A strategy for multivariate calibration based on modified single-index signal regression: Capturing explicit non-linearity and improving prediction accuracy |
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| Institution: | 1. State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi’an University of Technology, Xi’an 710048, China;2. Xi’an Municipal Engineering Design & Research Institute Co., Ltd., Xi’an 710068, China;3. Environmental Protection Institute, Sinopec Beijing Research Institute of Chemical Industry, Beijing 100103, China;4. Department of Equipment Engineering, Sinopec Luoyang Company, Luoyang 471012, China;5. School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China;6. Department of Environmental Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China;1. Department of Safety, Health and Environmental Engineering, National United University, Miao-Li 36063, Taiwan;2. Department of Chemical Engineering, National United University, Miao-Li 36063, Taiwan;3. Department of Chemical and Materials Engineering, Chang Gung University, Kwei-Shan, Taoyuan 33302, Taiwan;1. Zhoukou Key Laboratory of Environmental Pollution Prevention and Remediation, School of Chemistry and Chemical Engineering, Zhoukou Normal University, Zhoukou 466001, China;2. School of Environmental and Municipal Engineering, Qingdao University of Technology, Qingdao 266033, China;3. The Second Comprehensive Business Division of Huangdao Customs of the People''s Republic of China 266555, China;1. Chemical Engineering Department, Federal University of Rio Grande do Sul (UFRGS), Rua Engenheiro Luiz Englert s/n, Porto Alegre/RS 90040-040, Brazil;2. Institute of Technology in Food for Health (itt Nutrifor), University of Vale do Rio dos Sinos (UNISINOS), Avenida Unisinos, 950, São Leopoldo/RS 93022-000, Brazil;1. Department of Chemical and Biochemical Engineering, Western University, London, Ontario, Canada;2. Department of Chemical Engineering, University of Waterloo, Waterloo, Ontario, Canada |
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| Abstract: | In this paper, a modified single-index signal regression (mSISR) method is proposed to construct a nonlinear and practical model with high-accuracy. The mSISR method defines the optimal penalty tuning parameter in P-spline signal regression (PSR) as initial tuning parameter and chooses the number of cycles based on minimizing root mean squared error of cross-validation (RMSECV). mSISR is superior to single-index signal regression (SISR) in terms of accuracy, computation time and convergency. And it can provide the character of the non-linearity between spectra and responses in a more precise manner than SISR. Two spectra data sets from basic research experiments, including plant chlorophyll nondestructive measurement and human blood glucose noninvasive measurement, are employed to illustrate the advantages of mSISR. The results indicate that the mSISR method (i) obtains the smooth and helpful regression coefficient vector, (ii) explicitly exhibits the type and amount of the non-linearity, (iii) can take advantage of nonlinear features of the signals to improve prediction performance and (iv) has distinct adaptability for the complex spectra model by comparing with other calibration methods. It is validated that mSISR is a promising nonlinear modeling strategy for multivariate calibration. |
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| Keywords: | Multivariate calibration Modified single-index signal regression Non-linearity Spectrometric quantization |
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