The simultaneous quantification of metformin (MET) and glibenclamide (GLB) in fixed-dose pharmaceutical formulations remains analytically challenging due to the pronounced concentration disparity between the drugs and the extensive spectral overlap in the ultraviolet region. This study describes the development of a UV-Vis spectrophotometric method combined with partial least squares (PLS) regression and principal component analysis (PCA) for the simultaneous determination of MET and GLB without chromatographic separation. Three multivariate calibration strategies were evaluated, including MET-priority (200-400 nm), GLB-priority (250-400 nm), and a global model optimized by variable selection. The optimized PLS models showed satisfactory predictive performance, with R2 pred values >= 0.99 and root mean square error of prediction (RMSEP) values <= 3 & micro;g/mL for the selected models. The proposed methodology was successfully applied to commercial pharmaceutical formulations, including samples presenting significant matrix effects associated with high MET:GLB ratios. Validation parameters were evaluated according to analytical performance principles commonly applied to chemometric calibration methods, including precision, accuracy, robustness, and external prediction capability. In comparison with conventional high-performance liquid chromatography based assays reported in the literature, the proposed UV-Vis/PLS approach required substantially lower solvent consumption and reduced analytical complexity, supporting its applicability for routine quality-control analysis of antidiabetic formulations.
Biochar, due to its specific properties, can adsorb organic pollutants from the environment. To improve the adsorption performance of biochar, it is important to modify it. In this work, activated biochar (Bch-HCl) and magnetic biochar (Bch-HCl-Fe3O4) were characterized via transmission electron microscopy (TEM), energy-dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and Brunauer-Emmett-Teller (BET) techniques. The materials were evaluated for their efficacy in removing ciprofloxacin (CIP) from aqueous solution. The effects of adsorbent dose, contact time, temperature, and pollutant concentration were examined. The best removal degree of CIP for both adsorbents was obtained for an initial concentration of 5 mg L-1, adsorbent dose of 5 g L-1, 20 min, and 25 degrees C. The equilibrium data were best described by the Langmuir isotherm model for both adsorbents, with a maximum adsorption capacity of 2.04 mg g-1 for Bch-HCl and 4.14 mg g-1 for Bch-HCl-Fe3O4. Moreover, the adsorption process best fit with the pseudo-second-order model kinetics, and the thermodynamic analysis indicated a spontaneous and endothermic process for Bch-HCl and a spontaneous and exothermic process for Bch-HCl-Fe3O4.
The third-generation class of cephalosporins is the most commonly prescribed group of antibiotics. The cephalosporin is the latest drug against gram-negative bacteria, which works by inhibiting the cell wall of bacteria. They are used to treat various infections worldwide. Surface-enhanced Raman spectroscopy (SERS) has been utilized for the detection of the effect of 3rd generation cephalosporin on K. pneumoniae bacteria. SERS using silver nanoparticles as a substrate has great potential to provide a highly sensitive platform for studying bacterial responses to antibiotics at the molecular level. Chemometric techniques are utilized for the determination of the effect of cephalosporin on sensitive and resistant strains of K. pneumoniae. Principal component analysis (PCA) and K nearest neighbor model (KNN) have been used for differentiation of biochemical changes caused by exposure to cephalosporin on sensitive and resistant strains of K. pneumoniae. The binary classification yielded from the K-nearest neighbor model has produced a perfectly discriminative confusion matrix, with sensitivity and specificity both reaching 100%. The integration of PCA with KNN enhances the accuracy of strain classification by combining dimensionality reduction with a simple yet effective classification algorithm. This approach demonstrates the potential of SERS combined with machine learning as a rapid, noninvasive diagnostic tool for monitoring antibiotic response in clinical microbiology.
Near-infrared (NIR) spectroscopy is a rapid, nondestructive technique for quality assessment. A persistent challenge, however, is that optical component variations across different instruments introduce systematic distortions in spectral responses. As a result, calibration models developed on a master instrument typically lose predictive accuracy when applied to slave instruments. Calibration transfer techniques aim to correct such instrument-induced spectral differences without requiring the laborious recalibration work. DCT2PC (direct calibration transfer to principal components) is a framework that directly maps slave spectra to a principal component (PC) space derived from the master spectra via singular value decomposition (SVD). An ensemble of extreme learning machines (ELMs) is trained to learn the nonlinear mapping from slave spectra to the corresponding master PCs. Transferred spectra are subsequently reconstructed by multiplying the transferred PC scores with the right singular vectors obtained from SVD. DCT2PC requires no intermediate linear calibration steps and relies on a PC space independent of instrument response, which enables the model to accurately and efficiently transfer key information. Another ELM-based calibration model was developed to predict the component contents. The proposed method was validated on three public NIR benchmark datasets. Across all datasets, DCT2PC-ELM consistently achieved lower root mean square errors of prediction (RMSEP) compared to several established methods, including CTCCA, SST, PDS, TEAM, and a PLS-based variant (DCT2PC-PLS). Statistical significance testing confirmed the superiority of the proposed approach. This framework offers a practical solution for cross-instrument NIR calibration without costly spectral recalibration.
Trace-level hexavalent chromium in aquatic systems poses serious risks to human and ecological health. Polydopamine-functionalized silica (SiO2@PDA) composites were synthesized and applied in a micro-column format for the solid-phase extraction (SPE) of Cr(VI), followed by its determination by inductively coupled plasma mass spectrometry (ICP-MS). The method exhibited high selectivity for Cr(VI) within a pH range of 1.0-4.0, achieving a detection limit (LOD) of 0.0018 mu g/L and a relative standard deviation (RSD) of 4.65% (n = 20). The adsorption mechanism was primarily governed by electrostatic interactions, mesopore entrapment, and hydrogen bonding. To evaluate practical applicability, real water samples were analyzed, yielding satisfactory recoveries ranging from 92.62% to 97.47% for the analyte. This work represents the first application of SiO2@PDA in SPE for the determination of Cr(VI), demonstrating excellent sensitivity, selectivity, and practical applicability for trace chromium speciation in complex aqueous matrices.
Plasmonic Metal Nanoparticle (PMNPs) have emerged as transformative agent in the realm of spectroscopic biosensing, offering unparalleled sensitivity, tunable optical properties and surface enhanced signal amplification. This review explores recent innovations in label free detection strategies that harness the Localized Surface Plasmon Resonance (LSPR) and Surface-Enhanced Raman Scattering (SERS) phenomena intrinsic to (PNPs). Emphasis is placed on the design, functionalization and integration of gold, silver and hybrid nanostructures within spectroscopic platform for real-time, non-invasive biomolecular recognition. Advances in fabrication techniques, bio interface engineering and multiplexed detection are critically examined, alongside emerging applications in clinical diagnostic, environmental monitoring, and point of care technologies. The review also highlights challenges related to reproducibility, biocompatibility, and regulatory translation, offering insights into future detection for scalable, robust and miniaturized biosensor development. By bridging nanophotonics with analytical biochemistry, (PMNP-based spectroscopic biosensing is poised to redefine the landscape of rapid, label free diagnostics.
