An overview of regression methods in hyperspectral and multispectral imaging
Publikation: Bidrag til bog/antologi/rapport › Bidrag til bog/antologi › Forskning › fagfællebedømt
Pixel-wise and bulk-wise quantitation of compounds in surfaces of different nature using hyperspectral and multispectral images is of a major interest, especially in fields like food and pharmaceutical production. This chapter revises the most common linear methods together with a brief overview of nonlinear methods applied in the regression framework from a practical point of view. The main benefits and drawbacks are discussed focused on applications in food and pharmaceutical production. Moreover, precise guidelines are given to develop calibration/regression models.
Originalsprog | Engelsk |
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Titel | Hyperspectral Imaging |
Redaktører | José Manuel Amigo |
Antal sider | 26 |
Forlag | Elsevier |
Publikationsdato | 2020 |
Sider | 205-230 |
Kapitel | 2.8 |
ISBN (Trykt) | 978-0-444-63977-6 |
DOI | |
Status | Udgivet - 2020 |
Navn | Data Handling in Science and Technology |
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Vol/bind | 32 |
ISSN | 0922-3487 |
ID: 230849559