Medicinal Plants Classification Biosynthesis and ... - Index of

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Non-Invasive Near Infrared Spectroscopic Techniques for the Characterization... extracts were recorded in transflection mode using light fibre optics over a wavelength range from 4008 to 9996 cm -1 with a resolution of 12 cm -1 . Ten scans were used for one average spectrum to equilibrate in homogeneities. Figure 8a shows 80 original spectra of St. Johns Wort extract samples. Calculation of the first derivative spectra (Figure 8b) allowed identification of characteristic absorption bands. The most intensive band in the spectrum belonged to the vibration of the second overtone of the carbonyl group (5352 cm -1 ), followed by C-H stretch and C-H deformation vibration, the –OH vibration (4440 cm -1 ) and the –CH2 overtone (5742 cm -1 ). Normalisation allowed for minimisation of the baseline shift. In the following all 80 extracts were analysed four-fold by LC for establishing the quantitative regression model. Seventy percent of the spectra were randomly put into a learning-set and 30% into a validation-set. Five primary factors were necessary to reach the best calibration equation. The robustness of the established NIRS model is high, which is demonstrated in similarity of results for SEE and SEP: 0.52 and 0.50 µg mL -1 and 0.64 and 0.71 µg mL -1 for hypericin and hyperforin, respectively. Accuracy is expressed in the bias. The values are 1.6 and 4.2E-14. So the LC-UV results correspond to NIRS on average. Calculation of the regression equation for hypericin (Figure 9) and hyperforin (Figure 10) resulted in correlation coefficients of 0.994 and 0.985, which are slightly smaller than the correlation coefficients of the LC-UV method. The model can be used to predict the content of hypericine and hyperforine in liquid St. John´s Wort extracts. Results show the possibility for phytopharmaceutical industry to replace LC method, usually applied to determine hypericin and hyperforine in the routine analysis, with NIR method, guaranteeing a high degree of robustness and reproducibility. Due to less sensitivity of the NIRS method, it is evident that for the analysis of the lower concentrated naphthodianthrones proto-, pseudo-, protopseudo-, and cyclopseudohypericine as well as for the phloroglucine adhyperforine in particular LC must be preferred. CE was found to be less reproducible. NIRS has the great advantage of ensuring high sampel throughput and reduced costs. NIRS is a full spectrum method. It cannot only determine the content of the naphthodianthrones and phloroglucines but also of other analytes within one single measurement. Finally, also information about other parameters concerning the quality of the extract, e.g. the solvent composition, etc., can be gained. 4.3. Achillea Genus The genus Achillea exhibits extraordinary ecological amplitude and ranges with great morphological variation. It is hard to identify and discriminate its species due to its highly morphological diversity and chemical homogeneity. Some species, such as Achillea millefolium, ceretanica, collina, pratensis, etc., were recognized as one group, the Achillea millefolium complex, for their high homogeneity and successful crossing [54,55]. According to the species index of NCBI taxonomy, there are 65 species of Achillea, e.g. clypeolata, collina, millefolium, nobilis, wilsoniana, etc. A large number of Achillea species is endemic and restricted to certain regions in Europe or certain temperature areas in Asia. Only few of the other species are growing over a wide geographical range. 267
268 C.W. Huck and G.K. Bonn Figure 9. Predicted (NIRS) vs. true property (LC) for the determination of µg/ml hypericin in liquid St. John´s Wort extracts (n=80). R 2 =0.99; SEP=0.68 Figure 10. Predicted (NIRS) vs. true property (LC) for the determination of µg/ml hyperforin in liquid St. John´s Wort extracts (n=80). R 2 =0.99; SEP=0.72
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BIOTECHNOLOGY IN AGRICULTURE, INDUS
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Industrial Biotechnology Shara L. A
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Copyright © 2009 by Nova Science P
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viii Contents Chapter 8 Pharmacolog
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x Alejandro Varela and Jasiah Ibañ
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xii Alejandro Varela and Jasiah Iba
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xiv Alejandro Varela and Jasiah Iba
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In: Medicinal Plants: Classificatio
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Biological Effects of -Carotene acy
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Biological Effects of -Carotene cry
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Biological Effects of -Carotene fla
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Biological Effects of -Carotene Pre
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Biological Effects of -Carotene A (
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Biological Effects of -Carotene As
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Biological Effects of -Carotene car
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Biological Effects of -Carotene res
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Biological Effects of -Carotene the
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Biological Effects of -Carotene cat
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Biological Effects of -Carotene mos
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Biological Effects of -Carotene veg
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Biological Effects of -Carotene .Su
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Biological Effects of -Carotene Bat
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Biological Effects of -Carotene Dah
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Biological Effects of -Carotene Hal
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Biological Effects of -Carotene Kva
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Biological Effects of -Carotene Osh
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Biological Effects of -Carotene Van
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50 Gustavo J. Martínez, Mara Sato
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58 [2] Gustavo J. Martínez, Mara S
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Huperzia saururus (Lam.) Trevis. Mi
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PNEUMONOLOGY AND INFECTIOUS DISEASE
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98 Aracely E. Chávez-Piña and And
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100 Aracely E. Chávez-Piña and An
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118 HO HO HO HO HO HO OH OH M e Me
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120 Me Me OH Me O OH O O OH H Me OH
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122 HO HO HO Aracely E. Chávez-Pi
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140 Hua-Bin Li, Dan Li, Ren-You Gan
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152 6.4. Antibacterial Effects Hua-
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168 Vandana Gulati, Ian H. Harding
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Biomolecules with Anti-Mycobacteria
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204 Chien-Yi Chen Ligusticum chuanx
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206 Chien-Yi Chen (IAEA 336), also
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208 Chien-Yi Chen 4. Results and Di
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210 4.3. Fresh Leaves and Stems Chi
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212 Chien-Yi Chen collected from na
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214 Chien-Yi Chen primary enzyme in
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320 2.7. General Toxicity Tests Phi
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338 age-related macular degeneratio
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340 B lymphocytes, 7 babies, 60, 62
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342 cell surface, 229 cellular adhe
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344 cultivation, 75, 76, 83, 85, 14
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346 embryonic stem cells, 47 emotio
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348 gas, xiii, 21, 104, 131, 145, 1
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350 high-performance liquid chromat
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352 institutions, 84 instruments, 2
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354 lung, ix, 1, 6, 16, 17, 22, 23,
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358 oxidative, 4, 8, 9, 10, 12, 13,
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360 polyunsaturated fatty acids, 10
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362 Reserpine, 328 reservation, 143
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364 SPT, 135 squamous cell carcinom
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366 transcription, 8, 35, 152 trans
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