NIR spectroscopy-CNN-enabled chemometrics for multianalyte monitoring in microbial fermentation

• Published in: Biotechnology and bioengineering 2024-06, Vol.121 (6), p.1803-1819
• Main Authors: Banerjee, Shantanu, Mandal, Shyamapada, Jesubalan, Naveen G, Jain, Rijul, Rathore, Anurag S
• Format: Article
• Language: English
• Subjects: Acetic acid; Amino acids; Artificial neural networks; Biopharmaceuticals; Calibration; Cell culture; Chemometrics; Chemometrics - methods; Datasets; E coli; Escherichia coli - isolation & purification; Escherichia coli - metabolism; Fermentation; Glucose - analysis; Glucose - metabolism; Industry 4.0; Infrared analysis; Infrared spectroscopy; Lactose; Least-Squares Analysis; Microorganisms; Monitoring; Near infrared radiation; Neural networks; Neural Networks, Computer; Predictions; Preprocessing; Regression analysis; Regression models; Reliability analysis; Spectroscopy; Spectroscopy, Near-Infrared - methods; Spectrum analysis; Statistical analysis
• ISSN: 0006-3592; 1097-0290
• DOI: 10.1002/bit.28681

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 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.