analytical developments for identification and authentication of … · analytical developments for...
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Analytical Developments for Identification and Authentication of Botanicals
National Capital Area Chapter – Society of Toxicology Annual Meeting, March 23, 2017
James Harnly Food Composition and Methods Development Lab
Beltsville Human Nutrition Research Center Agricultural Research Service
U.S. Department of Agriculture Beltsville, MD, USA
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Authentication of Botanicals
Terminology: authentication, identification, similarity, phyto-equivalence, taxonomic exactness, or adulteration.
Perspective: these are all the same problem and best addressed with the same approach: non-targeted metabolite fingerprinting with chemometric analysis.
Non-targeted methods: information is limited to the composition of the reference materials. No target compounds, no target adulterants. As comprehensive as possible. Usually a form of metabolite fingerprinting. Every data point is used. None are arbitrarily discarded.
Chemometric analysis: use one-class modeling. Model is based only on reference samples. No other info needed.
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Authentication
Authentication is like a “Sesame Street Question” Peter Scholl, FDA
Which one is different?
Or, rephrased:
Is this one, , the same as the others?
• Requires reference samples. • Verification of the similarity/dissimilarity of the test
sample and the reference samples.
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Conceptually, Authentication is Simple
The Hard Part:
1. Specifying and collecting reference samples for the model that will encompass as many naturally occurring sources of variance as possible.
2. Specifying a method that will allow you to build a model that will encompass as many of the specific botanical features/properties as possible.
1. Build a model with reference samples. 2. Set statistical limits (how much deviation from
the model will be tolerated). 3. Then, compare the “test” sample to the model.
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1. Collecting Reference Samples
• Most critical aspect of the analysis and usually the most difficult.
• Samples may be vouchered, self collected, historical. It depends on the purpose.
• Samples must be representative of the material of interest & the expected variance.
• Differences may arise from many factors (G x E x M): Genetics: species & sub-species Environment: geography, weather Management: conventional/organic farming, post- harvest, processing • Must compare apples to apples!
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2. Selection of Methods
Many possible measures of botanical properties.
Modern methods:
Genetic: Full/partial sequencing, DNA barcoding, mini-barcoding, next generation sequencing. Chemical: Metabolomics (targeted, markers) Metabolic fingerprinting (non-targeted, patterns require multivariate analysis).
Prefer quantitative methods that produce values that can be treated statistically to assure objectivity.
The method must be appropriate for the question being asked.
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Non-Targeted Analysis - Metabolite Fingerprinting
High throughput qualitative screening of the metabolic composition of an organism or tissue with the primary aim of sample comparison and discrimination analysis. Generally no attempt is initially made to identify the metabolites present. All steps from sample preparation, separation, and detection should be rapid and as simple as is feasible.*
* Hall, RD. New Phytologist 169:453-468 (2006).
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Non-Targeted Analysis Chromatographic and Spectral Fingerprints
Fingerprints come from:
1) direct analysis of solids,
2) direct analysis of extracts
(no separation), or
3) chromatograms of extracts.
Fingerprints are complex
chromatograms or spectra.
Fingerprints require statistical
or chemometric analysis to
extract information.
Any chromatographic or
spectroscopic method can be
used.
Solid Sample Solid Sample
Fingerprints
Separation
Extract Solid
Direct
Chromatograms Spectra
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Basic Question: Do These Patterns Match?
NIR
MS NMR
HPLC-Any Detector
UV
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Approacha Method Supervision Model
Exploratory PCAb None All data
Class Modeling SIMCA ID classes 1 or more (soft modeling) (multi-PCA) classes
One-Class Modeling PCA ID 1 class 1 class
Classification PLS-DA ID classes All Classes (hard modeling)
a Richard G. Brerton, Chemometric for Pattern Recognition, John Wiley &
Sons, West Sussex, UK, 2009, ISBN 978-0-470-98725-4
b PCA - Principal Component Analysis, SIMCA - Soft independent modeling of
class analogy, PLS-DA – Partial Least Squares-Discriminant Analysis.
How Do We Determine if the Patterns Match?
Chemometric Methods!
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Hotelling T2 statistic – Multivariate analog to Student’s t value in univariate statistics. Characterizes the variance within the model.
One-Class Modeling Hotelling T2 and Q Statistics
Linear, 1D model (1 PC) fit to bivariate data:
(*) authentic samples
(mean-centered)
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Hotelling T2 statistic – Multivariate analog to Student’s t value in univariate statistics. Characterizes the variance within the model. Q Statistic – Characterizes the variance outside the model. No equivalent in univariate statistics.
Linear model (1 PC) fit to bivariate data:
(*) authentic samples
(*) test samples
One-Class Modeling Hotelling T2 and Q Statistics
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Simplest Approach to Authentication
Step Comments Collect authentic materials Desired reference materials (fit for purpose)
Obtain fingerprints Chromatographic or spectral
Build a model Fit a PCA model to authentic fingerprints
Establish statistical limits Use Q statistic
Compare test material Does test material lie outside the 95% confidence limits?
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Example #1: Authentication of Gingko biloba
Samples: 18 commercial samples from local stores . 2 NIST Ginkgo biloba standard reference materials: SRM 3247 Powdered Extract SRM 3248 Oral Dosage Product (tablet)
Analysis: HPLC-DAD (absorbance 220-400 nm)
Data processing (3 approaches): Computed areas for 22 peaks (normalized to 100%)
Chromatograms as images UV absorbance profiles as images (no separation) Model:
One-class SIMCA
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Chromatograms with UV Detection 18 Commercial Samples
Retention Times Aligned (areas not normalized)
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Samples
Pe
ak A
rea
Approach #1 - Measuring Individual Peaks Relative Peak Areas for 22 Flavonol glycosides
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PCA: Relative Peak Areas (numbers correspond to samples on previous slide)
17 2,10 11
1 5,8
3247,3248, 3,4,6,7,9,12, 13,14,15,16,18
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SIMCA: Authentic Samples Modeled Q Values vs Hotelling T2 Values
95% Confidence Limit
95
% C
on
fid
en
ce L
imit
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SRM3247,SRM3248, 3,4,7,9,12,13, 14,15,16,&18
6 1 5,8
2,10
17
11
PCA: Relative Peak Areas PC1, PC2, and PC3
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Relative Peak Areas Additional “unusual” sample detected (outlined)
Samples
Pe
ak A
rea
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Retention Times Aligned (areas not normalized)
Approach #2: Chromatograms as Images
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PCA: Chromatograms as Images
Retention Times Aligned (sample spectra normalized)
1,5,&8
2,10,11,&17
SRM3247,SRM3248, 3,4,6,7,9,12,13, 14,15,16,&18
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SIMCA: Chromatograms as Images Q Statistic vs Hotelling T2 Values
6
6
95% Confidence Limit
95
% C
on
fid
en
ce L
imit
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Wavelength (nm)
Ab
sorb
ance
Approach #3: UV Absorbance Spectra as Images
Typical UV spectra
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PCA: UV Spectra (no chromatographic separation)
18
6
1,2,5,8,10,11,&17
SRM3247,SRM3248, 3,4,7,9,12,13,14,15, 16,&18
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SIMCA: Authentic Samples Modeled Q Statistic vs Hotelling T2 Values
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3248 Tablets
3247 Extract
3246 Leaves
Leaves
Commercial Supplements
PCA: Ginkgo Leaves and Commercial Supplements Apples vs Oranges
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Example 2: American Ginseng Flow Injection MS
0.E+00
2.E+05
4.E+05
6.E+05
8.E+05
1.E+06
1.E+06
200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500
Rb1
Rb2/Rb3/Rc
Rd/Re
Rf/Rg1
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PCA: FIMS, Panax Species
K1
K5
K3
SC80
SC25
AHP
P ginseng P notoginseng P quinquefolius
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95
% C
L
95% CL
SIMCA P notoginseng
P ginseng
P quinquefolius
PCA
PCA & SIMCA: NIR, Panax Species
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PCA Wisconsin grown
Canadian grown
SIMCA
95
% C
L
95% CL
PCA & SIMCA: NIR, Panax Quinquefolius
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Example #3: Authentication of Black Cohosh
(Actaea racemosa)
• Obtained authentic A. racemosa and other species from American Herbal Pharmacopoiea (AHP), Strategic Sourcing (SS), North Carolina Arboretum (NCA), & NIST.
• Obtained commercial supplements and whole roots from the internet and stores in the US and China.
• Obtained DNA barcodes from AuthenTechnologies.
• Obtained spectral fingerprints using nuclear magnetic resonance spectrometry (NMR) and flow injection mass spectrometry (MS).
• Analyzed data using PCA and SIMCA.
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A. racemosa A. dahurica A. pachypoda A. podocarpa A. rubra
▲
■
◊
PCA
SIMCA
95% CL
95
% C
L
PCA & SIMCA: Black Cohosh FIMS for AHP Samples
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PCA
SIMCA
95% CL
95
% C
L
A. racemosa A. dahurica A. pachypoda A. podocarpa A. rubra
▲
■
◊
PCA & SIMCA: Black Cohosh 1H-NMR for AHP Samples
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PCA: Authentic Black Cohosh
NMR
American Herbal Pharmacopoeia
N Carolina Arboretum Germplasm Repository
Strategic Sourcing
NIST SRM 3295
▲
■
MS
NMR
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PCA: MS - A. racemosa from 22 Sites (Source - North Carolina Arboretum; Method - FIMS)
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SIMCA: A. racemosa from 2 Sites as Examples (Source - North Carolina Arboretum; Method - NMR)
Location #1
Location #22
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• NCA samples were collected from 22 sites along the eastern mountain range.
• They cluster together compared to the other species.
• Within the cluster, samples from the same location form sub-clusters.
• Variance within the A. racemosa samples may be due to:
Isolated genetic differences Local climate and soil conditions Different types and levels of endophytic fungi
• Work on this project continues.
A. racemosa cultivated from Different Sites
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PCA: All Authentic Black Cohosh and Commercial Roots & Supplements
SRM 3295 Rhizome
SRM 3297, 3298 Rhizome extract
& tablet Authentic Black Cohosh
Commercial Root Samples
Commercial Supplements
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SIMCA: Model Based on All Authentic Black Cohosh vs Commercial Roots & Supplements
SRM 3297, 3298 Rhizome extract
& tablet
SRM 3295 Rhizome
Authentic Black Cohosh
Commercial Root Samples
Commercial Supplements
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Example 4: Perspective on Chemical Identification PCA: Flow Injection MS Spectral Fingerprints
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PCA: Echinacea 2 Species - 2 Plant Parts
EAR – E. angustifolia root EPA – E. purpurea aerial EPR – E. purpurea root
EPR
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PCA: Echinacea 2 Species - 2 Plant Parts - Supplements
EAR EPA
EPR
EPA – E. purpurea aerial EPR – E. purpurea root EAR – E. angustifolia root S - Supplement
S S
S
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SIMCA: E. purpurea aerial Aerial Ingredient and Solid and Liquid Supplements
EPA
EPAL
EPAS
EPA – E. purpurea aerial EPAS – E. purpurea aerial solid supplement EPAL – E. purpurea aerial liquid supplement
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Q Statistic: Echinacea Fingerprints PCA based on E. Purpurea Aerial (1)
Q S
tati
stic
Sample
1
15
8
7
6
5
4
3
2 13
12
11 10
9
14
+2 Mean
-2
1 – E. purpurea aerial 4 – E. purpurea root 6 – E. angustifolia root 2,5,7 – solid single ingredient supplements 3,8 – liquid single ingredient supplements 9-15 – mixed ingredient supplements
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Summary
• Basic concept: build a model, set statistical limits, and compare test samples.
• PCA, SIMCA are essential mathematical tools.
• Must compare apples to apples.
• Must select appropriate method.
• Comparison of raw botanical materials and processed commercial supplements is difficult.
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Acknowledgements
USDA Agricultural Research Service,
Office of Dietary Supplements at the National Institutes of Health,
Kim Colson, Jimmy Yuk, Bruker BioSpin, Bellerica, MA, USA
Joe-Ann McCoy, North Carolina Arboretum, Bent Creek Germplasm Repository, Asheville, NC, USA
Danica Harbaugh Reynaud, AuthenTechnologies LLC, Richmond,CA, USA