Wavelength Selection Characterization for NIR Spectra

Abstract

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 time-consuming 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.