Assessing in vitro methodologies for the determination of protein digestibility, amino acid digestibility, and protein quality
Bibliographic record
Abstract
This study examined the potential of two in vitro static digestion models, namely the pH-drop and INFOGEST 2.0, to determine in vitro protein digestibility and amino acid digestibility in assessing protein quality. The pH-drop model directly measured in vitro protein digestibility for the subsequent calculation of the in vitro Protein Digestibility Corrected Amino Acid Score (IV-PDCAAS) value. However, the INFOGEST model digestion products were analyzed by three methods: i) OPA derivatization, ii) total nitrogen via Kjeldahl, and iii) individual amino acid analysis to determine in vitro protein digestibility and IV-PDCAAS. The latter analysis was additionally used to assess in vitro amino acid digestibility, subsequently used to calculate in vitro Digestible Indispensable Amino Acid Score (IV-DIAAS). Among the four assessment methods, the OPA assay and total amino acid analysis from the INFOGEST digestion products demonstrated closer associations with in vivo PDCAAS compared to the pH-drop model and the Kjeldahl analysis. However, the pH-drop model, with a straightforward and simple approach, exhibited better repeatability across measurements. Using three popular assays, the PDCAAS, the DIAAS, and the Protein Efficiency Ratio (PER), to evaluate protein quality and permitted protein content claims of samples, it was observed that there was no difference in terms of protein content claims determined when using the in vivo or four in vitro methodologies. The results indicated that the PDCAAS generally offered higher protein permitted claims than the DIAAS and the PER methods and their respective protein content claims, highlighting that the selection of assessment methods impacts the limiting amino acid and protein quality and extends to subsequent claims for the sample. In conclusion, the pH-drop model is straightforward and highly repeatable; however, the INFOGEST model showed a closer correlation with in vivo data. Additionally, the in vitro static INFOGEST digesta proved versatile in evaluating various aspects of protein quality in both in vitro protein and amino acid digestibility through amino acid analysis. Both models can be valuable screening tools for protein quality assessment. Further development of these in vitro methodologies can offer effective and sustainable non-animal testing alternatives for protein quality assessment and protein content claims on product packaging.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".