The most frequently employed calibration methods in near-infrared (NIR) analysis are partial least squares (PLS) and principal component regression (PCR). These methods yield satisfactory results when the relationship between spectral parameters and chemical measurements is linear. However, when there is a nonlinear relationship, particularly with a wide range of sample properties, these methods often fall short. In such cases, segmented calibration is commonly used, necessitating a substantial increase in the number of training samples to meet modeling requirements. Additionally, the robustness of mathematical models to minor disturbances—such as slight variations in light source energy, sample cell, or temperature—is a significant concern in practical NIR applications. The application of artificial neural networks (ANN), a method that has matured significantly, holds promise for unified modeling of complex systems and can withstand minor perturbations, exhibiting good robustness.