5-Hydroxymethyl-2-furfural and Derivatives Formed during Acid Hydrolysis of Conjugated and Bound Phenolics in Plant Foods and the Effects on Phenolic Content and Antioxidant Capacity
Bibliographic record
Abstract
A common protocol for the extraction of phenolic aglycons or bound phenolics in plants generally involves hydrothermal hydrolysis in an aqueous methanol or ethanol solution containing 2-4 N HCl. However, as shown in the present study, this process also forms 5-(hydroxymethyl)furan-2-carbaldehyde (HMF) and its derivative products 5-(methoxymethyl)furan-2-carbaldehyde (MMF) and 5-(ethoxymethyl)furan-2-carbaldehyde (EMF), as identified by HPLC-DAD-ESI-MS/MS and NMR. These compounds are commonly misidentified as phenolics due to similar UV absorption at 280 nm. In this study, production of HMF, MMF, and EMF was shown to be dependent on the solvent condition and duration and temperature of hydrolysis. Fruits and vegetables produced HMF more readily than grains. HMF and its derivatives were subjected to various spectrophotometric antioxidant assays [2-diphenyl-1-picryhydrazyl radical scavenging activity (DPPH), ferric-reducing antioxidant power (FRAP), and oxygen radical absorbing capacity (ORAC)] and displayed antioxidant activity mainly in the ORAC assay. Results of this study help avoid overestimation of phenolic content and antioxidant activities of plant foods.
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 | 0.000 | 0.000 |
| 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 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".