Analytica

Alumina–based electrodes offer promising possibilities for sensitive electrochemical sensors, but their development is hindered by poor intrinsic electrical conductivity. To overcome this limitation, new carbon-alumina composites were synthesized via a sol-gel method using two distinct calcination approaches: conventional and flash-calcination. The structural and textural properties of pure alumina samples were affected by the calcination approach. The introduction of carbon reduced the crystallite size and increased the specific micropore surface area and total pore volume of the samples. Electrochemical characterization by cyclic voltammetry and electrochemical impedance spectroscopy revealed that the optimal sample was obtained by conventional calcination for 30 min. This approach produced optimal structural integration, with a well-connected network and superior charge-transfer kinetics. The optimized carbon-alumina composite demonstrated excellent performance for the simultaneous electrochemical detection of methyl parathion and aclonifen by utilizing intermediate reduction products. The resulting sensor achieved wide linear ranges (0.5 to 20 µM) alongside low detection limits of 0.08 µM for methyl parathion and 0.14 µM for aclonifen, establishing these composites as effective materials for sensing applications.

Standardized interpretation of analytical responses in multi-herb formulations remains difficult: constituent measurements, composition ratios, and extraction conditions are rarely integrated quantitatively. Effect Factor (EF) provides a dimensionless, endpoint-aware index for matched-condition referencing, but constituent-level EF descriptors do not directly yield formulation-level expectations. Using an EF-indexed resource comprising 271 herbal materials and 22 multi-herb formulations with paired water and 30% ethanol extracts, we developed an EF-guided quantitative herbal medicine composition–activity relationship (QHMCAR) framework. Additive theoretical formulation EFs for 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), 2,2-diphenyl-1-picrylhydrazyl (DPPH), total phenolic content (TPC), and total flavonoid content (TFC) were derived from constituent EFs, composition ratios, and solvent-matched extraction yields. Analyses included predictor screening, least-squares calibration, additive-deviation analysis, diagnostics, fixed-form refitting, nested leave-one-prescription-identity-out (LOPO), and solvent sensitivity. Retained full-data models showed apparent R² of 0.748–0.830; fully nested grouped LOPO yielded Q² of 0.543–0.809. Against a grouped-LOPO untransformed linear baseline, transformation screening modestly improved DPPH prediction, matched ABTS, and yielded lower Q² for TPC and TFC. Positive additive deviations were endpoint-dependent, most pronounced for ABTS; solvent-sensitivity analysis identified limited additional variation except for an ABTS-specific intercept shift. QHMCAR enables transparent formulation-level expectation, calibration, and internal validation without implying pharmacological synergy, therapeutic efficacy, or external predictive validity.

Honey is recognized as a valuable tool for environmental biomonitoring, while its contaminant content is relevant to food safety, requiring analytical methods for the simultaneous determination of multiple contaminants. In this study, an ultrasound–vortex-assisted dispersive liquid–liquid microextraction (UVA-DLLME) procedure coupled with gas chromatography–ion trap mass spectrometry (GC–IT/MS) was developed for the simultaneous determination of pesticides, polycyclic aromatic hydrocarbons (PAHs), and polychlorinated biphenyls (PCBs) in honey. The extraction solvent composition was optimized using different n-heptane/toluene mixtures, and the selected conditions were validated in terms of linearity, recovery, sensitivity, and precision. The optimized method provided recoveries between 70.2 and 112%, coefficients of determination (R²) from 0.9389 to 0.9998, limits of detection ranging from 0.40 to 21 ng g⁻¹, and intraday and interday relative standard deviations below 15%. The environmental performance of the sample preparation procedure was evaluated using the AGREEprep metric, giving an overall score of 0.52. Application of the method to 21 artisanal honey samples showed a generally low occurrence of the investigated contaminants, with PAHs being the most frequently detected compounds, whereas pesticides and PCBs were mostly below the limits of detection or quantification. The proposed method represents a solvent-efficient analytical procedure suitable for the multiclass determination of organic contaminants in honey.