Assessment of the trace level metal ingredients that enhance the flavor and taste of traditionally crafted rice-based products
Bibliographic record
Abstract
This study investigated thirteen trace-level metals in a type of traditionally crafted rice-based product, namely Grape Well rice cake (GWRC), and in raw materials of Grape Well water (GWW) and Guichao rice, to study the potential impacts of these metals on its taste and flavour. For comparison purposes a second rice cake, Chengdu rice cake (CDRC) and its source water Chengdu water (CDW) were also assessed. Both Sr and Ba were found to have elevated concentrations in GWW samples with maximums of 482 and 92.0 μg L−1, respectively. Principal component analysis indicated Sr and Ba contributed significantly to distinguish GWRC from CDRC. Results of eTongue revealed differing taste of GWRC versus CDRC is likely due to distinct concentrations of metals (e.g., Sr and Ba) in these two groups of samples. Overall results warranted the necessity to develop appropriate quality control criteria for good manufacturing practices of traditionally crafted rice cake.
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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 | 0.001 | 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".