Deep calibration transfer : Transferring deep learning models between infrared spectroscopy instruments
| Autor: | Dário Passos, Puneet Mishra |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Standard-free
Artificial neural network Computer science business.industry Deep learning Model update Pattern recognition Condensed Matter Physics Convolutional neural network Transfer function Atomic and Molecular Physics and Optics Electronic Optical and Magnetic Materials Chemometrics symbols.namesake Fourier transform symbols Calibration Convolutional neural networks Artificial intelligence Post Harvest Technology Transfer of learning business Spectroscopy |
| Zdroj: | Infrared Physics and Technology 117 (2021) Infrared Physics and Technology, 117 Repositório Científico de Acesso Aberto de Portugal Repositório Científico de Acesso Aberto de Portugal (RCAAP) instacron:RCAAP |
| ISSN: | 1350-4495 |
| Popis: | 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. info:eu-repo/semantics/publishedVersion |
| Databáze: | OpenAIRE |
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