ANTIMICROBIAL EFFECT OF CINNAMON BARK EXTRACT ON <i>ESCHERICHIA COLI</i> O157:H7, <i>LISTERIA INNOCUA</i> AND FRESH‐CUT APPLE SLICES
Bibliographic record
Abstract
ABSTRACT The potential for natural antimicrobial compounds extracted from true cinnamon (Cinnamomum zeylanicum Nees ) to use as a food additive to extend the shelf life of fresh‐cut apples was investigated. Several different extracts were prepared using cinnamon bark and powder to evaluate their antimicrobial activity on two marker microorganisms, Escherichia coli O157:H7 and Listeria innocua. An ethanolic extract of cinnamon bark (2% w/v) inhibited the growth of E. coli and L. innocua by 94 and 87%, respectively. When incorporated in a commercial antibrowning dipping solution, FreshExtend, the cinnamon bark extract (1% w/v) reduced significantly (P < 0.05) the microbial growth on apple slices stored for 12 days at 6C in comparison to the control. The cinnamon extract had no influence on the antibrowning properties of FreshExtend. Liquid chromatography mass spectrometry analysis showed that the major chemical constituent of this extract is cinnamic aldehyde. PRACTICAL APPLICATIONS The consumer demand for convenient and nutritious, minimally processed produce like fresh‐cut apples has been steadily increasing. Identification of natural antimicrobial agents that are acceptable to the consumer is a challenge to the fresh‐cut industry. In this study, we discovered antimicrobial properties of a cinnamon extract and identified the principal antimicrobial compound as cinnamic aldehyde. For the first time, we demonstrated that this generally recognized as safe (GRAS) compound could be used with a commercial post‐cut dipping solution (FreshExtend) to inhibit significantly the microbial growth on refrigerated apple slices. Therefore, this innovative study provides a new insight into the possible use of cinnamon extracts or cinnamic aldehyde as natural antimicrobial agents in the processing of sliced apples and other minimally processed fruits and vegetables to assure the microbial food safety.
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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) | 0.001 | 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".