Advanced Processing Techniques and Applications for Value-Added Sweet Potato Products
Bibliographic record
Abstract
Sweet potato, a versatile crop, plays a significant role in both food production and industrial applications due to its nutritional value and functional properties. This study provides a comprehensive overview of sweet potato composition, including carbohydrates, proteins, fibers, and antioxidants, and discusses the physicochemical properties influencing processing outcomes across different cultivars. Key primary processing techniques, such as washing, peeling, slicing, drying, and freezing, are examined alongside advanced methods like extrusion, fermentation, starch modification, and high-pressure processing for value-added products. Emerging innovations, including pulsed electric field technology, microwave-assisted processing, enzyme-assisted extraction, and 3D food printing, are explored for their potential to enhance production efficiency. A case study on industrial-scale sweet potato flour production is provided, covering the processing steps, quality control, and market impact. This study also addresses challenges in processing, such as seasonal variability, shelf-life limitations, and environmental concerns, with recommendations for overcoming these barriers, and concludes by highlighting future trends, including functional food development, sustainable practices, and the integration of genetic engineering to optimize processing outcomes. This study aims to provide insights for stakeholders to leverage sweet potato’s potential and foster innovations in industrial applications.
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.
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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".