to-noise ratio and excellent photostability. Additionally, their high quantum yields (QYs) and strong fluorescence make them well-suited for various optical sensors based on LFAs, including both colorimetric and fluorescence-based LFAs. This review explores the potential of AIE materials in LFAs for food safety analysis. It also provides a detailed discussion of the operational principles of both AIE materials and LFAs. Furthermore, the design of these platforms and their recent advancements in food safety applications are thoroughly reviewed.

Aptamers are in vitro or in vivo generated bio-candidates from a synthetic library using amplification and separation procedures and have delicate options to label at the ends of properly folded secondary or tertiary structures. Aptamers are used as source molecules for sensor development since they can easily conjugate with nanomaterials and report the interactive output. Various sensing systems with enhanced sensitivity have been built in the past using aptamer conjugations. Despite the fact that the techniques for aptamer immobilization vary with functionalization chemistries, the essential strategy is reporting the presence of a specific target for sensing purposes, called an 'aptasensor'. Various attempts to analyze aptamer-ligand interaction advance the gallery of aptamer-based sensors. Aptamer administration is generally being investigated as the recognition element in high-performance analysis, and has been promoted as the exemplary molecule to mimic and replace antibody-based sensing. Using existing technologies, aptasensors are proven for more sensitive clinical and serological biomarkers sensing and high-throughput analysis.

Recently, residual pollutants in the environment have posed a serious threat to ecosystems. Therefore, it is crucial to establish effective environmental pollutant detection strategies. Nanozymes are widely favored in the field of sensing due to their enzyme-like activity, adjustable activity and high stability. Meanwhile, the design of sensors is increasingly emphasizing features such as ease operation and high-throughput recognition. Compared with traditional sensors, sensor arrays have become a research hotspot in sensor due to their advantages. At present, colorimetric sensor arrays constructed based on nanozymes have been widely studied. They overcome the limitations of traditional sensors and are gradually being applied to the precise detection of various environmental pollutants. In this work, we briefly discuss the characteristics of nanozymes, systematically reviews the principles and methods of sensor arrays based on nanozyme, and focuses on analyzing the practical application of sensor arrays. Finally, we put forward the current challenges and future development trends to promote the in-depth development of sensors. The array sensing method based on nanozymes construction has demonstrated significant application value and development potential in the field of environmental monitoring.

The visualization of latent fingerprints (LFPs) is undergoing a paradigm shift, driven by the unique photophysical properties of aggregation-induced emission (AIE) materials. Moving beyond the limitations of conventional methods, AIE luminogens (AIEgens) introduce a transformative approach. This review elucidates the working principles of AIEgens, primarily through the restriction of intramolecular motion (RIM) mechanism, and systematically charts their advancements in LFP visualization. The application of AIEgens is comprehensively discussed from multiple perspectives: the interaction mechanisms at the fingerprint residue interface (e.g., hydrophobic, electrostatic, and physical adsorption), the resulting luminescence modes (fluorescence and phosphorescence), the practical visualization methodologies (sole application or combination with traditional techniques), and the corresponding evaluation criteria (qualitative and quantitative). While significant progress has been made, we also address existing challenges, particularly the limited color contrast on complex backgrounds and the absence of unified quantitative standards. Next steps should prioritize three critical areas: establishing standardized assessment protocols, optimizing material and imaging performance, and pursuing breakthroughs in multiplexed evidence analysis. This work not only summarizes a pivotal shift in forensic visualization but also provides essential design principles and technical references for in-situ LFP detection, trace analysis, and the recovery of nondestructive biological evidence.

Raman spectroscopy is a powerful tool for probing the structural and electronic properties of graphene-based materials, however, a focused review on reduced graphene oxide (RGO) is limited This review critically examines the Raman signatures of RGO, with a particular emphasis on defect-related bands arising from structural disorder and residual functionalities. The reliability and limitations of widely used intensity ratios and quantitative models are discussed, highlighting unconventional ratios for better analysis. By comparing recent advances, defect evolution can be decoded to better connect Raman features with physical and chemical properties. Emerging machine learning approaches for automated Raman analysis including preprocessing pipelines and algorithm strategies are also discussed. This work provides both a consolidated reference and a forward-looking perspective on Raman spectroscopy as a noninvasive tool for functional graphene materials.

Licorice (Glycyrrhiza glabra L.) is a rich source of phytochemicals and bioactive compounds (BCs), and growing interest in plant-derived BCs has increased attention toward efficient extraction strategies. Extraction methodology plays a critical role in determining yield, efficiency, and sustainability. This systematic review evaluates the effectiveness of conventional and green extraction techniques for isolating BCs from licorice. A total of 42 eligible studies were identified from multiple databases, focusing on extraction, analysis, and identification of licorice BCs. Conventional methods, including Soxhlet and maceration, generally yielded 24.32-37.63 mg/g of glycyrrhizic acid, requiring prolonged extraction times (5-12 h) and large solvent volumes. In contrast, green extraction techniques significantly enhanced extraction efficiency within shorter durations (3-120 min). Glycyrrhizic acid yields ranged from 28.06-217.7 mg/g for ultrasound-assisted extraction, 22.6-45 mg/g for microwave-assisted extraction, 18.5-19.5 mg/g for subcritical water extraction, 340-544 mg/g for supercritical CO₂ extraction, 20.99 mg/g for pressurized liquid extraction, and 43.78 mg/g for infrared-assisted extraction. These variations highlight the influence of operational parameters such as temperature, time, solvent characteristics, and solid-liquid ratio. Overall, green extraction methods offer superior performance and environmental advantages, representing promising alternatives for sustainable and large-scale recovery of licorice BCs.

Phenolic acids (PAs) are among the most abundant phytochemicals in foods and plants, and their consumption is increasing due to well-established health benefits. Determining these compounds is challenging because of their structural diversity, wide concentration ranges, and complex matrices. This review summarizes the separate analysis of PAs in foods, plants, and plant-based foodstuffs, with a particular focus on gas chromatography (GC) methodologies. Since PAs are inherently nonvolatile, chemical derivatization is generally required to enhance volatility and reduce polarity, enabling accurate detection. The role of derivatization, together with the extraction approaches used to isolate PAs from complex matrices, is critically discussed. Additionally, the review covers key method validation parameters, such as analytical range, limit of detection, and limit of quantification, that are essential for accurate measurement of PAs. By focusing on GC-based approaches, this review highlights their contribution to improving analytical performance and supporting research on PAs in food and plant sciences.

Lidocaine (LID), an amide-type local anesthetic and antiarrhythmic agent, remains one of the most extensively used drugs in medical, dental, and surgical practice owing to its rapid onset, potent analgesic activity, and reversible nerve-blocking effects. However, its narrow therapeutic index, rapid hepatic metabolism, and typically low plasma concentrations necessitate the development of highly sensitive and selective analytical approaches for accurate quantification in biological, pharmaceutical, and environmental matrices. This review provides a comprehensive and critical evaluation of more than two decades of methodological progress in LID and metabolites analysis (monoethylglycinexylidide (MEGX) and glycinexylidide (GX)). High-performance liquid chromatography (HPLC) and liquid chromatography-tandem mass spectrometry (LC-MS/MS) have emerged as the gold-standard techniques due to their superior sensitivity, reproducibility, and specificity at trace levels. Complementary spectrophotometric, electroanalytical, and capillary electrophoresis methods also demonstrate distinct advantages in cost, simplicity, and eco-sustainability. Recent advancements highlight the integration of Artificial Intelligence (AI) and Machine Intelligence (MI) algorithms to enhance data processing, optimize chromatographic conditions, and improve pattern recognition in complex matrices, thereby enabling automated, real-time analysis with higher precision. Persistent analytical challenges such as matrix interference, compound instability, and ultra-trace detection underscore the need for continued innovation. Future perspectives emphasize the combination of AI-driven modeling, nanomaterial-based sensors, and green analytical platforms to establish smart, miniaturized, and environmentally sustainable systems for LID determination, facilitating reliable pharmacokinetic monitoring, clinical diagnostics, and pharmaceutical quality assurance.