What is he best known for?
The fields of study he is best known for:
The scientist’s investigation covers issues in Food science, Digestion, Casein, Enzyme and Whey protein.
His Food science research is multidisciplinary, incorporating perspectives in Lactic acid, Bacteria, Enterococcus hirae and Biochemistry.
His study in the field of Intestinal digestion and In vitro digestion is also linked to topics like Gastric lipase.
He combines subjects such as Hydrolyzed protein and Food research with his study of In vitro digestion.
His Casein research includes themes of Dairy cattle, Lactose and Raw milk.
His research integrates issues of Isoelectric focusing, Polyacrylamide gel electrophoresis and Proteolytic enzymes in his study of Raw milk.
His most cited work include:
- A standardised static in vitro digestion method suitable for food – an international consensus (1806 citations)
- INFOGEST static in vitro simulation of gastrointestinal food digestion (294 citations)
- Mineral-binding milk proteins and peptides; occurrence, biochemical and technological characteristics (139 citations)
What are the main themes of his work throughout his whole career to date?
Food science, Digestion, Biochemistry, Enzyme and Casein are his primary areas of study.
Gerd E. Vegarud combines Food science and Composition in his research.
His Digestion research is multidisciplinary, relying on both Ex vivo, Pepsin and Lipid digestion.
In his research, Lactoferrin is intimately related to In vivo, which falls under the overarching field of Lipid digestion.
His work investigates the relationship between Enzyme and topics such as In vitro that intersect with problems in Immune system.
In his research, Gerd E. Vegarud undertakes multidisciplinary study on Gastric lipase and Intestinal digestion.
He most often published in these fields:
- Food science (42.72%)
- Digestion (38.83%)
- Biochemistry (37.86%)
What were the highlights of his more recent work (between 2014-2021)?
- Digestion (38.83%)
- Biochemistry (37.86%)
- Ex vivo (14.56%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Digestion, Biochemistry, Ex vivo, Food science and Peptide.
His Digestion research integrates issues from Hydrolysis, Casein and Proteolysis.
His study in the field of Hydrolysate, Pepsin, Hydrolyzed protein and In vitro study also crosses realms of Angiotensin I converting enzyme.
His work carried out in the field of Ex vivo brings together such families of science as T cell and Gastrointestinal digestion.
In general Food science study, his work on Rennet and Ripening often relates to the realm of Norwegian goat, Regulation of gastric function and Composition, thereby connecting several areas of interest.
His work deals with themes such as Amino acid, Molecular biology, In vitro and Enzyme, which intersect with Peptide.
Between 2014 and 2021, his most popular works were:
- INFOGEST static in vitro simulation of gastrointestinal food digestion (294 citations)
- The harmonized INFOGEST in vitro digestion method: From knowledge to action (103 citations)
- Whey proteins and their antimicrobial properties in donkey milk: a brief review (38 citations)
In his most recent research, the most cited papers focused on:
His primary scientific interests are in Digestion, Biochemistry, Ex vivo, Food science and Antioxidant.
When carried out as part of a general Digestion research project, his work on Intestinal digestion is frequently linked to work in Lipolysis, therefore connecting diverse disciplines of study.
Gerd E. Vegarud focuses mostly in the field of Ex vivo, narrowing it down to matters related to Peptide and, in some cases, Proline, Genetic variation, Proteolysis and Molecular biology.
Gerd E. Vegarud performs multidisciplinary study in Food science and Gastric lipase in his work.
His study in the fields of DPPH and ABTS under the domain of Antioxidant overlaps with other disciplines such as Carp.
The Hydrolyzed protein study combines topics in areas such as In vitro digestion and Food research.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
