SPA Projection for Spectral Analysis

SPA Projection is a widely used spectral analysis method for selecting characteristic wavelengths that effectively represent sample information. The core principle involves dimensionality reduction of spectral data, transforming multidimensional spectra into two-dimensional or three-dimensional visualizations to intuitively display information embedded in spectral datasets. During implementation, key steps include spectral preprocessing (such as normalization or Savitzky-Golay filtering) and applying SPA algorithms (typically involving iterative wavelength selection based on projection error minimization). The analysis requires selecting appropriate preprocessing techniques and SPA algorithms according to specific sample characteristics and research objectives to achieve accurate and reliable results. Code implementation often utilizes matrix operations for projection calculations and optimization loops for wavelength selection. When employing SPA projection for spectral analysis, it should be combined with complementary analytical methods like Principal Component Analysis (PCA) for dimensionality validation and clustering analysis for pattern recognition. This integrated approach ensures comprehensive interpretation of spectral information, providing robust support for scientific research and practical applications.