Sensory characterization of conifer-based extracts in a culinary use perspective
Bibliographic record
Abstract
Wild foods have increased in popularity among Quebec’s chefs and the general public. Tree products, such as conifer needles and buds, are abundant resources that are very characteristic of Quebec’s forests. They are underutilized in culinary applications despite their great flavour potential.<br/><br/>The purpose of this study was to characterize the sensory properties of conifer-based extracts for culinary purposes. The extracts’ sensory properties were characterized by chefs and student chefs. In addition, suggestions for their culinary value were collected in order to further encourage the use of conifer products in cooking.<br/><br/>Needles and buds from three conifer species (Balsam fir, White spruce, Black spruce) were prepared following three methods: aqueous (maceration [4°C-48h], decoction [100°C-1min]) or lipid (sous-vide [55°C-90min]) extraction methods. Projective mapping combined with ultra-flash profiling was performed. Respondents specifically focused on odours and aromas. Culinary arts teachers and advanced students (n=21) evaluated 14 samples. Standard projective mapping data analysis methods (lemmatizing, semantic clustering) were applied, using R-packages (SensoMineR, FactoMineR and Factoshiny) for statistical procedures.<br/><br/>The descriptors were grouped based on categories from existing sensory vocabularies, and systematic differences between samples as a function of extraction method, tree part and tree species were seen (See figure). Lipid extracts were described with roasted, milky, fresh herbs notes and saltiness, while aqueous extracts were woodsy, resinous, earthy, bitter and astringent. Needles extracts were sweet, acidic with fruity and confectionery/pastry notes, whereas buds extracts were herbaceous (aromatic herbs, dry herbs, leaves).<br/><br/>Black spruce buds maceration and Balsam fir needles maceration were the most frequently identified to have good culinary potential, with 57 and 47% of participants, respectively. Varied proposals for culinary uses were collected, ranging from incorporation in an emulsion or a sauce to accompany halibut or duck, to use in a sorbet or a cranberry cocktail.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.175 | 0.002 |
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; both teacher heads agree on what is shown here.
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".