Construction of an online reduced-spectrum NIR calibration model from full-spectrum data

Simon A. Dodds; William P. Heath

doi:10.1016/j.chemolab.2004.09.002

Construction of an online reduced-spectrum NIR calibration model from full-spectrum data

Research output: Contribution to journal › Article › peer-review

Abstract

Near infrared (NIR) spectroscopy offers rapid and nondestructive estimation to a wide range of industries, but its acceptance has been slowed by the high costs of long-term use of full-spectrum instrumentation. From examining the terms produced in multivariate calibration of this full-spectrum data, it is possible to identify influential wavelengths, using either the regression vector b or a series of estimation prognostic vectors c, which is proposed in this paper. Once these wavelengths have been identified, the full-spectrum probe can be replaced with a series of monochromators, which is more commercially viable. In this paper, online NIR absorbance data from a pilot scale food extruder is used to estimate downstream product quality attributes (PQAs) via a full-spectrum calibration model followed by a reduced-spectrum model. © 2004 Elsevier B.V. All rights reserved.

Original language	English
Pages (from-to)	37-43
Number of pages	6
Journal	Chemometrics and Intelligent Laboratory Systems
Volume	76
Issue number	1
DOIs	https://doi.org/10.1016/j.chemolab.2004.09.002
Publication status	Published - 28 Mar 2005

Keywords

NIR spectroscopy
PCR
Prognostic vectors
Soft sensors

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10.1016/j.chemolab.2004.09.002

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title = "Construction of an online reduced-spectrum NIR calibration model from full-spectrum data",

abstract = "Near infrared (NIR) spectroscopy offers rapid and nondestructive estimation to a wide range of industries, but its acceptance has been slowed by the high costs of long-term use of full-spectrum instrumentation. From examining the terms produced in multivariate calibration of this full-spectrum data, it is possible to identify influential wavelengths, using either the regression vector b or a series of estimation prognostic vectors c, which is proposed in this paper. Once these wavelengths have been identified, the full-spectrum probe can be replaced with a series of monochromators, which is more commercially viable. In this paper, online NIR absorbance data from a pilot scale food extruder is used to estimate downstream product quality attributes (PQAs) via a full-spectrum calibration model followed by a reduced-spectrum model. {\textcopyright} 2004 Elsevier B.V. All rights reserved.",

keywords = "NIR spectroscopy, PCR, Prognostic vectors, Soft sensors",

author = "Dodds, {Simon A.} and Heath, {William P.}",

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doi = "10.1016/j.chemolab.2004.09.002",

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AU - Dodds, Simon A.

AU - Heath, William P.

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N2 - Near infrared (NIR) spectroscopy offers rapid and nondestructive estimation to a wide range of industries, but its acceptance has been slowed by the high costs of long-term use of full-spectrum instrumentation. From examining the terms produced in multivariate calibration of this full-spectrum data, it is possible to identify influential wavelengths, using either the regression vector b or a series of estimation prognostic vectors c, which is proposed in this paper. Once these wavelengths have been identified, the full-spectrum probe can be replaced with a series of monochromators, which is more commercially viable. In this paper, online NIR absorbance data from a pilot scale food extruder is used to estimate downstream product quality attributes (PQAs) via a full-spectrum calibration model followed by a reduced-spectrum model. © 2004 Elsevier B.V. All rights reserved.

AB - Near infrared (NIR) spectroscopy offers rapid and nondestructive estimation to a wide range of industries, but its acceptance has been slowed by the high costs of long-term use of full-spectrum instrumentation. From examining the terms produced in multivariate calibration of this full-spectrum data, it is possible to identify influential wavelengths, using either the regression vector b or a series of estimation prognostic vectors c, which is proposed in this paper. Once these wavelengths have been identified, the full-spectrum probe can be replaced with a series of monochromators, which is more commercially viable. In this paper, online NIR absorbance data from a pilot scale food extruder is used to estimate downstream product quality attributes (PQAs) via a full-spectrum calibration model followed by a reduced-spectrum model. © 2004 Elsevier B.V. All rights reserved.

KW - NIR spectroscopy

KW - PCR

KW - Prognostic vectors

KW - Soft sensors

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DO - 10.1016/j.chemolab.2004.09.002

M3 - Article

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VL - 76

SP - 37

EP - 43

JO - Chemometrics and Intelligent Laboratory Systems

JF - Chemometrics and Intelligent Laboratory Systems

IS - 1

ER -

Construction of an online reduced-spectrum NIR calibration model from full-spectrum data

Abstract

Keywords

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