Highly Sensitive and Interference-Free Detection of Multiple Drug Molecules in Serum Using Dual-Modified SERS Substrates Combined with AI Algorithm Analysis

Surface-enhanced Raman spectroscopy (SERS) technology has shown broad potential in drug concentration detection, but its application in blood drug monitoring faces significant challenges. The primary difficulty lies in overcoming the interference caused by various biomolecules present in serum, which can severely obscure the SERS signals of target drug molecules. Traditional enhancement substrates are often limited to detecting single drugs and are prone to interference, making the label-free detection of multiple drugs particularly challenging. To address these issues, we developed a novel SERS substrate based on Au@AgNRs, which undergoes a two-step modification to produce Au@AgDBCNRs. This innovative substrate provides exceptional signal amplification, simultaneously allowing the sensitive detection of multiple drug molecules. Moreover, our method eliminates the need for serum deproteinization, enabling the direct detection of drugs in serum while effectively mitigating interference from blood components. The cetyltrimethylammonium bromide coating on Au@AgDBCNRs is an internal standard for drug quantification without additional standards. The platform significantly improves detection accuracy and efficiency by automatically integrating artificial intelligence to recognize and analyze Raman spectral features. This novel SERS platform provides a new idea for therapeutic drug monitoring and is expected to provide rapid, accurate, and cost-effective drug detection in the clinical environment, which has great potential in improving patient care and optimizing drug dosage strategies.