1. Department of Materials Science and Engineering, University of California, Los Angeles;2. Department of Orthopaedic Surgery, University of California, Los Angeles
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 model‐metrics has the potential to improve the classification of low‐SNR Raman spectra from human normal and osteoarthritic (OA) cartilage over a single metric trained with spectra from the same healthy and OA tissues.