PULSED ELECTRIC FIELD PROCESSING EFFECTS ON PHYSICOCHEMICAL PROPERTIES, FLAVOR COMPOUNDS AND MICROORGANISMS OF LONGAN JUICE
Bibliographic record
Abstract
ABSTRACT Longan juice was processed using a pulsed electric field (PEF) treatment and compared with a conventional thermal pasteurization method. The PEF treatment was carried out using a laboratory unit, set with a bipolar pulse (3 µs wide), an intensity of 32 kV/cm. PEF-treated longan juice showed no noticeable difference compared with the untreated sample, although well-visible differences were found between untreated and thermal-treated juice. An analysis of variance for pH, titratable acidity and total soluble solids determinations showed no statistically significant difference between the untreated and both thermally pasteurized and PEF-treated samples. The effect of PEF on nonenzymatic browning index and hydroxymethylfurfurol is not significantly different, although thermal treatment show significant difference in comparison with untreated juice. Contents of total phenol compounds presented significant variability for the two compared pasteurization methods. The PEF-treated longan juice retained greater amounts of vitamin C and top five flavor compounds than thermally treated longan juice. PRACTICAL APPLICATIONS As a new nonthermal sterilization method, pulsed electric field (PEF) treatment is a useful tool in fruit processing. It has been used in some other fruit juice processing, and no apparent changes in physicochemical properties and flavor were directly caused by PEF treatment. We tried it on longan juice and obtained some data for longan juice processing. PEF treatment is a new processing technology that improves longan juice quality and enhances its value.
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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".