Micronutrient Fortification for Older Adults in Long-Term Care: Sensory Considerations
Bibliographic record
Abstract
Micronutrient fortification can improve nutrient intake in older adults residing in long-term care (LTC). However, previous studies indicate that micronutrient fortification can alter food sensory attributes. This thesis investigates a potential micronutrient fortification program for older adults in LTC, with a focus on food sensory properties. A micronutrient powder containing 9 vitamins and 3 minerals was sourced from Calico Ingredients (ON, Canada). Four foods were identified as potential carriers: tomato soup, mushroom soup, mashed potatoes and oatmeal. The micronutrient powder was added to the selected foods in varying amounts to produce samples with different fortification levels. Preparation methods used in LTC were also taken into consideration during the initial screening of these foods. Napping® with ultra-flash profiling (UFP) was first used as a screening tool to identify sensory differences present in each of the four selected foods. Descriptive Analysis was then completed for two foods – tomato soup and oatmeal. Sensory differences with fortification were identified in both foods. The acceptability and perception of these foods by younger (age 18-40, n=64) and older (age 65+, n=65) adults was completed using hedonic scales and check-all-that-apply (CATA) questions. Subgroups of participants with different patterns of liking were identified using agglomerative hierarchical cluster analysis (AHC). This was completed separately for each age group and for each of the two foods evaluated. In each case, three distinct consumer subgroups with different patterns of liking were identified. CATA and hedonic liking results were evaluated separately for each subgroup; differences in the way these subgroups described and rated food samples were identified. Finally, sensory acuity was evaluated in all participants to determine whether differences in taste and smell sensitivity existed between liking subgroups. Olfactory sensitivity was found to differ between older adult (OA) tomato soup subgroups. Mean olfactory score was lower for the subgroup that disliked fortified foods compared to the subgroup that experienced no change in liking. This research provides information about the sensory properties of selected fortified foods, and their acceptability to older adult consumers. The role of age and sensory acuity on liking of these foods was also evaluated.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".