Ensemble multivariate analysis to improve identification of articular cartilage disease in noisy Raman spectra.

ABSTRACT: The development of new methods for the early diagnosis of cartilage disease could offer significant improvement in patient care. Raman spectroscopy is an emerging biomedical technology with unique potential to recognize disease tissues, though difficulty in obtaining the samples needed to train a diagnostic and excessive signal noise could slow its development into a clinical tool. In the current report we detail the use of principal component analysis--linear discriminant analysis (PCA-LDA) on spectra from pairs of materials modeling cartilage disease to create multiple spectral scoring metrics, which could limit the reliance on primary training data for identifying disease in low signal-to-noise-ratio (SNR) Raman spectra. Our proof-of-concept experiments show that combinations of these m

SUBMITTER: Richardson W 

PROVIDER: S-EPMC4472573 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature