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Accurate method for rapid biomass quantification based on specific absorbance of microalgae species with biofuel importance
Author(s) -
AmbrizPérez D.L.,
OrozcoGuillen E.E.,
GalánHernández N.D.,
LunaAvelar K.D.,
ValdezOrtiz A.,
SantosBallardo D.U.
Publication year - 2021
Publication title -
letters in applied microbiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.698
H-Index - 110
eISSN - 1472-765X
pISSN - 0266-8254
DOI - 10.1111/lam.13519
Subject(s) - biofuel , biomass (ecology) , biochemical engineering , absorbance , bioprocess , raw material , production (economics) , process engineering , pulp and paper industry , environmental science , microbiology and biotechnology , biological system , biology , chemistry , chromatography , ecology , engineering , paleontology , macroeconomics , economics
The development of microalgae culture technology has been an integral part to produce biomass feedstock to biofuel production. Due to this, numerous attempts have been made to improve some operational parameters of microalgae production. Despite this, specialized research in cell growth monitoring, considered as a fundamental parameter to achieve profitable applications of microalgae for biofuels production, presents some opportunity areas mainly related to the development of specific and accurate methodologies for growth monitoring. In this work, predictive models were developed through statistical tools that correlate a specific micro‐algal absorbance with cell density measured by cell count (cells∙per ml), for three species of interest for biofuels production. The results allow the precise prediction of cell density through a logistic model based on spectrophotometry, valid for all the kinetics analysed. The adjusted determination coefficients ( r adj 2 ) for the developed models were 0·993, 0·995 and 0·994 for Dunaliella tertiolecta, Nannochloropsis oculata and Chaetoceros muelleri respectively. The results showed that the equations obtained here can be used with an extremely low error (≤2%) for all the cell growth ranges analysed, with low operational cost and high potential of automation. Finally, a user‐friendly software was designed to give practical use to the developed predictive models.