Publication
Deep calibration transfer: transferring deep learning models between infrared spectroscopy instruments
| dc.contributor.author | Mishra, Puneet | |
| dc.contributor.author | Passos, Dário | |
| dc.date.accessioned | 2021-09-14T11:47:07Z | |
| dc.date.available | 2021-09-14T11:47:07Z | |
| dc.date.issued | 2021-09 | |
| dc.description.abstract | Calibration transfer (CT) is required when a model developed on one instrument needs to be transferred and used on a new instrument. Several methods are available in the chemometrics domain to transfer the multivariate calibrations developed using modelling techniques such as partial least-square regression. However, recently deep learning (DL) models are gaining popularity to model spectral data. The traditional multivariate CT methods are not suitable to transfer a deep learning model which is based on neural networks architectures. Hence, this study presents the concept of deep calibration transfer (CT) for transferring a DL model made on one instrument onto a new instrument. The deep CT is based on the concept of transfer learning from the DL domain. To show it, two different CT cases are presented. The first case is the CT between benchtop FT-NIR (Fourier Transform Near Infrared) instruments, and the second case is the CT between handheld NIR (Near Infrared) instruments. In both the demonstrated cases, the transfer was performed standard-free i.e., no common standard samples were used to estimate any transfer function. The results showed that with deep CT, the DL models made on one instrument can be easily adapted and transferred to a new instrument. The main benefit of the deep CT is that it is a standard free approach and does not require any standard sample measurements. Such a standard free approach to transfer DL models between instruments can support a widespread sharing of chemometric DL models between the scientific practitioners. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.doi | 10.1016/j.infrared.2021.103863 | pt_PT |
| dc.identifier.eissn | 1879-0275 | |
| dc.identifier.uri | http://hdl.handle.net/10400.1/17105 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | Elsevier | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Standard-free | pt_PT |
| dc.subject | Spectroscopy | pt_PT |
| dc.subject | Model update | pt_PT |
| dc.subject | Convolutional neural networks | pt_PT |
| dc.title | Deep calibration transfer: transferring deep learning models between infrared spectroscopy instruments | pt_PT |
| dc.title.alternative | Transferência profunda de calibração: Transferência de modelos de aprendizagem profunda entre instrumentos de espectroscopia infravermelha | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.startPage | 103863 | pt_PT |
| oaire.citation.title | Infrared Physics & Technology | pt_PT |
| oaire.citation.volume | 117 | pt_PT |
| person.familyName | Passos | |
| person.givenName | Dário | |
| person.identifier | 324764 | |
| person.identifier.ciencia-id | 3D13-C289-0595 | |
| person.identifier.orcid | 0000-0002-5345-5119 | |
| person.identifier.scopus-author-id | 21743737200 | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | article | pt_PT |
| relation.isAuthorOfPublication | 30c8500a-c12b-47c3-845e-9b64957cf233 | |
| relation.isAuthorOfPublication.latestForDiscovery | 30c8500a-c12b-47c3-845e-9b64957cf233 |
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