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Raman microspectroscopy has been widely demonstrated as an ideal analytical tool for preclinical drug development and clinical applications. However, it is still not easy to accurately identify the subtle spectral variations in different biological samples, which requires a feasible combination between novel spectra collection instrumentation and effective data mining algorithms. In this study, three distinct multivariate classification approaches, which were principal component analysis-linear discriminant analysis (PCA-LDA), support vector machine (SVM), and principal component analysis-support vector machine (PCA-SVM), were validated and compared for obtaining reliable and chemically significant results from the analysis of bio-spectral data. Their performances were evaluated by classifying the spectral characteristics of osteosarcoma cells treated with N-[N-(3,5-difluorophenacetyl)-L-alanyl]-S-phenylglycine t-butyl ester (DAPT) from untreated cells. Based on the discriminated spectral variations, the results indicate that PCA combined with the radial basis function (RBF) kernel SVM model achieved the highest classification accuracy. In general, this study confirms that PCA-SVM algorithm improves the automatic processing accuracy and efficiency of micro-Raman spectroscopy, which may be adopted in further cell screening and analysis applications.

Validating Multivariate Classification Algorithms in Raman Spectroscopy-Based Osteosarcoma Cellular Analysis