FUTURE FOODS DEVELOPMENT: EFFECT OF TEMPERATURE, FEEDMOISTURE AND COCONUT ADDITION ON THE PHYSICOCHEMICALPROPERTIES OF EXTRUDED CORN SNACKS
Bibliographic record
Abstract
The study investigated the effect of temperature, feed moisture and coconut addition on the physicochemical properties of extruded corn snacks.The samples processed and data collected analyzed using standard methods.The results findings revealed that there were significant variations (P<0.05) in the proximate composition of the extruded corn-coconut snack samples.The ash, moisture, fire and fat contents ranged from 3.54-3.82%,4.94-5.60%,2.87-3.15%and 8.90-9.15%,respectively.The protein content of the extruded snack samples varied from 9.93 to 12.31%, while the carbohydrate content ranged from 66.36 to 69.40%.Carbohydrate content was highest in FGH (69.40%) and lowest in BCO (66.36%).The moderate fat content can aid in the absorption of fat-soluble vitamins (ADEK).Significant differences (p<0.05) were also observed in some of the functional properties of the extruded corn-coconut snacks with the emulsion stability ranging from 10.00 to 11.33%, while emulsion activity ranged from 9.14 to 9.91%.The oil absorption capacity, bulk density and foaming capacity ranged from 1.93-2.98%,0.57-0.76%and 11.17-15.60%,respectively.Oil absorption capacity varied significantly, with NPO and BCO samples exhibiting the highest values (2.98 g/g), reflecting their potential for flavor retention and mouthfeel enhancement.Bulk density ranged from 0.57 to 0.76 g/cm³, where higher values (e.g., QRW and YXZ) imply denser products, potentially influencing packaging and textural properties.Foaming capacity varied between 11.17% and 15.60%, with NPO demonstrating the most pronounced ability to entrap air, which is crucial for snack lightness and volume.The colour and physical properties of the extruded snacks also varied significantly (p<0.05)among the samples.The results of the sensory evaluation revealed that the sample VST was ranked the highest in terms of overall acceptability, while sample SOA was ranked least.
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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 | 0.001 |
| 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) | 0.002 | 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".