Abstract: In quantitative analysis using near-infrared spectroscopy combined with chemometric methods, multicollinearity among variables is a key issue affecting the performance of spectral models. This study investigates the impact of multicollinearity between component concentrations on chemometric quantitative models. Two systems were designed with strong and weak correlations between the concentrations of vitamin B6 (low concentration) and vitamin B1 (high concentration), respectively. Using vitamin B6 as the target component, prediction models for component concentrations were established using near-infrared spectral information combined with partial least squares regression. The results show that when there are coexisting components with high concentrations strongly correlated to the target component in the system, the model can utilize information from these coexisting components to achieve more accurate predictions of the lower-concentration target component, thereby improving the precision of quantitative analysis for the target component. The application of this approach to the detection of commercially available oral solutions containing vitamins B6 and B1 further verified that strong multicollinearity between component concentrations can enhance the quantitative predictive ability of near-infrared spectral models. The conclusions of this study have significant theoretical and practical application value and can be applied to the simultaneous quantitative analysis of components in complex mixture systems.