Effects of high power ultrasound on the enzymolysis and structures of sweet potato starch
Bibliographic record
Abstract
Abstract BACKGROUND The general enzymatic method for producing reducing sugar is liquefaction followed by saccharification of starch. This method results in lower yields, consuming high energy and time. Therefore, the present study evaluated a new approach for producing reducing sugar from sweet potato starch (SPS), including simultaneous liquefaction (by α‐amylase) and saccharification (by glucoamylase) of SPS pretreated with high power ultrasound. The effects of ultrasound parameters on the conversion rate of SPS and mechanism were investigated. RESULTS The optimum ultrasound pretreatment conditions were a frequency of 20 kHz, SPS concentration of 125 g L−1, temperature of 30 °C, pulsed on‐time of 3 s, pulsed off‐time of 5 s, power density of 8 W mL−1 and sonication time of 15 min. The ultrasound assisted enzymolysis resulted in a SPS conversion rate of 59.10%, which was improved by 56.35% compared to the control. The results of pasting properties and thermal analysis showed that ultrasound pretreatment decreased the peak viscosity, breakdown temperature, setback viscosity, gelatinization range (TC − TO) and enthalpy of gelatinization (ΔH) of SPS significantly (P < 0.05) by 12.1%, 7.6%, 6.6%, 18.8% and 44.4%, respectively. Fourier‐transform infrared spectroscopy indicated that ultrasound damaged the ordered structures and crystallization zone. This was confirmed by X‐ray diffraction analysis, which showed that the relative crystallinity was reduced by 15.0%. Scanning electron microscopy showed that ultrasound destroyed the surfaces and the linkages between starch granules. CONCLUSION Prior to simultaneous liquefaction and saccharification of SPS, high power ultrasound pretreatment is a promising method for improving the conversion rate of starch. © 2020 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".