2014Laser & InfraredRequires access

Sucrose concentration measurement by transmission and reflection spectroscopy based on PCR and PLS

Wang Ya-hon

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Abstract

Near-infrared spectra of trans-reflective is applied to determine the feasibility of nondestructive testing of the sucrose solution in the near-infrared wavelength range of 780 ~ 1100nm,and principal component regression( PCR) and partial least squares( PLS) are used to establish the near-infrared quantitative analysis model of sucrose solution. After the pretreatment of( Savitzky-Golay) 5 points smoothing and multiplicative scatter correction( MSC),the( PCR and PLS) calibration model is made. The results of quantitative analysis of PCR are the number of principal component PC = 7,the correlation coefficient RCV= 0. 957335,root mean square error of cross validation RMSECV = 0. 015859; The results of quantitative analysis of PLS are the number of principal component PC = 4,the correlation coefficient RCV= 0. 975789,root mean square error of cross-validation RMSECV = 0. 012251. The prediction samples are predicted respectively by the calibration model of PCR and PLS. Root mean square errors of prediction are 0. 0127,0. 0118 respectively. Both of the methods are satisfactory for the predicted results of high concentration samples. The relative errors of more than 77% of validation samples are below 10% using PLS. All of results show that PLS model is simpler and its prediction accuracy is higher,and short-wave near-infrared spectrometry is a valuable, rapid and nondestructive tool for the quantitative analysis of sucrose solution.

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What this paper is about

Near-infrared spectra of trans-reflective is applied to determine the feasibility of nondestructive testing of the sucrose solution in the near-infrared wavelength range of 780 ~ 1100nm,and principal component regression( PCR) and partial least squares( PLS) are used to establish the near-infrared quantitative analysis model of sucrose solution. After the pretreatment of( Savitzky-Golay) 5 points smoothing and multiplicative scatter correction( MSC),the( PCR and PLS) calibration model is made. The results of quantitative analysis of PCR are the number of principal component PC = 7,the correlation coefficient RCV= 0. 957335,root mean square error of cross validation RMSECV = 0. 015859; The results of quantitative analysis of PLS are the number of principal component PC = 4,the correlation coefficient RCV= 0. 975789,root mean square error of cross-validation RMSECV = 0. 012251. The prediction samples are predicted respectively by the calibration model of PCR and PLS. Root mean square errors of prediction are 0. 0127,0. 0118 respectively. Both of the methods are satisfactory for the predicted results of high concentration samples. The relative errors of more than 77% of validation samples are below 10% using PLS. All of results show that PLS model is simpler and its prediction accuracy is higher,and short-wave near-infrared spectrometry is a valuable, rapid and nondestructive tool for the quantitative analysis of sucrose solution.

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Available abstract

Near-infrared spectra of trans-reflective is applied to determine the feasibility of nondestructive testing of the sucrose solution in the near-infrared wavelength range of 780 ~ 1100nm,and principal component regression( PCR) and partial least squares( PLS) are used to establish the near-infrared quantitative analysis model of sucrose solution. After the pretreatment of( Savitzky-Golay) 5 points smoothing and multiplicative scatter correction( MSC),the( PCR and PLS) calibration model is made. The results of quantitative analysis of PCR are the number of principal component PC = 7,the correlation coefficient RCV= 0. 957335,root mean square error of cross validation RMSECV = 0. 015859; The results of quantitative analysis of PLS are the number of principal component PC = 4,the correlation coefficient RCV= 0. 975789,root mean square error of cross-validation RMSECV = 0. 012251. The prediction samples are predicted respectively by the calibration model of PCR and PLS. Root mean square errors of prediction are 0. 0127,0. 0118 respectively. Both of the methods are satisfactory for the predicted results of high concentration samples. The relative errors of more than 77% of validation samples are below 10% using PLS. All of results show that PLS model is simpler and its prediction accuracy is higher,and short-wave near-infrared spectrometry is a valuable, rapid and nondestructive tool for the quantitative analysis of sucrose solution.

Key concepts: Principal component regression, Partial least squares regression, Calibration, Principal component analysis, Correlation coefficient, Near-infrared spectroscopy, Mean squared error, Analytical Chemistry (journal)

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