Variation in Botanical Reference Materials: Similarity of Actaea Racemosa Analyzed by Flow Injection Mass Spectrometry

BACKGROUND

Botanical reference materials (BRMs) generally account for the species, cultivar, and year and location of harvest that result in variability in the chemical composition that may lead to statistically significant differences using chemometric methods.

OBJECTIVE

To compare the chemical composition of five species of Actaea root BRMs, four herbal sources of A. racemosa root BRMs, and A. racemosa BRMS, and commercial roots and supplements using chemometric methods and selected pre-processing approaches.

METHOD

Samples were analyzed by flow injection mass spectrometry (FIMS), principal component analysis (PCA), and factorial multivariate analysis of variance (mANOVA).

RESULTS

Statistically significant (P = 0.05) compositional differences were found between three genera (Actaea, Panax, and Ginkgo), five species of Actaea (A. racemosa, A. cimicifuga, A. dahurica, A. pachypoda, and A. rubra) root BRMs, four herbal sources of A. racemosa root BRMs, and A. racemosa BRMS and commercial roots and supplements. The variability of 6% of the BRM variables was found to be quantitatively conserved and reduced the compositional differences between the four sources of root BRMs. Compositional overlap of A. racemosa and other Actaea BRMs was influenced by variation in technical repeats, pre-processing methods, selection of variables, and selection of confidence limits. Sensitivity ranged from 94 to 97% and specificity ranged from 21 to 89% for the pre-processing protocols tested.

CONCLUSIONS

Environmental, genetic, and chemometric factors can influence discrimination between species and authentic botanical reference materials.

HIGHLIGHTS

Frequency distribution plots derived from soft independent modeling of class analogy provide excellent means for understanding the impact of experimental factors.

Botanical authentication using one-class modeling

Sample authentication using one-class modeling is simpler and more versatile than the approved AOAC Probability of Identification (POI) method. This approach develops a one-class model for authentic samples and does not identify or model non-authentic or adulterated samples. Unknown samples are classified as either authentic (in the same class as the authentic samples) or not authentic (outside the authentic model class). One-class modeling uses chemometric analysis based on soft independent modeling of class analogy and the specific pre-processing steps of sample vector normalization and autoscaling. Data from flow injection mass spectrometry are used to illustrate the method. Autoscaling reduces the impact of the relative ion intensities and provides much greater sensitivity to changes in the intensities of individual variables. Detection limits for each variable can be predicted based on an average spectrum and types of uncertainty (random or proportional noise). Method validation is readily achieved using cross validation. Detection of adulteration is spectrally oriented and requires no identification of adulterants. Thus, both the data acquisition and the selection of adulterants are untargeted.

Mouse Choroid Proteome Revisited: Focus on Aging

Age is a major risk factor for age-related macular degeneration (AMD), and age has a role in the disease phenotypes of heritable macular dystrophies. The proteomes of C57Bl6/J mouse choroids at 2 ages were analyzed to identify biochemical processes affected by aging. Proteins of interest were identified as those contributing most to the variance in principal component analysis and those showing the largest significant differences between ages. These proteins implicated altered ECM composition, immune system function, and lipid metabolism.

Multi-Glycomic Characterization of Fiber from AOAC Methods Defines the Carbohydrate Structures

Dietary fiber has long been known to be an essential component of a healthy diet, and recent investigations into the gut microbiome-health paradigm have identified fiber as a prime determinant in this interaction. Further, fiber is now known to impact the gut microbiome in a structure-specific manner, conferring differential bioactivities to these specific structures. However, current analytical methods for food carbohydrate analysis do not capture this important structural information. To address this need, we utilized rapid-throughput LC-MS methods to develop a novel analytical pipeline to determine the structural composition of soluble and insoluble fiber fractions from two AOAC methods (991.43 and 2017.16) at the total monosaccharide, glycosidic linkage, and free saccharide level. Two foods were chosen for this proof-of-concept study: oats and potato starch. For oats, both AOAC methods gave similar results. Insoluble fiber was found to be comprised of linkages corresponding to β-glucan, arabinoxylan, xyloglucan, and mannan, while soluble fiber was found to be mostly β-glucan, with small amounts of arabinogalactan. For raw potato starch, each AOAC method gave markedly different results in the soluble fiber fractions. These observed differences are attributable to the resistant starch content of potato starch and the different starch digestion conditions used in each method. Together, these tools are a means to obtain the complex structures present within dietary fiber while retaining "classical" determinations such as soluble and insoluble fiber. These efforts will provide an analytical framework to connect gravimetric fiber determinations with their constituent structures to better inform gut microbiome and clinical nutrition studies.

USDA's FoodData Central: what is it and why is it needed today?

FoodData Central (FDC) is the center of the USDA-based food-composition information web. It is an integrated data system that presently provides-in 1 place-5 distinct types of data containing information on food and nutrient profiles. Each data type has a unique purpose. Two of the data types-Foundation Foods (FF) and Experimental Foods (EF)-represent "a bridge to the future" in food and nutrient composition. They provide data and metadata that have never previously been available from a database. The other 3 data types are well established and familiar to many users: Standard Reference (SR) Legacy, Food and Nutrient Database for Dietary Studies (FNDDS), and Global Branded Foods Products Database (GBFPD). After >100 y of maintaining food-composition data within the USDA, it was clear that change was needed to respond to the rapid increase in the number and variety of foods in the food supply, evolution of analytical approaches, and new agricultural practices and products. FDC is USDA's answer to the challenge of providing reliable, web-based, transparent, and easily accessible information about the nutrients and other components of foods to meet the increasingly diverse needs of many audiences, including public health professionals, agricultural and environmental researchers, policy makers, nutrition professionals, health care providers, product developers, and the public at large.

Identification of High and Low Branched-Chain Fatty Acid-Producing Phenotypes in Holstein Cows following High-Forage and Low-Forage Diets in a Crossover Designed Trial

BACKGROUND

Branched-chain fatty acids (BCFAs) are rumen-derived fatty acids comprising ∼2% of bovine-milk fatty acids. BCFAs possess anti-inflammatory properties and enriching the BCFA content of bovine milk may provide human health benefits.

OBJECTIVE

We determined whether forage content impacts the BCFA content of milk from Holstein cows and identified fatty acid phenotypes in high vs. low BCFA-containing milks.

METHODS

Holstein cows (n = 62), fed for 67 d in a crossover design, consumed a diet with high forage and low concentrate (HF:C) and a diet with low forage and high concentrate (LF:C). Milk samples were collected at the end of each treatment period and fatty acid content determined. Paired t-tests, 1-factor ANOVA, sparse partial least-squares discriminant analysis (sPLSDA), and Pearson's correlation analysis were used to analyze the data.

RESULTS

The total milk fatty acid concentration for cows fed the HF:C diet was greater than that of cows fed the LF:C diet (4.2 ± 0.7 g/100 mL vs. 3.9 ± 0.9 g/100 mL). sPLSDA demonstrated separation of the dietary treatments, with BCFAs and odd-chain fatty acids as primary determinants. Total BCFA content in milk fat was elevated by HF:C intake compared with LF:C intake (1.80 vs. 1.68%). Quintile separation of high vs. low BCFA milks resulted in 4 groups: HF:C /low BCFAs, HF:C /high BCFAs; LF:C /low BCFAs, and LF:C /high BCFAs. Milks from the high BCFA quintiles had lower palmitic acid content (29.6% vs. 34.4%) but higher oleic acid content than milks from the low BCFA quintiles (19.7% vs. 17.0%). Some cows were identified as high BCFA producers or low BCFA producers regardless of diet.

CONCLUSIONS

BCFA content of milk is diet-sensitive but variation in responses exists. The potential to produce milk with high BCFA content and lower SFA content needs further study.

Identification of Branched-Chain Fatty Acid Producing Phenotypes in Holstein Cows

Objectives

Branched chain fatty acids (BCFA) are rumen-derived fatty acids comprising about 2% of bovine milk fatty acids. BCFA possess anti-inflammatory properties and enriching the BCFA content of bovine milk may provide human health benefits of milk consumption. In this work, we determined whether high vs low forage diets impact the BCFA content of milk from Holstein cows and identified fatty acid phenotypes in high vs low BCFA-containing milks.

Methods

In this study, 62 Holstein cows were fed for 70 days, in a cross-over design fashion, either a high forage: concentrate (HF: C) diet or a low forage: concentrate (LF: C) diet. At the end of the 70-day feeding period, cows were switched to the alternate feeding arm. Milk samples were collected prior to the start of the first feeding period and at the end of each treatment period. Milk fatty acid content was determined by fatty acid methyl ester analysis. Paired t-tests, one-way ANOVA, and

sparse partial least squares discriminant analysis (sPLSDA) were used to analyze the data.

Results

The total milk fatty acid concentration at the end of HF: C diet period was greater than that of the LF: C diet (4.2 ± 0.9 g/100 mL vs 3.95 ± 0.9 g/100 mL). sPLSDA demonstrated clear separation of the dietary treatments, with BCFA and odd-chain fatty acids as primary determinants. Total BCFA content was elevated by HF: C intake (1.86 ± 0.13%) vs LF: C intake (1.77 ± 0.14%). Quintile separation of high vs low BCFA milks resulted in 4 groups (n = 12) HF: C/low BCFA, HF: C/high BCFA; LF: C/low BCFA, and LF: C/high BCFA. The total BCFA contents of the low BCFA milks were not different regardless of forage content (1.70 ± 0.06% and 1.61 ± 0.04%) and were lower than the high BFCA milks (2.04 ± 0.11% and 1.98 ± 0.11%). Milks from the high BCFA quintiles had lower saturated fatty acid (SFA) content but higher oleic acid content than milks from the low BCFA quintiles. Of the 62 cows, 5 cows were identified as high BCFA producers and 6 cows as low BCFA producers regardless of the LF: C or HF: C diets.

Conclusions

The data demonstrate that the BCFA content of milk is diet-sensitive but variation in cow responses suggest factors such as genotype or rumen microbiome composition may play significant roles. The potential to produce milk with high BCFA content and lower SFA content needs further study.

Funding Sources

USDA-ARS Projects 3062–53000-001–00D, 5090–31000-025–00D, 80–8040-05–01-0000–0000.

Characterization of Maca (Lepidium meyenii/Lepidium peruvianum) Using a Mass Spectral Fingerprinting, Metabolomic Analysis, and Genetic Sequencing Approach

Maca (Lepidium meyenii, synonym L. peruvianum) was analyzed using a systematic approach employing principal component analysis of flow injection mass spectrometry fingerprints (no chromatographic separation) to guide the selection of samples for metabolite profiling and DNA next generation sequencing. Samples consisted of 39 commercial maca supplements from 11 manufacturers, 31 unprocessed maca tubers grown in Peru and China, and a historic non-tuber maca sample from Peru. Principal component analysis of flow injection mass spectrometry fingerprints initially placed all the maca samples in three classes with similar chemical composition: commercial maca samples, tubers grown in Peru, and tubers grown in China. Metabolite profiling identified 67 compounds in the negative mode and 51 compounds in the positive mode. Compounds identified by metabolite profiling (macamides, glucosinolates, amino acids, fatty acids, polyunsaturated fatty acids, saccharides, imidazoles) were then used to identify ions in the flow injection mass spectrometry fingerprints. The tuber fingerprints were analyzed by factorial multivariate analysis of variance revealing that black, red, and yellow maca from Peru and black and yellow maca from China were compositionally different with respect to color and country. Critical ions were identified that allowed for the differentiation of maca between colors from the same country or between two countries with the same color. Genetically, all samples were confirmed to be L. meyenii based on next generation sequencing at three gene regions (ITS2, psbA, and trnL) and comparison to recorded sequences of vouchered standards.

Elimination of the Variance Between Individuals Is Necessary to Evaluate the Impact of Garlic on the Metabolic Profile of Human Urine

Objectives

To determine the impact of garlic on the metabolic profile of urine.

Methods

On the first day 17 fasting subjects were fed a breakfast of bread and butter. Urines were collected before and 3 hours after the meal. On a second day, the same 17 fasting subjects were fed a meal of bread, butter, and garlic. Urines were again collected before and 3 hours after the meal. Samples were analyzed by metabolomics using liquid chromatography-mass spectrometry (LC-MS) and data were subjected to analysis of variance-principal component analysis (ANOVA-PCA).

Results

637 compounds were found in the urines and 277 were identified. PCA of urine profiles were dominated by variation between individual. Removal of individual variance by ANOVA allowed differentiation of fasting urines from bread and butter urines from bread, butter, and garlic urines. PCA loadings identified compounds that led to discrimination between treatments. Influence of the loading identified compounds were verified by examination of the LC-MS data for individual compounds. Three unique sulfur containing compounds were identified. Loadings showed, however, that a change in the metabolite profiles (ratios of compounds) and not the unique compounds) were most informative.

Conclusions

Removal of variance between individuals is essential to properly analyze the data. Changes in the patterns of compounds routinely observed in urine were the major result of the garlic meal. ANOVA-PCA is an excellent tool for isolating experimental factors.

Funding Sources

Agricultural Research Service, US Department of Agriculture and Office of Dietary Supplements, National Institutes of Health.

Variance of Commercial Powdered Milks Analyzed by Proton Nuclear Magnetic Resonance and Impact on Detection of Adulterants

Proton nuclear magnetic resonance spectra for 66 commercial powdered milk samples were analyzed by principal component analysis, soft independent modeling of class analogy, and pooled, crossed analysis of variance. It was found that the sample type (skim milk powder or non-fat dry milk), the supplier, the production site, the processing temperature (high, medium, or low temperature), and the day of analysis provided statistically significant sources of variation. Interestingly, inexact alignment (deviations of ±0.002 ppm) of the spectral reference peak was a significant source of variation, and fine alignment was necessary before the variation arising from the other experimental factors could be accurately evaluated. Using non-targeted analysis, the lowest detectable adulteration for dicyandiamide, melamine, and sucrose was 0.05%, the lowest detectable adulteration for maltodextrin and urea was 0.5%, the lowest detectable adulteration for ammonium sulfate and whey was 5%, and the lowest adulteration for soy protein isolate was undetectable using methods described herein. The measurement of variance and detection of adulteration were relatively unaffected by the resolution. Similar results were obtained with unbinned data (0.0003 ppm resolution) and binning of 333 data points (0.1 ppm resolution).

Influence of direct and sequential extraction methodology on metabolic profiling

A systematic comparison was made of the detected metabolite profiles for two plant materials (black beans and soybeans) and a dietary supplement (black cohosh) extracted using sequential (hexane, ethyl acetate, and 50% aqueous methanol) and direct extraction with three solvent systems (80% aqueous methanol, methanol/chloroform/water (2.5:1:1, v/v/v) and water). Extracts were analyzed by LC-MS (without derivatization) and GC-FID (with BSTFA/TMCS derivatizations). For sequential extraction, HPLC-UV and BSTFA/TMCS-derivatized GC-FID detection were more responsive to the polar molecules with a rough distribution of 10%, 10%, and 80% of the total signals in hexane, ethyl acetate, and 50% aqueous methanol, respectively. With HPLC-MS detection, the distribution of signals was more balanced, roughly 40%, 30%, and 30% for the same extracts (hexane, ethyl acetate, and 50% aqueous methanol). For direct extraction, HPLC-UV and BSTFA/TMCS-derivatized 4GC-FID provided signals between 60% and 150% of the total sequential extracted signals. The overlap of signals for the 3 sequential extracts ranged from 1% to 3%. The overlap of the signals for direct extraction with the total for sequential extraction ranged from 15% to 98%. With HPLC-MS detection, signals varied from 30% to 40% of the total signals for sequential extraction. Multivariate analysis showed that the components for the sequential and direct extracts were statistically different. However, each extract, sequential or direct, allowed discrimination between the 3 plant materials.

Importance of Accurate Measurements in Nutrition Research: Dietary Flavonoids as a Case Study

Accurate measurements of the secondary metabolites in natural products and plant foods are critical for establishing relations between diet and health. There are as many as 50,000 secondary metabolites that may influence human health. Their structural and chemical diversity presents a challenge to analytical chemistry. With respect to flavonoids, putative identification is accessible, but positive identification and quantification are limited by the lack of standards. Quantification has been tested with use of both nonspecific and specific methods. Nonspecific methods, which include antioxidant capacity methods, fail to provide information on the measured components, suffer from numerous interferences, are not equatable, and are unsuitable for health research. Specific methods, such as LC with diode array and mass spectrometric detection, require the use of internal standards and relative molar response factors. These methods are relatively expensive and require a high level of expertise and experimental verification; however, they represent the only suitable means of relating health outcomes to specific dietary components.

Comparison of Flow Injection MS, NMR, and DNA Sequencing: Methods for Identification and Authentication of Black Cohosh (Actaea racemosa)

Flow injection mass spectrometry and proton nuclear magnetic resonance spectrometry, two metabolic fingerprinting methods, and DNA sequencing were used to identify and authenticate Actaea species. Initially, samples of Actaea racemosa from a single source were distinguished from other Actaea species based on principal component analysis and soft independent modeling of class analogies of flow injection mass spectrometry and proton nuclear magnetic resonance spectrometry metabolic fingerprints. The chemometric results for flow injection mass spectrometry and proton nuclear magnetic resonance spectrometry agreed well and showed similar agreement throughout the study. DNA sequencing using DNA sequence data from two independent gene regions confirmed the metabolic fingerprinting results. Differences were observed between A. racemosa samples from four different sources, although the variance within species was still significantly less than the variance between species. A model based on the combined A. racemosa samples from the four sources consistently permitted distinction between species. Additionally, the combined A. racemosa samples were distinguishable from commercial root samples and from commercial supplements in tablet, capsule, or liquid form. DNA sequencing verified the lack of authenticity of the commercial roots but was unsuccessful in characterizing many of the supplements due to the lack of available DNA.

Feasibility of including green tea products for an analytically verified dietary supplement database

The Dietary Supplement Ingredient Database (DSID) is a federally funded, publicly accessible dietary supplement database that currently contains analytically derived information on micronutrients in selected adult and children's multivitamin and mineral (MVM) supplements. Other constituents in dietary supplement products such as botanicals are also of interest and thus are being considered for inclusion in the DSID. Thirty-eight constituents, mainly botanicals were identified and prioritized by a federal interagency committee. Green tea was selected from this list as the botanical for expansion of the DSID. This article describes the process for prioritizing dietary ingredients in the DSID. It also discusses the criteria for inclusion of these ingredients, and the approach for selecting and testing products for the green tea pilot study.

Certification of elements in and use of standard reference material 3280 multivitamin/multielement tablets

Standard Reference Material 3280 Multivitamin/ Multielement Tablets was issued by the National Institute of Standards and Technology in 2009, and has certified and reference mass fraction values for 13 vitamins, 26 elements, and two carotenoids. Elements were measured using two or more analytical methods at NIST with additional data contributed by collaborating laboratories. This reference material is expected to serve a dual purpose: to provide quality assurance in support of a database of dietary supplement products and to provide a means for analysts, dietary supplement manufacturers, and researchers to assess the appropriateness and validity of their analytical methods and the accuracy of their results.

Probability of identification: adulteration of American Ginseng with Asian Ginseng

The AOAC INTERNATIONAL guidelines for validation of botanical identification methods were applied to the detection of Asian Ginseng [Panax ginseng (PG)] as an adulterant for American Ginseng [P. quinquefolius (PQ)] using spectral fingerprints obtained by flow injection mass spectrometry (FIMS). Samples of 100% PQ and 100% PG were physically mixed to provide 90, 80, and 50% PQ. The multivariate FIMS fingerprint data were analyzed using soft independent modeling of class analogy (SIMCA) based on 100% PQ. The Q statistic, a measure of the degree of non-fit of the test samples with the calibration model, was used as the analytical parameter. FIMS was able to discriminate between 100% PQ and 100% PG, and between 100% PQ and 90, 80, and 50% PQ. The probability of identification (POI) curve was estimated based on the SD of 90% PQ. A digital model of adulteration, obtained by mathematically summing the experimentally acquired spectra of 100% PQ and 100% PG in the desired ratios, agreed well with the physical data and provided an easy and more accurate method for constructing the POI curve. Two chemometric modeling methods, SIMCA and fuzzy optimal associative memories, and two classification methods, partial least squares-discriminant analysis and fuzzy rule-building expert systems, were applied to the data. The modeling methods correctly identified the adulterated samples; the classification methods did not.

Quantitation of the hydroxycinnamic acid derivatives and the glycosides of flavonols and flavones by UV absorbance after identification by LC-MS

A general approach was developed to quantify hydroxycinnamic acid derivatives and the glycosides of flavonols and flavones using UV molar relative response factors (MRRFs). More than 90 standards were analyzed by LC-MS and divided into five groups based on the λ(max) of their band I absorbance profiles. For each group, a commercially available standard was chosen as the group reference standard. Response factors were determined for each standard in each group as purchased (MRRF) and, when possible, after vacuum drying (MRRF(D)). The MRRF(D) values for 17 compounds whose λ(max) values fell within ±2 nm of the group reference standard were 1.01 ± 0.03. MRRF values for compounds whose λ(max) values fell within ±10 nm of the group reference standard were 0.96 ± 0.13. Group reference standards were used to quantify 44 compounds in Chinese lettuce, red onion, and white tea. This approach allows quantitation of numerous compounds for which there are no standards.

UHPLC-PDA-ESI/HRMS/MS(n) analysis of anthocyanins, flavonol glycosides, and hydroxycinnamic acid derivatives in red mustard greens (Brassica juncea Coss variety)

An UHPLC-PDA-ESI/HRMS/MS(n) profiling method was used for a comprehensive study of the phenolic components of red mustard greens ( Brassica juncea Coss variety) and identified 67 anthocyanins, 102 flavonol glycosides, and 40 hydroxycinnamic acid derivatives. The glycosylation patterns of the flavonoids were assigned on the basis of direct comparison of the parent flavonoid glycosides with reference compounds. The putative identifications were obtained from tandem mass data analysis and confirmed by the retention time, elution order, and UV-vis and high-resolution mass spectra. Further identifications were made by comparing the UHPLC-PDA-ESI/HRMS/MS(n) data with those of reference compounds in the polyphenol database and in the literature. Twenty-seven acylated cyanidin 3-sophoroside-5-diglucosides, 24 acylated cyanidin 3-sophoroside-5-glucosides, 3 acylated cyanidin triglucoside-5-glucosides, 37 flavonol glycosides, and 10 hydroxycinnamic acid derivatives were detected for the first time in brassica vegetables. At least 50 of them are reported for the first time in any plant materials.

Dietary supplement ingredient database (DSID): Preliminary USDA studies on the composition of adult multivitamin/mineral supplements

The Nutrient Data Laboratory of the United States Department of Agriculture (USDA) is collaborating with the Office of Dietary Supplements (ODS), the National Center for Health Statistics (NCHS), and other government agencies to design and populate a dietary supplement ingredient database (DSID). This analytically based, publicly available database will provide reliable estimates of vitamin and mineral content of dietary supplement (DS) products. The DSID will initially be populated with multivitamin/mineral (MVM) products because they are the most commonly consumed supplements. Challenges associated with the analysis of MVMs were identified and investigated. A pilot study addressing the identification of appropriate analytical methods, sample preparation protocols, and experienced laboratories for the analysis of 12 vitamins and 11 minerals in adult MVM supplement products was completed. Preliminary studies support the development of additional analytical studies with results that can be applied to the DSID. Total intakes from foods and supplements are needed to evaluate the associations between dietary components and health. The DSID will provide better estimates of actual nutrient intake from supplements than databases that rely on label values alone.

Progress in developing analytical and label-based dietary supplement databases at the NIH Office of Dietary Supplements

Although an estimated 50% of adults in the United States consume dietary supplements, analytically substantiated data on their bioactive constituents are sparse. Several programs funded by the Office of Dietary Supplements (ODS) at the National Institutes of Health enhance dietary supplement database development and help to better describe the quantitative and qualitative contributions of dietary supplements to total dietary intakes. ODS, in collaboration with the United States Department of Agriculture, is developing a Dietary Supplement Ingredient Database (DSID) verified by chemical analysis. The products chosen initially for analytical verification are adult multivitamin-mineral supplements (MVMs). These products are widely used, analytical methods are available for determining key constituents, and a certified reference material is in development. Also MVMs have no standard scientific, regulatory, or marketplace definitions and have widely varying compositions, characteristics, and bioavailability. Furthermore, the extent to which actual amounts of vitamins and minerals in a product deviate from label values is not known. Ultimately, DSID will prove useful to professionals in permitting more accurate estimation of the contribution of dietary supplements to total dietary intakes of nutrients and better evaluation of the role of dietary supplements in promoting health and well-being. ODS is also collaborating with the National Center for Health Statistics to enhance the National Health and Nutrition Examination Survey dietary supplement label database. The newest ODS effort explores the feasibility and practicality of developing a database of all dietary supplement labels marketed in the US. This article describes these and supporting projects.

Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI)

There is a general consensus that supports the need for standardized reporting of metadata or information describing large-scale metabolomics and other functional genomics data sets. Reporting of standard metadata provides a biological and empirical context for the data, facilitates experimental replication, and enables the re-interrogation and comparison of data by others. Accordingly, the Metabolomics Standards Initiative is building a general consensus concerning the minimum reporting standards for metabolomics experiments of which the Chemical Analysis Working Group (CAWG) is a member of this community effort. This article proposes the minimum reporting standards related to the chemical analysis aspects of metabolomics experiments including: sample preparation, experimental analysis, quality control, metabolite identification, and data pre-processing. These minimum standards currently focus mostly upon mass spectrometry and nuclear magnetic resonance spectroscopy due to the popularity of these techniques in metabolomics. However, additional input concerning other techniques is welcomed and can be provided via the CAWG on-line discussion forum at http://msi-workgroups.sourceforge.net/ or http://Msi-workgroups-feedback@lists.sourceforge.net. Further, community input related to this document can also be provided via this electronic forum.

Chromatographic fingerprint analysis for evaluation of Ginkgo biloba products

The flavonoids and the terpene lactones are regarded as the two main active components of Ginkgo biloba that affect human health. In the work discussed in this paper, two analytical methods for the characterization of G. biloba authentic materials and commercial products, an LC-UV chromatographic fingerprinting method and a traditional flavonol quantification method, were compared. The traditional method was used to determine the total flavonol content (as glycosides) after acid hydrolysis. The fingerprinting method examined the chromatographic profiles of methanol-water extracts using chemometric methods. The traditional method showed that all the commercial products met the current voluntary standard of 24% flavonols by weight. The chromatographic fingerprinting method revealed significant variations in the commercial products with regard to the relative concentration of individual flavonols.

Measuring vitamins and minerals in dietary supplements for nutrition studies in the USA

This article illustrates the importance of having analytical data on the vitamin and mineral contents of dietary supplements in nutrition studies, and describes efforts to develop an analytically validated dietary supplement ingredient database (DSID) by a consortium of federal agencies in the USA. Preliminary studies of multivitamin mineral supplements marketed in the USA that were analyzed as candidates for the DSID are summarized. Challenges are summarized, possible future directions are outlined, and some related programs at the Office of Dietary Supplements, National Institutes of Health are described. The DSID should be helpful to researchers in assessing relationships between intakes of vitamins and minerals and health outcomes.

Flavonoids and heart health: proceedings of the ILSI North America Flavonoids Workshop, May 31-June 1, 2005, Washington, DC

This article provides an overview of current research on flavonoids as presented during a workshop entitled, "Flavonoids and Heart Health," held by the ILSI North America Project Committee on Flavonoids in Washington, DC, May 31 and June 1, 2005. Because a thorough knowledge and understanding about the science of flavonoids and their effects on health will aid in establishing dietary recommendations for bioactive components such as flavonoids, a systematic review of the science of select flavonoid classes (i.e., flavonols, flavones, flavanones, isoflavones, flavan-3-ols, anthocyanins, and proanthocyanidins) was presented. The objectives of the workshop were to 1) present and discuss current research on flavonoid intake and the relation between flavonoids and heart health; 2) develop information that could lead to expert consensus on the state-of-the-science of dietary intake of flavonoids on heart health; and 3) summarize and prioritize the research needed to establish the relations between specific flavonoids and heart health. Presentations included the basics of the biology of flavonoids, including the types and distribution in foods, analytical methodologies used to determine the amounts in foods, the bioavailability, the consumption patterns and potential biomarkers of intake, risk assessment and safety evaluation, structure/function claims, and the proposed mechanism(s) of the relation between certain flavonoids and heart health endpoints. Data presented support the concept that certain flavonoids in the diet can be associated with significant health benefits, including heart health. Research gaps were identified to help advance the science.

Determination of the flavonoid components of cashew apple (Anacardium occidentale) by LC-DAD-ESI/MS

Liquid chromatography, with diode array detection and electrospray ionization mass spectrometry (LC-DAD-ESI/MS), was used to identify and quantify flavonoids in cashew apple. One anthocyanin and thirteen glycosylated flavonols were detected in a methanol-water extract. Among them, the 3-O-galactoside, 3-O-glucoside, 3-O-rhamnoside, 3-O-xylopyranoside, 3-O-arabinopyranoside and 3-O-arabinofuranoside of quercetin and myricetin, as well as kaempferol 3-O-glucoside were identified by direct comparison with standards or positively identified flavonoids in cranberry. The anthocyanin was the 3-O-hexoside of methyl-cyanidin. Trace amounts of delphinidin and rhamnetin were detected in the hydrolyzed extract, suggesting their glycosides were present, but undetectable, in the original extract. The concentrations of the 14 flavonoids in the tested sample were determined. This is the first report of these flavonoids in cashew apple.

Progress in development of an integrated dietary supplement ingredient database at the NIH Office of Dietary Supplements

Several activities of the Office of Dietary Supplements (ODS) at the National Institutes of Health involve enhancement of dietary supplement databases. These include an initiative with US Department of Agriculture to develop an analytically substantiated dietary supplement ingredient database (DSID) and collaboration with the National Center for Health Statistics to enhance the dietary supplement label database in the National Health and Nutrition Examination Survey (NHANES). The many challenges that must be dealt with in developing an analytically supported DSID include categorizing product types in the database, identifying nutrients, and other components of public health interest in these products and prioritizing which will be entered in the database first. Additional tasks include developing methods and reference materials for quantifying the constituents, finding qualified laboratories to measure the constituents, developing appropriate sample handling procedures, and finally developing representative sampling plans. Developing the NHANES dietary supplement label database has other challenges such as collecting information on dietary supplement use from NHANES respondents, constant updating and refining of information obtained, developing default values that can be used if the respondent cannot supply the exact supplement or strength that was consumed, and developing a publicly available label database. Federal partners and the research community are assisting in making an analytically supported dietary supplement database a reality.