4. Conclusions
This work presents a novel technique for the fast analysis of microalgae biochemical composition. The main advantages of the proposed technique are:
1.
Fast analysis for thee parameters (~ 1 h).
2.
Minimal sample amount required (< 0.1 mg).
3.
No toxic or harmful chemicals.
4.
Minimal sample handling.
5.
Only one piece of equipment required for the full biochemical characterisation.
It is shown that by identifying the peak areas of three marker compounds for the main biochemical components, lipids, carbohydrates and proteins, the levels of these components can be estimated in the original biomass. Protein levels could be determined with a good correlation to the Lowry method of R2 = 0.8 over the entire data set. The technique showed improved correlation when only data points from separate microalgae strains were investigated. This suggests that the method can faithfully predict the microalgal composition during growth trials from one strain at different conditions or growth stages. Py–GC–MS lipid analysis was compared to the Folch method and achieved a correlation of R2 = 0.65. The carbohydrate analysis achieved a correlation of R2 = 0.61, most likely due to the difference in different microalgae strain carbohydrate composition. Individual carbohydrate algae strain correlation was as high as R2 = 0.91. Overall this work presents a fast and easy technique for microalgae researchers when a vast number or sample points in growth trials are analysed. The novel technique is not as accurate as established techniques but gives a reasonable estimation of lipid, protein and carbohydrate contents of microalgae in one easy step.