Monitoring E. coli Cell Integrity by ATR-FTIR Spectroscopy and Chemometrics: Opportunities and Caveats

Abstract

During recombinant protein production with E. coli, the integrity of the inner and outer membrane changes, which leads to product leakage (loss of outer membrane integrity) or lysis (loss of inner membrane integrity). Motivated by current Quality by Design guidelines, there is a need for monitoring tools to determine leakiness and lysis in real-time. In this work, we assessed a novel approach to monitoring E. coli cell integrity by attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy. Various preprocessing strategies were tested in combination with regression (partial least squares, random forest) or classification models (partial least squares discriminant analysis, linear discriminant analysis, random forest, artificial neural network). Models were validated using standard procedures, and well-performing methods were additionally scrutinized by removing putatively important features and assessing the decrease in performance. Whereas the prediction of target compound concentration via regression was unsuccessful, possibly due to a lack of samples and low sensitivity, random forest classifiers achieved prediction accuracies of over 90% within the datasets tested in this study. However, strong correlations with untargeted spectral regions were revealed by feature selection, thereby demonstrating the need to rigorously validate chemometric models for bioprocesses, including the evaluation of feature importance.

Improving Sports Outcome Prediction Process Using Integrating Adaptive Weighted Features and Machine Learning Techniques

“…By combining several randomized decision trees and aggregates their predictions by averaging, the approach of RF has shown excellent performance in the dataset with large amount of variables and observations. It is also flexible enough to be implemented to large-scale task, is conveniently adapted to various ad hoc learning problems, and returns measures of variable importance [50][51][52][53][54][55][56][57][58][59][60]. The "randomForest" R package of version 4.6-14 [79] was used to construct the RF model.…”

“…There is relatively fewer research applying machine learning methods for NBA game outcomes prediction and NBA game final score prediction [16][17][18][19][20][21][22][23][24]. Therefore, five machine learning methods, including classification and regression trees (CART) [42][43][44][45][46][47][48][49], random forest (RF) [50][51][52][53][54][55][56][57][58][59][60], stochastic gradient boosting (SGB) [24,45,52,[61][62][63][64][65][66][67], eXtreme gradient boosting (XGBoost) [24,[68][69][70][71][72][73], and extreme learning machine (ELM) [24,[73][74]…”

“…CART is a statistical procedure often used as a regression tool to analyze continuous data [42][43][44][45][46][47][48][49]. The approach of RF has shown excellent performance in the dataset with large amount of variables and observations by combining several randomized decision tress and aggregates their predictions by averaging [50][51][52][53][54][55][56][57][58][59][60]. SGB is a hybrid method that merging boosting and bagging techniques, and the input data are selected by random sampling at each stage of the steepest gradient algorithm-based boosting procedure [24,45,52,[61][62][63][64][65][66][67].…”

About Model Validation in Bioprocessing

“…The data needed to calibrate and validate a model is heavily reliant on the type of model in question. For example, a mechanistic model would require a much smaller dataset in comparison to a hybrid [13] or chemometric model [70]. Therefore, validation statistics and approaches are also dependent on the sample size of the datasets during model development.…”

In Situ Quantification of Polyhydroxybutyrate in Photobioreactor Cultivations of Synechocystis sp. Using an Ultrasound-Enhanced ATR-FTIR Spectroscopy Probe

“…The changes in light intensities are recorded, and the spectra can be evaluated by univariate calibration or chemometric tools [ 16 , 18 ]. ATR-FTIR instruments are commercially available, and applications for in-line, on-line, or at-line monitoring are established PAT tools, e.g., for P. chrysogenum or E. coli processes [ 19 , 20 , 21 ]. Further, ATR-FTIR studies were done for characterization of PHAs [ 22 ], and for PHB production processes using bacteria [ 23 , 24 , 25 ].…”