dc.contributor.author | Monago-Maraña, Olga | |
dc.contributor.author | Wold, Jens Petter | |
dc.contributor.author | Rødbotten, Rune | |
dc.contributor.author | Dankel, Elin Katinka | |
dc.contributor.author | Afseth, Nils Kristian | |
dc.date.accessioned | 2021-01-27T14:18:42Z | |
dc.date.available | 2021-01-27T14:18:42Z | |
dc.date.created | 2021-01-14T13:11:34Z | |
dc.date.issued | 2021 | |
dc.identifier.issn | 2331-513X | |
dc.identifier.uri | https://hdl.handle.net/11250/2725018 | |
dc.description.abstract | Raman, near-infrared and fluorescence spectroscopy were evaluated for determination of collagen content in ground meat. Two sample sets were used (i.e. ground beef and ground poultry by-products), and collagen concentrations (measured as hydroxyproline) varied in the ranges 0.1–3.3% in the beef samples and 0.4–1.5% in the poultry samples. Similar validation results for hydroxyproline were obtained for NIRS (R2 = 0.82 and RMSECV = 0.11%) and Raman (R2 = 0.81 and RMSECV = 0.11%) for the poultry samples. For the beef samples, NIRS obtained slightly less accurate results (R2 = 0.89, RMSECV = 0.25%) compared to Raman (R2 = 0.94, RMSECV = 0.19%), most likely due to less representative sampling. Fluorescence spectroscopy gave higher prediction errors (RMSECV = 0.50% and 0.13% for beef and poultry, respectively). This shows that Raman spectroscopy employing a scanning approach for representative sampling is a potential tool for on-line determination of collagen in meat. | |
dc.language.iso | eng | |
dc.subject | Ground meat | |
dc.subject | Ground meat | |
dc.subject | Raman | |
dc.subject | Raman | |
dc.subject | NIR | |
dc.subject | NIR | |
dc.subject | Fluorescence | |
dc.subject | Fluorescence | |
dc.subject | Collagen | |
dc.subject | Collagen | |
dc.title | Raman, near-infrared and fluorescence spectroscopy for determination of collagen content in ground meat and poultry by-products. | |
dc.type | Journal article | |
dc.description.version | acceptedVersion | |
dc.source.volume | 140 | |
dc.source.journal | Food Science and Technology | |
dc.identifier.doi | 10.1016/j.lwt.2020.110592 | |
dc.identifier.cristin | 1871334 | |
dc.relation.project | Nofima AS: 201702 | |
dc.relation.project | Norges forskningsråd: 262308 | |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.qualitycode | 0 | |