NIR spectroscopy—CNN‐enabled chemometrics for multianalyte monitoring in microbial fermentation

Abstract As the biopharmaceutical industry looks to implement Industry 4.0, the need for rapid and robust analytical characterization of analytes has become a pressing priority. Spectroscopic tools, like near‐infrared (NIR) spectroscopy, are finding increasing use for real‐time quantitative analysis. Yet detection of multiple low‐concentration analytes in microbial and mammalian cell cultures remains an ongoing challenge, requiring the selection of carefully calibrated, resilient chemometrics for each analyte. The convolutional neural network (CNN) is a puissant tool for processing complex data and making it a potential approach for automatic multivariate spectral processing. This work proposes an inception module‐based two‐dimensional (2D) CNN approach (I‐CNN) for calibrating multiple analytes using NIR spectral data. The I‐CNN model, coupled with orthogonal partial least squares (PLS) preprocessing, converts the NIR spectral data into a 2D data matrix, after which the critical features are extracted, leading to model development for multiple analytes. Escherichia coli fermentation broth was taken as a case study, where calibration models were developed for 23 analytes, including 20 amino acids, glucose, lactose, and acetate. The I‐CNN model result statistics depicted an average R 2 values of prediction 0.90, external validation data set 0.86 and significantly lower root mean square error of prediction values ∼0.52 compared to conventional regression models like PLS. Preprocessing steps were applied to I‐CNN models to evaluate any augmentation in prediction performance. Finally, the model reliability was assessed via real‐time process monitoring and comparison with offline analytics. The proposed I‐CNN method is systematic and novel in extracting distinctive spectral features from a multianalyte bioprocess data set and could be adapted to other complex cell culture systems requiring rapid quantification using spectroscopy.