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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. [Display omitted] • Modeling from Raman spectra using PLS for rabies VLP production monitoring. • Raman spectral range pick for monitoring biochemically rabies VLP process. • Effective online biochemical monitoring over rabies VLP production with Raman data. • Suitable biochemical monitoring over rabies VLP production with offline Raman data. • Assessed parameters relative error was less than 15 % using online or offline data. [ABSTRACT FROM AUTHOR]

This work aimed to compare the predictive capacity of linear (Partial Least Squares; Principal Component Regression) and non-linear (Artificial Neural Network, ANN; Support Vector Machine, SVM) chemometric models from Raman spectroscopy spectra acquired offline with (unclarified samples) and without cells (clarified samples), and biochemical parameters (Viable cell density, cell viability, glucose, lactate, glutamine, glutamate, ammonium, and potassium) quantified by reference methods in a rabies VLP production bioprocess. The nonlinear models, ANN and SVM were the more suitable models with the lowest absolute errors. No effective differences in accuracy were detected for models from spectral data with and without cells. 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−1), lactate (18.6–152.7 mg L−1), glutamine (158–1761 mg L−1), glutamate (807.6–2159.7 mg L−1), ammonium (62.8–117.8 mg L−1), and potassium (531.6–685. 3 mg L−1) were 0.17 ± 0.13 × 106 cells/mL, 6.8 ± 7.1%, 0.24 ± 0.14 g L− 1, 3.3 ± 3.5 mg L− 1, 61 ± 52 mg L− 1, 52 ± 51 mg L− 1, 2.1 ± 1.9 mg L− 1 and 8.6 ± 8.6 mg L− 1, respectively. The errors achieved were like those for bioprocesses using a highlighted animal cell host (CHO cell) in similar ranges. [Display omitted] • Monitoring of Rabies virus-like particles production using offline Raman spectra. • Accuracy was alike between chemometrics models for samples with and without cells. • Nonlinear models showed a better fit than linear ones in biochemical monitoring. • The fitted models can be used in high-throughput upstream process development. • Variables absolute error was like those for other host cells covering alike ranges. [ABSTRACT FROM AUTHOR]