Generative data augmentation and automated optimization of convolutional neural networks for process monitoring.

ABSTRACT: Chemometric modeling for spectral data is considered a key technology in biopharmaceutical processing to realize real-time process control and release testing. Machine learning (ML) models have been shown to increase the accuracy of various spectral regression and classification tasks, remove challenging preprocessing steps for spectral data, and promise to improve the transferability of models when compared to commonly applied, linear methods. The training and optimization of ML models require large data sets which are not available in the context of biopharmaceutical processing. Generative methods to extend data sets with realistic in silico samples, so-called data augmentation, may provide the means to alleviate this challenge. In this study, we develop and implement a novel data

SUBMITTER: Schiemer R 

PROVIDER: S-EPMC10864647 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature