Advancing 2D fluorescence online monitoring in microtiter plates by separating scattered light and fluorescence measurement, using a tunable emission monochromator

Online fluorescence monitoring has become a key technology in modern bioprocess development, as it provides in-depth process knowledge at comparably low costs. In particular, the technology is widely established for high-throughput microbioreactor cultivation systems, due to its noninvasive character. For microtiter plates, previously also multi-wavelength 2D fluorescence monitoring was developed. To overcome an observed limitation of fluorescence sensitivity, this study presents a modified spectroscopic setup, including a tunable emission monochromator. The new optical component enables the separation of the scattered and fluorescent light measurements, which allows for the adjustment of integration times of the charge-coupled device detector. The resulting increased fluorescence sensitivity positively affected the performance of principal component analysis for spectral data of Escherichia coli batch cultivation experiments with varying sorbitol concentration supplementation. In direct comparison with spectral data recorded at short integration times, more biologically consistent signal dynamics were calculated. Furthermore, during partial least square regression for E. coli cultivation experiments with varying glucose concentrations, improved modeling performance was observed. Especially, for the growth-uncoupled acetate concentration, a considerable improvement of the root-mean-square error from 0.25 to 0.17 g/L was achieved. In conclusion, the modified setup represents another important step in advancing 2D fluorescence monitoring in microtiter plates.

Online Monitoring of Sourdough Fermentation Using a Gas Sensor Array with Multivariate Data Analysis

Sourdough can improve bakery products' shelf life, sensory properties, and nutrient composition. To ensure high-quality sourdough, the fermentation has to be monitored. The characteristic process variables for sourdough fermentation are pH and the degree of acidity measured as total titratable acidity (TTA). The time- and cost-intensive offline measurement of process variables can be improved by utilizing online gas measurements in prediction models. Therefore, a gas sensor array (GSA) system was used to monitor the fermentation process of sourdough online by correlation of exhaust gas data with offline measurement values of the process variables. Three methods were tested to utilize the extracted features from GSA to create the models. The most robust prediction models were achieved using a PCA (Principal Component Analysis) on all features and combined two fermentations. The calibrations with the extracted features had a percentage root mean square error (RMSE) from 1.4% to 12% for the pH and from 2.7% to 9.3% for the TTA. The coefficient of determination (R2) for these calibrations was 0.94 to 0.998 for the pH and 0.947 to 0.994 for the TTA. The obtained results indicate that the online measurement of exhaust gas from sourdough fermentations with gas sensor arrays can be a cheap and efficient application to predict pH and TTA.

Assessing the capabilities of 2D fluorescence monitoring in microtiter plates with data-driven modeling for secondary substrate limitation experiments of Hansenula polymorpha

BACKGROUND

Non-invasive online fluorescence monitoring in high-throughput microbioreactors is a well-established method to accelerate early-stage bioprocess development. Recently, single-wavelength fluorescence monitoring in microtiter plates was extended to measurements of highly resolved 2D fluorescence spectra, by introducing charge-coupled device (CCD) detectors. Although introductory experiments demonstrated a high potential of the new monitoring technology, an assessment of the capabilities and limits for practical applications is yet to be provided.

RESULTS

In this study, three experimental sets introducing secondary substrate limitations of magnesium, potassium, and phosphate to cultivations of a GFP-expressing H. polymorpha strain were conducted. This increased the complexity of the spectral dynamics, which were determined by 2D fluorescence measurements. The metabolic responses upon growth limiting conditions were assessed by monitoring of the oxygen transfer rate and extensive offline sampling. Using only the spectral data, subsequently, partial least-square (PLS) regression models for the key parameters of glycerol, cell dry weight, and pH value were generated. For model calibration, spectral data of only two cultivation conditions were combined with sparse offline sampling data. Applying the models to spectral data of six cultures not used for calibration, resulted in an average relative root-mean-square error (RMSE) of prediction between 6.8 and 6.0%. Thus, while demanding only sparse offline data, the models allowed the estimation of biomass accumulation and glycerol consumption, even in the presence of more or less pronounced secondary substrate limitation.

CONCLUSION

For the secondary substrate limitation experiments of this study, the generation of data-driven models allowed a considerable reduction in sampling efforts while also providing process information for unsampled cultures. Therefore, the practical experiments of this study strongly affirm the previously claimed advantages of 2D fluorescence spectroscopy in microtiter plates.

Modeling and optimization of bakery production scheduling to minimize makespan and oven idle time

Makespan dominates the manufacturing expenses in bakery production. The high energy consumption of ovens also has a substantial impact, which bakers may overlook. Bakers leave ovens running until the final product is baked, allowing them to consume energy even when not in use. It results in energy waste, increased manufacturing costs, and CO₂ emissions. This paper investigates three manufacturing lines from small and medium-sized bakeries to find optimum makespan and ovens' idle time (OIDT). A hybrid no-wait flow shop scheduling model considering the constraints that are most common in bakeries is proposed. To find optimal solutions, non-dominated sorting genetic algorithm (NSGA-II), strength Pareto evolutionary algorithm (SPEA2), generalized differential evolution (GDE3), improved multi-objective particle swarm optimization (OMOPSO), and speed-constrained multi-objective particle swarm optimization (SMPSO) were used. The experimental results show that the shortest makespan does not always imply the lowest OIDT. Even the optimized solutions have up to 231 min of excess OIDT, while the makespan is the shortest. Pareto solutions provide promising trade-offs between makespan and OIDT, with the best-case scenario reducing OIDT by 1348 min while increasing makespan only by 61 min from the minimum possible makespan. NSGA-II outperforms all other algorithms in obtaining a high number of good-quality solutions and a small number of poor-quality solutions, followed by SPEA2 and GDE3. In contrast, OMOPSO and SMPSO deliver the worst solutions, which become pronounced as the problem complexity grows.

Generic Chemometric Models for Metabolite Concentration Prediction Based on Raman Spectra

Chemometric models for on-line process monitoring have become well established in pharmaceutical bioprocesses. The main drawback is the required calibration effort and the inflexibility regarding system or process changes. So, a recalibration is necessary whenever the process or the setup changes even slightly. With a large and diverse Raman dataset, however, it was possible to generate generic partial least squares regression models to reliably predict the concentrations of important metabolic compounds, such as glucose-, lactate-, and glutamine-indifferent CHO cell cultivations. The data for calibration were collected from various cell cultures from different sites in different companies using different Raman spectrophotometers. In testing, the developed “generic” models were capable of predicting the concentrations of said compounds from a dilution series in FMX-8 mod medium, as well as from an independent CHO cell culture. These spectra were taken with a completely different setup and with different Raman spectrometers, demonstrating the model flexibility. The prediction errors for the tests were mostly in an acceptable range (<10% relative error). This demonstrates that, under the right circumstances and by choosing the calibration data carefully, it is possible to create generic and reliable chemometric models that are transferrable from one process to another without recalibration.

Application of Two-Dimensional Fluorescence Spectroscopy for the On-Line Monitoring of Teff-Based Substrate Fermentation Inoculated with Certain Probiotic Bacteria

There is increasing demand for cereal-based probiotic fermented beverages as an alternative to dairy-based products due to their limitations. However, analyzing and monitoring the fermentation process is usually time consuming, costly, and labor intensive. This research therefore aims to apply two-dimensional (2D)-fluorescence spectroscopy coupled with partial least-squares regression (PLSR) and artificial neural networks (ANN) for the on-line quantitative analysis of cell growth and concentrations of lactic acid and glucose during the fermentation of a teff-based substrate. This substrate was inoculated with mixed strains of Lactiplantibacillus plantarum A6 (LPA6) and Lacticaseibacillus rhamnosus GG (LCGG). The fermentation was performed under two different conditions: condition 1 (7 g/100 mL substrate inoculated with 6 log cfu/mL) and condition 2 (4 g/100 mL substrate inoculated with 6 log cfu/mL). For the prediction of LPA6 and LCGG cell growth, the relative root mean square error of prediction (pRMSEP) was measured between 2.5 and 4.5%. The highest pRMSEP (4.5%) was observed for the prediction of LPA6 cell growth under condition 2 using ANN, but the lowest pRMSEP (2.5%) was observed for the prediction of LCGG cell growth under condition 1 with ANN. A slightly more accurate prediction was found with ANN under condition 1. However, under condition 2, a superior prediction was observed with PLSR as compared to ANN. Moreover, for the prediction of lactic acid concentration, the observed values of pRMSEP were 7.6 and 7.7% using PLSR and ANN, respectively. The highest error rates of 13 and 14% were observed for the prediction of glucose concentration using PLSR and ANN, respectively. Most of the predicted values had a coefficient of determination (R²) of more than 0.85. In conclusion, a 2D-fluorescence spectroscopy combined with PLSR and ANN can be used to accurately monitor LPA6 and LCGG cell counts and lactic acid concentration in the fermentation process of a teff-based substrate. The prediction of glucose concentration, however, showed a rather high error rate.

Chia Oil Adulteration Detection Based on Spectroscopic Measurements

Chia oil is a valuable source of omega-3-fatty acids and other nutritional components. However, it is expensive to produce and can therefore be easily adulterated with cheaper oils to improve the profit margins. Spectroscopic methods are becoming more and more common in food fraud detection. The aim of this study was to answer following questions: Is it possible to detect chia oil adulteration by spectroscopic analysis of the oils? Is it possible to identify the adulteration oil? Is it possible to determine the amount of adulteration? Two chia oils from local markets were adulterated with three common food oils, including sunflower, rapeseed and corn oil. Subsequently, six chia oils obtained from different sites in Kenya were adulterated with sunflower oil to check the results. Raman, NIR and fluorescence spectroscopy were applied for the analysis. It was possible to detect the amount of adulterated oils by spectroscopic analysis, with a minimum R² of 0.95 for the used partial least square regression with a maximum RMSEP_range of 10%. The adulterations of chia oils by rapeseed, sunflower and corn oil were identified by classification with a median true positive rate of 90%. The training accuracies, sensitivity and specificity of the classifications were over 90%. Chia oil B was easier to detect. The adulterated samples were identified with a precision of 97%. All of the classification methods show good results, however SVM were the best. The identification of the adulteration oil was possible; less than 5% of the adulteration oils were difficult to detect. In summary, spectroscopic analysis of chia oils might be a useful tool to identify adulterations.

Spectroscopic analysis of chia seeds

Chia seeds are becoming more and more popular in modern diets. In this contribution NIR and 2D-fluorescence spectroscopy were used to determine their nutritional values, mainly fat and protein content. 25 samples of chia seeds were analysed, whereof 9 samples were obtained from different regions in Kenya, 16 samples were purchased in stores in Germany and originated mostly from South America. For the purchased samples the nutritional information of the package was taken in addition to the values obtained for fat and protein, which were determined at the Hohenheim Core Facility. For the first time the NIR and fluorescence spectroscopy were used for the analysis of chia. For the spectral evaluation two different pre-processing methods were tested. Baseline correction with subsequent mean-centring lead to the best results for NIR spectra whereas SNV (standard normal variate transformation) was sufficient for the evaluation of fluorescence spectra. When combining NIR and fluorescence spectra, the fluorescence spectra were also multiplied with a factor to adjust the intensity levels. The best prediction results for the evaluation of the combined spectra were obtained for Kenyan samples with prediction errors below 0.2 g/100 g. For all other samples the absolute prediction error was 0.51 g/100 g for fat and 0.62 g/100 g for protein. It is possible to determine the amount of protein and fat of chia seeds by fluorescence and NIR spectroscopy. The combination of both methods is beneficial for the predictions. Chia seeds from Kenya had similar protein and lipid contents as South American seeds.

Cover Feature: Parameter and state estimation of backers yeast cultivation with a gas sensor array and unscented Kalman filter

DOI: 10.1002/elsc.202000058 Successful operation, control and optimization of biotechnological process depend on reliable real-time available measurements of the process variables. Although some hardware sensors are readily available, they often have several drawbacks: cost, sample destruction, discrete-time measurements, processing delay, sterilization, disturbances in the hydrodynamic conditions inside the bioreactor, etc. It is therefore of interest to use software sensors [29, 30]. The central idea behind a soft sensor is to use easily accessible on-line data for the estimation of other process variables that are either difficult to measure or only measured at low frequency [30]. The figure illustrates a software sensor for on-line monitoring of substrate and biomass production in backers yeast cultivation. For details see article DOI 10.1002/elsc.202000058 on page 169.

The Kalman Filter for the Supervision of Cultivation Processes

In the era of technology and digitalization, the process industries are undergoing a digital transformation. The available process models, advance sensor technologies, enhanced computational power and a broad set of data analytical techniques enable solid bases for digital transformation in the biopharmaceutical industry.Among various data analytical techniques, the Kalman filter and its non-linear extensions are powerful tools for prediction of reliable process information. The combination of the Kalman filter with a virtual representation of the bioprocess, called digital twin, can provide real-time available process information. Incorporation of such variables in process operation can provide improved control performance with enhanced productivity.In this chapter the linear discrete Kalman filter, the extended Kalman filter and the unscented Kalman filters are described and a brief overview of applications of the Kalman filter and its non-linear extensions to bioreactors are presented. Furthermore, in a case study an example of the digital twin of the backer's yeast batch cultivation process is presented. A digital twin of a bioreactor mirrors the processes of the real bioreactor. It contains the physical parts, the process model and prediction algorithm to predict the bioprocess variables. These values could be used for optimization and control of the process.

Artificial neural network for bioprocess monitoring based on fluorescence measurements: Training without offline measurements

The feasibility of using a feed-forward neural network in combination with 2D fluorescence spectroscopy to monitor the state of Saccharomyces cerevisiae fermentation was investigated. The main point is that for the backpropagation training of the neural network, no offline measurement value was used, which is the ordinary approach. Instead, a theoretical model of the process has been applied to simulate the process state (biomass, glucose, and ethanol concentration) at any given time. However, the kinetic parameters of the simulation model are unknown at the beginning of the training. It will be demonstrated that the kinetic parameters of the theoretical process model as well as the parameters of the feed-forward neural network to predict the process state from 2D fluorescence spectra can be acquired from the 2D fluorescence spectra alone. Offline measurements are not actually required. The resulting trained neural network can predict the process state as accurate as a conventionally (with offline measurements) trained neural network. The calculated parameters result in a simulation model that is at least as accurate as a model with parameters acquired by least squares fitting to the offline measurements.

How long does a photoreceptor cell take to die? Implications for the causative cell death mechanisms

The duration of cell death may allow deducing the underlying degenerative mechanism. To find out how long a photoreceptor takes to die, we used the rd1 mouse model for retinal neurodegeneration, which is characterized by phosphodiesterase-6 (PDE6) dysfunction and photoreceptor death triggered by high cGMP levels. Based on cellular data on the progression of cGMP accumulation, cell death, and survival, we created a mathematical model to simulate the temporal development of the degeneration and the clearance of dead cells. Both cellular data and modelling suggested that at the level of the individual cell, the degenerative process was rather slow, taking around 80 h to complete. Organotypic retinal explant cultures derived from wild-type animals and exposed to the selective PDE6 inhibitor zaprinast, confirmed the surprisingly long duration of an individual photoreceptor cell's death. We briefly discuss the possibility to link different cell death stages and their temporal progression to specific enzymatic activities known to be causally connected to cell death. This in turn opens up new perspectives for the treatment of inherited retinal degeneration, both in terms of therapeutic targets and temporal windows-of-opportunity.

Retinitis pigmentosa: rapid neurodegeneration is governed by slow cell death mechanisms

For most neurodegenerative diseases the precise duration of an individual cell's death is unknown, which is an obstacle when counteractive measures are being considered. To address this, we used the rd1 mouse model for retinal neurodegeneration, characterized by phosphodiesterase-6 (PDE6) dysfunction and photoreceptor death triggered by high cyclic guanosine-mono-phosphate (cGMP) levels. Using cellular data on cGMP accumulation, cell death, and survival, we created mathematical models to simulate the temporal development of the degeneration. We validated model predictions using organotypic retinal explant cultures derived from wild-type animals and exposed to the selective PDE6 inhibitor zaprinast. Together, photoreceptor data and modeling for the first time delineated three major cell death phases in a complex neuronal tissue: (1) initiation, taking up to 36 h, (2) execution, lasting another 40 h, and finally (3) clearance, lasting about 7 h. Surprisingly, photoreceptor neurodegeneration was noticeably slower than necrosis or apoptosis, suggesting a different mechanism of death for these neurons.