Effects of Feed Moisture Content on the Physical and Nutritional Quality Attributes of Sunflower Meal-based High-Moisture Meat Analogues
Bibliographic record
Abstract
Abstract Adding value to food industry by-products, like sunflower meal (SFM), through their utilization as ingredients in new food products can improve sustainability of food systems. This research investigated extrusion cooking to produce high-moisture meat analogues (HMMAs) made from blends of soy protein isolate and expeller-pressed SFM. The effects of feed moisture content [FMC] (60, 65, and 70%, wet basis) and SFM concentration (37.5, 50, and 62.5%, total blend weight basis) on physical and protein nutritional quality attributes of HMMAs were investigated. The processing temperatures (including cooling die), screw speed and feed rate were kept constant at 60-80-115-125-50-25 °C (from feeder to the die end), 200 rpm and 0.5 kg/h (dry basis), respectively. An increase in SFM concentration and FMC significantly (p < 0.05) reduced the mechanical energy requirements for extrusion. Cutting strength and texture profile analysis of HMMAs indicated softer texture with increases in SFM and FMC. X-ray microcomputed tomography analysis revealed that the microstructure of the HMMAs at the centre and towards the surface was different and affected by SFM concentration and FMC. The in vitro–protein digestibility corrected amino acid score of the HMMAs ranged between 85 and 91% and did not show significant (p < 0.05) changes as a function of FMC or SFM concentration. HMMAs produced from 37.5% SFM at 70% FMC showed no deficiency in essential amino acids for all age categories except for infants, suggesting the high potential of SFM and soy protein blends for creating nutritious meat alternative products. Overall, this work provided valuable insights regarding the effects of soy protein replacement by SFM on the textural, microstructural and nutritional quality of HMMA applications, paving the way for value-addition to this underutilized food industry by-product.
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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".