In situ microscopy: A perspective for industrial bioethanol production monitoring

Belini, Valdinei Luís; Wiedemann, Philipp; Suhr, H.

doi:10.1016/j.mimet.2013.03.009

Cited by 22 publications

(18 citation statements)

References 36 publications

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“…Due to the flash illumination, the cells moving by the window are imaged without blurring, despite their considerable speed (0.1 to 0.5 m/s). For critical reviews on in situ microscopy, please refer to, e.g., Höpfner et al (2010) and Belini et al (2013).…”

Section: Image Acquisitionmentioning

confidence: 99%

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Yeast fermentation of sugarcane for ethanol production: Can it be monitored by using in situ microscopy?

Belini

Caurin

Wiedemann

et al. 2017

Braz. J. Chem. Eng.

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This paper addresses some key issues related to the automation of fermentation process analysis in the context of industrial-scale ethanol production from sugarcane substrates. As the current methods for the determination of cell density and viability are time consuming and laborious, high resolution in situ microscopy (0.5µm) is proposed as a promising alternative. Laboratory-scale experiments presented here show that this imaging technique allows automatic, on-line, and real-time monitoring of yeast cells suspended in sugarcane molasses used in the ethanol industry. In particular, the feasibility of cell concentration measurements of Saccharomyces cerevisiae SA-1 in industrial sugarcane molasses is demonstrated. Automated concentration measurements exhibit a linear correlation with manual reference values using a Neubauer chamber from 3×10 6 cells/mL up to a saturation level at approximately 2×10 8 cells/mL. Furthermore, it was demonstrated that the microscopic resolution of this technique, combined with its large statistics, allows a morphological assessment of the size, shape and some internal structures of the yeast cells. On average, the accuracy of the algorithm´s yeast cells classification was 0.80. The results obtained suggest that the ISM is a suitable tool to perform in-line sugarcane fermentation monitoring.

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Section: Image Acquisitionmentioning

confidence: 99%

“…Unlike other in situ sampling techniques [i.e., mechanical sampling and decelerated flow through an observation zone; see Belini et al (2013) for detailed information], the optical sampling ISM used here is a system that lets the suspension flow freely through the observation zone.…”

Section: Image Acquisitionmentioning

confidence: 99%

Yeast fermentation of sugarcane for ethanol production: Can it be monitored by using in situ microscopy?

Belini

Caurin

Wiedemann

et al. 2017

Braz. J. Chem. Eng.

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“…Furthermore, TB paired with a haemocytometer grid (Fig. 1) is regarded as the standard approach for estimating the cell population density [17], i.e. the total number of living cells in the culture [18].…”

Section: Introductionmentioning

confidence: 99%

Cell Counting and Viability Assessment of 2D and 3D Cell Cultures: Expected Reliability of the Trypan Blue Assay

et al. 2017

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BackgroundWhatever the target of an experiment in cell biology, cell counting and viability assessment are always computed. The Trypan Blue (TB) assay was proposed about a century ago and is still the most widely used method to perform cell viability analysis. Furthermore, the combined use of TB with a haemocytometer is also considered the standard approach to estimate cell population density. There are numerous research articles reporting the use of TB assays to compute cell number and viability of 2D and 3D cultures. However, the literature still lacks studies regarding the reliability of the TB assay in terms of assessment of its repeatability and reproducibility.MethodsWe compared the TB assay's measurements obtained by two biologists who analysed 105 different samples in double-blind for a total of 210 counts performed. We measured: (a) the repeatability of the count performed by the same operator; (b) the reproducibility of counts performed by the two operators.ResultsThere were no significant differences in the results obtained with 2D and 3D cell cultures: we estimated an approximate variability of 5% when the TB assay was used to assess the viability of the culture, and a variability of around 20% when it was used to determine the cell population density.ConclusionsThe main aim of this study was to make researchers aware of potential measurement errors when TB is used with a haemocytometer for counting and viability measurements in 2D and 3D cultures. We believe that these results can help researchers to determine whether the expected reliability of the TB assay is compliant with their applications.

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“…In addition to conventional sensors, optical sensors are competitive candidates for bioprocess control. Diverse optical principles have been used to measure different physiochemical variables, including the measurement of vitamins, coenzymes [ 1 ] and pyruvate [ 2 ] by fluorescence spectroscopy, the determination of glucose concentrations by near-infrared spectroscopy [ 3 , 4 ], and the observation of cell morphology by in situ microscopy [ 5 , 6 ]. Light scattering can also be used to determine the density of many different microorganisms with high sensitivity [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

An Innovative Optical Sensor for the Online Monitoring and Control of Biomass Concentration in a Membrane Bioreactor System for Lactic Acid Production

Fan

Ebrahimi

Quitmann

et al. 2016

Sensors

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Accurate real-time process control is necessary to increase process efficiency, and optical sensors offer a competitive solution because they provide diverse system information in a noninvasive manner. We used an innovative scattered light sensor for the online monitoring of biomass during lactic acid production in a membrane bioreactor system because biomass determines productivity in this type of process. The upper limit of the measurement range in fermentation broth containing Bacillus coagulans was ~2.2 g·L−1. The specific cell growth rate (µ) during the exponential phase was calculated using data representing the linear range (cell density ≤ 0.5 g·L−1). The results were consistently and reproducibly more accurate than offline measurements of optical density and cell dry weight, because more data were gathered in real-time over a shorter duration. Furthermore, µmax was measured under different filtration conditions (transmembrane pressure 0.3–1.2 bar, crossflow velocity 0.5–1.5 m·s−1), showing that energy input had no significant impact on cell growth. Cell density was monitored using the sensor during filtration and was maintained at a constant level by feeding with glucose according to the fermentation kinetics. Our novel sensor is therefore suitable for integration into control strategies for continuous fermentation in membrane bioreactor systems.

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In situ microscopy: A perspective for industrial bioethanol production monitoring

Cited by 22 publications

References 36 publications

Yeast fermentation of sugarcane for ethanol production: Can it be monitored by using in situ microscopy?

Yeast fermentation of sugarcane for ethanol production: Can it be monitored by using in situ microscopy?

Cell Counting and Viability Assessment of 2D and 3D Cell Cultures: Expected Reliability of the Trypan Blue Assay

An Innovative Optical Sensor for the Online Monitoring and Control of Biomass Concentration in a Membrane Bioreactor System for Lactic Acid Production

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