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This work aimed to set inline Raman spectroscopy models to monitor biochemically (viable cell density, cell viability, glucose, lactate, glutamine, glutamate, and ammonium) all upstream stages of a virus-like particle-making process. Linear (Partial least squares, PLS; Principal components regression, PCR) and nonlinear (Artificial neural networks, ANN; supported vector machine, SVM) modeling approaches were assessed. The nonlinear models, ANN and SVM, were the more suitable models with the lowest absolute errors. The mean absolute error of the best models within the assessed parameter ranges for viable cell density (0.01-8.83 × 106 cells/mL), cell viability (1.3-100.0 %), glucose (5.22-10.93 g/L), lactate (18.6-152.7 mg/L), glutamine (158-1761 mg/L), glutamate (807.6-2159.7 mg/L), and ammonium (62.8-117.8 mg/L) were 1.55 ± 1.37 × 106 cells/mL (ANN), 5.01 ± 4.93 % (ANN), 0.27 ± 0.22 g/L (SVM), 4.7 ± 2.6 mg/L (SVM), 51 ± 49 mg/L (ANN), 57 ± 39 mg/L (SVM) and 2.0 ± 1.8 mg/L (ANN), respectively. The errors achieved, and best-fitted models were like those for the same bioprocess using offline data and others, which utilized inline spectra for mammalian cell lines as a host.

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This work assessed the impact of laser wavelength and sample conditioning on offline monitoring (viable cell density, cell viability, virus titer, glucose, lactate, glutamine, glutamate, and ammonium) of SARS-CoV-2 viruslike particles production upstream stage by Raman spectroscopy. The evaluated chemometrics techniques were Partial Least Squares (PLS) and Artificial Neural Networks (ANN), and different spectral filtering approaches were also considered. ANN showed better prediction capacity for most of the parameters, but ammonium and lactate, better predicted by PLS, and glutamine, no difference between modeling techniques was detected. For cell growth parameters and virus titer, samples without cells measured at 785 nm Raman laser wavelength originated better-adjusted models. This laser wavelength was also more appropriate for the remaining monitored experimental parameters except for glucose, in which the best model came from the spectral database acquired at 1064 nm wavelength. Cell remotion significantly increased the accuracy of viable cell density, cell viability, glutamate, and virus titer models. However, glucose, lactate, and ammonium models showed better prediction capacity for samples containing cells. Thus, it was demonstrated that laser wavelength, sample conditioning, spectral preprocessing, and chemometric modeling techniques need to be tailored for each experimental parameter to improve accuracy.

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This work aimed to set inline Raman spectroscopy models to monitor biochemically (viable cell density, cell viability, glucose, lactate, glutamine, glutamate, and ammonium) all upstream stages of a virus-like particlemaking process. Linear (Partial least squares, PLS; Principal components regression, PCR) and nonlinear (Artificial neural networks, ANN; supported vector machine, SVM) modeling approaches were assessed. The nonlinear models, ANN and SVM, were the more suitable models with the lowest absolute errors. The mean absolute error of the best models within the assessed parameter ranges for viable cell density (0.01–8.83 × 106 cells/mL), cell viability (1.3–100.0 %), glucose (5.22–10.93 g/L), lactate (18.6–152.7 mg/L), glutamine (158–1761 mg/L), glutamate (807.6–2159.7 mg/L), and ammonium (62.8–117.8 mg/L) were 1.55 ± 1.37 × 106 cells/mL (ANN), 5.01 ± 4.93 % (ANN), 0.27 ± 0.22 g/L (SVM), 4.7 ± 2.6 mg/L (SVM), 51 ± 49 mg/L (ANN), 57 ± 39 mg/L (SVM) and 2.0 ± 1.8 mg/L (ANN), respectively. The errors achieved, and best-fitted models were like those for the same bioprocess using offline data and others, which utilized inline spectra for mammalian cell lines as a host.

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This work aimed to quantify growth and biochemical parameters (viable cell density, Xv; cell viability, CV; glucose, lactate, glutamine, glutamate, ammonium, and potassium concentrations) in upstream stages to obtain rabies virus-like particles (rabies VLP) from insect cell-baculovirus system using on-line and off-line Raman spectra to calibrate global models with minimal experimental data. Five cultivations in bioreactor were performed. The first one comprised the growth of uninfected Spodoptera frugiperda (Sf9) cells, the second and third runs to obtain recombinant baculovirus (rBV) bearing Rabies G glycoprotein and matrix protein, respectively. The fourth one involved the generation of rabies VLP from rBVs and the last one was a repetition of the third one with cell inoculum infected by rBV. The spectra were acquired through a Raman spectrometer with a 785-nm laser source. The fitted Partial Least Square models for nutrients and metabolites were comparable with those previously reported for mammalian cell lines (Relative error < 15 %). However, the use of this chemometrics approach for Xv and CV was not as accurate as it was for other parameters. The findings from this work established the basis for bioprocess Raman spectroscopical monitoring using insect cells for VLP manufacturing, which are gaining ground in the pharmaceutical industry.

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BACKGROUND This study aimed to establish chemometric models using Raman spectroscopy data for biochemical monitoring of rabies Virus-Like Particles (VLP) production based on baculovirus/insect cell system. The models were developed using fresh and stored samples from the initial development stages (Schott culture flasks). The following modeling techniques were assessed: partial least squares (PLS) and artificial neural networks (ANN). The effects of spectral filtering approaches, spectral ranges (400–1850 cm−1; 100–3425 cm−1), and sample cryopreservation were also considered. The applicability of the models was evaluated using experimental data from assays carried out in a benchtop bioreactor. RESULTS The results showed that the prediction capacity of the chemometric models was negatively impacted when samples from rabies VLP production were cryopreserved. Further studies are needed to confirm the maximum storage time for samples (< 4 months) without a significant difference in model predictions compared to those from an in line database. The dilution of the sample should be kept constant throughout the rabies VLP development stages. A nonlinear correlation was observed between dilution and the predicted values of biochemical parameters from Raman spectral data. The choice of spectral filtering has a major impact on the prediction accuracy of chemometric models. CONCLUSION The optimal filtering approach should be individually optimized for each biochemical parameter. The ANN models were significantly more suitable for biochemical monitoring than the PLS approach. The 400–1850 cm−1 Raman shift range is recommended for biochemical monitoring of rabies VLP using a baculovirus/insect cell platform when samples are cell-free.

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This work aimed to quantify growth and biochemical parameters (viable cell density, Xv; cell viability, CV; glucose, lactate, glutamine, glutamate, ammonium, and potassium concentrations) in upstream stages to obtain rabies virus-like particles (rabies VLP) from insect cell-baculovirus system using on-line and off-line Raman spectra to calibrate global models with minimal experimental data. Five cultivations in bioreactor were performed. The first one comprised the growth of uninfected Spodoptera frugiperda (Sf9) cells, the second and third runs to obtain recombinant baculovirus (rBV) bearing Rabies G glycoprotein and matrix protein, respectively. The fourth one involved the generation of rabies VLP from rBVs and the last one was a repetition of the third one with cell inoculum infected by rBV. The spectra were acquired through a Raman spectrometer with a 785-nm laser source. The fitted Partial Least Square models for nutrients and metabolites were comparable with those previously reported for mammalian cell lines (Relative error < 15 %). However, the use of this chemometrics approach for Xv and CV was not as accurate as it was for other parameters. The findings from this work established the basis for bioprocess Raman spectroscopical monitoring using insect cells for VLP manufacturing, which are gaining ground in the pharmaceutical industry.

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The properties of the convergence region of the estimation error of a robust observer for second-order systems are determined, and a new algorithm is proposed for setting the observer parameters, considering persistent but bounded disturbances in the two observation error dynamics. The main contributions over closely related studies of the stability of state observers are: (i) the width of the convergence region of the observer error for the unknown state is expressed in terms of the interaction between the observer parameters and the disturbance terms of the observer error dynamics; (ii) it was found that this width has a minimum point and a vertical asymptote with respect to one of the observer parameters, and their coordinates were determined. In addition, the main advantages of the proposed algorithm over closely related algorithms are: (i) the definition of observer parameters is significantly simpler, as the fulfillment of Riccati equation conditions, solution of LMI constraints, and fulfillment of eigenvalue conditions are not required; (ii) unknown bounded terms are considered in the dynamics of the observer error for the known state. Finally, the algorithm is applied to a model of microalgae culture in a photobioreactor for the estimation of biomass growth rate and substrate uptake rate based on known concentrations of biomass and substrate.

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Microbial physiology is an essential characteristic to be considered in the research and industrial use of microorganisms. Conventionally, the study of microbial physiology has been limited to carrying out qualitative and quantitative analysis of the role of individual components in global cell behaviour at a specific time and under certain growth conditions. In this framework, groups of observable cell physiological variables that remain over time define the physiological states. Recently, with advances in omics techniques, it has been possible to demonstrate that microbial physiology is a dynamic process and that, even with low variations in environmental culture conditions, physiological changes in the cell are provoked. However, the changes cannot be detected at a macroscopic level, and it is not possible to observe these changes in real time. As an alternative to solve this inconvenience, dielectric spectroscopy has been used as a complementary technique to monitor on-line cell physiology variations to avoid long waiting times during measurements. In this review, we discuss the state-of-the-art application of dielectric spectroscopy to unravel the physiological state of microorganisms, its current state, prospects and limitations during fermentation processes. Key points • Summary of the state of the art of several issues of dielectric spectroscopy. • Discussion of correlation among dielectric properties and cell physiological states. • View of the potential use of dielectric spectroscopy in monitoring bioprocesses.

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Rapid, efficient, and low-cost technologies for monitoring the fermentation process during second generation (2G) or cellulosic ethanol production are essential for the successful implementation of this process at the commercial scale. Here, the use of near-infrared (NIR) spectroscopy associated with partial least squares (PLS) regression was investigated as a tool for monitoring the production of 2G ethanol from lignocellulosic sugarcane residues including bagasse, straw, and tops. The spectral data was based on a set of 103 alcoholic fermentation samples. Models based on different pre-processing techniques were evaluated. The best root mean square error of prediction (RMSEP) values obtained in the external validation were around 3.02 g/L for ethanol and 6.60 g/L for glucose. The findings showed that the PLS-NIR methodology was efficient in accurately predicting the glucose and ethanol concentrations during the production of 2G ethanol, demonstrating potential for use in monitoring and control of large-scale industrial processes.

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