Machine Learning-Enabled Raman Spectroscopy for Process Analytical Technology and Real-Time Release Testing in Bioprocess Manufacturing: A Comparative Predictive Modeling Study
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Analytical technologies that can provide quick, precise, and continuous information regarding process performance are necessary for the development of biopharmaceutical manufacturing. Conventional bioprocess monitoring is largely dependent on laboratory-based data and offline sampling, which can restrict process management and cause delays in decision-making. This study develops a machine learning-enabled Raman spectroscopy framework for Process Analytical Technology (PAT) and Real-Time Release Testing (RTRT) applications in bioprocess manufacturing. Five predictive modeling techniques—Partial Least Squares (PLS) regression, Support Vector Regression (SVR), Random Forest, Extreme Gradient Boosting (XGBoost), and Neural Networks—were used to analyze Raman spectral data from an Escherichia coli fermentation dataset. The models were assessed using the coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE) to predict two crucial fermentation parameters: the concentrations of glucose and acetate. The superior performance of PLS regression for glucose prediction and the improved prediction accuracy of XGBoost for acetate concentration demonstrated the importance of selecting modeling techniques based on biological complexity. Explainable artificial intelligence using SHAP analysis was incorporated to improve model transparency by identifying Raman spectral regions contributing to predictions. The suggested architecture shows how Raman spectroscopy and machine learning can be combined to assist automated process monitoring, enhance process comprehension, and hasten the implementation of real-time quality judgments in next-generation biomanufacturing.
Graphical Abstract
Overall workflow of the Raman spectroscopy-based machine learning framework for PAT and RTRT implementation. Raman spectra collected from E. coli fermentation were preprocessed and analyzed using multiple machine learning algorithms for the prediction of glucose and acetate concentrations. Model performance evaluation and SHAP-based explainable AI analysis enabled the identification of important spectral features for real-time bioprocess monitoring.
Highlights
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Developed a Raman spectroscopy-based machine learning framework for real-time monitoring of critical bioprocess parameters.
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Compared traditional chemometric modeling (PLS regression) with advanced machine learning approaches, including SVR, Random Forest, XGBoost, and neural networks.
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Showed that the biochemical target affects the model’s performance, with XGBoost improving acetate prediction and PLS offering better glucose prediction.
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Integrated explainable artificial intelligence to identify Raman spectral regions contributing to bioprocess predictions.
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Established a pathway toward interpretable Raman-based Process Analytical Technology (PAT) and Real-Time Release Testing (RTRT) implementation.
