CHAPTER 6. GENERAL CONCLUSIONS
6.1 GENERAL DISCUSSION
In the dissertation, independent component analysis (ICA) was first adopted as the
sole tool for NIR quantitative analyses of biomaterials, including wax jambu fruit
(Chuang et al., 2010; Chuang et al., 2012c), medicinal plant Gentiana scabra Bunge
(Chen et al., 2010; Chuang et al., 2012b; Chuang et al., 2013), and milled white rice
(Chuang et al., 2012a), to evaluate the applicability of this method. Influence due to
various format types of samples (sucrose solution, intact fruit, dry powder of Gentiana
scabra Bunge, and rice kernel) was also studied.
In the first part, ICA was applied as the sole tool to integrate with NIR spectroscopy
for rapid quantification of sugar content in sucrose solutions and wax jambu. ICA gave
a comprehensive approach to characterize the NIR spectra with respect to the sugar
content in wax jambu and sucrose solutions that other multivariate analysis methods
cannot deal with. The spectral calibration models built by ICA had high predictability
for both wax jambu and sucrose solutions. Compared to PLSR, ICA can identify the
sugar features in the spectra of wax jambu and then evaluate their concentrations more
effectively. Therefore, it offers a reliable tool for quantitative analysis of sugar content
in wax jambu by NIR spectroscopy. ICA in conjunction with NIR spectroscopy also has
a potential to be applied to identify multiple constituents and evaluate their
concentrations of agricultural products.
Regarding medicinal plants, NIR was applied for quantitative analysis of
gentiopicroside which was one of the bioactive components in the medicinal plant
Gentiana scabra Bunge. It was found that the spectral pretreatments of MSC in
combination of 2nd derivative reduced the spectral noise caused by the nonuniform
particle sizes of Gentiana 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 Gentiana scabra Bunge tissue
culture and grown plant, which enable quantitative inspection of the bioactive
component gentiopicroside in Gentiana 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
Gentiana scabra Bunge using multi-spectral imaging.
For further evaluation, this study applied ICA in NIR spectroscopy analysis on
gentiopicroside and swertiamarin - bioactive components of Gentiana scabra Bunge
and discussed relevant tissue culture and grown plant (including shoot and root). By
selecting ICs that were highly correlated to the bioactive components, the space of ICs
could clearly show the distribution of gentiopicroside and swertiamarin in different
parts of Gentiana scabra Bunge. Additionally, the predictability of the spectral
calibration models on the two bioactive components was adequate for establishing
qualitative and quantitative correlations. Therefore, by combining ICA with NIR
spectroscopy, fast and accurate inspection of gentiopicroside and swertiamarin in
Gentiana scabra Bunge at different growth stages could be achieved. This technology
could contribute substantially to the quality management of Gentiana scabra Bunge or
other medicinal plants (e.g. Herba Saussureae Involucratae) during and post cultivation.
On the other hand, ICA was integrated with NIR spectral analysis to quantify the
internal quality of rice. A quantitative model was developed using ICA factors to predict
the pH value of ground white rice in solution as a proxy for rice freshness. The results
show that ICA quantitative analysis methods with near infrared spectroscopy can
successfully distinguish rice freshness and can serve as a nondestructive rapid analytical
screening tool.
In conclusion, by combining ICA with NIR spectroscopy, fast and accurate evaluation
of constituents in biomaterials could be achieved. ICA offers a rapid and reliable tool
for quantitative analysis of constituents in biomaterials by NIR spectroscopy. This
technology could contribute substantially to identify multiple constituents of
biomaterials and evaluate their concentrations.
6.2 RECOMMENDATIONS FOR FUTURE RESEARCH
There are several ways for the application of ICA for future research. First, ICA can
be applied to deal with more research topics according to their needs or requirements.
The analysis results of ICA by conducting JADE algorithm can also be assessed by
comparing to other algorithms like FastICA and kernel ICA. Second, the combination of
ICA and other multivariate analysis methods such as ICA-ANN, ICA-LS-SVM, and
ICA-SVM may be available to deal with nonlinear problems instead of using ICA.
Third, ICA can be integrated with spectral imaging or fluorescence imaging technology
for inspection of biomaterials and food products in food safety and quality assurance
issues. Finally, the relationship between the values of CV and the ICA calibration
models could be explored for further research.
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