Integrating transformer-based machine learning with SERS technology for the analysis of hazardous pesticides in spinach.

This study introduces an innovative strategy for the rapid and accurate identification of pesticide residues in agricultural products by combining surface-enhanced Raman spectroscopy (SERS) with a state-of-the-art transformer model, termed SERSFormer. Gold-silver core-shell nanoparticles were synthesized and served as high-performance SERS substrates, which possess well-defined structures, uniform dispersion, and a core-shell composition with an average diameter of 21.44 ± 4.02 nm, as characterized by TEM-EDS. SERSFormer employs sophisticated, task-specific data processing techniques and CNN embedders, powered by an architecture features weight-shared multi-head self-attention transformer encoder layers. The SERSFormer model demonstrated exceptional proficiency in qualitative analysis, successfully classifying six categories, including five pesticides (coumaphos, oxamyl, carbophenothion, thiabendazole, and phosmet) and a control group of spinach data, with 98.4% accuracy. For quantitative analysis, the model accurately predicted pesticide concentrations with a mean absolute error of 0.966, a mean squared error of 1.826, and an R2 score of 0.849. This novel approach, which combines SERS with machine learning and is supported by robust transformer models, showcases the potential for real-time pesticide detection to improve food safety in the agricultural and food industries.

Fabrication and characterization of mucoadhesive bioplastic patch via coaxial polylactic acid (PLA) based electrospun nanofibers with antimicrobial and wound healing application.

Polylactic acid (PLA) is the second-highest consumed bioplastic in the world. PVP/PLA-PEO complex nanofibers encapsulating collagen and cefazolin dressing scaffold were fabricated using a coaxial electrospinning method to target the release of the encapsulated compounds. It was observed that in collagen doses of 10 and 20%, the speed of healing showed a significant difference with the control sample, but the dose of 40% caused a decrease in wound healing rate in mice. The nanofibers' morphology and surface roughness were investigated by scanning electron microscopy (SEM) and atomic force microscopy (AFM), respectively. The mechanical properties and adhesion strength of the scaffolds were investigated. The scaffolds' antimicrobial activity was evaluated by disk diffusion method against the E. coli, S. aureus, and P. aeruginosa. The results indicated a positive effect on the antimicrobial activity of the samples. In this study, we were able to prolong the effect of scaffolds by changing the pattern of release of cefazolin from inside the nanofibers. Possible interactions between the polymers and the encapsulated compounds were investigated using Fourier-transform infrared spectroscopy (FTIR). Finally, in-vivo and histological tests were performed to evaluate the efficacy of the scaffolds in accelerating wound healing.