Aktiviteter pr. år
Projektdetaljer
Beskrivelse
Potatoes are among the most important crops globally, and in spite of the modest protein content (1-3% w/w), around 200,000 tons of potato protein is produced globally each year. Due to the high abundance of specific proteins types in potatoes, they have a large unused potential for extraction of bioactive peptides for use as additives in food. Natural and plant-based bioactive peptide alternatives have the potential to replace chemical additives used presently. By using protein-based additives, it is possible to both extend the digestive durability of foods and improve the nutritional profile.
Recently, we identified a number of peptides derived from potato proteins with an exceptional potential as e.g. emulsifiers (two manuscripts attached). Although mass spectrometry (MS) can be used to quantify proteins in a complex mixture, the quantitative information on the peptide-level is sparse. Consequently, it is challenging to determine the content quantitatively and thereby qualitatively estimate the financial potential in extracting particularly bioactive peptides.
We aim to develop a simple and universal method to quantify peptides in a highly complex mixture without the need of expensive and tedious processing. The method will be used to evaluate the unused potential of bioactive food peptides in potato protein but will also be applicable for other protein rich side stream in the food industry (and much more).
Recently, we identified a number of peptides derived from potato proteins with an exceptional potential as e.g. emulsifiers (two manuscripts attached). Although mass spectrometry (MS) can be used to quantify proteins in a complex mixture, the quantitative information on the peptide-level is sparse. Consequently, it is challenging to determine the content quantitatively and thereby qualitatively estimate the financial potential in extracting particularly bioactive peptides.
We aim to develop a simple and universal method to quantify peptides in a highly complex mixture without the need of expensive and tedious processing. The method will be used to evaluate the unused potential of bioactive food peptides in potato protein but will also be applicable for other protein rich side stream in the food industry (and much more).
Akronym | Q-BIOPEP |
---|---|
Status | Igangværende |
Effektiv start/slut dato | 15/06/2021 → 14/06/2024 |
Samarbejdspartnere
- AKV Langholt (Projektpartner)
Finansiering
FN's verdensmål
I 2015 blev FN-landene enige om 17 verdensmål til at bekæmpe fattigdom, beskytte planeten og sikre velstand for alle. Dette projekt bidrager til følgende verdensmål:
Fingerprint
Udforsk forskningsemnerne, som dette projekt berører. Disse etiketter er oprettet på grundlag af de underliggende bevillinger/legater. Sammen danner de et unikt fingerprint.
Aktiviteter
- 1 Peer reviewer/fagfællebedømmer af manuskripter
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Food Hydrocolloids (Tidsskrift)
Simon Gregersen (Fagfællebedømmer) & Naim Abdul Khalek Gharzeddine (Fagfællebedømmer)
4 jan. 2022Aktivitet: Redaktionelt arbejde og fagfællebedømmelse › Peer reviewer/fagfællebedømmer af manuskripter › Forskning
Projekter
- 1 Afsluttet
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PROVIDE: Protein valorization through informatics, hydrolysis, and separation
Gregersen, S., Overgaard, M. T., Hansen, E. B., Bang-Berthelsen, I., Jacobsen, C., García Moreno, P. J., Marcatili, P. & Yesiltas, B.
01/09/2017 → 30/12/2022
Projekter: Projekt › Forskning
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Insight on Physicochemical Properties Governing Peptide MS1 Response in HPLC-ESI-MS/MS: A Deep Learning Approach
Abdul-Khalek, N., Wimmer, R., Overgaard, M. T. & Echers, S. G., 27 jul. 2023, I: Computational and Structural Biotechnology Journal. 21, s. 3715-3727 13 s.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review
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Insight on physicochemical properties governing peptide MS1 response in HPLC-ESI-MS/MS proteomics: A deep learning approach
Khalek, N. A., Wimmer, R., Overgaard, M. T. & Echers, S. G., 13 feb. 2023, bioRxiv, 32 s.Publikation: Working paper/Preprint › Preprint
Åben adgang -
Variance Analysis of LC-MS Experimental Factors and Their Impact on Machine Learning
Rehfeldt, T. G., Krawczyk, K., Echers, S. G., Marcatili, P., Palczynski, P., Röttger, R. & Schwämmle, V., 2 maj 2023, bioRxiv.Publikation: Andet › Andet bidrag › Forskning