Provider: ingentaconnect
Database: ingentaconnect
Content: application/x-research-info-systems
TY - ABST
AU - Brenchley, Jason M.
AU - Horchner, Uwe
AU - Kalivas, John H.
TI - Wavelength Selection Characterization for NIR Spectra
JO - Applied Spectroscopy
PY - 1997-05-01T00:00:00///
VL - 51
IS - 5
SP - 689
EP - 699
KW - NEAR-INFRARED WAVELENGTH SELECTION SIMULATED ANNEALING
N2 - For quantitative analysis of samples based on near-infrared (NIR) spectra, it is common practice to use full spectra in conjunction with partial least-squares (PLS) or principal component regression. Alternatively,
least-squares (LS) can be used provided that proper wavelengths have been selected. Recently, optimization algorithms such as simulated annealing and the genetic algorithm have been applied to the selection
of individual wavelengths. These algorithms are touted as global optimizers capable of locating the best set of parameters for a given large-scale optimization problem. Optimization methods such as simulated
annealing and the genetic algorithm can become time intensive. Excessive computer time may be due not to computations but to the need to determine proper operational parameters to ensure acceptable optimization
results. In order to reduce the time to select wavelengths, a different approach consists of selecting wavelengths directly on the basis of spectral criteria. This paper shows that results are not acceptable
when one is separately using the criteria of large wavelength correlations to the prediction property, wavelengths associated with large values in loading vectors from PLS or derived from the singular value
decomposition (SVD) of the spectra, and wavelengths associated with large PLS regression coefficients. However, it is demonstrated that acceptable results can be produced by using wavelength regions simultaneously
associated with large correlations and loading values provided that the level of noise for identified wavelengths is also acceptable. Thus, this paper shows that, rather than using timeconsuming optimization
algorithms that generally select individual wavelengths, one can achieve improved results based on wavelength windows directly selected. In other words, the described approach is founded on the exclusion
of spectral regions rather than the search for distinct wavelengths. As part of the NIR spectral characterization, it is shown that certain loading vectors from the SVD of spectra are equivalent to correlograms
for prediction properties. The same is shown to be true for PLS loading vectors. This type of analysis is useful for determining dominant properties of spectra, i.e., primary properties responsible for
spectral variations.
UR - http://www.ingentaconnect.com/content/sas/sas/1997/00000051/00000005/art00013
M3 - doi:10.1366/0003702971940837
UR - http://dx.doi.org/10.1366/0003702971940837
ER -