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

Document Type : Research Paper



2 Persian Type Culture Collection(PTCC), Iranian Research Organization for Science and Technology (IROST)


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.


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