Combined Effect of Cold Atmospheric Plasma and Chitooligosaccharide–EGCG Conjugate on Quality and Shelf-Life of Depurated Asian Green Mussel
Bibliographic record
Abstract
The combined effects of chitooligosaccharide–epigallocatechin gallate conjugate (CEC) at different concentrations (1, 2, and 3%, w/w) and cold atmospheric plasma (CAP) on the depurated Asian green mussel edible portion (AGM-EP) were investigated during refrigerated storage for 15 days. Among all the treatments, the microbial counts, total volatile bases (TMA-N and TVB-N), and lipid oxidation of AGM-EP-treated 3% CEC in conjunction with CAP (CEC-3-CAP) were lower than the other samples during 15-day storage (p < 0.05). Total viable bacteria (6.16 log CFU/g sample), psychrotrophic bacteria (3.24 log CFU/g sample), Vibrio spp. (2.47 log CFU/g sample), presumptive Pseudomonas (5.93 log CFU/g sample), and H2S-producing bacteria (5.05 log CFU/g sample) counts of the CEC-3-CAP were lower than samples treated with 1 and 2% (w/w) CEC on day 15, as well as samples solely treated using CAP during refrigerated storage, irrespective of storage time. Additionally, CEC-3-CAP had significantly lower lipid oxidation (PV: 8.36 mg cumene hydroperoxide/kg sample and TBARS: 2.65 mg MDA/kg sample) as compared to those without CEC added and other samples (p < 0.05). The incorporation of CEC effectively mitigated lipid oxidation as supported by lower reduction of PUFAs in AGM-EP. Moreover, on day 0, no significant differences were observed in cooking loss or textural parameters (firmness and toughness) among the treatments (p > 0.05). However, as storage progressed, cooking loss increased in the CEC-3-CAP sample, while a noticeable decline in firmness and toughness was recorded (p < 0.05). This further attributed to the lower likeness attained for CAP-3-CAP on day 12, but the score was higher than the acceptable limit (5.0). Therefore, CAP together with CEC is a promising technology to prolong the shelf-life of depurated AGM-EP by at least 9 days as compared to the control (3 days), but it certainly needs further studies for the retention of textural properties and sensorial attributes.
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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".