A reversed-phase high-performance liquid chromatography method with a diode-array detector (RP-HPLC-DAD) was developed and validated for the simultaneous determination of 3,4-dimethoxybenzaldehyde (3,4-DMBA), benzaldehyde (BA), and 4-methoxybenzaldehyde (4-MBA) in acid whey fermented with Ischnoderma benzoinum. These structurally related aromatic aldehydes are valuable aroma compounds formed during basidiomycete-mediated fermentation of dairy by-products; however, no method enabling their simultaneous quantification in this complex matrix without prior derivatization has been reported to date. Chromatographic separation was achieved on an XBridge BEH C18 column (250 × 4.6 mm, 5 μm) using gradient elution with water and methanol as mobile phases, with a total runtime of 30 min. The method was validated following the relevant ICH Q2(R2) validation characteristics and showed linearity (R2 > 0.999) over ranges of 0.1-100 mg L-1 (BA, 4-MBA) and 0.1-75 mg L-1 (3,4-DMBA). Limits of detection and quantification ranged from 0.32 to 0.66 mg L-1 and 0.96-1.98 mg L-1, respectively. Accuracy (94.9-111.5%), intra- and inter-day precision (RSD ≤ 3.16%), and matrix recovery (90.7-105.5%) confirmed the reliability of the method for quantitative analysis in real fermentation samples. The developed method provides a practical, direct analytical tool for monitoring aromatic aldehyde production at mg L-1 level in complex aqueous fermentation matrices, rather than for trace-level determination.

Semaglutide is a potent long-acting glucagon-like peptide-1 receptor agonist with outstanding therapeutic efficacy for type 2 diabetes and obesity. Because of its widespread clinical use, there is an increasing demand for analytical techniques to identify semaglutide in pharmaceutical formulations and biological matrices. The current study provides an overview of analytical methodologies for determining semaglutide across various disciplines. The material may be analysed using the following techniques: chromatography, spectroscopy, electrophoresis, and mass spectrometry. Some of these approaches include reversed-phase high-performance liquid chromatography, liquid chromatography/tandem mass spectrometry, ultraviolet-visible spectrophotometry, Fourier-transform infrared spectroscopy, Raman spectroscopy and others. The reported methodological characteristics, including validation parameters are discussed. In addition, a comparison and discussion of analytical performance, approaches, advantages, and disadvantages are presented. Chromatographic techniques are presented as the most effective, sensitive, and reliable analytical methods for semaglutide determination, but alternative approaches are also described. The current study may provide a comprehensive and informative guide for further investigations into semaglutide analysis and related research.

Deep eutectic solvents (DESs) have recently emerged as promising alternatives to conventional separation media in capillary electrophoresis (CE). However, nearly all existing studies have confined DESs to the role of minor additives, with their application as primary separation media remaining largely unexplored. In this work, we investigated the feasibility of employing a DES, Proline:Urea (PU), as the primary separation medium in electrokinetic chromatography (EKC), with a particular focus on its compatibility with cyclodextrins (CDs), one of the most important classes of pseudo-stationary phases in CE. The results showed that PU exhibited excellent solubility for all three native and five derivatized CDs tested, with a particularly significant enhancement (2 to 16 times higher) in the solubility of native CDs compared to water. More importantly, the host-guest properties (mainly the hydrophobic effects) of these CDs were well-preserved in the DES, enabling excellent separations of 3 groups of complex samples, covering 8 anions, 4 cations, and 8 neutrals. By contrast, these separations could not be achieved under conventional aqueous CD-EKC conditions. These findings demonstrate the significant potential of DESs as primary separation media for addressing specific challenges in conventional CE while providing a more sustainable separation strategy.

Liquid crystal monomers (LCMs) have recently emerged as contaminants of increasing environmental concern owing to their widespread use in liquid crystal display devices, environmental persistence, bioaccumulation potential, and adverse toxicological effects. Indoor dust represents a major exposure pathway for human intake of LCMs; however, their ultra-trace concentrations and the complexity of dust matrices pose substantial challenges for accurate determination. Herein, a fluorinated porous organic polymer (F-POP)-coated solid-phase microextraction (SPME) fiber was developed for the selective enrichment of fluorinated liquid crystal monomers (FLCMs). F-POP provides multiple complementary adsorption mechanisms, including fluorous interaction, π-π stacking, hydrophobic interactions, and pore-filling effects, while a newly designed cooling-assisted headspace SPME (CA-HS-SPME) device equipped with dual-cooling structures provides an enhanced temperature gradient to promote analyte partitioning and mass transfer. The proposed CA-HS-SPME strategy increased extraction efficiencies by 40-108% compared with conventional HS-SPME. Coupled with gas chromatography-mass spectrometry (GC-MS), the developed method achieved enrichment factors of 1360-5672, excellent linearity over 10-1000 pg mL-1 (r ≥ 0.9997), limits of quantification of 1.05-2.74 pg mL-1, and satisfactory recoveries of 89.3-118.0%. The method was successfully applied to the determination of four FLCMs in indoor dust samples, demonstrating its capability for sensitive and reliable quantification of ultra-trace fluorinated contaminants in complex environmental matrices. The proposed analytical method provides an effective strategy for environmental monitoring and human exposure assessment of FLCMs and may be extended to the determination of other fluorinated emerging contaminants.

The venom of most poisonous snakes consists of active proteins that are responsible for most of the lethal effects of snakebite envenomation. To guide the development of targeted antivenom, a deeper understanding of venom compositions is necessary. The present study aimed to characterize the protein composition and to evaluate the lethality of Naja melanoleuca venom obtained in Ghana. Crude venom was collected, lyophilized and protein concentration quantified using the Bradford Assay. The protein profile and molecular weight distribution of the venom were evaluated using sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE), while reverse-phase high-performance liquid chromatography coupled with mass spectrometry (RP-HPLC-MS) was employed to separate protein fractions and to determine intact protein masses. The venom toxicity was assessed by determining the median lethal dose (LD50) in mice using the probit regression method. The protein concentration of the venom was 2.917 mg/mL, while the SDS-PAGE analysis showed protein bands with estimated molecular weights ranging from 10.98 kDa to 122.53 kDa. The relative abundance of protein bands in the LC-MS data suggested that low-molecular-weight proteins dominated the venom. The average LD50 of the venom was 1.17 mg/kg within the range of 0.50-1.84 mg/kg, which confirmed its high toxicity. This study provided one of the first proteomic datasets describing Ghanaian N.melanoleuca venom with baseline information on its protein composition and toxicity. These findings laid the foundation for future proteomic, pharmacological and toxicological investigations to develope local antivenoms for improved snakebite management.

Asphodelus aestivus L. is a traditionally used medicinal plant with significant therapeutic and nutritional value. This study investigated the phytochemical composition, antioxidant activity, phenolic and flavonoid contents, cytotoxic effects, and nutritional profile of different organs of A. aestivus L. extracted with aqueous, methanol, and 80% ethanol. Major bioactive compounds were further evaluated using DFT analysis, molecular docking, and network pharmacology approaches, including GO-BP and KEGG pathway enrichment analyses. LC-ESI-MS/MS analysis identified chlorogenic acid, p-coumaric acid, vanillin, trans-ferulic acid, and gentisic acid as the major phenolic compounds, with chlorogenic acid being the predominant constituent across most plant organs and extracts. Leaf extracts, particularly the methanol extract, exhibited the strongest antioxidant activity in both DPPH and ABTS assays, whereas stem and flower extracts showed the weakest activities. Among all plant organs, leaf extracts showed the greatest accumulation of phenolics and flavonoids. ICP-MS analysis demonstrated a mineral-rich profile in the leaves, dominated by K, Ca, Na and Mg. Among the extracts, the aqueous leaf extract showed the highest cytotoxicity against A549 cells while exhibiting low toxicity toward healthy L929 cells. DFT analysis revealed comparable electronic properties among the major compounds, with sinapic acid exhibiting the strongest electron-donating ability. The docking analysis identified chrysin and chlorogenic acid as the compounds with the most favorable predicted interactions toward AChE and BChE, respectively; GO-BP and KEGG analyses revealed enrichment of inflammation, apoptosis, and cancer-related pathways targeted by the identified phenolic compounds. Overall, the integrated in vitro and in silico findings demonstrate that A. aestivus L. represents a valuable natural resource with significant potential for the development of functional foods, nutraceuticals, and novel therapeutic agents.

Mycotoxin contamination in medicinal and edible plants threatens food safety and public health, and developing effective adsorbents for their enrichment and detection is essential. A novel magnetic cyclodextrin-functionalised microporous organic network (MON), TEPA-Fe3O4@α-CD-MON, was synthesized via an in situ growth strategy. The adsorbent efficiently enriched trace levels of aflatoxin B1 (AFB1), aflatoxin B2 (AFB2), zearalenone (ZEN) and ochratoxin A (OTA), simultaneously. Experimental results combined with density functional theory (DFT) calculations confirmed that the adsorbent exhibited excellent extraction performance and high adsorption capacity (e.g., AFB2: 79.9 mg/g-the highest among reported adsorbents) through π-π interactions, hydrogen bonding and van der Waals forces. At optimized conditions, the established TEPA-Fe3O4@α-CD-MON-MSPE-HPLC-DAD method was successfully applied to 20 medicinal and edible plants, demonstrating a wide linearity range (25-5000 μg/L), good precision (RSD < 8.76%) and high stability. The method proves simple, efficient, and highly promising for monitoring trace mycotoxins in the complex matrices.

The increasing complexity of food matrices and the demand for comprehensive foodomics have exposed the limitations of conventional LC-MS workflows in chemical coverage, structural annotation, and quantitative analysis. Chemical derivatization provides an effective strategy to address these challenges through selective functional-group modification and integration with advanced analytical approaches, enabling improved chromatographic performance, ionization efficiency, and structural characterization. Recent advances integrating derivatization with high-resolution mass spectrometry, stable isotope encoding, retention time prediction, and computational annotation have further expanded its role from sensitivity enhancement to information-rich analysis. This review summarizes recent developments in derivatization-assisted LC-MS strategies for food analysis from 2013 to 2026. Derivatization approaches targeting amino, hydroxyl, carbonyl, carboxyl, and other functional groups are systematically discussed, with emphasis on reaction principles, analytical advantages, representative applications, and methodological limitations. Particular attention is given to the role of derivatization in improving molecular coverage, structural confidence, and quantitative reliability in complex food matrices. Remaining challenges include balancing reaction selectivity and applicability, improving workflow standardization, and enhancing automation compatibility. Future advances will rely on integrating selective derivatization chemistry with advanced MS technologies and computational approaches to achieve more comprehensive, reliable, and accurate characterization of chemically diverse compounds in complex food systems.

Headspace solid-phase microextraction (HS-SPME) is widely used for volatile profiling, with method development typically guided by responses that reward signal maximisation, such as total area or peak count. On porous adsorptive coatings (e.g., divinylbenzene/Carboxen/polydimethylsiloxane), competitive adsorption complicates this strategy: under aggressive extraction, compounds that are not preferentially adsorbed on the fibre are displaced from the finite number of adsorption sites by more strongly retained volatiles, reducing the contribution of minor compounds observed in the chemical profile. Here, a multi-response Derringer-Suich desirability framework is developed that introduces two responses beyond conventional sensitivity measures: a minor-peak area fraction, serving as an indicator of competitive adsorption, and a two-dimensional separation orthogonality term, capturing how extracted volatiles populate the available separation space. The framework was demonstrated on GC×GC-TOF MS analysis of extra virgin olive oil. A central composite design (extraction temperature, equilibration time, extraction time) was modelled against these two responses alongside three conventional measures: peak count, lipoxygenase-pathway volatile sum, and nonanal area as an oxidation indicator. Across complementary optimisation scenarios, the optimum consistently converged at approximately 46-47 °C, with 25 min equilibration and 60 min extraction. This temperature sits at the lower end of the reported 40-70 °C range, reflecting the suppression of thermally driven oxidation products that increase with extraction temperature. All responses are derived directly from the standard GC×GC peak table, requiring no additional measurement. Together, these responses extend HS-SPME method development beyond signal maximisation, towards volatile profiles that are both representative and well-separated.

To give guidance to future research on computer-aided method development, the present study aimed at quantifying the accuracy with which Gaussian processes can represent typical Chromatographic Response Function (CRF) surfaces underlying chromatographic method development. Gaussian processes make up the core of Bayesian optimization, an optimization method that currently shows great promise to solve chromatographic method development problems. CRF surfaces are known to be very complex and jagged, with many local minima and maxima. On the other hand, the Matérn-based kernels commonly used in Gaussian processes typically impose strong smoothness assumptions that conflict with sharp variations and many closely spaced local extrema. To obtain an unbiased assessment of the reconstruction accuracy, grid- and Bayesian optimization-based Gaussian process fittings were investigated. Both showed poor reconstruction of the complete CRF surface, even for relatively simple 10-component samples. This suggests a limited ability of the Gaussian process-based surrogate models making use of Matérn-based kernels to accurately capture the fine details of the CRF surfaces and delineate robustness regions. Grid-based approaches generally outperformed Bayesian optimization-based methods in terms of global reconstruction accuracy, while Bayesian optimization-based approaches were more successful at locating acceptable optima. However, both required substantially more experimental data points than conventional model-based strategies. Furthermore, for the most challenging scenario, in which "only" 25 evaluations were performed for a 20-component sample, the median difference with the true optimal method time ranged from 27% (10-component sample) to 102% (20-component sample), provided a correct solution was found.

While natural red colorants are generally safe, some synthetic red dyes have been linked to adverse effects, including allergenicity, neurodevelopmental toxicity, and confirmed carcinogenicity. Detecting trace levels in food is challenging due to complex matrices (proteins, lipids, natural pigments). Hence, efficient sample pretreatment is critical for ensuring food safety, underpinning analytical selectivity and reliability. This review provides a comprehensive overview of pretreatment methods for red pigments in complex foods, systematically comparing established techniques-liquid-phase microextraction, solid-phase extraction, solid-phase microextraction, and matrix solid-phase dispersion-and their modern variants, evaluating extraction efficiency, convenience, and environmental impact. Particular focus is on key innovations like integrating ionic liquids into dispersive liquid-liquid microextraction to enhance selectivity and minimize solvent use. By critically assessing each approach's strengths and limitations, it illustrates the trend toward greener, more efficient, and automated sample preparation technologies.

The QSRR modeling framework enables researchers to use molecular descriptors for predicting chromatographic retention, including HPLC retention based on their physicochemical characteristics, as it can predict chromatographic retentions and not only HPLC retention. QSRR models show low transferability between different laboratories and instruments and experimental protocols because their performance depends on experimental conditions which remain poorly documented. The present study investigates the way experimental conditions affect both model architecture and descriptor selection through their experimental design which has not been studied in previous reviews. The review demonstrates how organic modifier type and concentration, mobile phase pH and buffer identity, stationary phase chemistry, and column temperature functions as the fundamental drivers of analyte retention which leads to QSRR model transferability problems. The research examines how dataset variation and laboratory differences and variable relationships affect study results. The study evaluates traditional modeling methods which include Linear Solvation Energy Relationships and Multiple Linear Regression and Partial Least Squares together with modern machine learning techniques which use Random Forests and Gradient Boosting and Support Vector Regression and Graph Neural Networks. The research establishes model validation standards which include applicability domain assessment and descriptor selection and standardized reporting.

A sensitive and robust micro-solid phase extraction LC-tandem mass spectrometry (μSPE-LC-MS/MS) method was established for quantifying 23 per- and polyfluoroalkyl substances (PFAS) in mouse feces and liver. Method development identified interferences due to the complex fecal matrix, especially at unit-mass resolution detection, where co-eluting endogenous compounds caused transition-specific interferences. Conventional C18 stationary phases failed to adequately resolve these matrix components, particularly for perfluoroalkyl sulfonic acids (PFSAs), resulting in compromised selectivity. To reduce these matrix effects, alternative stationary phases, including Phenyl-Hexyl (Phe) and Pentafluorophenyl (PFP) columns, were systematically evaluated. The PFP column, operated with acetonitrile in the mobile phase, demonstrated superior chromatographic selectivity and effectively eliminated co-eluting interferences. Notably, transition-specific interferences previously observed for PFOS on C18 columns were resolved, allowing accurate quantification using both m/z 499 → 80 and 499 → 99 transitions. Using the improved selectivity of the PFP column, automated μSPE cleanup combined with isotope-labelled internal standards achieved high extraction efficiencies (96-104%), ensuring reliable accuracy and precision (recoveries 96-99%, RSD <5%) for most analytes despite matrix complexity. These findings demonstrate the importance of chromatographic selectivity, rigorous cleanup, and isotopic correction for quantifying PFAS in highly complex biological matrices using unit-resolution mass spectrometry. The developed workflow provides a reliable analytical platform for toxicokinetic studies and may be applicable to other challenging biological matrices.