Applications of Algal Biomass in Global Food and Feed Markets: From Traditional Usage to the Potential for Functional Products
Bibliographic record
Abstract
The many beneficial applications available from the wide diversity of algal species (both macro and micro) are only beginning to be unraveled. The list of known species used by humans is quite long. In this chapter we review the most functionally significant compounds identified in algae and their variations in concentration and structure. This chapter discusses the presence and value of structural components such as proteins, lipids, and polysaccharides, as well as the structure and function of molecules of commercial interest. The primary utilization of algae is in the form of raw biomass, either fresh or dried. Secondary use is in the form of their processed extracts that have interesting rheological or bioactive properties for the cosmetic, food supplement, and animal feed markets. The major utilization of macroalgae is in human food. An important application of seaweed extracts is based upon special marine colloids, with three main polysaccharide types: alginate, carrageenan, and agar. The primary functionality of colloids occurs in the processed food market, as texturizing and structural compounds in formulations. Given the great diversity of both algal species and the molecules they contain, the potential for their utilization in a broad range of applications is large and currently underexplored.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".