Moisture content determination along production line of ibuprofen soft gelatin capsule manufacturing by near infrared spectroscopy and ensemble deep neural networks

Abstract

In the manufacturing process of ibuprofen soft gelatin capsules, controlling moisture content at each production line stage, including in their components—gelatin, fill content, and shell—is vital to ensure quality and stability. This study developed and assessed an analytical method for rapid and non-destructive moisture determination using near-infrared spectroscopy (NIRS) coupled with deep neural networks (DNN) on all stages of the ibuprofen production line. The NIRS-DNN classifier models were able to distinguish between the three components, achieving accuracy scores of up to 99%. The DNN models for moisture quantification also demonstrated high accuracy, with R² values exceeding 0.997 across all production stages and prediction errors to those of previously reported models. A significant advantage of the NIRS-DNN approach was its ability to maintain accuracy over a wide moisture concentration range, from 7% to 48%. The ensemble model, NIRS-EDNN, seamlessly integrated classification and quantification, revealing its potential for real-time process control in soft gel manufacturing. The comprehensive sampling approach ensured a diverse representation of moisture content, thereby enhancing the understanding of its impact on the final product stability, demonstrating that this methodology is potentially applicable to any soft gelatin capsule tracking worldwide.