An overview of regression methods in hyperspectral and multispectral imaging
Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review
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.
Original language | English |
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Title of host publication | Hyperspectral Imaging |
Editors | José Manuel Amigo |
Number of pages | 26 |
Publisher | Elsevier |
Publication date | 2020 |
Pages | 205-230 |
Chapter | 2.8 |
ISBN (Print) | 978-0-444-63977-6 |
DOIs | |
Publication status | Published - 2020 |
Series | Data Handling in Science and Technology |
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Volume | 32 |
ISSN | 0922-3487 |
- ANN, Food, MLR, PCR, Pharma, PLS, SVM, Validation
Research areas
ID: 230849559