Laser tweezers Raman spectroscopy (LTRS) combines optical tweezers
technology and Raman spectroscopy to obtain biomolecular compositional
information from a single cell without invasion or destruction, so it
can be used to “fingerprint” substances to characterize
numerous types of biological cell samples. In the current study, LTRS
was combined with two machine learning algorithms, principal component
analysis (PCA)-linear discriminant analysis (LDA) and random forest,
to achieve high-precision multi-species blood classification at the
single-cell level. The accuracies of the two classification models
were 96.60% and 96.84%, respectively. Meanwhile,
compared with PCA-LDA and other classification algorithms, the random
forest algorithm is proved to have significant advantages, which can
directly explain the importance of spectral features at the molecular
level.
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