Application of absorbent resins in protein extraction to reduce off-Flavours in flaxseed proteins
Bibliographic record
Abstract
This study investigated the use of adsorbent resins (Amberlite XAD-16 N, Sepabeads SP-207, and Diaion HP-20) during flaxseed protein extraction to reduce the volatile organic (VOCs) and non-volatile compounds (non-VOCs). These compounds could limit the application and consumption of flaxseed proteins by imparting off-aromas and flavours, including bitterness and astringency. The reduction of these compounds using resins during protein extraction could potentially enhance the sensory quality of flaxseed proteins, making them more suitable for incorporation into a wider range of food products such as plant-based meat alternatives, beverages, and protein supplements. Headspace solid phase microextraction- gas chromatography–mass spectrometry (HS-SPME-GC–MS) analysis was performed to examine VOCs in flaxseed meal, untreated flaxseed protein isolate (FPI- control), and resin-treated protein isolates. The flaxseed meal was predominant in hydrocarbons. After extraction, the flavour profile of FPI exhibited aldehydes, ketones, alcohols, and hydrocarbons. Resin treatment indicated that Amberlite XAD-16 N and Diaion HP-20 were more effective at reducing total peak areas for hydrocarbons, aldehydes, alcohols, and acids/esters than Sepabeads SP-207 resin. Analysis of non-VOC using proton nuclear magnetic resonance ( 1 H NMR) showed that resin treatments decreased the sinapine content twelve times compared to the control and thirty-eight times compared to the flaxseed meal. The phenolic reductions were resin-dependent: Amberlite XAD-16 N achieved the highest reduction for gallic acid, ferulic acid reduction was similar across all resins, while chlorogenic acid reduction was approximately 50 % for all resins used. Additionally, the levels of cyanogenic glucosides were significantly reduced by resin treatment. The functional properties of the resin-treated protein isolates were comparable to the control. Overall, the combination of protein extraction with resin treatment was more effective than the extraction alone at reducing both VOCs and non-VOCs, supporting the use of resins to mitigate flaxseed off-flavours. • The use of adsorbent resins during protein extraction reduced the off-flavour compounds in flaxseed during protein extraction. • Amberlite XAD-16 N and Diaion HP-20 resins were the most effective at lowering VOCs. • Phenolics were reduced significantly by resin treatment. • Protein functionality remained unchanged after resin treatment.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".