Effect of Hibicus sabdariffa on the Nutritional and Sensory Properties of Novel Naem Product
Bibliographic record
Abstract
Naem is a traditional Asian fermented sausage made from the mixture of raw meat, cooked rice, garlic, salt, sugar, spices, and sodium nitrite. With consumers more concerned with healthy food choices, Hibiscus sabdariffa (HS) can be used as substitute for sodium nitrite in Naem preparation. Therefore, the objective of this study was to evaluate the effects of HS on the sensory, quality, and physicochemical analyses of Naem products. Four concentrations of HS were evaluated in this experiment: 1) 0%, 2) 1%, 3) 3% and 4) 5% HS. Treatments were analyzed for sensory evaluation by using a 9-point hedonic scale (trained panelists = 38). Physicochemical characteristics were evaluated for color (L*, a*, and b* values), pH, water activity, moisture (%), ash content, and lipid stability (TBARS). In addition, nutrition profiles, lactic acid bacteria, aerobic plate counts, Escherichia coli, and Listeria spp. were completed. SPSS with one-way ANOVA was used to evaluate any significant differences with p<0.05. The sensory evaluation revealed that Naem prepared with 3% HS had the highest overall acceptance scores (5.76), flavor (5.81), and taste (5.66). In addition, fermented Naem with 3% HS showed the highest scores of acceptability (92.2%), purchase intent (71.1%), and lactic acid bacteria counts (5.41 log CFU/g). The initial pH values, water activity, moisture (%), and ash content in this experiment ranged from 5.44-5.57, 0.93-0.94, 63.17-64.94%, and 1.51-1.74%, respectively. There was a significant (p<0.05) on color after 7 days storage at 3°C. Specifically, a* values were decreased in all treatments. The control treatment obtained the highest TBARS values (0.83 mg MDA/kg). No E. coli or Listeria spp. were detected. The results of this study indicate that Hibiscus sabdariffa can be used as a natural spice for Naem products which may help the meat industry increase market share through this innovative product.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".