Rapid, non-invasive screening of keratitis based on Raman spectroscopy combined with multivariate statistical analysis

This study proposes a multivariate statistical analysis method based on Raman spectroscopy and different dimensionality reduction methods combined with the support vector machine (SVM) algorithm for rapid, noninvasive, high-accuracy classification of keratitis screenings. In this experiment, tear samples from 19 subjects with keratitis and 27 healthy subjects were detected, Raman spectra of the two groups of subjects were compared and analysed, and we found that their spectral intensities were different at 1005 cm(-1) and 1155 cm(-1) Principal component analysis (PCA) and partial least squares (PLS) were used for feature extraction, which greatly reduced the dimensionality of the high-dimensional spectral data. Then, the above two feature extraction methods were used as input to an SVM to build the discriminant diagnosis model. The average accuracy obtained from the PCA-SVM and PLS-SVM models was 77.86 % and 100 %, respectively. Our results suggest that tear Raman spectroscopy combined with multivariate statistical analysis has great potential in screening for keratitis. We expect this technology to could lead to the development of a portable, non-invasive and highly accurate keratitis screening device.