1a. Objectives (from AD-416)
Objective 1: Develop and evaluate procedures for quantitative extraction and/or fractionation of food materials by polarity. Sub-objective 1.A.: Develop an extraction procedure for sequential fractionation of the major groups of components from plant materials. Sub-objective 1.B.: Develop optimized extraction procedures for accurate quantification of individual phytochemicals in plant materials. Objective 2: Develop and evaluate spectral fingerprinting and chromatographic profiling methods to characterize components in lipid soluble, water soluble, and intermediate fractions of food materials. Sub-objective 2.A.: Develop spectral fingerprinting methods for identification of plant materials and individual components using direct analysis (no prior chromatographic separation) and pattern recognition algorithms. Sub-objective 2.B.: Develop chromatographic profiling methods for identification and quantification of individual components in plant materials. Objective 3: Develop methods to determine variability of biologically active components in food materials through profiles and/or fingerprints.
1b. Approach (from AD-416)
Objective 1: Methods will be developed for the quantitative extraction of macro and micro components from plant materials using commercial, high pressure/temperature extraction instrumentation. Soybeans will be tested initially as they contain both lipid soluble and highly polar molecules of health interest. Sequential and parallel extraction will be investigated. Extracted materials will be characterized using liquid chromatography with diode array and electrospray ionization/mass spectrometric detection (LC-DAD-ESI/MS). The new method will be applied to foods and botanical materials. In addition, optimized methods will be developed for specific families of compounds such as water-soluble vitamins, lipid-soluble vitamins, phenolic acids, and flavonoids. Objective 2: Spectral fingerprinting methods will be developed based on ultraviolet and visible molecular absorption (UV/Vis), infrared (IR), near-infrared (NIR), and mass spectrometric (MS) detection. The overlapping complex spectra will be interpreted using pattern recognition programs. The patterns will be used to determine the sensitivity of the different detection systems for discriminating between plant materials based on genera, species, variety, growing year, growing site, and processing conditions. These methods will be developed using 3 food materials and 3 botanical supplement materials. Repeat samples will be examined over a period of years to determine the stability of the spectra and the ability to compare spectra of new materials to archived spectra. The phenolic and vitamin content of the plant materials will also be determined using chromatographic profiling using LC-DAD-ESI/MS. This will make it possible to determine which compounds are contributing most to differences arising from the various growing factors. Objective 3: The spectral fingerprints can be used with nested analysis of variance to determine the relative variance contributed by each growing factor: species, variety, site, year, plant-to-plant variation, and analytical uncertainty. Samples will be obtained from collaborators across the country and representing a variety of foods and botanical supplements. UV/Vis, IR, and NIR spectra will provide variance data for the integrated chemical composition of the plant materials and MS will provide variance data for specific masses and, with the assistance of chromatographic profiling, specific compounds of health interest.
3. Progress Report
FCMDL developed optimized parameters for the accelerated solvent extractor (ASE from Dionex) for the determination of a wide range of polar (lactones, anthocyanins), semi-polar (flavonoids, terpenoids), and non-polar (methoxy flavonoids, lipids) compounds in soybeans, wheat, and Artemesia annua. In wheat, ASE parameters were optimized for extracting phenolic compounds from different particle size fractions of wheat (1235-52000-060-14R). Oils from citronella and lemon grass were compared using ASE and classical micro-hydrodistillation. A sequential extraction method was developed for soybeans which allowed extraction of the polar isoflavones and then the non-polar lipids. The ASE method prevented interaction of residues of the two solvents. This method is necessary for complete characterization of plant and animal metabolomes. An ASE method was optimized for Artemisinin, an anti-viral, anti-carcinogenic, sesquiterpene lactone found in Artemesia, that is of medical interest. We optimized our method to investigate the effect of drying procedures and growing conditions (field, greenhouse, or tissue culture) on artemisinin, dihydroartemisinic acid (a precursor), and the phenolic content of the tissue. This work was in collaboration with Applachian Farming Systems Research Center (ARS, USDA, Beaver, WV). FCMDL continued development of methods using spectral fingerprinting and pattern recognition programs to characterize botanicals. In 2010 we examined 3 ginseng species: American (Panax quinquefolius), Chinese ginseng (P. quinquefolius), and P. notoginseng. This project was supported by the Office of Dietary Supplements at NIH and is described in more detail in project report 1235-52000-060-11R. Direct analysis of solids or extracts by ultraviolet (UV), near-infrared (NIR), or mass spectrometry (MS) readily identified the 3 species and the “red” and “white” preparations of the “Chinese” ginseng. Mass spectrometry (MS) and near-infrared (NIR) could also be used to identify the growing location (China, U.S., or Canada) for American ginseng. Computer programs used for pattern recognition were developed in collaboration with Ohio University (project report 1235-52000-060-08G). Using statistical analysis programs we were able to locate spectral regions (by UV and NIR) or masses (by MS) that were significantly different for the species and provided enhanced ability to distinguish between them. The standardized method for analysis of polyphenols (developed at FCMDL) was used to analyze flavonoids and phenolic acids in Brassica olaracea (e.g. kale, collard greens, and broccolis) and other Brassica species (e.g. mustard greens, bok choy, and napa). More than 100 phenolic compounds were identified in both groups, many for the first time. A method is being developed for the quantification of glycosylated flavonoids based on general rules for shifts in the peak wavelengths and absorption coefficients for the conjugated forms.
1. Measuring the effect of genetic and location differences on dry beans using UV spectrophotometry. The chemical composition of plants can vary significantly as a function of genetics (species and sub-species) and the environment (weather, soil, and other biota). This research used the UV spectra of aqueous alcohol extracts of dry beans and pattern recognition computer programs to demonstrate significant differences in the chemical composition for 9 cultivars and for 3 growing location (MD, MI, and NE). In addition, it was determined that the plant-to-plant variation was approximately 70%. This research demonstrated that an inexpensive analytical method can be used to detect subtle differences in chemistry arising from micro- (plant-to-plant), and macro- (different states in the US) environments and genetics (cultivar). The low cost of the method makes it an easily accessible tool for all plant researchers.
2. Measuring the effect of growing conditions on grapefruit using spectral fingerprinting. The chemical composition of plants can vary significantly as a function of genetics (species and sub-species) and the environment (weather, soil, and other biota). This research demonstrated that mass spectral fingerprints (spectra obtained by analysis of the grapefruit juice with no chromatographic separation) used with pattern recognition computer programs can be used to differentiate between the fruit with respect to the growing year, growing mode (conventional vs. organic), and harvest time (early, mid, and late season). In addition, the specific data analysis program allowed the percentage of the total variance of the samples to be assigned to these 3 factors. This methodology makes it possible for researchers to answer critical questions regarding the effect of growing conditions on the chemical composition of plant materials.Singh, A.P., Luthria, D., Wilson, T., Vorsa, N., Singh, V., Banuelos, G.S., Pasakdee, S. 2009. Polyphenol content and antioxidant capacity of eggplant pulp. Food Chemistry. 114(3):955-961.