How can we unravel complicated near infrared spectra? Recent progress in spectral analysis methods for resolution enhancement and band assignments inthe near infrared region

This review paper reports recent progress in spectral analysis methods for resolution enhancement and band assignments in the near infrared (NIR) regi on. Spectra in the NIR region are inherently rich with information on the p hysical and chemical properties of molecules, However, it is not always str aightforward to analyse the spectra because an NIR spectrum consists of a n umber of overlapped bands due to overtones and combination modes. An NIR sp ectrum may be analysed by conventional spectral analysis methods, chemometr ics or two-dimensional correlation spectroscopy. The following conventional methods are currently utilised to analyse NIR spectra: (a) derivatives, (b ) difference spectroscopy, (c) Fourier self-deconvolution and (d) curve fit ting. The derivative method is powerful in separating superimposed bands an d correcting for a baseline slope. Conventional experimental methods for sp ectral analysis, such as isotope exchange and measurement of polarisation s pectra, are also valid in the NIR region. Chemometrics is very useful for e xtracting information from NIR spectra. Among a variety of chemometrics met hods, multiple linear regression, principal component analysis, principal c omponent regression and partial least squares regression are most often use d for qualitative and quantitative analysis. Recently, chemometrics has bee n used for resolution enhancement of NIR spectra. Particularly, loadings pl ots or regression coefficients are useful for separating overlapped bands a nd for making band assignments. Notable recent advances in the analysis of NIR spectroscopy are the development or introduction of new spectral analys is methods such as two-dimensional (2D) correlation spectroscopy and self-m odelling curve resolution methods (SMCR). 2D correlation analysis enables e nhancement of apparent spectral resolution by spreading spectral peaks over a second dimension. SMCR allows one to resolve the experimental matrix int o concentration profiles and pure spectra of the involved species without p rior knowledge of any of these features.