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FEDERAL INSTITUTE FOR RISK ASSESSMENT Authenticity Testing of Food - State of Play and Future Challenges - Carsten Fauhl-Hassek

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NT Authenticity Testing of Food

- State of Play and Future Challenges -

Carsten Fauhl-Hassek

Carsten Fauhl-Hassek, 2015-02-18, 32nd Meeting of the Forum of FLEP Page 2

WHO AM I ?WHERE AM I

FROM ?

Authenticity Testing

Authentication: confirmation of all requirements regarding the legal product description

or the detection of the fraudulent statements, particularly in view of:

(i) the substitution by cheaper but similar ingredients,

(ii) extension of food using adulterant (e.g. water, starch including exogenous material)

or blending and/or undeclared processes (e.g. irradiation, extraction)

(iii) the origin, e.g. geographic, species or method of production.

Esslinger, S., Riedl, J., Fauhl-Hassek, C., Food Research International, 60, 189-204 (2014)

Carsten Fauhl-Hassek, 2015-02-18, 32nd Meeting of the Forum of FLEP Page 3

Authenticity of food

Authenticity

Labelling

GeographicalOrigin

Identity (Composition)

Carsten Fauhl-Hassek, 2015-02-18, 32nd Meeting of the Forum of FLEP Page 4

Authenticity of food

Motivation

Food

QualityFood Fraud I 1 Gain: Economic

Food

Safety

Food Fraud II

Food Defense

Harm:

Public Health, Economic or Terror

Unintentional Intentional

Action1 Includes the subcategory of economically motivated adulteration

and food counterfeiting

Journal of Food Science, 76(9), 157-163 (2011) Spink, J. and Moyer, D.C.

Carsten Fauhl-Hassek, 2015-02-18, 32nd Meeting of the Forum of FLEP Page 5

Codex Alimentarius: „Traceability/product tracing:

the ability to follow the movement of a food through specified stage(s) of production, processing and distribution.“

(Regulation (EC) No 178/2002 § 3 p 15)

Traceability systems trace and track “food packaging”

Definition: Traceability

Carsten Fauhl-Hassek, 2015-02-18, 32nd Meeting of the Forum of FLEP Page 6

1. Analysis of composition• Classical analysis, wet chemistry, chromatography, spectroscopy,• Detection of non-natural food constitutes

2. Analysis of stable isotopes• (D/H, 13C/12C, 18O/16O, 15N/14N)

3. Enantioselective analysis

4. Molecular biological methods

5. Non-targeted analysis (analysis of composition)• Spectroscopy, spectrometry

Analytical methods for authentication

Carsten Fauhl-Hassek, 2015-02-18, 32nd Meeting of the Forum of FLEP Page 7

P = 0,95

α = 0,025α = 0,025

± Student Factor x σ

Authenticity range

Authentic or unsuspicious samples

Analytical methods for authentication

Classical approachReference Data (bases)

e.g. German RSK system Fruit Juices(Standard Values and Ranges of certain analytical characteristics)

Carsten Fauhl-Hassek, 2015-02-18, 32nd Meeting of the Forum of FLEP Page 8

• Glycerol 4,8-14 g/l

• Methods: wet chemistry, GC, HPLC, NMR

Water

Wine

Alc.Acid

Addition of glycerol to wine

Composition1) Composition

Cyclic Diglycerols (Dioxan, Dioxepan)

O HO

CH3

OH

3-Methoxy-propandiol

O

O

OH

HO

O

O

OH

HO

OO

OH

OH

OO

OH

OH

O

O

HOHO

Fauhl C, Wittkowski R, Lofthouse J, Hird S, Brereton P, Versini G, Lees M, Guillou C (2004), Journal of AOAC International 87: 1179-1188

• in 1997: 140 of 850 samples were

“positive” (16 %)

• in 1999: 3 of 150 samples were “positive”O

O

HO

HO

• By-products in technical glycerin (exogenous wine substances)

Carsten Fauhl-Hassek, 2015-02-18, 32nd Meeting of the Forum of FLEP Page 9

Stable Isotope Ratios “Fingerprint”

Stable isotopes2) Stable isotopes

Carsten Fauhl-Hassek, 2015-02-18, 32nd Meeting of the Forum of FLEP Page 10

δ 18O-value of wine

water (‰ vs VSMOW)-1,0

+7,5

+8,5

+3,5

-0,5 - + 3,0

~ + 6

Stable isotopes

Geographical origin of wine

2) Stable isotopes

Carsten Fauhl-Hassek, 2015-02-18, 32nd Meeting of the Forum of FLEP Page 11

Jamin et al. J. Agric. Food Chem., 2003

Tap water is enriched:-7 to -15 ‰ δ 18O

Stable isotopes

Addition of water direct juices δ 18O

2) Stable isotopes

Carsten Fauhl-Hassek, 2015-02-18, 32nd Meeting of the Forum of FLEP Page 12

-8 -6 -4 -2 0 2 4 6

Discriminant function 1

-3

-2

-1

0

1

2

3

4

Dis

crim

inant fu

nct

ion 2

EU

China

USA

EUChina USA group centroids

Canonical discriminant analysis (CDA)

Especially for EU-USA differentiation

www.qsaffe.eu

Journal of Agricultural and Food Chemistry, 61, 7225-7233(2013) Nietner,T.; Pfister, M.; Glomb, M.A.; Fauhl-Hassek, C.

Stable isotopes

Geographical origin of feed

2) Stable isotopesAnalysis of Distillers Dried Grains and

Solubles (DDGS) by FT-IR

(directly and after sample extraction)

• DDGS is a global commodity,

• Co-product of ethanol production,

• High nutrient content (protein, fat)

Carsten Fauhl-Hassek, 2015-02-18, 32nd Meeting of the Forum of FLEP Page 13

� 1,500-3,000 $ kg-1 natural vanillin

� 40 t year-1

� 15000 t year-1

� 15 $ kg-1

Vanillin

Stable isotopes2) Stable isotopes

SNIF®-NMR

Carsten Fauhl-Hassek, 2015-02-18, 32nd Meeting of the Forum of FLEP Page 14

Analysis of Bergamot oil

AcO HO

1 (R), 2 (S) 3 (R), 4 (S)

**

LinaloolLinalyl acetate

� Wine analysis �Flavour additions (peach)

Enantioselective

analysis3) Chiral analysis

Carsten Fauhl-Hassek, 2015-02-18, 32nd Meeting of the Forum of FLEP Page 15

Microarray technology, microsatellite analysis,

real time PCR, proteomics applicationsProduct adulteration in meat

products, e.g.

• horsemeat,

• Pork in Halal products

Challenges:

• highly processed products

(oil, wine, meat-

and-bone meal)

• targeted !!

Molecular-biological

methods4) Differentiation of species

Carsten Fauhl-Hassek, 2015-02-18, 32nd Meeting of the Forum of FLEP Page 16

Aim: Identification of deviations

Sample preparation

2D data 3D data

FT-IRNMR LC-HR-MS

Applicability:

• Comprehensive characterization

• Differentiation of samples due to:

� Botanical origin

� Geographical origin

� Adulterations

� …

• Detection of emerging adulterated products

Early detection of risks/hazards

Non-targeted analysis5) Fingerprinting

Carsten Fauhl-Hassek, 2015-02-18, 32nd Meeting of the Forum of FLEP Page 17

Non-targeted analysis

variables (e.g. analytical results, meta data)n s

am

ple

s

(poss

ibly

diff

ere

nt

sam

ple

gro

ups)

Cluster 1 2 3 4 5

ppm 4.36 4.35 4.34 4.33 4.32

Sample Code Colour Origin

1379_1_1 red Hungary 0.031 0.054 0.024 0.074 0.100

1380_1_1 red Hungary 0.030 0.129 0.094 0.176 0.192

1381_1_1 white Hungary 0.317 0.267 0.287 0.273 0.179

1388_1_1 red Hungary 0.022 0.116 0.031 0.157 0.086

1389_1_1 red Hungary 0.275 0.180 0.273 0.159 0.184

1390_1_1 red Hungary 0.084 0.140 0.031 0.159 0.087

1391_1_1 red Hungary 0.610 0.419 0.413 0.436 0.398

• Data matrix:

5) Fingerprinting - Multivariate statistics

• Unsupervised methods (pattern recognition)

e.g. Cluster Analysis, Principal Component Analysis (PCA)

• Supervised methods (examine structure)

Discriminant analysis (DA), Class modeling (e.g. SIMCA)

• Quantification Partial Least Squares (PLS) Regression

Carsten Fauhl-Hassek, 2015-02-18, 32nd Meeting of the Forum of FLEP Page 18

Example 1: Determination of melamine

• Investigation of different milk powders

(bought in 2008)

• Analysis using 1H-NMR (400 MHz)

• Identification of melamine via exogenous

signal at 5.93 ppm (NH2 groups)

lactose HO1-α

lactose HO1-βurea

CH3/CH2 fatty acids

lactoce

TMS

DMSO

TMU

7.5 5.0 2.5 ppm

Non-targeted analysis5) Fingerprinting

Carsten Fauhl-Hassek, 2015-02-18, 32nd Meeting of the Forum of FLEP Page 19

• Scope: development of non-targeted analytical procedures

to identify chemical hazards in spices and herbs

• Analysis using 1H-NMR spectroscopy after simple sample

preparation

• Analysis of paprika powder (250 samples), spiked with e.g.

sudan dyes or beetroot

Non-targeted analysis5) Fingerprinting

Example 2: Adulterated paprika powder

http://www.spiced.eu

-160000

-120000

-80000

-40000

0

40000

80000

120000

-120000 -80000 -40000 0 40000 80000 120000 160000

PC

-2 (

21

%)

PC-1 (35 %)

Authentic samples

5 % beetroot

10 % beetroot

20 % beetroot

-160000

-120000

-80000

-40000

0

40000

80000

120000

-120000 -80000 -40000 0 40000 80000 120000 160000

PC

-2 (

21

%)

PC-1 (35 %)

Carsten Fauhl-Hassek, 2015-02-18, 32nd Meeting of the Forum of FLEP Page 20

Non-targeted analysis5) Fingerprinting

BfR study

• 495 commercial wines

• Vintages 2006 - 2011

• Different geographical origin

• Steel tank/barrique

• Different quality attributes

• PLS-DA of 353 white wine

samples

Commercial system:

WineScreenerTM

Example 3: Differentiation of white wine by 1H-NMR spectroscopy

Carsten Fauhl-Hassek, 2015-02-18, 32nd Meeting of the Forum of FLEP Page 21

• Scope: discrimination between different seed oils and

authentic/adulterated sunflower oils using FT-IR spectroscopy

• Spiked samples: addition of mineral oil

• Direct analysis of 419 edible oil samples

Wavenumbers (cm-1)

Abso

rbance

mineral oilsunflower oil

� Detection of 0.5 % mineral oilis possible

unpublished Pfister, M.K.-H., Gründler, A., Esslinger, S., Fauhl-Hassek, C.

Non-targeted analysis5) Fingerprinting

Example 4: Identification of adulterated edible oils

Carsten Fauhl-Hassek, 2015-02-18, 32nd Meeting of the Forum of FLEP Page 22

Probennr. 164_367893_3678 64_3678 121_3756136_375693_3756 79_3756 57_3868 74_3868 89_3868 101_386888_3869 129_386969_3869 191_4167…

3456 112 112 112 112 112 112 112 453 3425 45324 234 345 4567 234 112

456 678 3489 356 234 345 4567 4567 4567 723 5647 234 345 4567 453 3421

1234 112 112 112 112 112 112 112 234 563 45673 4563 567 3456 567 23455678 216 1890 567 4563 567 3456 784 112 112 112 112 112 112 112 112

2974 4563 567 3456 216 1890 567 345 678 3489 356 234 345 4567 4567 112

1964 234 345 4567 456 321 56745 456 112 112 112 112 112 112 112 1124503 237 4567 5678 4567 723 5647 934 216 1890 567 4563 567 3456 784 456

45 112 112 112 112 112 112 112 4563 567 3456 216 1890 567 345 234

389 456 4563 45673 4567 723 5647 523 234 345 4567 456 321 56745 456 678476 453 3425 45324 234 345 4567 234 237 4567 5678 4567 723 5647 934 956

4387 112 112 112 112 112 112 112 112 112 112 112 112 112 112 234

34 3467 3456 67845 456 321 56745 678 456 4563 45673 4567 723 5647 523 1234231964 456 321 56745 4563 567 3456 345 453 3425 45324 234 345 4567 234 67436

5629 112 112 112 112 112 112 112 112 112 112 112 112 112 112 45896

456 3421 453 45673 4567 723 5647 130 3467 3456 67845 456 321 56745 678 34213459 2345 784 45673 456 321 56745 453 456 321 56745 4563 567 3456 345 4538

4782 112 112 112 112 112 112 112 112 112 112 112 112 112 112 8978

32 4567 723 5647 234 345 4567 453 3421 453 45673 4567 723 5647 130 563784 234 563 45673 4563 567 3456 567 2345 784 45673 456 321 56745 453 456

...

N

K

H

Chromatography-MS

H

Sample1

Sample N

data preprocessing

m/z_Scan

Sample

Rubert J., Lacina O., Fauhl-Hassek C., Hajslova J. Anal Bioanal Chem 2014 May 28.

Springer AE, Riedl J, Esslinger S, Roth T, Glomb MA, Fauhl-Hassek C, J Agr.Food Chem. 2014 62(28): 6844-51

Non-targeted analysis5) Fingerprinting – 3 dimensional data

Example 5: Wine authentication (grape varieties)

Carsten Fauhl-Hassek, 2015-02-18, 32nd Meeting of the Forum of FLEP Page 23

• „good practice“

• conduction

• publication

within one lab

BfR research: “Non Targeted Analysis” in foodauthentication (2007-2013):

Out of 267 only 196 publications state n n (Ø)= 118 samples

Validation of non-targeted methodsNon-targeted analysis

Carsten Fauhl-Hassek, 2015-02-18, 32nd Meeting of the Forum of FLEP Page 24

Lab 1

1

2

3

4

n

Research

Food Screener™ from Bruker

Proprietary measurement procedure

Lab 1

n + x

Lab 2 Lab 3

n + x n + x

Lab 4 Lab n

?

Data consistencyNon-targeted analysis

Carsten Fauhl-Hassek, 2015-02-18, 32nd Meeting of the Forum of FLEP Page 25

• 38 partners

• funded by EU with 9,000,000 €

• Jan 2014 – Dec 2018

Addressing the gap between existing knowledge & accessibility

• Network of experts

• Data sharing

• Food Fraud Early Warning System

EU-project ‚Food Integrity‘ – Ensuring the Integrity of the European food chain

Carsten Fauhl-Hassek, 2015-02-18, 32nd Meeting of the Forum of FLEP Page 26

• Globalization also in case of fraud, prediction hardly possible

• Fraudsters taking health risks to consumers

• Divers range analytical approaches for food authentication

• Authentication needs „reference data“

Application of consistent MethodsAcceptance of authentic ranges

• Trend to multivariate data evaluation

Often feasibility studies with limited data sets

Not yet fully applicable in official control

Summary 1

Carsten Fauhl-Hassek, 2015-02-18, 32nd Meeting of the Forum of FLEP Page 27

Management activities :

• Food Fraud Net

• Authentication in VO (EG) Nr. 882/2004

• EU-RL Food Authenticity ?

• Research: „FoodIntegrity“

Melamine (2008)

Horsemeat (2013)

Summary 2

Scientific activities:

• Detection of unknown additives

• Non-targeted analysis

Carsten Fauhl-Hassek, 2015-02-18, 32nd Meeting of the Forum of FLEP Page 28

• Non-targeted strategies, e.g. using NMR, MS, FT-IR will

become increasingly important

• Combination with targeted applications

• Implementation of methods/approaches from research to

routine analysis/official control

Control 2035 ?

Outlook

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Thank you for your attention

Carsten Fauhl-Hassek

Federal Institute for Risk Assessment

Max-Dohrn-Str. 8-10 � 10589 Berlin, GERMANY

Tel. +49 30 - 184 12 - 3393 �

[email protected] � www.bfr.bund.de