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

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 ۶۰۰-۷۵۰ 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 (۶۸۰ 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 (R۲) 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 ۵% and ۱۰% could be used to estimate densities up to ۱۰۷ cells mL-۱ and dry weight of ۰.۰۲-۱.۲۴ and ۰.۰۳-۱.۱۸ g L-۱ in Chlorella sorokiniana and Chlorella sp. cultures.

کلیدواژه ها:

Microalgae ، cell density ، growth curve ، optical density ، biomass estimation