Multivariate calibration of near-infrared spectra by using influential variables

2012 ◽  
Vol 4 (2) ◽  
pp. 467 ◽  
Author(s):  
Xueguang Shao ◽  
Min Zhang ◽  
Wensheng Cai
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Near Infrared Spectra

Pure Component Selectivity Analysis of Multivariate Calibration Models from Near-Infrared Spectra

2004 ◽  
Vol 76 (9) ◽  
pp. 2583-2590 ◽  
Author(s):  
Mark A. Arnold ◽  
Gary W. Small ◽  
Dong Xiang ◽  
Jiang Qui ◽  
David W. Murhammer
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Pure Component ◽  
Near Infrared Spectra ◽  
Calibration Models

Multivariate calibration of near infrared spectra by orthogonal WAVElet correction using a genetic algorithm

2006 ◽  
Vol 555 (1) ◽  
pp. 84-95 ◽  
Author(s):  
I. Esteban-Díez ◽  
J.M. González-Sáiz ◽  
D. Gómez-Cámara ◽  
C. Pizarro Millan
Keyword(s):  
Genetic Algorithm ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Orthogonal Wavelet ◽  
Near Infrared Spectra

Reagentless Near-Infrared Determination of Glucose in Whole Blood Using Multivariate Calibration

1992 ◽  
Vol 46 (10) ◽  
pp. 1575-1578 ◽  
Author(s):  
David M. Haaland ◽  
M. Ries Robinson ◽  
Gary W. Koepp ◽  
Edward V. Thomas ◽  
R. Philip Eaton
Keyword(s):  
Infrared Spectra ◽  
Glucose Concentration ◽  
Whole Blood ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Blood Chemistry ◽  
Standard Errors ◽  
Diabetic Patients ◽  
Blood Samples ◽  
Near Infrared Spectra

Noninvasive monitoring of glucose in diabetic patients is feasible with the use of near-infrared spectroscopic measurements. As a step toward the final goal of the development of a noninvasive monitor, the near-infrared spectra (4250 to 6600 cm−1) of glucose-doped whole blood samples were obtained along with reference glucose values. Glucose concentrations and spectra of blood samples obtained from four subjects were subjected to multivariate calibration with the use of partial least-squares (PLS) methods. The cross-validated PLS standard errors of prediction for glucose concentration based on data obtained from each individual subject's blood samples averaged 33 mg/dL over the range from 3 to 743 mg/dL. Cross-validated standard errors for glucose concentration from PLS calibrations based on data from all four subjects were 39 mg/dL. However, when PLS models based upon three subjects' data were used for prediction on the fourth, glucose prediction abilities were poor. It is suggested that blood chemistry differences were sufficiently different for the four subjects to require that a larger number of subjects be included in the calibration for adequate prediction abilities to be obtained from near-infrared spectra of blood from subjects not included in the calibration.

Multivariate Calibration of Sulfur in Sour Crude Oils via Near-Infrared Spectra

2021 ◽  
Author(s):  
Slobodan Šašić ◽  
Howard Yokelson ◽  
Todd Kotecki ◽  
Stacy Austin ◽  
Kevin Thacker ◽  
...  
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Crude Oils ◽  
Near Infrared Spectra ◽  
Sour Crude

A new ensemble modeling method for multivariate calibration of near infrared spectra

2021 ◽  
Vol 13 (11) ◽  
pp. 1374-1380
Author(s):  
Kaiyi Wang ◽  
Xihui Bian ◽  
Xiaoyao Tan ◽  
Haitao Wang ◽  
Yankun Li
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Modeling Method ◽  
Ensemble Modeling ◽  
Near Infrared Spectra

A new ensemble modeling method based on PLS, MC sampling and LASSO was proposed for multivariate calibration of near infrared spectra.

A weighted multiscale regression for multivariate calibration of near infrared spectra

The Analyst ◽  
2009 ◽  
Vol 134 (2) ◽  
pp. 261-266 ◽  
Author(s):  
Zhichao Liu ◽  
Wensheng Cai ◽  
Xueguang Shao
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Near Infrared Spectra
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