Grape Berry Secondary Metabolites and Their Modulation by Abiotic Factors in a Climate Change Scenario–A Review
Bibliographic record
Abstract
Temperature, water, solar radiation, and atmospheric CO 2 concentration are the main abiotic factors that are changing in the course of global warming. These abiotic factors govern the synthesis and degradation of primary (sugars, amino acids, organic acids, etc.) and secondary (phenolic and volatile flavor compounds and their precursors) metabolites directly, via the regulation of their biosynthetic pathways, or indirectly, via their effects on vine physiology and phenology. Several hundred secondary metabolites have been identified in the grape berry. Their biosynthesis and degradation have been characterized and have been shown to occur during different developmental stages of the berry. The understanding of how the different abiotic factors modulate secondary metabolism and thus berry quality is of crucial importance for breeders and growers to develop plant material and viticultural practices to maintain high-quality fruit and wine production in the context of global warming. Here, we review the main secondary metabolites of the grape berry, their biosynthesis, and how their accumulation and degradation is influenced by abiotic factors. The first part of the review provides an update on structure, biosynthesis, and degradation of phenolic compounds (flavonoids and non-flavonoids) and major aroma compounds (terpenes, thiols, methoxypyrazines, and C13 norisoprenoids). The second part gives an update on the influence of abiotic factors, such as water availability, temperature, radiation, and CO 2 concentration, on berry secondary metabolism. At the end of the paper, we raise some critical questions regarding intracluster berry heterogeneity and dilution effects and how the sampling strategy can impact the outcome of studies on the grapevine berry response to abiotic factors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".