Potential impacts of bioprocessing of sweet potato: Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it