Exploring the phytoremediation potential of water hyacinth by FTIR Spectroscopy and ICP-OES for treatment of heavy metal contaminated water.

Toxic heavy metal pollution of water is a major environmental problem and the current remediation approaches are not optimal as they are non-eco-friendly and lacking in efficiency. As such phytoremediation, a green remediation technology is recognized as a better approach. In this study, both Fourier Transform Infrared (FTIR) Spectroscopy and Inductive Coupled Plasma-Optical Emission Spectroscopy (ICP-OES) were used to investigate the capability of an aquatic plant, water hyacinth (Eichhornia crassipes) to remove heavy metals of lead, copper, cadmium and arsenic from aqueous solution at concentrations of 2 mg/L and 8 mg/L. Overall, the results showed that the uptake was rapid with the plants removing >80% of all the heavy metals at both concentrations. This uptake was proven by the detection of metal accumulation in plant tissues. Roots proved to be better accumulator than leaves. Maximum bioconcentration factor values indicating that the plant is a hyperaccumulator for lead and a moderate accumulator for the other heavy metals. Ligands such as O-H, C-O, C-C and C-H were found to aid the plant in accumulating heavy metal in its tissues. This study concludes that water hyacinth can be utilized as a phytoremediation agent to clean up heavy metal polluted water.

Penalized discriminant analysis for the detection of wild-grown and cultivated Ganoderma lucidum using Fourier transform infrared spectroscopy.

An effective and simple analytical method using Fourier transform infrared (FTIR) spectroscopy to distinguish wild-grown high-quality Ganoderma lucidum (G. lucidum) from cultivated one is of essential importance for its quality assurance and medicinal value estimation. Commonly used chemical and analytical methods using full spectrum are not so effective for the detection and interpretation due to the complex system of the herbal medicine. In this study, two penalized discriminant analysis models, penalized linear discriminant analysis (PLDA) and elastic net (Elnet),using FTIR spectroscopy have been explored for the purpose of discrimination and interpretation. The classification performances of the two penalized models have been compared with two widely used multivariate methods, principal component discriminant analysis (PCDA) and partial least squares discriminant analysis (PLSDA). The Elnet model involving a combination of L1 and L2 norm penalties enabled an automatic selection of a small number of informative spectral absorption bands and gave an excellent classification accuracy of 99% for discrimination between spectra of wild-grown and cultivated G. lucidum. Its classification performance was superior to that of the PLDA model in a pure L1 setting and outperformed the PCDA and PLSDA models using full wavelength. The well-performed selection of informative spectral features leads to substantial reduction in model complexity and improvement of classification accuracy, and it is particularly helpful for the quantitative interpretations of the major chemical constituents of G. lucidum regarding its anti-cancer effects.