NMR-Based Quantification of Collagen Content in Protein Hydrolysates

Certain functional food and dietary supplement ingredients contain bioactive peptides whose bioactivity is determined by their chain length, composition, and specific sequence of the amino acids. This study investigated collagenous peptides from the valorization of poultry side streams, which, for example, have inhibitory activities against therapeutic targets for the management of blood pressure and blood glucose. To overcome the specificity challenges of conventional amino acid analysis, the aim was to establish and validate a quantitative <sup>1</sup>H NMR (qHNMR) method for the quantification of hydroxyproline as a marker of collagen content in poultry hydrolysates. Indeed, qHNMR provides a rapid structure-specific quantification, and it is highly informative, reproducible, and accurate. The effect of different acidic solvents was investigated using quantum mechanics (QM)-based <sup>1</sup>H iterative functionalized spin analysis (HifSA). Comparison of conventional integration (int-) with QM-qHNMR revealed limitations of the former approach and highlighted the increased accuracy and specificity of QM-qHNMR.

Authors

Institutions

Publication Details

Journal
Journal of Agricultural and Food Chemistry
Published
2026-02-09
DOI
https://doi.org/10.1021/acs.jafc.5c13447
Primary Topic
Protein Hydrolysis and Bioactive Peptides
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

NMR-Based Quantification of Collagen Content in Protein Hydrolysates

Kristian Hovde Liland, Nils Kristian Afseth, Sileshi Gizachew Wubshet, Guido F. Pauli et al.
Journal of Agricultural and Food Chemistry
Protein Hydrolysis and Bioactive Peptides
article

NMR-Based Quantification of Collagen Content in Protein Hydrolysates

Kristian Hovde Liland, Nils Kristian Afseth, Sileshi Gizachew Wubshet, Guido F. Pauli, Kenneth Aase Kristoffersen, Wilkins Al, Greta Nardini, Frode Rise, Matthias Niemitz
article en

Abstract

Certain functional food and dietary supplement ingredients contain bioactive peptides whose bioactivity is determined by their chain length, composition, and specific sequence of the amino acids. This study investigated collagenous peptides from the valorization of poultry side streams, which, for example, have inhibitory activities against therapeutic targets for the management of blood pressure and blood glucose. To overcome the specificity challenges of conventional amino acid analysis, the aim was to establish and validate a quantitative <sup>1</sup>H NMR (qHNMR) method for the quantification of hydroxyproline as a marker of collagen content in poultry hydrolysates. Indeed, qHNMR provides a rapid structure-specific quantification, and it is highly informative, reproducible, and accurate. The effect of different acidic solvents was investigated using quantum mechanics (QM)-based <sup>1</sup>H iterative functionalized spin analysis (HifSA). Comparison of conventional integration (int-) with QM-qHNMR revealed limitations of the former approach and highlighted the increased accuracy and specificity of QM-qHNMR.

Journal of Agricultural and Food ChemistryVol. 74(6)
University of Oslo (NO), Albany College of Pharmacy and Health Sciences (US), University of Illinois Chicago (US), Division of Chemistry (US), Nofima (NO), Institute of Fisheries and Aquaculture (BG), Materials Solutions (United Kingdom) (GB), University of Waikato (NZ), Norwegian University of Life Sciences (NO)
Zero hunger
Openalex Percentile: Top 63%
Protein Hydrolysis and Bioactive Peptides
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.