Machine Learning-Assisted Characterization of Cervid Skin Tissues with Chronic Wasting Disease by nano-enabled Raman Spectroscopic biosensing

Abstract

Chronic wasting disease (CWD) is a contagious neurodegenerative disease in cervids and its spread threatens the health of wild and farm-raised animals. A rapid screening method for CWD is in great demand. Compared to current diagnostic methods, ELISA and immunohistochemistry, Raman spectroscopic biosensing offers a potential approach to screen for CWD in real time and onsite, which is currently lacking. In this study, to evaluate the effectiveness of Raman spectroscopic biosensing for CWD detection, Raman spectra were collected by a Raman microscope as well as a portable Raman spectrometer from cervid skin tissue samples sprayed by gold nanoparticle signal enhancers collected from both heathy (i.e., control, CWD-negative) and diseased (i.e., CWD-positive) white-tailed deer. The spectral data were subject to analysis by two machine learning (ML) algorithms, i.e., support vector machine (SVM) and artificial neural network (ANN). The results suggest that ML-assisted Raman spectroscopic biosensing can indeed offer a rapid first screening for CWD, with a highest accuracy of 94.4% which is superior to existing methods. It has the potential to become a useful tool for in-field screening and detection of CWD.