WAVELENGTH SPECTRA
MPLSR analysis of silicon CCD sensing bands (400 to 1098 nm) was shown in Table
3.4. The best calibration model of G. scabra Bunge tissue culture was acquired when the
2nd derivative spectra and 3 factors were employed, where both smoothing points and
gap were at 2, with wavelength range of 400 to 500 nm and 800 to 1000 nm, and the
results were Rc = 0.865, SEC = 0.611%, SEV = 0.772%, of bias = 0.025%, RPD = 1.47.
The best calibration model of G. scabra Bunge grown plant was found with the 1st
derivative spectra and 5 factors, smoothing points and gap at 2, with wavelength range
of 400 to 600 nm and 900 to 1098 nm, resulting in Rc = 0.904, SEC = 0.649%, SEV =
0.724%, bias = –0.089%, RPD = 2.08. Regardless of the samples being tissue culture or
grown plant, the calibration models built based on 1st derivative spectra and 2nd
derivative spectra were better than those based on the original spectra, indicating that
spectral pretreatments indeed enhanced the predictability of the calibration models. The
spectral calibration models of grown plant were all better than those of the tissue culture
with fewer spectral pretreatments, which was consistent with the results shown in Table
3.3. The specific wavelength regions of tissue culture and grown plant were mainly
distributed in 400 to 600 nm (blue and red light) and 800 to 1098 nm (the 2nd and 3rd
overtone of C-H bond). The absorption capacity of these bands was a little lower than
the combination and the 1st overtone of C-H bond, producing a fewer spectral
absorption performances of gentiopicroside, so the predictability declined slightly when
using silicon CCD sensing band to build the calibration models.
Table 3.4 Prediction of the gentiopicroside content in tissue culture and grown plants of G. scabra Bunge by MPLSR models in the
1st Derivative 400-600, 2
900-1098, 2
2 / 2 5 0.904 0.649 0.724 -0.089 2.08
2nd Derivative 400-650, 2
950-1098, 2
3 / 3 3 0.888 0.697 0.750 -0.100 2.00
a Interval is 2 nm
In addition, the SMLR analysis results of silicon CCD sensing band (400 to 1098 nm)
was shown in Table 3.5. The best calibration model of G. scabra Bunge tissue culture
was found when the 2nd derivative spectra were used. Both smoothing points and gap
were at 3, with the specific wavelength of 846 nm and 932 nm, of which Rc = 0.750,
SEC = 0.806%, SEV = 0.990%, bias = 0.270%, RPD = 1.15 were achieved. The best
calibration model of grown plant was attained when the 2nd derivative spectra were
employed, where both smoothing points and gap were set at 3, in the combination of the
4 wavelengths of 670 nm, 786 nm, 474 nm and 826 nm, of which Rc = 0.860, SEC =
0.775%, SEV = 0.848%, bias = –0.134%, RPD = 1.77 were achieved. The calibration
models built based on the 1st and 2nd derivative spectra were all better than those based
on the original spectra, indicating that the spectral pretreatments reduced the noise
influence and made the combination of selected wavelengths more consistent when the
number of wavelengths increased. The specific wavelengths selected in Table 3.5 were
similar to those in Table 3.3 and Table 3.4, only with a small number of specific
wavelengths beyond those selected through the MPLSR analysis. Since the silicon CCD
sensing band contains fewer information of gentiopicroside, and SMLR built the
spectral calibration model based on the combination of a small number of wavelengths,
which gives less spectral information than MPLSR, so the analysis results seemed a
little worse than those in Table 3.3 and Table 3.4. Compared to the tissue culture which
can only apply 2 wavelengths at most for inspection, grown plant can apply 4
wavelengths to build the calibration model, consequently improving its predictability.
Table 3.5 Prediction of the gentiopicroside content in tissue culture and grown plants of G. scabra Bunge by SMLR models in the
966, 420, 408, 436 0.802 0.906 1.186 -0.178 1.27
1st Derivative 730
2 / 2
0.590 1.225 1.265 -0.074 1.19
462, 676 0.725 1.044 0.889 -0.041 1.69
684, 780, 462 0.806 0.897 0.936 0.072 1.61
650, 780, 462, 512 0.850 0.799 0.823 0.008 1.83
2nd Derivative 468
3 / 3
0.626 1.182 1.122 0.001 1.34
460, 634 0.736 1.027 1.011 -0.250 1.49
666, 788, 474 0.834 0.838 0.897 -0.144 1.67
670, 786, 474, 826 0.860 0.775 0.848 -0.134 1.77
3.4 CONCLUSIONS
This study applied NIR for quantitative analysis of gentiopicroside in the medicinal
plant G. scabra Bunge. It was found that the spectral pretreatments of MSC in
combination of 2nd derivative reduced the spectral noise caused by the heterogeneous
particle size of G. scabra Bunge powder. The specific wavelength regions or specific
wavelengths selected based on their characteristic response to gentiopicroside could
effectively improve the predictability of the calibration models. This study successfully
built the spectral calibration models for G. scabra Bunge tissue culture and grown plant,
which enable quantitative inspection of the bioactive component gentiopicroside in G.
scabra Bunge during different growth stages. The specific wavelengths selected in
Silicon CCD sensing band can be used as the foundation to establish a nondestructive
and rapid method to assess the quality of G. scabra Bunge using multi-spectral imaging.
ACKNOWLEDGMENTS
This work was supported by Industrial Development Bureau, Ministry of Economic
Affairs (09611101087-9601). I would like to thank Mr. Cheng-Wei Huang, Mr. Yu-Song
Chen, and Mr. Chun-Chi Chen for their assistance.
CHAPTER 4. INTEGRATION OF INDEPENDENT COMPONENT ANALYSIS WITH NEAR INFRARED SPECTROSCOPY FOR ANALYSIS OF BIOACTIVE COMPONENTS IN A MEDICINAL PLANT
herbaceous plant, is mainly grown in temperate regions such as Taiwan, China, Japan,
South Korea, Russia, and North America. Gentiana scabra Bunge has been proven to
achieve good effect in pharmacology, its dried root and rootstock are commonly used as
pharmaceutical raw materials, since they are rich in many secondary metabolites such as
gentiopicroside, swertiamarin and sweroside (Kakuda et al., 2001). In particular,
gentiopicroside has been shown to protect liver, inhibit liver dysfunction, and promote
gastric acid secretion in addition to its antimicrobial and anti-inflammatory effects, and
swertiamarin is anti-inflammatory, antiepileptic, analgesic, and sedative, making it a
popular ingredient in Chinese herbal medicine and health products (Kim et al., 2009).
In early days, Gentiana scabra Bunge was mainly collected in the wild. As the
demand for Gentiana scabra Bunge increases, change of natural environment and
became an important issue in order to protect and sustainably utilize rare plants (Zhang
et al., 2010). Studies in recent years used tissue culture technology to make artificial
cultivation of Gentiana scabra Bunge (Cai et al., 2009), by domesticating the tissue
culture of Gentiana scabra Bunge, then transplanting it to the greenhouse for cultivation.
In order to monitor the change of Gentiana scabra Bunge during the growth process, it
is necessary to measure the bioactive components of Gentiana scabra Bunge. However,
the commonly used methods such as micellar electrokinetic capillary chromatography
(MECC) (Glatz et al., 2000), high performance liquid chromatography (HPLC)
(Kikuchi et al., 2005) and liquid chromatography-mass spectrometry (LC-MS)
(Aberham et al., 2011) are all time-consuming and energy-intense, hence cannot be
applicable for daily quality inspection of Gentiana scabra Bunge during cultivation.
Near infrared (NIR) spectroscopy is a nondestructive inspection method that has been
widely used in dispensation (Zhang et al., 2005; Wang et al., 2007; Chen et al., 2011).
Generally, spectrum of a mixture is a linear combination of spectra of various components and can be considered as the ‘blind sources’ when the components are
unknown. A fast and reliable algorithm - independent component analysis (ICA) can
deal with the issue of blind source separation (BSS) (Hyvärinen and Oja, 2000) and
identify components of a mixture effectively (Pasadakis and Kardamakis, 2006;
Kardamakis et al., 2007). In recent years, ICA has been used in medicinal tests (Fang
and Lin, 2008; Wang et al., 2009; Shao et al., 2009). Considering there hasn’t been any
study applying NIR spectroscopy in inspection on internal components of Gentiana
scabra Bunge currently, it is the intent of this study to apply ICA, which could analyze
various components simultaneously, in NIR spectroscopy analysis on gentiopicroside
and swertiamarin to discuss qualitative and quantitative relationships of the two
bioactive components. Efforts were also made to build spectral calibration models with
high predictability in order to evaluate the potentiality of NIR for quality inspection on
Gentiana scabra Bunge.
4.2 MATERIALS AND METHODS