Effects of protein-lipid interactions on physiochemical and functional properties of food proteins
Bibliographic record
Abstract
Protein-lipid complexes are known to result from complex molecular interactions which contribute to physiochemical and functional properties of foods. To identify the interactions of food proteins with lipids and the associated changes in properties, the following factors were investigated: incubation temperature, pH, type of lipids (phospholipid, triglyceride and fatty acids) and different proteins (ovalbumin and soybean glycinin). The effects of lipids on physiochemical and functional characteristics of ovalbumin and glycinin were investigated using polyacrylamide gel electrophoresis (PAGE), fluorescence, differential scanning calorimetry (DSC), Fourier transform infrared (FTIR) spectroscopy, and emulsification and gelation properties. The results showed that pH, temperature and the type of lipids affected both ovalbumin-lipid interactions and physiochemical properties of ovalbumin. Changes in the electrophoretic behavior of ovalbumin were related to the presence of lipids, and the relative fluorescence of ovalbumin decreased in the presence of lipids at different pH values. In addition, lipids increased the stability of ovalbumin as revealed by the thermal denaturation (Td) and by the enthalpy transition (DeltaH). FTIR spectra in the amide I absorption region revealed that lipids affected the secondary structure of ovalbumin. Changes in the integrated intensity of the amide II band between (1520--1555) cm -1 in the presence of D2O showed that H-D exchange of ovalbumin decreased in the presence of lipids. Emulsifying properties, gel strength and water holding capacity (WHC) of ovalbumin increased significantly (P < 0.05) in the presence of lipids. Scanning electron microscopy (SEM) showed difference in the microstructure of ovalbumin gel in the presence of different lipids. The more pronounced effect of lipids was observed with lecithin and the lowest was with stearic acid. The order of magnitude for the effects of lipids on physiochemical and functional properties of ovalbumin was: lecithin > cocoa butter > oleic acid > linoleic acid > linolenic acid > stearic acid. It is likely that the degree of polarity of lipids play an important role in protein-lipid interactions and in the enhancement of the functional properties of ovalbumin. The effects of soybean oil (SO), soybean lecithin (SL) and a mixture of both (SOL) on physiochemical and functional properties of soybean glycinin were studied at pH 8.0, with incubation at 40°C. Changes in the electrophoretic behavior were related to the presence of lipids. Relative fluorescence of glycinin decreased in the presence of lipids. Lipids increased the thermal stability (Td) of glycinin from 89.7°C to 92.0°C, 94.3°C and 93.4°C with SO, SL and SOL respectively. FTIR spectra indicated changes in both; the secondary structure and H-D exchange of glycinin in the presence of lipids. Gel strength, WHC and emulsifying properties of glycinin increased significantly (P < 0.05) in the presence of lipids; the order of consequence increase was: SL > SOL > SO. SEM showed difference in the microstructure of glycinin gels with the different lipids used. Overall, the results demonstrate both quantitative and qualitative effects on the physiochemical and functional properties of ovalbumin and glycinin as a result of protein-lipid interactions.
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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 | 0.001 |
| 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".