Related Experiment Video
Updated: Jul 19, 2026
Quantitative Multispectral Analysis Following Fluorescent Tissue Transplant for Visualization of Cell Origins, Types, and Interactions
Published on: September 22, 2013
Comparison of the performance of linear multivariate analysis methods for normal and dyplasia tissues differentiation
Shou Chia Chu1, Tzu-Chien Ryan Hsiao, Jen K Lin
1Institute of Biomedical Engineering, National Yang-Ming University, Taipei, Taiwan ROC. d49004003@ym.edu.tw
Abstract:
We compared the performance of three widely used linear multivariate methods for autofluorescence spectroscopic tissues differentiation. Principal component analysis (PCA), partial least squares (PLS), and multivariate linear regression (MVLR) were compared for differentiating at normal, tubular adenoma/epithelial dysplasia and cancer in colorectal and oral tissues. The methods' performances were evaluated by cross-validation analysis. The group-averaged predictive diagnostic accuracies were 85% (PCA), 90% (PLS), and 89% (MVLR) for colorectal tissues; 89% (PCA), 90% (PLS), and 90% (MVLR) for oral tissues. This study found that both PLS and MVLR achieved higher diagnostic results than did PCA.
