Enhancing color and bioactive retention during purple eggplant drying via sonicated pretreatments and optimized blanching
Bibliographic record
Abstract
The retention of color, bioactive compounds, and texture during drying remains a challenge for the food processing industry. This study investigates the effects of sonication-assisted calcium chloride and ascorbic acid (1–2% solution) pretreatments combined with optimized blanching on the quality retention of dried purple eggplant. Different pretreatment methods, including sonication and blanching, were applied to eggplant slices followed by hot-air drying. Results showed that sonication significantly improved color retention, with sonicated samples maintaining higher lightness (L∗-value of 7.8 ± 0.4 for SSnB2) compared to non-sonicated controls (L∗-value of 6.6 ± 0.4 for SB2). The ΔE∗ (total color difference) for sonicated treatments was lower (7.5 ± 1.5 for SSnB2) compared to non-sonicated treatments (10.5 ± 1.2 for SB1), indicating superior color stability. Bioactive compound retention was enhanced, with sonicated samples exhibiting higher levels of polyphenols (12.8 ± 0.6 mg GAE/g DW), flavonoids (4.4 ± 0.5 mg QE/g DW), and anthocyanins (8.9 ± 0.6 mg C3G/g DW) compared to non-sonicated samples. DPPH scavenging activity was also higher in sonicated samples (75% ± 1.5 for SSnB2), compared to non-sonicated samples (75% ± 1.6 for SB2). Sensory evaluations showed that sonicated samples scored higher in color (7.8 ± 0.4), texture (7.5 ± 0.4), and overall acceptability (8.0 ± 0.3) compared to non-sonicated samples. These findings confirm that sonication, combined with blanching, significantly enhances the quality, bioactive retention, and consumer acceptability of dried purple eggplant, offering a promising, scalable technique for the food 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 | 0.001 |
| 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".