Artificial networks for spectral resolution of antibiotic residues in bovine milk; solidification of floating organic droplet in dispersive liquid-liquid microextraction for sample treatment

Abstract

The intensive use of antibiotics in livestock practice has a negative impact on human health and increases the antibiotic resistance. In this study feasible data interpretation algorithm along with efficient extraction protocol were combined for selective analysis of three antibiotics in milk samples. Trimethoprim, sulphamethoxazole and oxytetracycline are widely used antibiotics in veterinary pharmaceuticals. The studied antibiotics were efficiently extracted from milk samples with solidification of floating organic droplet in dispersive liquid-liquid microextraction. This extraction protocol was optimized not only to maximize extraction recoveries but also to approach the lower residue limits specified by European Union. Artificial neural networks succeeded in resolving spectral overlap between the studied drugs. The network architecture was optimized and validated for accurate and precise analysis. The proposed method outweighs the reported chromatographic methods for being simple and inexpensive and compared favorable to official methods.

Keywords: Antibiotic residue, milk analysis; Artificial neural networks; Dispersive liquid-liquid microextraction; Solidification of floating organic droplet.