Multivariate calibration methods are crucial in near-infrared (NIR) spectroscopy, enabling the extraction of chemically specific information from complex spectra. Conventional performance metrics provide a basic framework for assessing model accuracy, but they tend to oversimplify the inherently multidimensional nature of the problem, ignoring the detailed chemical information being processed underneath. In NIR spectroscopy, predictions are fundamentally based on molecular absorption characteristics, yet the interpretability of these models frequently poses challenges. The inherent complexity of NIR spectra complicates the interpretation of the relationships between absorption features and the variables in the calibration model. Recent advances in quantum chemical simulations hold promise for overcoming these difficulties. This paper presents a proof-of-concept for the use of spectra simulation in interpreting the meaningful variables in multivariate regression models, monitoring the spectral pretreatment quality, evaluating instrumental differences and enhancing the predictive power of the model itself. PLS-R models are obtained and examined for paracetamol, caffeine and cellulose in a model pharmaceutical mixture, with an extension to a comparative analysis of three NIR spectrometers: the Hefei SouthNest Technology nanoFTIR, Viavi MicroNIR 1700 ES, and the benchtop Büchi NIRFlex N-500. Additionally, the study highlights the importance of supervised feature selection and identifies specific signatures of model overfitting. The findings demonstrate the utility of incorporating interpreted data into analytical method development and show the potential for informed sensor selection tailored to the unique characteristics of the analytical problem at hand.