Limnetica 34

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A quick and effective estimation of algal density by turbidimetry developed with Chlorella vulgaris cultures

Noelia S. Ferrando, Hernán H. Benítez, Néstor A. Gabellone, María C. Claps & Pablo R. Altamirano
2015
34
2
397-406
DOI: 
10.23818/limn.34.30

The use of Chlorella vulgaris Beijerinck as a food source for zooplankton requires the optimization of algal-culture conditions for prolonged growth maintenance. In this study, we developed a method that relates algal density to culture turbidity to estimate culture biomass. This method was improved by applying digital analysis for algal counting, which promotes accuracy, low culture disturbance, easy repetition and the rapid acquisition of results. Two 3-L cultures of C. vulgaris, maintained for two weeks with continuous lighting (eight light-emitting diodes at 50 µmol photons m-2. s–1, at 660 nm) and aerators to prevent algal sedimentation, reached turbidities of 214 and 280 NTUs, respectively. Sample counting was performed using digital images obtained with an inverted microscope. Aliquot sedimentation was compared with or without previous homogenization through photographs taken in the central, middle, and peripheral sectors of the Utermöhl settling chambers. For each procedure, we counted between 17 and 404 individuals image–1, requiring, on average, one minute image–1. At low turbidity (< 40 NTU), the data dispersion was similar between the two protocols (error range, 16 to 60 %); at higher turbidity, the direct sedimentation alone gave a larger error (31–50 %) than with prior homogenization (5–13 %). Regression analysis at low data fit (67 %) suggested that the sedimentation heterogeneity of non-homogenized samples corresponded to a pattern of settled algae having increasing density from the periphery to the centre of the chamber, but with homogenization, a better model fitting (99 %) resulted, contributing to greater consistency with that procedure. We consider that this turbidometric protocol could be used successfully with cultures of algae that have geometrical shapes recognizable by the image software.

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