Near-infrared (NIR) spectroscopy is a powerful tool for qualitative and quantitative phytoanalysis. The backbone of NIR spectroscopy, multi-variate data analysis, provides no physical insight into the molecular system. In our research [1] we employ the methods of computational chemistry to unveil the origins of NIR bands, and to establish the basic relationship between the wavenumbers influential in quantitative models, and the underlying molecular background. Examples of substances with importance in phytoanalysis are presented, rosmarinic acid [2],[3] and thymol [4], as prototypic polyphenol and monoterpene.
NIR spectra simulations not only provide deep understanding of the spectral bands. In addition, the features of quantitative models obtained in analytical routines may be interpreted. Fundamental relationships with the basic factors may be established, e.g. how the sensitivity of the molecule to its chemical environment is reflected in the models, and thus an understanding of how these factors affect the analytical spectroscopy is obtained.
This work was supported by the Austrian Science Fund (FWF), M2729-N28.
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