Meta‐analysis of apple (<i>Malus</i> × <i>domestica</i> Borkh.) fruit and juice quality traits for potential use in hard cider production
Bibliographic record
Abstract
Societal Impact Statement The production of hard cider is a burgeoning industry in North America. However, traditional European cider‐specific apples are largely unsuitable for production in modern systems and have not been widely adopted. Evaluation of the biochemical variability in apples can provide a better characterization of varieties for use in hard cider production. Many varieties exhibited extensive variability in biochemical traits. Varieties with high phenolics and acidity displayed greater plasticity of these traits between seasons, which supports the concept of “vintage quality” and opens new marketing opportunities. This study provides valuable insight for apple producers to integrate cider‐specific and nonspecific varieties into their production systems. Summary The North American fermented beverage market has undergone extensive growth in the fermented apple juice (hard cider) sector over the last decade. Traditional hard cider producing regions such as England, France, and Spain have historically utilized cider‐specific apples that have desirable balances between sugar, organic acid, and phenolics. However, in North America, culinary apples (lower acidity and phenolics) are predominantly used, and information distinguishing the varieties with novel use for hard cider production is lacking. In this study, we evaluated variation of four major apple biochemical traits (total soluble solids, pH, titratable acidity, and total phenolics) as they relate to use for hard cider production by using a meta‐analysis approach. We analyzed the variation in cider quality traits across 47 published works and more than 800 unique apple varieties, including an additional analysis of 142 varieties from cider apple germplasms in the United States. The biochemical traits of many varieties exhibited plasticity, and the characterization of a subset of varieties using two cider classification system revealed that most belonged to the “sweet” or “sharp” (acidic) categories rather than “bitter” (high phenolic content). We also compared the classification of a subset of varieties from two US germplasm resources across two seasons and found varieties that have high percent phenolics and/or malic acid experienced greater biochemical plasticity and potential classification change across vintages. Ultimately, this work provides information regarding the variability in apple biochemical traits and also suggests a unique opportunity for producers to market “vintage” quality.
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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) | 0.001 | 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) | 0.001 | 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 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".