Provider: ingentaconnect
Database: ingentaconnect
Content: application/x-research-info-systems
TY - ABST
AU - Kalivas, John H.
AU - Green, Robert L.
TI - Pareto Optimal Multivariate Calibration for Spectroscopic Data
JO - Applied Spectroscopy
PY - 2001-12-01T00:00:00///
VL - 55
IS - 12
SP - 1645
EP - 1652
KW - PARTIAL LEAST-SQUARES
KW - CALIBRATION
KW - GENERALIZED RIDGE REGRESSION
KW - SIMPLEX
KW - PARETO OPTIMAL
KW - RIDGE REGRESSION
KW - PRINCIPAL COMPONENT REGRESSION
N2 - Multivariate calibration of spectral data is considered with an emphasis on prediction. An abundance of methods are available to develop such calibration models. Using a harmonious approach with target
vector optimization, the best calibration models are identified relative to the criteria used. Criteria utilized to determine the adequacy of models are minimization of the root mean square error of calibration
(RMSEC) and the norm of the regression vector. Because of the simplicity of the optimization response surfaces, the method of simplex was found to function much faster than generalized simulated annealing.
Using a near infrared spectral example to demonstrate concepts, a family of models are established to be good, i.e., Pareto optimal models. For the data set investigated, it is found that the Pareto optimal
models are essentially the same as models obtained by ridge regression and generalized ridge regression and are more harmonious than models obtained by principal component regression (PCR), partial least-squares
(PLS), continuum regression, and cyclic subspace regression as the Pareto optimal models have smaller regression vector norms, RMSEC, and root mean square error of validation (RMSEV) values. The PLS models
are found to be Pareto optimal relative to the PCR models. The paper presents an explanation of when the RR model will not be acceptable.
UR - http://www.ingentaconnect.com/content/sas/sas/2001/00000055/00000012/art00011
M3 - doi:10.1366/0003702011953955
UR - http://dx.doi.org/10.1366/0003702011953955
ER -