The protein content of seaweeds: a universal nitrogen-to-protein conversion factor of five
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A global drive to source additional and sustainable biomass for the production of protein has resulted in a renewed interest in the protein content of seaweeds. However, to determine accurately the potential of seaweeds as a source of protein requires reliable quantitative methods. This article systematically analysed the literature to assess the approaches and methods of protein determination and to provide an evidence-based conversion factor for nitrogen to protein that is specific to seaweeds. Almost 95 % of studies on seaweeds determined protein either by direct extraction procedures (42 % of all studies) or by applying an indirect nitrogen-to-protein conversion factor of 6.25 (52 % of all studies), with the latter as the most widely used method in the last 6 years. Meta-analysis of the true protein content, defined as the sum of the proteomic amino acids, demonstrated that direct extraction procedures underestimated protein content by 33 %, while the most commonly used indirect nitrogen-to-protein conversion factor of 6.25 over-estimated protein content by 43 %. We therefore determined whether a single nitrogen-to-protein conversion factor could be used for seaweeds and evaluated how robust this would be by analysing the variation in this factor for 103 species across 44 studies that span three phyla, multiple geographic regions and a range of nitrogen contents. An overall median nitrogen-to-protein conversion factor of 4.97 was established and an overall mean nitrogen-to-protein conversion factor of 4.76. We propose that the overall median value of 5 be used as the most accurate universal seaweed nitrogen-to-protein (SNP) conversion factor.
KeywordsAmino acid Macroalgae Meta-analysis Nitrogen-to-protein factor Protein Protein determination Seaweed
This research is part of the MBD Energy Research and Development programme for Biological Carbon Capture and Storage. The project is supported by the Advanced Manufacturing Cooperative Research Centre (AMCRC), funded through the Australian Government’s Cooperative Research Centre Scheme. We thank Simon Angell for developing an Excel macro program that enabled efficient extraction of data from the literature.
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