Properties and applications of <i>α</i>‐galactosidase in agricultural waste processing and secondary agricultural process industries
Bibliographic record
Abstract
Agriculture products form the foundation building blocks of our daily lives. Although they have been claimed to be renewable resources with a low carbon footprint, the agricultural community is constantly challenged to overcome two post-harvest bottlenecks: first, farm bio-waste, a substantial economic and environmental burden to the farming sector, and second, an inefficient agricultural processing sector, plagued by the need for significant energy input to generate the products. Both these sectors require extensive processing technologies that are demanding in their energy requirements and expensive. To address these issues, an enzyme(s)-based green chemistry is available to break down complex structures into bio-degradable compounds that source alternate energy with valuable by-products and co-products. α-Galactosidase is a widespread class of glycoside hydroxylases that hydrolyzes α-galactosyl moieties in simple and complex oligo and polysaccharides, glycolipids, and glycoproteins. As a result of its growing importance, in this review we discuss the source of the enzyme, production and purification systems, and enzyme properties. We also elaborate on the enzyme's potential in agricultural bio-waste management, secondary agricultural industries like sugar refining, soymilk derivatives, food and confectionery, and animal feed processing. Insight into this vital enzyme will provide new avenues for less expensive green chemistry-based secondary agricultural processing and agricultural sustainability. © 2023 Society of Chemical Industry.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".