Derivative Spectroscopy Analysis Method for Detecting Characteristic Absorption Bands

Derivative spectroscopy analysis is a widely used technique in hyperspectral image processing, primarily employed for detecting characteristic absorption bands of materials. This method enhances subtle features in original spectral data through mathematical transformations, proving particularly effective for analyzing overlapping absorption peaks. The fundamental principle involves computing first or higher-order derivatives of the original spectral curve, converting gentle absorption valleys into distinct extremum points. First-order derivatives effectively eliminate baseline drift influences, while second-order derivatives further separate overlapping absorption peaks. In practical implementations, appropriate smoothing preprocessing of raw spectra is typically required to prevent noise amplification. Code implementations often utilize Savitzky-Golay filters for smoothing combined with numerical differentiation algorithms using finite difference methods. The detection workflow for characteristic absorption bands generally comprises three key steps: First, performing necessary noise filtering and baseline correction on original spectral data using algorithms like moving average filters or polynomial fitting. Second, selecting appropriate differentiation orders for derivative transformation, typically implemented through convolution operations with derivative kernels. Finally, identifying characteristic absorption positions by locating extremum points or zero-crossings in derivative spectra using peak detection algorithms with threshold-based approaches. This method demonstrates unique advantages in hyperspectral remote sensing applications, significantly improving accuracy in mineral identification and vegetation analysis. Particularly when processing complex mixed spectra, derivative analysis effectively decomposes overlapping absorption features, providing reliable basis for quantitative inversion of material composition. Implementation typically involves spectral processing libraries like ENVI or Python's SciPy for numerical differentiation and peak detection functions.