Chemical and physiological properties are related to individual or bioactive compounds such as essential oils, terpenoids, flavonoids, volatile compounds and other chemicals which are present in natural products in low concentrations (e.g. ppm or ppb). For many years, classical separation, chromatographic and spectrometric techniques such as high performance liquid chromatography (HPLC), gas chromatography (GC), liquid chromatography (LC) and mass spectrometry (MS) have been used for the elucidation of isolated compounds from different matrices. Hence, the use of standard separation, chromatographic and spectrometric methods was found useful in chemical and both plant and animal physiology studies, for fingerprinting and comparing natural and synthetic samples, as well as to identify single active compounds. It has been generally accepted that a single analytical technique will not provide sufficient visualization of the metabolome, hence holistic techniques are needed for comprehensive analysis. In the last 40 years near infrared (NIR) spectroscopy became one of the most attractive and used methods of analyzing agricultural related products and plant materials which provide simultaneous, rapid and non-destructive quantitation of major. This technique has been reported to determine other minor compounds in plant materials such as volatile compounds and elements. The aim of this short review is to describe some recent applications of NIR spectroscopy combined with multivariate data analysis for high throughput screening of metabolites with an emphasis on food and medical applications.
Keywords: Near infrared, metabolomics, chemometrics, food, medical, spectroscopy, fingerprinting, high performance liquid chromatography, HPLC, gas chromatography, GC, liquid chromatography, LC, NIR, electro spray ionization mass spectrometry, capillary electrophoresis, microchip arrays, high throughput screening, INFRARED SPECTROSCOPY, molecular spectroscopy, NIR spectroscopy, Principal component analysis, PLS-DA, linear discriminant analysis, LDA, artificial neural networks, RMSEC, RMSEP, SEP, IR spectroscopy, Hordeum vulgare, FTNIR spectroscopy, SIMCA, Saccharomyces cerevisiae