Evaluation of Rice-based Alternatives to Titanium Dioxide for Colour-masking in Iron-Fortified Salts
Bibliographic record
Abstract
Titanium dioxide (TiO2) is a common whitening agent used in the food industry, used in candies, baked goods and confectionaries. Several food regulatory agencies have banned or severely restricted TiO2 use due to potential carcinogenic/ genotoxicity. Rice starch and rice flour were investigated as alternatives to TiO2, since they are cheap, opaque, white and are widely used in industry. Due to its amorphous granules and resulting low electrostatic forces, the adhesion of rice starch to extruded materials was much weaker than that of TiO2. Adhesives were synthesized from crosslinking citric acid with rice starch and rice flour through esterification reaction pathways. The results were tested on extruded ferrous fumarate cylinders used in salt fortification and compared with TiO2 was as control. The results show that rice starch as a whitening agent with a modified rice starch adhesive was a promising option for replacing TiO2 as a colour masking/whitening agent. It was observed that higher mass fractions of citric acid in the adhesive produced better results. Rice flour performed comparably to the rice starch in adhesives however, the ease of use was poorer due to higher viscosity and clumping. The cost for using rice starch was a cost-effective alternative to TiO2 as rice starch is a cheaper, widely available food additive.
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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".