Rapid Detection of Stabilizer Content in Double‐Base Propellant Based on Artificial Neural Network Combined With Near‐Infrared Spectroscopy

ABSTRACT During long‐term storage, double‐base propellants are prone to chemical decomposition of internal nitrate esters, leading to decreased burn rate, reduced strength, and degraded ballistic performance. Adding an appropriate amount of Centralite‐II is crucial for ensuring storage safety. This study proposes a novel method combining near‐infrared spectroscopy (NIRS) with artificial intelligence to rapidly and non‐destructively detect the content of Centralite‐II in double‐base propellants. The optimal modeling wavelength ranges of 4000–4600 cm−1 and 5700–6100 cm−1 were identified, and the raw spectral data were preprocessed using standard normal variate (SNV) transformation to improve the signal‐to‐noise ratio. Principal component analysis (PCA) was then applied to reduce data dimensionality, and the first three principal components were used as inputs for a backpropagation (BP‐ANN) neural network. The resulting PCA‐BP‐ANN model showed excellent performance on the training set, with an Rc2$$ {R}_c^2 $$ of 0.9830 and an RMSEC$$ RMSEC $$ of 0.0376%. During independent validation, the model demonstrated strong generalization ability, achieving an Rp2$$ {R}_p^2 $$ of 0.9824 and an RMSEP$$ RMSEP $$ of 0.3179%, comparative analysis with other models, including BP, PLS, ELM, SVR, and LSTM, indicated that the PCA‐BP‐ANN model exhibited superior prediction accuracy and generalization capability. This method provides a rapid and non‐destructive approach for assessing the stabilizer content in double‐base propellants and expands the application of NIRS and AI techniques in the field of energetic materials.