The quantitative analysis model for infrared spectroscopy primarily relies on regression methods. Partial Least Squares (PLS) is proposed to overcome the small sample problem through dimensionality reduction. However, spectral data may still include orthogonal variation components. Orthogonal Signal Correction (OSC) methods are developed to remove these orthogonal components, improving analysis accuracy, but they require orthogonality assumptions. Total Least Squares (TLS) regression is introduced to suppress noise and perturbations in both predictor and response variables, yet it does not sol...
