The Australian Wine Research Institute, Waite Campus, PO Box 197, Urrbrae, 5064, Australia.
AbbreviationsANN
artificial neural network;
DAdiscriminant analysis;
dwdry weight;
FT-NIRFourier transform near infrared;
GCgas chromatography;
HPLChigh performance liquid chromatography;
IRinfrared;
LCliquid chromatography;
MIRmid infrared;
MLRmultiple linear regression;
MPLSmodified PLS;
MSmass spectrometry;
NIRnear infrared reflectance;
PCAprincipal component analysis;
PCRprincipal component regression;
PLSpartial least squares;
SECVstandard error of cross validation;
SEPstandard error of prediction;
RPDresidual predictive deviation;
R2coefficient of determination;
RMSECVroot mean square error of cross validation;
RMSEProot mean square error of prediction;
VISvisible
Medicinal plant properties are related to individual compounds such as essential oils, terpenoids, flavonoids, which are present in natural products in low concentrations (e.g. ppm or ppb). For many years, the use of classical separation, chromatographic and spectrometric techniques such as high performance liquid chromatography (HPLC), gas chromatography (GC), liquid chromatography (LC) and mass spectrometry (MS) were initially focused for the elucidation of isolated compounds from different plant matrices. In the last 40 years near infrared (NIR) spectroscopy became one of the most attractive and used methods of analysis which provides simultaneous, rapid and non-destructive quantitation of major components (e.g. protein, dry matter, carbohydrates) in many agriculture related products and plant materials. More recently, the use of NIR spectroscopy has been reported to determine other minor compounds in plant materials. This paper reviews recent applications on the use of NIR spectroscopy to quantitatively analyse plant natural products.
Near infrared spectroscopy, natural products, chemometrics, quantitative analysis
