Coupling Non- Linear Regression Analysis with Predictive Model by Spectrophotometric Data for Estimation Microalgae Concentration

Document Type : Research Paper

Authors

Department of Biotechnology, Iranian Research Organization for Science and Technology (IROST), P. O. Box 3353-5111, Tehran, Iran

Abstract

The estimation of algal biomass requires monitoring the growth of microalgae. In contrast to time-consuming methods such as cell counting, spectrophotometry was developed as a straightforward, quick, and explicit method to measure biomass concentration. Non-linear models can appropriately describe the patterns of growth and product formation, which are necessary for any biotechnological process using microorganisms. This study investigated the relationship between algal concentration and absorbance in the 600-750 nm wavelength range. Four mathematical growth non-linear models were utilized to analyze and confirm growth curve-based absorbance data obtained from Chlorella sorokiniana and Chlorella sp. The calibration curve was then created by relating the absorbance value (680 nm) with the cell density and dry weight measurements and calculating the correlation coefficient. The absorbance derivative was estimated in order to improve the algal concentration detection limit. A prediction model was created that considered the application of spectrophotometry data to the growth of Chlorella sorokiniana and Chlorella sp. The Exponential Plateau model should be selected to describe the growth of both Chlorella sorokiniana and Chlorella sp. The significance criteria, such as high regression coefficients (R2) and low root-mean-square error (RMSE), indicated that the models used were well-fitted to experimental data and may be considered sufficient to characterize biomass concentration. In addition, percentile deviation revealed that the obtained equations in this study with an error of less than 5% and 10% could be used to estimate densities up to 107 cells mL-1 and dry weight of 0.02-1.24 and 0.03-1.18 g L-1 in Chlorella sorokiniana and Chlorella sp. cultures.

Keywords

Main Subjects

References

Adar, O., Kaplan-Levy, R. N., & Banet, G. (2016). High temperature Chlorellaceae (Chlorophyta) strains from the Syrian-African Rift Valley: the effect of salinity and temperature on growth, morphology and sporulation mode. European Journal of Phycology, 51(4), 387–400. https://doi.org/10.1080/09670262.2016.1193772
Ajala, S. O., & Alexander, M. L. (2020). Assessment of Chlorella vulgaris, Scenedesmus obliquus, and Oocystis minuta for Removal of Sulfate, Nitrate, and Phosphate in Wastewater. International Journal of Energy and Environmental Engineering, 311–326. https://doi.org/10.1007/s40095-019-00333-0
Ale, M. T., & Meyer, A. S. (2013). Fucoidans from brown seaweeds: An update on structures, extraction techniques and use of enzymes as tools for structural elucidation. RSC Advances, 3(22), 8131–8141. https://doi.org/10.1039/c3ra23373a
Ambriz-Pérez, D. L., Orozco-Guillen, E. E., Galán- Hernández, N. D., Luna-Avelar, K. D., Valdez-Ortiz, A., & Santos-Ballardo, D. U. (2021). Accurate method for rapid biomass quantification based on specific absorbance of microalgae species with biofuel importance. Letters in Applied Microbiology, 73(3),  343–351. https://doi.org/10.1111/lam.13519
Asadi, P., Rad, H. A., & Qaderi, F. (2019). Comparison of Chlorella vulgaris and Chlorella sorokiniana pa.91 in Post Treatment of Dairy Wastewater Treatment Plant Effluents. Environmental Science and Pollution Research. https://doi.org/10.1007/s11356-019-06051-8
Badar, S. N., Yaakob, Z., & Timmiati, S. N. (2017). Growth Evaluation of Microalgae Isolated from Palm Oil Mill Effluent in Synthetic Media. Malaysian Journal of Analytical Science, 21(1), 82–94. https://doi.org/10.17576/mjas-2017-2101-10
Baldisserotto, C., Sabia, A., Giovanardi, M., Ferroni, L., Maglie, M., & Pancaldi, S. (2022). Chlorophyta microalgae as dietary protein supplement: a comparative analysis of productivity related to photosynthesis. Journal of Applied Phycology, 34(3),     1323–1340. https://doi.org/10.1007/s10811-022-02724-z
Basak, R., Wahid, K. A., & Dinh, A. (2021). Estimation of the Chlorophyll-a Concentration of Algae Species Using Electrical Impedance Spectroscopy. Water, 13(1223), 1–18. https://doi.org/10.3390/w13091223
Bricaud, A., Morel, A., Babin, M., Allali, K., & Claustre, H. (1998). Variations of Light Absorption by Suspended Particles with Chlorophyll a Concentration in Oceanic (case 1) Waters: Analysis and Implications for Bio-Optical Models. Journal of Geophysical Research: Oceans, 103(C13), 31033–31044. https://doi.org/10.1029/98jc02712
Chioccioli, M., Hankamer, B., & Ross, I. L. (2014). Flow cytometry pulse width data enables rapid and sensitive estimation of biomass dry weight in the microalgae Chlamydomonas reinhardtii and Chlorella vulgaris. PLoS ONE, 9(5), 1–12. https://doi.org/10.1371/journal.pone.0097269
Chirivella-Martorell, J., Briz-Redón, Á., & Serrano- Aroca, Á. (2018). Modelling of biomass concentration, multi-wavelength absorption and discrimination method for seven important marine microalgae species. Energies, 11(5). https://doi.org/10.3390/en11051089
Córdova, O., Ruiz-Filippi, G., Fermoso, F. G., & Chamy, R. (2018). Influence of Growth Kinetics of Microalgal Cultures on Biogas Production. Renewable Energy, 122, 455–459. https://doi.org/10.1016/j.renene.2018.01.125
Cunha, L., & Grenha, A. (2016). Sulfated seaweed polysaccharides as multifunctional materials in drug delivery applications. In Marine Drugs (Vol. 14, Issue 42). MDPI AG. https://doi.org/10.3390/md14030042
Godoy-Hernández, G., & Vázquez-Flota, F. A. (2006). Growth Measurements Estimation of Cell Division and Cell Expansion Gregorio. In Plant Cell Culture Protocols-Methods in Molecular Biology (Vol. 318, pp. 51– 58). Gómez, M. P., Romeral, J. G., Martorell, J. C., & Aroca, Á. S. (2015). Direct spectrophotometric method to determine cell density of Isochrysis galbana in serial batch cultures from a larger scale fed-batch culture in exponential phase. NEREIS, 8, 35–43. https://doi.org/10.3390/mol2net-03-04632
Griffiths, M. J., Garcin, C., van Hille, R. P., & Harrison, S. T. L. (2011). Interference by Pigment in the Estimation of Microalgal Biomass Concentration by Optical Density. Journal of Microbiological Methods, 85, 119–123. https://doi.org/10.1016/j.mimet.2011.02.005
Guedes, C., & Malcata, X. F. (2012). Nutritional Value and Uses of Microalgae in Aquaculture. In Aquaculture (pp. 59–78). https://doi.org/10.5772/30576
Hajjar Rakhmadumila, D., & Setiani Muntalif, B. (2020). Artificial Produced Water as a Medium to Grow Chlorella sp. for Biodiesel Production. E3S Web of Conferences, 148. https://doi.org/10.1051/e3sconf/202014802005
Hanief, S., Prasakti, L., Pradana, Y. S., Cahyono, R. B., & Budiman, A. (2020). Growth Kinetic of Botryococcus braunii Microalgae Using Logistic and Gompertz Models.AIP Conference Proceedings, 2296(November). https://doi.org/10.1063/5.0030459
He, L., Chen, Y., Wu, X., Chen, S., Liu, J., & Li, Q. (2020). Effect of Physical Factors on the Growth of Chlorella vulgaris on Enriched Media Using the Methods of Orthogonal Analysis and Response Surface Methodology. Water, 12(34). https://doi.org/10.3390/w12010034
Hotos, G. N., Avramidou, D., & Bekiari, V. (2020). Calibration Curves of Culture Density Assessed by Spectrophotometer for Three Microalgae (Nephroselmis sp., Amphidinium carterae and Phormidium sp.). European Journal of Biology and Biotechnology, 1(6), 1–7. https://doi.org/10.24018/ejbio.2020.1.6.132
Islam, M. S., Senaha, I., Matiar Rahman, M., Yoda, Y., & Saha, B. B. (2022). Mathematical modelling and statistical optimization of fast cultivation of Agardhiella subulata: Response surface methodology. Energy Nexus, 7. https://doi.org/10.1016/j.nexus.2022.100115
Jia, F., Kacira, M., & Ogden, K. L. (2015). Multi- Wavelength Based Optical Density Sensor for Autonomous Monitoring of Microalgae. Sensors, 15, 22234–22248. https://doi.org/10.3390/s150922234
Lacerda, L. M. C. F., Queiroz, M. I., Furlan, L. T., Lauro, M. J., Modenesi, K., Jacob-Lopes, E., & Franco, T. T. (2011). Improving Refinery Wastewater for Microalgal Biomass Production and CO2 Biofixation: Predictive Modeling and Simulation. Journal of Petroleum Science and Engineering, 78, 679–686. https://doi.org/10.1016/j.petrol.2011.07.003
Mahlmann, D. M., Jahnke, J., & Loosen, P. (2008). Rapid Determination of The Dry Weight of Single, Living Cyanobacterial Cells Using the Mach-Zehnder Double- Beam Interference Microscope. European Journal of Phycology, 43(4), 355–364. https://doi.org/10.1080/09670260802168625
Mansouri, M. (2017). Predictive modeling of biomass production by Chlorella vulgaris in a draft-tube airlift photobioreactor. Advances in Environmental Technology, 2(3), 119-126. doi: 10.22104/aet.2017.433
Pruitt, K. M., & Kamau, D. N. (1993). Mathematical Models of Bacterial Growth, Inhibition and Death Under Combined Stress Conditions. Journal of Industrial Microbiology, 12, 221–231. https://doi.org/10.1007/BF01584194
Rodrigues, L. H. R., Arenzon, A., Raya-Rodriguez, M. T., & Ferreira Fontoura, N. (2011). Algal Density Assessed by Spectrophotometry: A Calibration Curve for The Unicellular Algae Pseudokirchneriella subcapitata. Journal of Environmental Chemistry and Ecotoxicology, 3(8), 225–228. https://doi.org/10.5897/jece2011.025
Rosenberg, J. N., Kobayashi, N., Barnes, A., Noel, E. A., Betenbaugh, M. J., & Oyler, G. A. (2014). Comparative Analyses of Three Chlorella species in Response to Light and Sugar Reveal Distinctive Lipid Accumulation Patterns in the Microalga C. sorokiniana. PLoS ONE, 9(4). https://doi.org/10.1371/journal.pone.0092460
Santos-Ballardo, D. U., Rossi, S., Hernández, V., Gómez, R. V., del Carmen Rendón-Unceta, M., Caro- Corrales, J., & Valdez-Ortiz, A. (2015). A simple Spectrophotometric Method for Biomass Measurement of Important Microalgae Species in Aquaculture. Aquaculture, 448, 87–92. https://doi.org/10.1016/j.aquaculture.2015.05.044
Schagerl, M., Siedler, R., Konopáčová, E., & Ali, S.S. (2022). Estimating Biomass and Vitality of Microalgae for Monitoring Cultures: A Roadmap for Reliable Measurements. Cells, 11(15). https://doi.org/10.3390/cells11152455
Spolaore, P., Joannis-Cassan, C., Duran, E., & Isambert, A. (2006). Commercial applications of microalgae. Journal of Bioscience and Bioengineering, 101(2), 87–96. https://doi.org/10.1263/jbb.101.87
Su, Y., Mennerich, A., & Urban, B. (2016). A Comparison of Feasible Methods for Microalgal Biomass Determinations During Tertiary Wastewater Treatment. Ecological  Engineering, 94, 532–536. https://doi.org/10.1016/j.ecoleng.2016.06.023
Thatipamala, R., & Hill, G. A. (1991). Spectrophotometric method for high biomass concentration measurements. Biotechnology and Bioengineering, 38(9), 1007–1011. https://doi.org/10.1002/bit.260380908
 
Microbiology, Metabolites and Biotechnology
Volume 5, Issue 1
June 2022
Pages 42-55

Files

Share

How to cite

Statistics