The samples of Gentiana scabra Bunge were provided by Taiwan Sugar Research
Institute (TSRI; Tainan City, Taiwan). The total of 94 tissue culture samples and 68
grown plant samples of different cultivation time was acquired (Yang et al., 2008;
Cheng, 2009). The shoot and root of the grown plant samples were measured separately
in order to compare their differences. The Gentiana scabra Bunge samples were first
dried for 48 hours in a dryer (50°C), then milled with a high speed grinder (RT-02A,
Sun-Great Technology Co., Ltd., New Taipei City, Taiwan). The dried powder was
filtered with 100 mesh and stored in amber sample vials to avoid light exposure that
may cause degradation of the functional ingredients in Gentiana scabra Bunge (Yang et
al., 2008; Cheng, 2009).
4.2.2 NIR SPECTRA AND HPLC MEASUREMENT
Dry powder of Gentiana scabra Bunge was gently poured into a small ring cup (i.d. 5
cm) and subjected to NIR measurement (NIRS 6500, FOSS NIRSystems, Inc., Laurel,
MD, U.S.A.). The reflectance spectra of the samples ranged from 400 to 2498 nm with
2 nm intervals, and the NIR spectrum of each sample was the average of 32 scans (Yang
et al., 2008; Cheng, 2009).
To attain the reference value of the two bioactive components, the authors measured
gentiopicroside and swertiamarin using HPLC (DX 500 ion chromatograph, Dionex
Corporation, Sunnyvale, CA, U.S.A.) equipped with a DIONEX C18 column (250 mm
× 4.6 mm i.d.). The peak of gentiopicroside and swertiamarin showed up at 250 nm
when applying methanol:water (20:80) in the mobile phase at a flow rate of 1 mL/min.
A high-precision scale was used to measure the gentiopicroside and the swertiamarin
standard powder, and diluted into 1000, 500, and 250 ppm with 70 % methanol as the
standard solutions for the three-point calibration of HPLC. A quantitative linear
relationship was established between the standard concentration and the peak area
(Yang et al., 2008; Cheng, 2009).
4.2.3 DATA ANALYSIS
After the reflectance spectra of Gentiana scabra Bunge powder and the contents of
two bioactive components of Gentiana scabra Bunge were measured, ICA was applied
to explore the relationship between them and spectral calibration models of the two
bioactive components were then built, respectively.
4.2.3.1 INDEPENDENT COMPONENT ANALYSIS (ICA)
Independent component analysis (ICA) is a method used to transform the observed
multivariate data to statistically independent components (ICs) and present them as a
linear combination of observation variables. The number of receptors defined by ICA
algorithm must be more than or equal to the number of sources, and the signals emitted
by the sources are in non-Gaussian distribution (Hyvärinen and Oja, 2000). The ICs are
latent variables; therefore, they cannot be directly observed, indicating that the mixing
matrix is also unknown. The purpose of the ICA algorithm is to determine the mixing
matrix (M) or the separating matrix (W). In order to predict the unknown source, it is
assumed that W = M-1,
ŝ = Wx = M-1Ms (4.1)
where ŝ is the estimation of the sources (s) and x represents the observed spectra of
the objects.
In the present study JADE (joint approximate diagonalization of eigenmatrices)
algorithm (Cardoso and Souloumiac, 1993; Cardoso, 1999) was employed to conduct
ICA analysis. In general, JADE offers rapid performance for dealing with spectra data
due to it works off-the-shelf, an improvement over other multivariate approaches like
PCR and PLSR. Assuming that the spectra obtained through measurement of the
unknown mixtures were the linear combination of various components’ spectra, it can
be expressed as:
A = MI (4.2)
The spectra of samples were all linearly composed of m ICs. Matrix Al×n stands for l
samples containing n values; Im×n stands for the matrix of ICs, including m independent
components. Ml×m stands for the mixing matrix, which is related to the component
concentration in the mixture. The linear relationship between the mixing matrix (M) and
the component concentration (C) can be expressed as:
C = MB (4.3)
Among them, B referred to the matrix of regression coefficient. In doing so, the
concentration of each component in the mixture could be determined by the
combination of ICA and linear regression.
4.2.3.2 SPECTRAL PRETREATMENTS
The purpose of spectral pretreatments was to eliminate the spectral variation not
caused by chemical information contained in the samples (de Noord, 1994; Fearn, 2001).
Since inevitable light scattering could be added into the spectra when using NIR to
measure powder samples, especially when the particle size is not uniform,
multiplicative scatter correction (MSC) was used to allow additive and multiplicative
transformation of the spectra (Eq. 4.4). It was conducted using the average spectrum of
all samples as the reference value, and calculating the parameters a and b with the least
square. After MSC treatment, the spectra of Gentiana scabra Bunge powder not only
reduced the physical impact of non-uniform particles (Helland et al., 1995; Maleki et al.,
2007), but also confirmed the linearity of the spectral information (Isaksson and Næ s,
1988), which would contribute to subsequent linear regression analysis (Thennadil et al.,
2006).
independent treatments, namely (1) smoothing; (2) smoothing with 1st derivative; and (3)
smoothing with 2nd derivative, in order to choose the best pretreatment parameters,
including the smoothing points and the gap ranging from 2 to 50, with the gap being
greater than or equal to the smoothing points.
4.2.3.3 MODEL ESTABLISHMENT
This research used MATLAB version 7.5.0 (The MathWorks, Inc., Natick, MA,
U.S.A.) to edit program of ICA spectra analysis. The ICA analysis procedure included:
(1) selecting calibration set and validation set; (2) spectral pretreatments; (3) selecting
the specific wavelength regions; and (4) determining best calibration model. A 2:1 ratio
of calibration to validation samples was adopted according to the concentrations of
bioactive components in the sample. All samples were ranked ascendantly according to
their concentrations of gentiopicroside and swertiamarin, with the concentrations in the
calibration set higher than the validation set, yet both sets contained similar
concentration distributions of all samples. When selecting the best calibration model, in
order to avoid over-fitting caused by use of excessive ICs, the following principles were
adhered to: (1) the maximum number of ICs is one tenth of the number of calibration
sets + 2 to 3; (2) stop if the adding of a new IC makes the SEV rise; and (3) when the
SEV is lower than the SEC, stop adding new IC.
After the respective spectral calibration models of gentiopicroside and swertiamarin
were built, these models were then used to predict the concentrations of the calibration
and the validation set. The predictability of the models was evaluated based on the
following statistical parameters, including coefficient of correlation of calibration set
(Rc), standard error of calibration (SEC), standard error of the validation (SEV), bias
where Yc and Yv represent the estimated concentration of the calibration set and the
validation set, respectively. Yr is the reference concentration; nc and nv are the number
of samples in the calibration set and validation set; and SD is the standard deviation of
concentration within the validation set.
4.3 RESULTS AND DISCUSSION