Towards the use of microalgae as a source of alternative protein : using plant extract as a growth enhancer, protein quality quantification, and a method of biochemical content estimation

Abstract

Food security, increase in global population, ecological impacts of climate change, and greater health awareness have led to the recent rise in alternative protein products. One such product is microalgae. However, its production is not yet viable because of the high cost, technological immaturity, and lack of understanding. A simpler and more direct way to improve the economics is to increase the biomass productivity or output of the process. Hence, the first objective of this thesis is to investigate the use of herbs and spices to determine if its use could improve the growth of microalgae. Different herbs and spices were screened (and different concentrations and forms). It was found that onion extract was the most effective and managed to double the growth rate and increase the cell density to about 6 times that of control. The second objective is to determine the protein digestibility of microalgae, specifically the PDCAAS method to determine the protein quality of microalgae. This is important if the microalgae (whether whole or extracted proteins) is to be used as food to supply humans with essential amino acids (and nutrients). The PDCAAS value was found to be relatively high, 0.88, comparable to beef and soy. The digestibility was found to have a maximum value of 1 but dropped to 0.88 due to the lack a certain amino acid (methionine). It is said that the biorefinery approach is the way to go for a microalgae production process that is viable and economical. High value products are produced and sold alongside cheaper and lower value commodity products. However, the technologies available for microalgae are still at an infancy and a lack of understanding of the processes is hindering its further development. The third objective of this thesis is thus to develop a method to estimate the biochemical composition of the biomass that avoids the laborious and time-consuming disruption/extraction/measurement processes. Data available from literature were used to evaluate the performance of the prediction model and it was found to be highly predictive with low root mean squared errors.