Bioreactor monitoring with spectroscopy and chemometrics: a review

Lourenço, Nídia D.; Lopes, João A.; Almeida, Cláudia; Sarraguça, Mafalda C.; Pinheiro, H.M.

doi:10.1007/s00216-012-6073-9

Cited by 221 publications

(226 citation statements)

References 104 publications

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“…At this time, most of the research is focused on two probing technologies, i.e. optical analysis [88][89][90] and spectroscopy [91]. Spectroscopy-based methods are mainly focused on the excretion of metabolites in the extracellular medium and do not take into account physiology-related parameters.…”

Section: Alternatives To Flow Cytometry For Monitoring Microbial Hetementioning

confidence: 99%

Microbial heterogeneity affects bioprocess robustness: Dynamic single‐cell analysis contributes to understanding of microbial populations

Delvigne

Goffin

2013

Biotechnology Journal

127

102

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Heterogeneity or segregation of microbial populations has been the subject of much research, but the real impact of this phenomenon on bioprocesses remains poorly understood. The main reason for this lack of knowledge is the difficulty in monitoring microbial population heterogeneity under dynamic process conditions. The main concepts resulting in microbial population heterogeneity in the context of bioprocesses have been summarized by two distinct hypotheses. The first involves the individual history of microbial cells or the "path" followed during their residence time inside the process equipment. The second hypothesis involves a coordinated response by the microbial population as a bet-hedging strategy, in order to cope with process-related stresses. The respective contribution of each hypothesis to microbial heterogeneity in bioprocesses is still unclear. This illustrates the fact that, although microbial phenotypic heterogeneity has been thoroughly investigated at a fundamental level, the implications of this phenomenon in the context of microbial bioprocesses are still subject to debate. At this time, automated flow cytometry is the best technique for investigating microbial heterogeneity under process conditions. However, dedicated software and relevant biomarkers are needed for the proper integration of flow cytometry as a bioprocess control tool.

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Section: Alternatives To Flow Cytometry For Monitoring Microbial Hetementioning

confidence: 99%

Microbial heterogeneity affects bioprocess robustness: Dynamic single‐cell analysis contributes to understanding of microbial populations

Delvigne

Goffin

2013

Biotechnology Journal

127

102

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“…cell density, metabolite concentration etc.) (Lourenco et al 2012). Spectroscopy-based technologies e.g.…”

Section: Electromagnetic Spectroscopic Measurements (Nir Mir Raman)mentioning

confidence: 99%

Monitoring of Cell Culture

2014

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Cell culture monitoring is essential for the continuous characterisation of cultivated cells. This helps us to understand each cell line's growth and metabolism properties and to establish and ensure reproducible cell cultivations. Monitoring not only comprises biological parameters, like cell count and viability, but also physiological parameters, like substrate, metabolite and possibly product concentrations. Furthermore, a large set of physicochemical parameters such as pH, temperature and osmolarity is routinely monitored in cell culture applications. A multiplicity of monitoring technologies exists. Often, there are several methods for one monitoring parameter to choose from and the choice depends on application and process needs. Moreover, new methods are developed at ever increasing speed. Today, cell culture processes and regulatory agencies demand detailed process understanding, which extensive monitoring is a prerequisite for. In this chapter, the current state of monitoring technologies and applications is reviewed. Particular emphasis is placed on biological parameters, i.e. cell density and viability as well as substrates, metabolites, and product concentration but also on cell stress and apoptosis. Furthermore, promising exploratory technologies are surveyed. Lastly, the chapter is meant to bridge the gap between existing technology-driven and more biologyoriented publications in this field.

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“…Nevertheless, the use of chemometric techniques has revealed that these spectra comprise information that can be extracted and used for the quality monitoring of foods and bioprocesses, in general, as well as specifically, in beer classification. [13,15,16] Multivariate tools of data analysis for linear relationships (principal component analysis (PCA), principal component regression (PCR), partial least squares (PLS)) and artificial intelligence approaches (artificial neural networks (ANNs)) are the chemometric techniques that have been more extensively used to correlate spectral data with critical parameters in bioprocessing and quality attributes in the final bio-products, including beer. [17,18] ANNs for these purposes have shown an improved or similar prediction capacity with respect to multivariate methodologies.…”

Section: Introductionmentioning

confidence: 99%

“…[12] In general, several spectroscopic techniques have been used to monitor the critical parameters during bioprocessing as well as the quality attributes of the final derived products. [13] Among the spectroscopy types with successful applications in bioprocess monitoring are ultraviolet-visible (UVVis), near-infrared (NIR), mid-infrared (MIR), and fluorescence spectroscopy. [13] However, specifically in the monitoring of the brewing process and beer characterization, the utilization of spectroscopic methods has been confined almost totally to vibrational spectroscopies, such as NIR, MIR, and Raman spectroscopy.…”

Section: Introductionmentioning

confidence: 99%

“…[13] Among the spectroscopy types with successful applications in bioprocess monitoring are ultraviolet-visible (UVVis), near-infrared (NIR), mid-infrared (MIR), and fluorescence spectroscopy. [13] However, specifically in the monitoring of the brewing process and beer characterization, the utilization of spectroscopic methods has been confined almost totally to vibrational spectroscopies, such as NIR, MIR, and Raman spectroscopy. [14] Generally, applications of UV-Vis spectroscopy are not as widespread as vibrational techniques in the monitoring of bioprocesses and bio-products, because of the poor information of the corresponding spectra (absorption spectra with broader bands).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Rapid monitoring of beer-quality attributes based on UV-Vis spectral data

Oliveira

Filho

Rocha

et al. 2017

International Journal of Food Properties

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This work aimed to determinate eight beer properties using UV-Vis spectra in combination with principal component regression (PCR) or artificial neural network (ANN) models. A statistical experimental design was performed to generate the calibration data. First, principal component analysis (PCA) was applied to the original spectral data, and the scores in significant PCs were utilized to calibrate both models. PCR showed poor correlation for beer parameters (R 2 < 0.61). The ANNs showed satisfactory correlations (R 2 = 0.74-0.92) and low relative error considering a variable range (E r < 9%) for most of the beer-quality attributes, but vicinal diketones (R 2 = 0.56, E r = 16.69%). Once implemented, this method would be fast and low cost. ARTICLE HISTORY

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Bioreactor monitoring with spectroscopy and chemometrics: a review

Cited by 221 publications

References 104 publications

Microbial heterogeneity affects bioprocess robustness: Dynamic single‐cell analysis contributes to understanding of microbial populations

Microbial heterogeneity affects bioprocess robustness: Dynamic single‐cell analysis contributes to understanding of microbial populations

Monitoring of Cell Culture

Rapid monitoring of beer-quality attributes based on UV-Vis spectral data

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