Impact of Genetics and Environment on Cranberry Fruit Metabolites
Bibliographic record
Abstract
BACKGROUND: Cranberry (Vaccinium macrocarpon Ait.) is a highly consumed fruit found in foods and supplements and grown throughout northern North America. Its tart flavor makes it a common food ingredient, rather than being directly consumed as fresh fruit. OBJECTIVE: Cranberry fruit samples of 15 genotypes (cultivars and accessions) grown in 16 locations in 4 states (MA, NJ, OR, and WI) and a Canadian province (British Columbia) were analyzed by mass spectrometry. Data were analyzed using chemometric methods to determine the correlation of composition with geographic location. METHOD: 214 cranberry samples were analyzed by nontargeted fuzzy chromatography-direct injection mass spectrometry. Data were collected for 206 ions and analyzed by multifactorial multivariate-analysis of variance-principal component analysis (MFMV-ANOVA-PCA). RESULTS: Sample composition varied statistically (P < 0.001) with respect to the major experimental factors (state/province, growing location, genotype, and analytical batch) and cross factors (genotype-state/province and genotype-growing location). PCA score plots verified a systematic variation with respect to 42 genotype-state/province pairs and 82 genotype-growing location pairs. PCA variable loadings identified major ions that varied with each of the major factors and cross factors and 56 ions were annotated. The location-ion count matrix was transposed and analyzed by hierarchical cluster analysis (HCA) producing dendrograms that grouped ions with respect to metabolic pathways for either the genotype-state/province or genotype-growing location pairs. Annotation of the ions in the hierarchical clusters allowed evaluation of the impact of genetics and location on compounds of interest. Ions expected to correlate with fruit quality measurements (brix, titratable acid, total anthocyanins, and total pro-anthocyanidins) were identified. CONCLUSIONS: This study demonstrates that mass spectral data coupled with chemometric analysis is a valuable tool for predicting the composition of specific genotypes for specific growing locations. HIGHLIGHTS: The general design of this study can be used as a model for other food plants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.001 | 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".