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Record W4388737438 · doi:10.53555//sfs.v10i3.1666

Synergistic Effects of Nisin-Assisted Thermosonication on Quality Characteristics of Apple Juice

2023· article· en· W4388737438 on OpenAlexvenueno aff
Nadir Ali, Javed Shoukat, Mahwish Tanveer, Muhammad Usman Shoukat, Tanveer Ahmad, Abdullah Abdullah, Sajid Ali, Muskan Fatima

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Inactivation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsNisinFood sciencePreservativeChemistryAscorbic acidFood preservationAntimicrobial

Abstract

fetched live from OpenAlex

Awareness in consumers towards the safety and quality of food is increasing day by day, that is why the demand for safe and nutritious food has been increased. The present study was conducted to evaluate the synergistic effects of bio preservative nisin (NS) with thermosonication (TS) on the quality and safety attributes of apple juice. The purpose of conducting the given research was to check the effects of chemically assisted TS and to provide microbial safety to the apple juice without thermal degradation. Treatments were applied by using TS (24kHz, 30oC, 50min) in T1 and at 60oC in T2, after that NS was added in treated samples at the concentration of 75 mg/L in T3 and 100 mg/L in T4, samples were analyzed with the interval of 5 days during storage of 15 days. Final results, stated that value of acidity and cloud value increased from (0.34±0.04) to (0.41±0.09) and (0.09 ± 0.02) to (1.13 ± 0.05) respectively after applying the treatment of TS and TS+NS, but decreasing trend were seen during storage interval. Treatments increased the total phenolic contents of juice from (274.53) to (291.60) in T4 followed by T3 but decrease to (288.45) after storage interval of 15 days. On the other hand, the significant decrease was seen during treatment and storage in the pH from (3.93) to (3.88) in T4 and ascorbic acid from (4.09) to (3.68 mg/100mg) due to heating and increase in storage time. Nonsignificant results showed in the TSS during storage and after treatments. Treatments like TS and TS+NS improved the color of apple juice, which gave decreasing trend during storage. Total plate count decreased significantly after the treatments like T4 (TS+NS) and T3 from 1.97 log to 1.53 log, but microbial load increased a little bit during storage; efficiently retained by T4 treatment. All the treatments improved in the overall acceptability of apple juice. Therefore, the findings of the given study analyzed that TS+NS treatment (T4 & T3) are better methods for providing quality retention and microbial safety of apple juice without thermal degradation as compared to simple TS treatment (T1 & T2), but both TS and TS+NS have the potential to retain quality in apple juice. Hence; the study concluded that TS+NS is an effective method to decrease microbial load without thermal degradation of quality characteristics.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.173
GPT teacher head0.363
Teacher spread0.190 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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