Sensory Analysis of New Varieties of Citrus as a Complementary Strategy to the Brazilian Citriculture
Bibliographic record
Abstract
In Brazil new varieties of citrus were selected along the years, but none sensory analysis is usually made to verify the acceptance as one of the bottleneck for fresh citrus juice industry and before the commercial release. We have evaluated the response of consumers (n=62) for eight new hybrids of the crossing between sweet orange and mandarin in five sensory attributes and used analysis of variance Tukey's procedure (HSD) and internal preference mapping for the data processing. The results were compared in relation to their standard physical-chemical characteristics and with commercial varieties: Murcott tangor (Citrus sinensis (L.) Osbeck x Citrus reticulata Blanco), Pera sweet orange (Citrus sinensis (L.) Osbeck, Cravo mandarin (Citrus reticulata Blanco). Hybrids TM x LP 222 and TC x LP 5 are candidates to become variety and TM x LP 94 was chosen for new sensory analysis. Flavor featured as the most important parameter for orange juice and some hybrids with adequate physical-chemical parameters presented low acceptance, while others with inadequate parameters showed good acceptability, what suggests a new way to fruit selection.
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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".