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Selected papers from the latest issue:
ICRM-2011 international chemometrics research meeting
14 December 2011, 22:21:16
Publication year: 2011
Source: Chemometrics and Intelligent Laboratory Systems, Available online 13 December 2011
Steven D. Brown, Anna de Juan
Source: Chemometrics and Intelligent Laboratory Systems, Available online 13 December 2011
Steven D. Brown, Anna de Juan
Highlights
► The ICRM conference held in Berg en Dal is reviewed ► A brief overview is given of the topics presented ► A discussion of the organization of the conference is providedA novel nonlinear adaptive Mooney-viscosity model based on DRPLS-GP algorithm for rubber mixing process
11 December 2011, 02:10:17
Publication year: 2011
Source: Chemometrics and Intelligent Laboratory Systems, Available online 9 December 2011
Ze Zhang, Kai Song, Tuo-Peng Tong, Fang Wu
Rubber-mixing process is a typical non-linear batch process with very short operation time (commonly, 2 ~ 5 min). The large measurement delay of Mooney-viscosity, one of the key quality indexes of mixed rubber, strongly restricts further improvement of the quality of final rubber products and the development of rubber-mixing process control. A novel nonlinear adaptive Mooney-viscosity prediction model based on Discounted-measurement Recursive Partial Least Squares-Gaussian Process (DRPLS-GP) algorithm is developed. Using rheological parameters as the input variables, which could be measured online, the measurement delay of Mooney-viscosity is markedly reduced from about 240 min to 2 min. In DRPLS-GP model, to overcome the noise and the multi-collinearity of original data, orthogonal latent variables (LVs) are extracted by Discounted-measurement Recursive Partial Least Squares (DRPLS) firstly, and then the LVs are inputted to Gaussian Process (GP) as predictors for further regression. Thus relying on the nonlinear regression power of GP and the multivariate regression power of DRPLS, the nonlinear relationship between rheological parameters and Mooney-viscosity could be regressed successfully by DRPLS-GP. In particular, this method could update Mooney-viscosity prediction model without increasing the computation and sampling burden, so it is very practical for industrial application. Moreover, the flexibility of discounted-measurement factor of the novel method ensures the high precise prediction of Mooney-viscosity of different mixed rubber formulas. The results which are obtained by using of 1006 industrial data sampled in a large-scale tire factory located in east China confirm that the predictive performance of DRPLS-GP is superior to other approaches.
Source: Chemometrics and Intelligent Laboratory Systems, Available online 9 December 2011
Ze Zhang, Kai Song, Tuo-Peng Tong, Fang Wu
Rubber-mixing process is a typical non-linear batch process with very short operation time (commonly, 2 ~ 5 min). The large measurement delay of Mooney-viscosity, one of the key quality indexes of mixed rubber, strongly restricts further improvement of the quality of final rubber products and the development of rubber-mixing process control. A novel nonlinear adaptive Mooney-viscosity prediction model based on Discounted-measurement Recursive Partial Least Squares-Gaussian Process (DRPLS-GP) algorithm is developed. Using rheological parameters as the input variables, which could be measured online, the measurement delay of Mooney-viscosity is markedly reduced from about 240 min to 2 min. In DRPLS-GP model, to overcome the noise and the multi-collinearity of original data, orthogonal latent variables (LVs) are extracted by Discounted-measurement Recursive Partial Least Squares (DRPLS) firstly, and then the LVs are inputted to Gaussian Process (GP) as predictors for further regression. Thus relying on the nonlinear regression power of GP and the multivariate regression power of DRPLS, the nonlinear relationship between rheological parameters and Mooney-viscosity could be regressed successfully by DRPLS-GP. In particular, this method could update Mooney-viscosity prediction model without increasing the computation and sampling burden, so it is very practical for industrial application. Moreover, the flexibility of discounted-measurement factor of the novel method ensures the high precise prediction of Mooney-viscosity of different mixed rubber formulas. The results which are obtained by using of 1006 industrial data sampled in a large-scale tire factory located in east China confirm that the predictive performance of DRPLS-GP is superior to other approaches.
Highlights
► The proposed Mooney-viscosity prediction model can reduced measurement delay from about 240 min to 2 min, and improves the product quality guarantee and reduces the factory's production loss greatly. ► The proposed method can overcome some shortages of the other methods. ► The method is confirmed by the 1006 actual industrial data sampled in a large-scale tire factory located in east China.Selection of representative calibration sample sets for near-infrared reflectance spectroscopy to predict nitrogen concentration in grasses
04 December 2011, 21:54:36
Publication year: 2011
Source: Chemometrics and Intelligent Laboratory Systems, Available online 2 December 2011
Nisha Shetty, Åsmund Rinnan, René Gislum
The effect of using representative calibration sets with fewer samples was explored and discussed. The data set consisted of near-infrared reflectance (NIR) spectra of grass samples. The grass samples were taken from different years covering a wide range of species and cultivars. Partial least squares regression (PLSR), a chemometric method, has been applied on NIR spectroscopy data for the determination of the nitrogen (N) concentration in these grass samples. The sample selection method based on NIR spectral data proposed by Puchwein and the CADEX (computer aided design of experiments) algorithm were used and compared. Both Puchwein and CADEX methods provide a calibration set equally distributed in space, and both methods require a minimum prior of knowledge. The samples were also selected randomly using complete random, cultivar random (year fixed), year random (cultivar fixed) and interaction (cultivar x year fixed) random procedures to see the influence of different factors on sample selection. Puchwein's method performed best with lowest RMSEP followed by CADEX, interaction random, year random, cultivar random and complete random. Out of 118 samples of the complete calibration set, 19 samples were selected as minimal number of representative samples. RMSEP values obtained for subsets selected using Puchwein, CADEX and using full calibration set were 0.099% N, 0.109% N and 0.092% N respectively. The result indicated that the selection of representative calibration samples can effectively enhance the cost-effectiveness of NIR spectral analysis by reducing the number of analyzed samples in the calibration set by more than 80%, which substantially reduces the effort of laboratory analyses with no significant loss in prediction accuracy.
Source: Chemometrics and Intelligent Laboratory Systems, Available online 2 December 2011
Nisha Shetty, Åsmund Rinnan, René Gislum
The effect of using representative calibration sets with fewer samples was explored and discussed. The data set consisted of near-infrared reflectance (NIR) spectra of grass samples. The grass samples were taken from different years covering a wide range of species and cultivars. Partial least squares regression (PLSR), a chemometric method, has been applied on NIR spectroscopy data for the determination of the nitrogen (N) concentration in these grass samples. The sample selection method based on NIR spectral data proposed by Puchwein and the CADEX (computer aided design of experiments) algorithm were used and compared. Both Puchwein and CADEX methods provide a calibration set equally distributed in space, and both methods require a minimum prior of knowledge. The samples were also selected randomly using complete random, cultivar random (year fixed), year random (cultivar fixed) and interaction (cultivar x year fixed) random procedures to see the influence of different factors on sample selection. Puchwein's method performed best with lowest RMSEP followed by CADEX, interaction random, year random, cultivar random and complete random. Out of 118 samples of the complete calibration set, 19 samples were selected as minimal number of representative samples. RMSEP values obtained for subsets selected using Puchwein, CADEX and using full calibration set were 0.099% N, 0.109% N and 0.092% N respectively. The result indicated that the selection of representative calibration samples can effectively enhance the cost-effectiveness of NIR spectral analysis by reducing the number of analyzed samples in the calibration set by more than 80%, which substantially reduces the effort of laboratory analyses with no significant loss in prediction accuracy.
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