Application of Hyperspectral Imaging to Differentiate <scp>COPD</scp> and Normal Lung Tissue in Mouse Lung Histopathological Sections

This study investigates the potential of hyperspectral imaging (HSI) as a label-free, quantitative tool for differentiating chronic obstructive pulmonary disease (COPD) from normal mouse lung tissues based on their intrinsic optical signatures. High-resolution HSI data (400-1000 nm) were acquired from paraffin-embedded sections and analyzed using principal component analysis (PCA) for dimensionality reduction, followed by partial least squares-discriminant analysis (PLS-DA) and support vector machine (SVM) classification. Distinct spectral disparities, particularly in hemoglobin and water-associated absorption bands, enabled accurate discrimination with classification performance exceeding 93%. These findings establish HSI as a non-destructive and objective modality capable of capturing subtle pathological alterations in pulmonary tissue, offering a robust experimental and theoretical foundation for its translation into pathological diagnostics and quantitative respiratory disease assessment.