Advanced Processing Techniques and Applications for Value-Added Sweet Potato Products
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, 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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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