Standardization of NIR Spectra between Different Instruments by Support Vector Machines
Date Issued
2008
Date
2008
Author(s)
Wu, Chih-Hung
Abstract
Recently, due to the advance of the Near Infrared technology, the relationships between NIR spectra and various components of materials have been established and generally used in industrial and academic areas. The differences of spectra among different spectrophotometers prevent the effective utilization of existed calibration models and database; however, the above-mentioned drawback can be overcome by spectral standardization. In the study, apart from the linear spectral standardization methods, such as Piecewise Direct Standardization (PDS), a non-linear method, Support Vector Standardization (SVS) which was developed based on Support Vector Regression (SVR) from statistical learning theory, was used. Support Vector Standardization can deal with the non-linear spectral differences among different spectrophotometers more effectively than conventional methods. The development of calibration models in this study used the non-linear method Least Squares Support Vector Regression (LS-SVS) to obtain the better results of prediction. In order to verify the standardization capabilities of SVS, this study used two spectral data sets: the first set was the standard spectral data set available in the literature, which used to compare the past commonly used method PDS with SVS on standardization capabilities; the second one was the spectral data set of powder samples experimented in this study, which used to investigate the spectral differences made from different spectrophotometers in practice, in which the same instrument configuration equipped different module and different instrument configurations were used. In judging the standardization capabilities of PDS and SVS, the Spectral Reconstruction Error (SRE) for spectral reconstruction capability and prediction errors for prediction capability were adopted after standardization procedures. As results showed, the calibration models developed by LS-SVR gained good prediction; and after standardization, SVS had better spectral reconstruction and prediction capability than those by PDS. In the case of same instrument configuration equipped different module with powder samples, the prediction result of calibration model developed by LS-SVR, the Relative Standardization Error of Calibration (RSEC) and the Relative Standardization Error of Prediction (RSEP) were 4.390% and 8.638% respectively. In the results of spectral reconstruction capability, the SRE by using PDS for calibration and prediction sets were 0.0339 and 0.0451 respectively; and when using SVS, SRE errors were 0.0205 and 0.0245 for calibration and prediction. Comparing with unstandardization case, SRE errors were 0.5299 and 0.5227. It was obvious that the SRE errors were reduced substantially after standardization; and SVS had better performance than PDS did. Regarding the results of prediction capability, before standardization, the RSEC and RSEP were 134.840% and 123.670% respectively; the RSEC and RSEP were 42.355% and 22.485% after using PDS, and they were 18.061% and 21.441% after using SVS. After standardization, RSEC and RSEP errors were reduced extensively and SVS gave better results than PDS did.
Subjects
Near Infrared
Spectral Standardization
Support Vector Regression
Support Vector Standardization
Piecewise Direct Standardization
Least Square Support Vector Regression
Type
thesis
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