Label-free SERS-ML detection of cocaine trace in human blood plasma.

ABSTRACT

The widespread consumption of cocaine poses a significant threat to modern society. The most effective way to combat this problem is to control the distribution of cocaine, based on its accurate and sensitive detection. Here, we proposed the detection of cocaine in human blood plasma using a combination of surface enhanced Raman spectroscopy and machine learning (SERS-ML). To demonstrate the efficacy of our proposed approach, cocaine was added into blood plasma at various concentrations and drop-deposited onto a specially prepared disposable SERS substrate. SERS substrates were created by deposition of metal nanoclusters on electrospun polymer nanofibers. Subsequently, SERS spectra were measured and as could be expected, the manual distinguishing of cocaine from the spectra proved unfeasible, as its signal was masked by the background signal from blood plasma molecules. To overcome this issue, a database of SERS spectra of cocaine in blood plasma was collected and used for ML training and validation. After training, the reliability of proposed approach was tested on independently prepared samples, with unknown for SERS-ML cocaine presence or absence. As a result, the possibility of rapid determination of cocaine in blood plasma with a probability above 99.5% for cocaine concentrations up to 10-14 M was confirmed. Therefore, it is evident that the proposed approach has the ability to detect trace amounts of cocaine in bioliquids in an express and simple manner.