Valorization of Heat-Treated Brewers' Spent Grain Through the Identification of Bioactive Phenolics by UPLC-PDA and Evaluation of Their Antioxidant Activities
Bibliographic record
Abstract
Thermal processing not only disrupts cell membranes and cell walls, but also cleaves covalent bonds releasing low molecular phenolic. This study examined the impact of various heat treatments (100, 140, and 160°C) on the composition of phenolic acids and antioxidant activities in extracts obtained from defatted brewers spent grain (BSG) meal. Heating BSG at 160°C resulted in a 2-fold increase in total phenolic content [TPC, 172.98 ± 7.3 mg Gallic acid equivalent (GAE)/100 g defatted meal] and total flavonoid content [TFC, 16.15 ± 2.22 catechin equivalents (CE)/100 g defatted meal] compared to the untreated BSG extracts. The antioxidant activities of treated BSG extracts, determined by radical scavenging and ferric reducing antioxidant power (FRAP) were significantly ( p < 0.5) higher than the corresponding untreated BSG extracts. Eleven phenolic acids were identified and quantified in BSG extracts by Ultra Performance Liquid Chromatography with Photodiode Array (UPLC-PDA). The amounts varied significantly ( p < 0.05) depending on the degree of toasting BSG was subjected to. Chlorogenic acid, an ester of caffeic and quinic acid was the predominant phenolic acid present in all fractions. Significant ( p < 0.05) increases in TPC, TFC, individual phenolic acids and antioxidant activity were observed in BSG extracts exposed to increasing oven temperatures. These results confirm the ability of heat processing to release bioactive phenolic from their bound forms thereby enhancing the phenolic acids and the digestibility of BSG meal in the intestinal tract.
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 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 | 0.000 | 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) | 0.000 | 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".