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Planta Med 2009; 75(7): 746-756
DOI: 10.1055/s-0028-1112220
Plant Analysis
Mini-Review
© Georg Thieme Verlag KG Stuttgart · New York

Near Infrared Spectroscopy in Natural Products Analysis

Daniel Cozzolino1
  • 1The Australian Wine Research Institute, Waite Campus, Urrbrae, Australia
Further Information

Publication History

Received: July 22, 2008 Revised: November 23, 2008

Accepted: November 27, 2008

Publication Date:
22 January 2009 (online)

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Abstract

Several medicinal and herbal plants 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 and chromatographic and spectrometric techniques such as high performance liquid chromatography (HPLC), gas chromatography (GC), liquid chromatography (LC) and mass spectrometry (MS) were initially used for the elucidation of isolated compounds from different plant matrices. Spectroscopic techniques in the infrared (IR) wavelength region of the electromagnetic spectrum have been used in the food industry to monitor and evaluate the composition of foods. Although Herschel discovered light in the near-infrared (NIR) region as early as 1800, most spectroscopists of the first half of the last century ignored it, in the belief that it lacked any analytical interest. However, during the last 40 years NIR spectroscopy has become one of the most attractive and used methods for analysis. This mini-review highlights recent applications of NIR spectroscopy to the qualitative and quantitative analysis of plant natural products.

Abbreviations

ANN:artificial neural network

AOTF:acousto-optic tunable filter

ATR:attenuated total reflection

CP:crude protein

DA:discriminant analysis

DM:dry matter

DOSC:direct orthogonal signal correction

dw:dry weight

FT-NIR:Fourier transform near infrared

GC:gas chromatography

G-G:glycosylated glucose

GSL:glucosinolates

InGaAs:indium gallium arsenide

iPLS:interval partial least squares

IR:infrared

LC:liquid chromatography

LCTF:liquid crystal tunable filters

MEMS:micro electro mechanical systems

MIR:mid infrared

MLR:multiple linear regression

MPLS:modified PLS

MSC:multiplicative scatter correction

MS:mass spectrometry

NIR:near infrared reflectance

NOCH:NIR on combine harvest

PCA:principal component analysis

PDA:photodiode array

PLS:partial least squares

SECV:standard error of cross validation

SEP:standard error of prediction

Si:silicon

siPLS:synergy interval partial least squares

SVM:support vector machine

SIMCA:soft independent modelling of class analogies

R:correlation coefficient

R2:coefficient of determination

RPD:residual predictive deviation

RMSEC:root mean square error of calibration

RMSECV:root mean square error of cross validation

RMSEP:root mean square rrror of prediction

REAC:trolox equivalent antioxidant capacity

TLC:thin layer chromatography

THC:tetrahydrocannabinol

UV:ultraviolet

VIS:visible

WT:wavelet transformation

Key words

near infrared spectroscopy - natural products - chemometrics - quantitative analysis

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Daniel Cozzolino

The Australian Wine Research Institute

Waite Campus

PO Box 197

Urrbrae – 5064

Australia

Fax: +61-8-8303-6601

Email: daniel.cozzolino@awri.com.au