dc.contributor.author | Kuchta, Miroslav | |
dc.contributor.author | Wubshet, Sileshi Gizachew | |
dc.contributor.author | Afseth, Nils Kristian | |
dc.contributor.author | Mardal, Kent-Andre | |
dc.contributor.author | Liland, Kristian Hovde | |
dc.date.accessioned | 2023-04-21T09:28:14Z | |
dc.date.available | 2023-04-21T09:28:14Z | |
dc.date.created | 2022-07-28T13:00:50Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Journal of Biophotonics. 2022, 1-18. | |
dc.identifier.issn | 1864-063X | |
dc.identifier.uri | https://hdl.handle.net/11250/3064212 | |
dc.description.abstract | In the process of converting food-processing by-products to value-addedingredients, fine grained control of the rawmaterials, enzymes and process conditionsensures the best possible yield and eco-nomic return. However, when raw mate-rial batches lack good characterization andcontain high batch variation, online or at-line monitoring of the enzymatic reac-tions would be beneficial. We investigate the potential of deep neural networks inpredicting the future state of enzymatic hydrolysis as described by Fourier-trans-form infrared spectra of the hydrolysates. Combined with predictions of averagemolecular weight, this provides a flexible and transparent tool for process moni-toring and control, enabling proactive adaption of process parameters. | |
dc.language.iso | eng | |
dc.subject | FTIR | |
dc.subject | FTIR | |
dc.subject | Process control | |
dc.subject | Process control | |
dc.subject | Enzymatic protein hydrolysis | |
dc.subject | Enzymatic protein hydrolysis | |
dc.subject | Deep learning | |
dc.subject | Deep learning | |
dc.subject | Encoder decoder | |
dc.subject | Encoder decoder | |
dc.title | Encoder–decoder neural networks for predicting future FTIR spectra – application to enzymatic protein hydrolysis | |
dc.title.alternative | Encoder–decoder neural networks for predicting future FTIR spectra – application to enzymatic protein hydrolysis | |
dc.type | Peer reviewed | |
dc.type | Journal article | |
dc.description.version | publishedVersion | |
dc.source.pagenumber | 1-18 | |
dc.source.journal | Journal of Biophotonics | |
dc.identifier.doi | 10.1002/jbio.202200097 | |
dc.identifier.cristin | 2039937 | |
dc.relation.project | Norges forskningsråd: 300305 | |
dc.relation.project | Norges forskningsråd: 309259 | |
dc.relation.project | Norges forskningsråd: 280709 | |
dc.relation.project | Norges forskningsråd: 303362 | |
dc.relation.project | Norges forskningsråd: 314111 | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |