An overview of regression methods in hyperspectral and multispectral imaging

Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review

Standard

An overview of regression methods in hyperspectral and multispectral imaging. / Torres, Irina; Amigo, José Manuel.

Hyperspectral Imaging. ed. / José Manuel Amigo. Elsevier, 2020. p. 205-230 (Data Handling in Science and Technology, Vol. 32).

Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review

Harvard

Torres, I & Amigo, JM 2020, An overview of regression methods in hyperspectral and multispectral imaging. in JM Amigo (ed.), Hyperspectral Imaging. Elsevier, Data Handling in Science and Technology, vol. 32, pp. 205-230. https://doi.org/10.1016/B978-0-444-63977-6.00010-9

APA

Torres, I., & Amigo, J. M. (2020). An overview of regression methods in hyperspectral and multispectral imaging. In J. M. Amigo (Ed.), Hyperspectral Imaging (pp. 205-230). Elsevier. Data Handling in Science and Technology Vol. 32 https://doi.org/10.1016/B978-0-444-63977-6.00010-9

Vancouver

Torres I, Amigo JM. An overview of regression methods in hyperspectral and multispectral imaging. In Amigo JM, editor, Hyperspectral Imaging. Elsevier. 2020. p. 205-230. (Data Handling in Science and Technology, Vol. 32). https://doi.org/10.1016/B978-0-444-63977-6.00010-9

Author

Torres, Irina ; Amigo, José Manuel. / An overview of regression methods in hyperspectral and multispectral imaging. Hyperspectral Imaging. editor / José Manuel Amigo. Elsevier, 2020. pp. 205-230 (Data Handling in Science and Technology, Vol. 32).

Bibtex

@inbook{0918261a6e6d49beaebf77b55b58a882,
title = "An overview of regression methods in hyperspectral and multispectral imaging",
abstract = "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.",
keywords = "ANN, Food, MLR, PCR, Pharma, PLS, SVM, Validation",
author = "Irina Torres and Amigo, {Jos{\'e} Manuel}",
year = "2020",
doi = "10.1016/B978-0-444-63977-6.00010-9",
language = "English",
isbn = "978-0-444-63977-6",
series = "Data Handling in Science and Technology",
publisher = "Elsevier",
pages = "205--230",
editor = "Amigo, {Jos{\'e} Manuel}",
booktitle = "Hyperspectral Imaging",
address = "Netherlands",

}

RIS

TY - CHAP

T1 - An overview of regression methods in hyperspectral and multispectral imaging

AU - Torres, Irina

AU - Amigo, José Manuel

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - ANN

KW - Food

KW - MLR

KW - PCR

KW - Pharma

KW - PLS

KW - SVM

KW - Validation

U2 - 10.1016/B978-0-444-63977-6.00010-9

DO - 10.1016/B978-0-444-63977-6.00010-9

M3 - Book chapter

AN - SCOPUS:85072664042

SN - 978-0-444-63977-6

T3 - Data Handling in Science and Technology

SP - 205

EP - 230

BT - Hyperspectral Imaging

A2 - Amigo, José Manuel

PB - Elsevier

ER -

ID: 230849559