Title: UV Spectral Fringerprinting and Analysis of Variance-Principal Component Analysis: A Tool for Characterizing Sources of Variance in Plant Materials Authors
|Finley, J - USDA-GFHNRC, GRAND FOLKS|
Submitted to: Journal of Agricultural and Food Chemistry
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: May 20, 2008
Publication Date: May 20, 2008
Citation: Luthria, D.L., Mukhopadhyay, M., Finley, J., Banuelos, G.S., Harnly, J.M. 2008. UV spectral fringerprinting and analysis of variance-principal component analysis: a tool for characterizing sources of variance in plant materials. Journal of Agricultural and Food Chemistry. http://dx.doi.org/10.10.21/jf0734572. Interpretive Summary: Genetics and a variety of environmental factors (such as rainfall, pests, soil, irrigation levels, and fertilization) can lead to chemical differences in the same plant materials. A simple and inexpensive method is described that allows the overall chemical composition of plant materials to be compared. This method uses an extraction of a powdered plant material that is examined using a spetrophotometer to measure the absorbance from 220 to 380 nm. The spectra of different plant materials are then analyzed using a pattern recognition program called analysis of variance-principal components analysis (ANOVA-PCA). With this method, differences in materials show up as a horizontal separation on the PCA plots. Further calculations allow the influence (% variance) of the experimental factors (cultivar and treatment in this study) to be quantified. This method will allow the rapid and inexpensive testing of plant materials to determine if there are chemical differences introduced by the cultivar, the growing conditions, or the processing conditions. In addition, this method makes it possible to determine if the observed differences are significant compared to naturally occurring plant-to-plant variations.
Technical Abstract: UV spectral fingerprints, in combination with analysis of variance-principal components analysis (ANOVA-PCA), was used to identify sources of variance in 7 broccoli samples composed of two cultivars and seven different growing condition (four levels of Se irrigation, organic farming, and conventional farming with full and 80% irrigation). Freeze dried powdered samples were extracted with methanol-water (60:40, v/v) and analyzed with no prior separation. Spectral fingerprints were acquired for the UV region (220 to 400 nm) using a 50 fold dilution of the extract. ANOVA-PCA was used to construct subset matrices that permitted easy testing of hypothesis that cultivar and treatment contributed to a difference in the chemical expression of the broccoli. The sums of the squares of the same matrices were used to show that cultivar, treatment, and analytical repeatability contributed 30.5%, 68.3%, and 1.2% of the variance, respectively.