Analytical and biochemical aspects of wine constituents that affect human health
Bibliographic record
Abstract
I have developed and evaluated improved methods with excellent analytical characteristics permitting: (a) the simultaneous quantitation of 17 pesticides from the families of organochlorines, organophosphorus and carbamate compounds in wine by GC-MSD, which I employed to investigate the potential role of clarifying agents in the removal of pesticide residues during wine production and their effects upon wine quality, and to survey more than 3000 unfermented grape juices and raw wines for their pesticide residue content; (b) the simultaneous assay of nine biogenic amines by HPLC, using precolumn derivatization and fluorescence detection, which I utilized to study cultivar-related differences in wines from Ontario; (c) the routine assay of concentrations of OTA in wines and beers by GC-MSD and BPLC-PDA, used to survey the concentrations of OTA in >1000 wines and beers from around the globe; and (d) the simultaneous assay of TCA and TCP in wine and corks by GC-MSD, which I applied to elucidate the potential cause of cork taint from natural and artificial corks in wine, and to survey 2400 commercial wines from around the globe. Utilizing a previously-developed assay for the multiresidue analysis of polyphenolic compounds, I investigated the kinetics of polyphenol release into wine must during fermentation of different cultivars, to determine optimal conditions for polyphenol enrichment. I developed an ultrasensitive assay for the analysis of catechin, quercetin and trans-resveratrol and their conjugates in biological fluids utilizing GC-MSD and, along with the usage of trans-resveratrol radiolabeled with [ 3H], I studied the absorption of these polyphenols in rats and humans, demonstrating that they are present systemically almost exclusively as conjugates. The putative anticarcinogenic properties of the wine constituents catechin, quercetin, trans-resveratrol and caffeic acid were investigated. They showed poor ability to modulate p53 expression in three human breast cancer cell lines and one human colon cancer cell line. Employing a two stage CD-1 mouse skin cancer model using DMBA as initiator and TPA as promoter, I compared the anticarcinogenic activities of catechin, quercetin, trans-resveratrol and gallic acid; quercetin was the most effective. Finally, I demonstrated that a previously utilized rabbit model was misleading as a guide to the relative efficacy of different alcoholic beverages in preventing atherosclerosis, because of uncontrolled interference with nutrition.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".