On‐Line Determination of Biomass in a Microalga Bioreactor Using a Novel Computerized Flow Injection Analysis System

Meireles, Luís A.; Azevedo, José Luís; Cunha, João Paulo Silva; Malcata, F. Xavier

doi:10.1021/bp020283u

Cited by 38 publications

(23 citation statements)

References 12 publications

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“…The aim of the current development was to combine the advantages of several types of PBRs [10][11][12][13] and to build a helix-tube reactor with the volume of 10 L. The photobioreactor was designed as three-layer structure (Fig. 1).…”

Section: Biomass and Cell Numbermentioning

confidence: 99%

Continuous cultivation of Dunaliella salina in photobioreactor for the production of β-carotene

Zhu

Jiang

2007

Eur Food Res Technol

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A helix tube photobioreactor with a volume of 10 L was designed and manufactured. The facade of the reactor was designed as three-layer frame. The illumination helixes units were 16, illumination area volume was 1.5 L, illuminate speciWc area was 95 m ¡1 . A marine micro-alga Dunaliella salina was used as a model organism in this study. Results showed that the optimum inoculum concentration was OD 630 = 0.15 and the optimum circulation rate of culture Xuid was 630 mL/h for the bioreactor. In continuous cultivation, in order to determine the optimum collection of alga solution for the highest yield of -carotene, three diVerent alga solution collection and addition fresh culture medium volumes were applied: 0.8, 1.5, 2.0 L/day. The biomass and extraction of -carotene were determined in diVerent cultivation periods. The result suggested that the optimum daily harvest of alga solution was 1.5 L/day for the cultivation of D. salina to obtain the highest production of -carotene.

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Section: Biomass and Cell Numbermentioning

confidence: 99%

Continuous cultivation of Dunaliella salina in photobioreactor for the production of β-carotene

Zhu

Jiang

2007

Eur Food Res Technol

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“…Nonetheless, manual counting of cells with an optical microscope using a hemacytometer is time consuming and tedious, and an aliquot must be taken (Imamoglu et al 2007). Another common method of cell number estimation makes use of optical density as a turbidimetric measure of cell numbers by taking an aliquot and using a spectrophotometer (Wilde and Gibbs 1998;Meireles et al 2002).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Isochrysis galbana (clone T-ISO) cell numbers by digital image analysis of color intensity

2009

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In microalgal cultivation, measuring cell numbers as a means to monitor growth rates is a long-standing problem. Many automated counting systems and schemes have been developed; among these are image analysis systems. However, such imaging systems have presented difficulties in dealing with the complexities of computer recognition of individual microscopic cells. It is known that the coloration of microalgae suspension is species specific and that color intensity increases are typically associated with increasing numbers. Using this qualitative insight, the present work describes the design, construction, and comparative performance of an inexpensive digital imaging system optimized for counting microalgal cells. The system circumvents the need to count individual cells and extracts cell numbers directly from the macroscopic color intensity of a microalgal suspension. The results suggest, using Isochrysis galbana (T-ISO) as an illustrative example, that this scheme is potentially useful for inexpensive and automated biomonitoring of microalgal cell numbers. Percentage difference comparisons with a standard Coulter Counter indicated that the three algorithms tested provided better than 10% accuracy over density thresholds of 1.52× 10 6 to 8.10×10 6 cells mL −1 with precision of 4% attainable at high density concentrations.

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“…In manual sampling, at least two independent estimates are recommended when monitoring biomass in a mass culture system [108], and this recommendation could be extended to the online measurement in the form of fairly frequent calibration because of sensor fouling. Experimentally, several methods have been tested at the laboratory scale, including automatic sampling and dilution in a flow injection analysis (FIA) system coupled with a spectrophotometer [109,110]. PAM fluorometry was used for determining the biomass concentration in a shake flask by measuring the in vivo maximum fluorescence yield F m through the flask bottom after a dark period and assuming proportionality between average F m and biomass concentration [111].…”

Section: Biomass Concentrationmentioning

confidence: 99%

Monitoring of Microalgal Processes

Havlík

Scheper

Reardon

2015

Microalgae Biotechnology

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Process monitoring, which can be defined as the measurement of process variables with the smallest possible delay, is combined with process models to form the basis for successful process control. Minimizing the measurement delay leads inevitably to employing online, in situ sensors where possible, preferably using noninvasive measurement methods with stable, low-cost sensors. Microalgal processes have similarities to traditional bioprocesses but also have unique monitoring requirements. In general, variables to be monitored in microalgal processes can be categorized as physical, chemical, and biological, and they are measured in gaseous, liquid, and solid (biological) phases. Physical and chemical process variables can be usually monitored online using standard industrial sensors. The monitoring of biological process variables, however, relies mostly on sensors developed and validated using laboratory-scale systems or uses offline methods because of difficulties in developing suitable online sensors. Here, we review current technologies for online, in situ monitoring of all types of process parameters of microalgal cultivations, with a focus on monitoring of biological parameters. We discuss newly introduced methods for measuring biological parameters that could be possibly adapted for routine online use, should be preferably noninvasive, and are based on approaches that have been proven in other bioprocesses. New sensor types for measuring physicochemical parameters using optical methods or ion-specific field effect transistor (ISFET) sensors are also discussed. Reviewed methods with online implementation or online potential include measurement of irradiance, biomass concentration by optical density and image analysis, cell count, chlorophyll fluorescence, growth rate, lipid concentration by infrared spectrophotometry, dielectric scattering, and nuclear magnetic resonance. Future perspectives are discussed, especially in the field of image analysis using in situ microscopy, infrared spectrophotometry, and software sensor systems.

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On‐Line Determination of Biomass in a Microalga Bioreactor Using a Novel Computerized Flow Injection Analysis System

Cited by 38 publications

References 12 publications

Continuous cultivation of Dunaliella salina in photobioreactor for the production of β-carotene

Continuous cultivation of Dunaliella salina in photobioreactor for the production of β-carotene

Evaluation of Isochrysis galbana (clone T-ISO) cell numbers by digital image analysis of color intensity

Monitoring of Microalgal Processes

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