pH indicator films fabricated from soy protein isolate modified with chitin nanowhisker and Clitoria ternatea flower extract
Bibliographic record
Abstract
Sensor films are finding wide range of applications. Different type of sensing films is fabricated for the identification of chemicals, ions, heavy metals, changes in the pH, etc. The present report is on the fabrication of pH sensitive films from completely natural sources-soy protein isolate, chitin nano whiskers and flower extract. The highly crystalline chitin nano whiskers (CNW) were extracted from prawn shell under neutral condition via steam explosion technique. Multifunctional Soy protein isolate (SPI) films were prepared by adding chitin nanowhisker and Clitoria ternatea flower extract and its effect on thermal, mechanical and moisture properties of SPI film was investigated. The isolated CNW presented a needle like morphology with a diameter of 10–50 nm and a crystallinity index of 99.67%. The extracted chitin nanowhisker was used to prepare biodegradable films with soy protein isolate immobilized with anthocyanin from Clitoria ternatea flower extract. The prepared Soy protein -chitin nanowhisker films was found to have a tensile strength of about 15.45 ± 0.97 MPa with 8% chitin nanowhisker addition. The addition of CTE was found to decrease the tensile strength of SPI-CNW film but was found to make the film pH sensitive. The developed indicator film showed visible color changes in acidic and basic medium and hence can be used to monitor the freshness of food materials.
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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".