Chemometric techniques for multivariate calibration and their application in spectroscopic sensors

Chemometric modeling for multivariate calibration of spectroscopy is a crucial technique to ensure product quality and process performance at low cost in many industries. This technique provides fast, noninvasive and nondestructive analysis of sample/process by predicting analyte properties from mea...

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Main Author: Ke, Wang
Other Authors: Yang Yanhui
Format: Theses and Dissertations
Language:English
Published: 2012
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Online Access:https://hdl.handle.net/10356/48640
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-486402023-03-03T15:57:11Z Chemometric techniques for multivariate calibration and their application in spectroscopic sensors Ke, Wang Yang Yanhui School of Chemical and Biomedical Engineering DRNTU::Science::Chemistry::Biochemistry::Spectroscopy Chemometric modeling for multivariate calibration of spectroscopy is a crucial technique to ensure product quality and process performance at low cost in many industries. This technique provides fast, noninvasive and nondestructive analysis of sample/process by predicting analyte properties from measured spectra. Traditional multivariate calibration methods, such as principal component regression (PCR) and partial least squares (PLS), are only reliable when the relationship between analyte properties and spectra is linear. In practice, external disturbances, such as light scattering and baseline noise, will introduce non-linearity into spectral data, deteriorating the prediction accuracy of PCR and PLS. In this thesis, several chemometric strategies will be investigated to address this challenge, including pre-processing and non-linear calibration techniques. Pre-processing methods of the first (D1) and second derivatives (D2), standard normal variate (SNV), extended multiplicative signal correction (EMSC), and extended inverted signal correction (EISC), are proposed to remove the impact of disturbances first, so that the linear calibration method of PCR or PLS can be applied. In addition, a unique linear calibration strategy of optical path length estimation and correction (OPLEC), which involving the building of two linear calibration models, is also investigated. Non-linear calibration techniques aim to model the non-linearity directly, including the methods of artificial neural network (ANN), least squares support vector machine (LS-SVM), and Gaussian process regression (GPR). Through comparison of different linear and non-linear calibration techniques, it is found non-linear calibration techniques give more accurate prediction performance than linear methods in most cases. However, non-linear calibration models are not robust enough and small changes in training data or model parameters may result in significant changes in prediction. Therefore, the strategies of bagging/subagging are investigated to improve the prediction robustness of non-linear calibration models. Furthermore, when using spectroscopic data to predict the analyte property, not all of the variables have contribution to the calibration model. Therefore, selecting the useful variables is effective to improve the prediction performance of calibration models. Two penalized regression algorithms with variable selection using LASSO (least absolute shrinkage and selection operator), penalized linear regression (PLR) and penalized Gaussian process regression (PGPR), are investigated to solve the variable selection problem. Finally, chemometric calibration techniques are applied for solving practical problems which involve predicting the length distribution of single walled carbon nanotubes (SWCNTs) through ultraviolet-visible near-infrared (UV-vis-NIR) spectroscopy and designing a soft sensor to monitor an industrial anaerobic wastewater treatment process. DOCTOR OF PHILOSOPHY (SCBE) 2012-05-04T07:57:15Z 2012-05-04T07:57:15Z 2012 2012 Thesis Ke, W. (2012). Chemometric techniques for multivariate calibration and their application in spectroscopic sensors. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/48640 10.32657/10356/48640 en 148 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Science::Chemistry::Biochemistry::Spectroscopy
spellingShingle DRNTU::Science::Chemistry::Biochemistry::Spectroscopy
Ke, Wang
Chemometric techniques for multivariate calibration and their application in spectroscopic sensors
description Chemometric modeling for multivariate calibration of spectroscopy is a crucial technique to ensure product quality and process performance at low cost in many industries. This technique provides fast, noninvasive and nondestructive analysis of sample/process by predicting analyte properties from measured spectra. Traditional multivariate calibration methods, such as principal component regression (PCR) and partial least squares (PLS), are only reliable when the relationship between analyte properties and spectra is linear. In practice, external disturbances, such as light scattering and baseline noise, will introduce non-linearity into spectral data, deteriorating the prediction accuracy of PCR and PLS. In this thesis, several chemometric strategies will be investigated to address this challenge, including pre-processing and non-linear calibration techniques. Pre-processing methods of the first (D1) and second derivatives (D2), standard normal variate (SNV), extended multiplicative signal correction (EMSC), and extended inverted signal correction (EISC), are proposed to remove the impact of disturbances first, so that the linear calibration method of PCR or PLS can be applied. In addition, a unique linear calibration strategy of optical path length estimation and correction (OPLEC), which involving the building of two linear calibration models, is also investigated. Non-linear calibration techniques aim to model the non-linearity directly, including the methods of artificial neural network (ANN), least squares support vector machine (LS-SVM), and Gaussian process regression (GPR). Through comparison of different linear and non-linear calibration techniques, it is found non-linear calibration techniques give more accurate prediction performance than linear methods in most cases. However, non-linear calibration models are not robust enough and small changes in training data or model parameters may result in significant changes in prediction. Therefore, the strategies of bagging/subagging are investigated to improve the prediction robustness of non-linear calibration models. Furthermore, when using spectroscopic data to predict the analyte property, not all of the variables have contribution to the calibration model. Therefore, selecting the useful variables is effective to improve the prediction performance of calibration models. Two penalized regression algorithms with variable selection using LASSO (least absolute shrinkage and selection operator), penalized linear regression (PLR) and penalized Gaussian process regression (PGPR), are investigated to solve the variable selection problem. Finally, chemometric calibration techniques are applied for solving practical problems which involve predicting the length distribution of single walled carbon nanotubes (SWCNTs) through ultraviolet-visible near-infrared (UV-vis-NIR) spectroscopy and designing a soft sensor to monitor an industrial anaerobic wastewater treatment process.
author2 Yang Yanhui
author_facet Yang Yanhui
Ke, Wang
format Theses and Dissertations
author Ke, Wang
author_sort Ke, Wang
title Chemometric techniques for multivariate calibration and their application in spectroscopic sensors
title_short Chemometric techniques for multivariate calibration and their application in spectroscopic sensors
title_full Chemometric techniques for multivariate calibration and their application in spectroscopic sensors
title_fullStr Chemometric techniques for multivariate calibration and their application in spectroscopic sensors
title_full_unstemmed Chemometric techniques for multivariate calibration and their application in spectroscopic sensors
title_sort chemometric techniques for multivariate calibration and their application in spectroscopic sensors
publishDate 2012
url https://hdl.handle.net/10356/48640
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