Effective application of biosensor analytical techniques in drug testing

Abstract

This study explores biosensor technology, focusing on its application in drug detection through advanced quantitative analysis methods: partial least squares (PLS) and probabilistic principal component analysis (PPCA). We developed a rapid quantitative calibration model using azure A, B, and C—metabolites of pefloxacin mesylate and methylene blue— demonstrated through surface-enhanced Raman spectroscopy. The findings highlight the superior accuracy of PLS and PPCA in predicting drug concentrations, with pefloxacin mesylate detection deviations maintained between 0.24%-0.98% and 0.35%-1.02%, respectively. PLS proved to be slightly more effective. This study confirms the potential of biosensor technology in ensuring drug safety, offering substantial support for public health protection and regulatory compliance.

Keywords

• partial least squares
• probabilistic principal component analysis
• biosensor technology
• drug detection
• 00a05