Techno-functional gelling mechanism and rheological properties of gelatin capsule-waste gel modified with kappa-carrageenan for future functional food applications
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Bibliographic record
Abstract
Gelatin capsule production generates large amounts of waste. Repurposing this waste into hydrogels offers a sustainable solution. This study investigated the techno-functional gelling mechanism and rheological properties of gelatin waste gel with added carrageenan, aiming to enhance the gel’s potential for future functional food applications. Gelatin capsule waste (GCW) was substituted with carrageenan at different levels (0%, 10%, 20%, 30%, 40%, or 50% w/w). The texture profile analysis and gel strength increased with the addition of carrageenan, accompanied by changes in the spectra obtained from Synchrotron Radiation-based Fourier-Transform Infrared Spectroscopy (SR-FTIR), which identified intermolecular and intramolecular interactions between the gelatin and carrageenan. The rheological measurements revealed that the incorporation of carrageenan influenced the elastic modulus (G’) and loss modulus (G’’) of the substituted GCW gel, as observed from the temperature sweep, which coincided with the higher gelling and melting temperatures (p<0.05). The substituted GCW gels had a higher turbidity than the GCW gel without substitution, as confirmed by a decreased L* value. The water activity of the GCW gel remained stable with the addition of carrageenan. Based on the gel microstructure, the GCW gel had a looser gel network with larger voids, whereas the substituted gels had a finer network with smaller voids. However, there was a decrease in the overall likeness score for the gels substituted with carrageenan, especially at higher levels. In summary, the incorporation of carrageenan into GCW gel significantly influenced its rheological and textural properties. At an optimal substitution level, the GCW gel with carrageenan had strong potential as a functional material for food applications, particularly in soft confectionery.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it