Potential impacts of bioprocessing of sweet potato: Review
Bibliographic record
Abstract
Sweet potato (Ipomoea batatas L.) is among the major food crops in the world and is cultivated in all tropical and subtropical regions particularly in Asia, Africa, and the Pacific. Asia and Africa regions account for 95% of the world's production. Among the root and tuber crops grown in the world, sweet potato ranks second after cassava. In previous decades, sweet potato represented food and feed security, now it offers income generation possibilities, through bioprocessing products. Bioprocessing of sweet potato offers novel opportunities to commercialize this crop by developing a number of functional foods and beverages such as sour starch, lacto-pickle, lacto-juice, soy sauce, acidophilus milk, sweet potato curd and yogurt, and alcoholic drinks through either solid state or submerged fermentation. Sweet potato tops, especially leaves are preserved as hay or silage. Sweet potato flour and bagassae are used as substrates for production of microbial protein, enzymes, organic acids, monosodium glutamate, chitosan, etc. Additionally, sweet potato is a promising candidate for production of bioethanol. This review deals with the development of various products from sweet potato by application of bioprocessing technology. To the best of our knowledge, there is no review paper on the potential impacts of the sweet potato bioprocessing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".