Objective To address the issues of reliance on exhaustive “blind” trial-and-error and the lack of theoretical guidance in the modeling process of near-infrared spectroscopy (NIRS), this study used the solid content prediction model of Jinzhen Oral Liquid (JOL, 金振口服液) as a case study. It aimed to reveal the direction for optimizing the best model from the perspective of spectral information quality and verify its application value in model maintenance. Methods The NIRS and solid content data of 380 samples were collected. After being processed by nine preprocessing methods, prediction models for solid content were established using partial least squares (PLS) and support vector regression (SVR), respectively. An evaluation framework was innovatively constructed by introducing Shannon entropy, principal component analysis (PCA), and autoencoders to quantify spectral information quality from three dimensions: information richness, linear structure concentration, and non-linear structure capturability. Finally, by systematically analyzing the correlation between spectral information characteristics and model performance, the direction for the optimal preprocessing method was revealed. This correlation rule was then applied to model maintenance involving 294 newly added samples to screen for the optimal spectral dataset. Results It was found that for NIRS characterized by broad and overlapping peaks, both information density and information retention rate were negatively correlated with PLS model performance. Based on this correlation rule, the optimal dataset for model maintenance was successfully predicted, achieving a modeling performance (R_p² = 0.990 9) significantly superior to that of other datasets. Conclusion The correlation rules identified in this study effectively explain the impact of preprocessing on model performance. They provide a theoretical basis and guiding tools for the optimization and maintenance of spectral models, facilitating a shift from “blind trial-and-error” to “active improvement”. This offers new insights for establishing a standardized and intelligent workflow for NIRS model construction and maintenance.

solid content prediction model  /  model maintenance  /  near-infrared spectroscopy  /  pretreatment  /  Shannon entropy  /  principal component analysis  /  autoencoder