Medicinal Plants Classification Biosynthesis and ... - Index of

Non-Invasive Near Infrared Spectroscopic Techniques for the Characterization...
Figure 11. 3-dimensional factor plot, representing principal components (PC) one, two and three, for
classifying the aerial parts of A. millefolium s.l..
In our study, we investigated Achillea millefolium L and three of its related species. The
main objective of this GC-MS supported NIRS study was (1) to discriminate between
Achillea millefolium flowers and leaves, (2) to differentiate between varying sample
preparation procedures (air-dried, oven-dried) and (3) to classify in the future Achillea
species by means of NIRS combined with multivariate data analysis (MVA). Recording 240
spectra (75 spectra for air-dried flowers, 75 for air-dried flowers and leaves) allowed
discriminating by cluster analysis (Figure 11).
The averaged and normalized (between 0 and 1) absorbance spectra are displayed in
Figure 12.
In a quantitative NIR study partial least square regression (PLSR) was used to create 14
single-compound models (SCM, one regression model for each compound) on one hand and
one multi-compound model (MCM, one regression model for 14 compounds) on the other
hand. The averaged standard error of prediction (SEP) showed 0.49 % for SCM and 0.62 %
for MCM. Differences between SCM and the MCM for the quantities of the compounds in
Achillea millefolium were compared by a t-test and one way analysis of variance (ANOVA).
Furthermore, the MCM was used to optimize and evaluate the best suitable sample extraction
procedure for the raw plant material prior to NIR analysis, namely air-dried, oven-dried and a
mixture of both extracts. Gas chromatography - mass spectrometry (GC-MS) was used as
reference technique to quantitatively calibrate the NIR system. Multivariate data analysis
(MVA) in form of Pearson bivariate correlation, principle component analysis (PCA) and
269