Abstract
Nondestructive, sensitive, near-real-time quantitative analysis approaches are gaining popularity and attention, especially in clinical diagnosis and detection. There is a need to propose an alternative scheme using surface-enhanced Raman spectroscopy (SERS) assisted by chemometrics to improve some defects existing using other analytical instruments to meet clinical demands. In this study, clinical drug oxcarbazepine (OXC) in human blood plasma has been quantified and detected using this method. Partial least squares regression (PLSR) modeling was employed to assess the relationship between full SERS spectral data and OXC concentration. The calibration set's correlation coefficient of the model is > 0.9, the result suggests that this method is favorable and feasible. Furthermore, other multivariate calibration algorithms like Monte Carlo cross-validation (MCCV) sample set partitioning based on joint XY distances (SPXY), adaptive iteratively reweighted penalized least squares (AIR-PLS), moving window partial least squares regression (MWPLS), and leave-one-out cross-validation were used to handle these spectral data to obtain an accurate predictive model. The results achieved in this study provide a possibility and availability for us to apply SERS in combination with chemometrics to diagnosis detection.
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