Reformulation of Biscuit and Oatcake Products With Nutritional and Environmental Benefits Using a Novel Palm Shortening Substitute
Bibliographic record
Abstract
ABSTRACT This paper assessed the feasibility of replacing palm shortening with a novel sustainable ingredient composed of rapeseed oil, linseed meal, and beta‐glucan (PALM‐ALT) in hard‐texture bakery products (biscuit and oatcake). There is currently no palm shortening alternative that is functional, sustainable, nutritionally‐balanced, and competitive. The PALM‐ALT ingredient was characterized by oil droplet size distribution, rheology, confocal microscopy, and scanning‐electron microscopy, whilst the biscuits and oatcakes were profiled by sensory, texturometry, colorimetry, water activity, and moisture analyses. The PALM‐ALT ingredient exhibited a stable emulsion‐gel structure (32 µm oil droplet size, 62 Pa.s viscosity, 40 Pa yield stress). Linseed proteins contributed to the formation and stabilization of oil droplets, whilst linseed mucilage, oat beta‐glucan, and aggregates of linseed proteins and/or beta‐glucan (observed in the continuous phase) further stabilized the emulsion. Three formulations were prepared with either palm shortening, rapeseed oil, or PALM‐ALT. PALM‐ALT biscuits and oatcakes respectively showed an 86% and 75% saturated fat reduction in comparison with their palm‐based control product. PALM‐ALT products displayed similar sensory and instrumental profiles to their palm‐based controls, whereas rapeseed oil formulations exhibited significantly different color, odor, and texture profiles than the controls ( p < 0.05). Rapeseed oil biscuits showed a lower overall sensory quality than their palm‐based control, whilst PALM‐ALT oatcakes displayed a higher acceptability than the control and rapeseed oil products ( p < 0.05). This study showed that PALM‐ALT was able to replace palm shortening in hard‐texture bakery formulations with maintenance of their sensory profiles, whilst providing nutritional and sustainability benefits.
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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".