Genomic insights into apple aroma diversity
Bibliographic record
Abstract
An apple's aroma is a major determinant of its desirability by consumers. To better understand the aroma of apples, 2-dimensional gas-chromatography mass-spectrometry (2D-GCMS) was used to quantify 106 volatile organic compounds (VOCs) from 515 apple varieties. We identified esters and aldehydes as the most abundant classes of VOCs, with butyl acetate and hexyl acetate being present in nearly every variety. Principal component analysis (PCA) revealed that the primary axis of variation in the apple volatilome is correlated with harvest date, with early-harvested apples expressing a greater number and higher concentration of VOCs compared to late-harvested apples. Genome-wide association studies (GWAS) using 250,579 single nucleotide polymorphisms (SNPs) identified a significant association between SNPs near the alcohol acyltransferase (AAT1) locus and the abundance of several esters. Additionally, strong associations were observed between SNPs at the NAC18.1 transcription factor locus and the abundances of 1-hexanol and 1-butanol, which serve as precursors for hexyl acetate and butyl acetate, respectively. These findings provide a foundation for understanding the genetic basis of apple aroma production and pave the way for the genomics-assisted enhancement of the aroma profiles of apple varieties to meet consumer preferences.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.004 |
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 teacher head, 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".