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

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.

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.

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.

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!

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.

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).

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

Basic Question: Do These Patterns Match?

NIR

MS NMR

HPLC-Any Detector

UV

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!

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)

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

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?

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

Chromatograms with UV Detection 18 Commercial Samples

Retention Times Aligned (areas not normalized)

Samples

Pe

ak A

rea

Approach #1 - Measuring Individual Peaks Relative Peak Areas for 22 Flavonol glycosides

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

SIMCA: Authentic Samples Modeled Q Values vs Hotelling T2 Values

95% Confidence Limit

95

% C

on

fid

en

ce L

imit

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

Relative Peak Areas Additional “unusual” sample detected (outlined)

Samples

Pe

ak A

rea

Retention Times Aligned (areas not normalized)

Approach #2: Chromatograms as Images

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

SIMCA: Chromatograms as Images Q Statistic vs Hotelling T2 Values

6

6

95% Confidence Limit

95

% C

on

fid

en

ce L

imit

Wavelength (nm)

Ab

sorb

ance

Approach #3: UV Absorbance Spectra as Images

Typical UV spectra

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

SIMCA: Authentic Samples Modeled Q Statistic vs Hotelling T2 Values

3248 Tablets

3247 Extract

3246 Leaves

Leaves

Commercial Supplements

PCA: Ginkgo Leaves and Commercial Supplements Apples vs Oranges

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

PCA: FIMS, Panax Species

K1

K5

K3

SC80

SC25

AHP

P ginseng P notoginseng P quinquefolius

95

% C

L

95% CL

SIMCA P notoginseng

P ginseng

P quinquefolius

PCA

PCA & SIMCA: NIR, Panax Species

PCA Wisconsin grown

Canadian grown

SIMCA

95

% C

L

95% CL

PCA & SIMCA: NIR, Panax Quinquefolius

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.

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

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

PCA: Authentic Black Cohosh

NMR

American Herbal Pharmacopoeia

N Carolina Arboretum Germplasm Repository

Strategic Sourcing

NIST SRM 3295

▲

■

MS

NMR

PCA: MS - A. racemosa from 22 Sites (Source - North Carolina Arboretum; Method - FIMS)

SIMCA: A. racemosa from 2 Sites as Examples (Source - North Carolina Arboretum; Method - NMR)

Location #1

Location #22

• 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

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

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

Example 4: Perspective on Chemical Identification PCA: Flow Injection MS Spectral Fingerprints

PCA: Echinacea 2 Species - 2 Plant Parts

EAR – E. angustifolia root EPA – E. purpurea aerial EPR – E. purpurea root

EPR

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

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

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

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.

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