29.9.2019

1

Analytical capability related to detecting fraud and counterfeiting

Annikki Welling

Head of Unit

Chemistry Unit

Laboratory and Research Division

III Food Safety Conference

Tallinn26.9.2019

Content

• Legislation and control

• Examples of food frauds and how to detect them

• DNA methods in food fraud investigations

• Strawberry project to create a tool to identify Finnish strawberries

29.9.2019

2

Controls the safety of food chain from farm to fork

Operates as the EU’s paying agency

Provides analytical and expert services for food chain

29.9.2019

3

Chemistry Unit

• 20 Researchers and 33 Technicians

• Three sections: • Composition and origin

• e.g. basic composition of food and feed, vitamins, authenticity, GMO

• Organic residues• e.g. residues in food and feed, mycotoxins, processing contaminants

• Inorganic Chemistry• e.g. chemical elements in food, feed and fertilizers

▪ 3 GC MS/MS (Waters, Thermo, Shimadzu)

▪ 2 GC-MS, GC-FID and GC-ECD (Agilent)

▪ 2 ICP MS with HPLC (Thermo, Perkin-Elmer)

▪ ICP OES

▪ NIR, UV-spectrometers…

▪ PCR and Immunological techniques (Elisa

etc.) are used for screening of contaminants

and in GMO analysis

Laboratory facilities

▪ 4 Triple quadrupoles

▪ 2 Waters QuattroMicro (HPLC)

▪ Waters Xevo (UHPLC)

▪ Shimadzu 8050 (UHPLC)

▪ Q-TOF (Premier, Waters UHPLC)

▪ Several HPLC’s with UV, FL, Corona, refractive

(Agilent, Waters).

▪ Waters UHPLC (FL, UV)

▪ HT-EA-IRMS

29.9.2019

4

Food Fraud• There is no definition for ”food fraud” in EU

legislation. However, the ”General food law” (EC) No 178/2002 aims at preventing fraudulent or deceptive practices, the adulteration of food and any other practices which may mislead the consumer.

• Proving food fraud is difficult since the intention needs to be demonstrated

• Usually there is always economical interests involved in food fraud

• “Successful fraud is the one that nobody notices!”

Violation of EU legislation

Intentional

Economical gain

Customer deception

Examples of food frauds

• Dilution• juices are diluted with water or with other, less valuable juices• extra-virgin olive oil is diluted with other oils

• Substitution• Expensive fish species are replaced with less valuable one• Ordinary rice is mixed with basmati-rice, ordinary rice is perfumed to resemble basmati

• Artificial increase of characteristics• sudan-colour is added to chili powder to increase colour and weight• melamine is added to milk to increase protein content (protein content is usually analyzed

indirectly by measuring nitrogen content)

• Production method is mislabeled• Organic food, free-range eggs

• Geographical origin is mislabelled

8

29.9.2019

5

• Is the meat beef or horse?

• Is the ruminant DNA in feed form whey or meat and bonemeal?

• Is the meat from Finland or from Argentina?

• The choice of method depends on the nature of food fraud, requirement of the legislation and how much knowledge there is about the food or what is the typical way to manipulate it

Laboratory analysis of food fraud

Laboratory analysis of food fraud

• Specific methods• Methods can be very specific when the typical fraud is known (buffalo mozzarella)

• When characteristics of authentic food is determined very precisely (extra-virgin olive oil)

29.9.2019

6

Analysis of extra-virgin olive oil contains several steps:

11

1) Fatty acid ethyl esters (FAEEs) (mg/kg)2) Acidity (%)3) Peroxide índex (mEq O2/kg)4) Waxes (mg/kg)5) 2-glyceril monopalmitate (%)6) Stigmastadienes (mg/kg )7) Difference: ECN42 (HPLC) and ECN42 (theoretical

calculation)8) K 2329) K 27010) Delta-K11) Organoleptic evaluation:

Median of defect (Md)Fruity median (Mf)

12) Fatty acid composition:Myristic (%)Linolenic (%)Arachidic (%)Eicosenoic (%)Behenic (%)Lignoceric (%)Total transoleic isomers (%)Total translinoleic + translinolenic isomers

13) (%) Sterols composition:Cholesterol (%)Brassicasterol (%)Campesterol (%)Stigmasterol (%)App β–sitosterol (%) Delta-7- stigmastenol (%)Total sterols (mg/kg)Erythrodiol and uvaol (%)

Laboratory analysis of food fraud

• Specific methods• Methods can be very specific when the typical fraud is known (buffalo mozzarella)

• When characteristics of authentic food is determined very precisely (extra-virgin olive oil)

• Generic methods• There is increasing demand on large-scale methods with no requirements of information about the

food or the fraud

• Different profiles, different omics

29.9.2019

7

Different ”omics”

• Tendency is to develop broad-spectrum methods, using different “omics” and create a profile that characterize a specific food. When food is analyzed, deviations from normal profile refers to a manipulation of food

• Mass-spectrometry profiles (Q-TOF)

• Metabolomics

• Fat content of the meat

• Sugar profile

• Fatty acid profile

• Stable isotope profile: geographical origin

13

Non-selective sample preparation

Non-targeted sample analysis

DATA-matrix Analyses using multicomponent-analyses

PC1PC2

PC3EA-IRMS

ICP-OES

value 1 alue 2 value 3 value 4 value 5

1.0 1.0 1.0 1.0 1.0

0.0 0.0 0.0 0.0 0.0

Variation in reference

Authentic food

Non-authentic food

Adaptation form Julia Raeken presentation

29.9.2019

8

DNA methods in food fraud investigations

Why DNA methods are good in food fraud investigations?• DNA methods are especially good in species identification

• If there is no morphological characteristics left, species can be detected using DNA• traditional medicines where endangered species are pulverized

• Herbs: dried oregano is substituted with other herbs, such as olive and myrtle leaves

29.9.2019

9

Why DNA methods are good in food fraud investigations?

• DNA molecules are rather stable → it is possible to investigate raw, cooked or processed food

• There is plenty of target DNA in all cells → all tissues

• DNA is even more stable in mitochondria and chloroplasts and since they are very abundant in cells → more DNA to analyze

DNA-based methods

18

• Identification of protein epitopes (allergen research)

• Species-specific methods

• Method to detect specific group of animals (ruminant identification method)

• DNA-barcoding (any species in simple matrix)

• DNA meta-barcoding (all species in complex matrix)

• WGS sequencing

Specific

Broad-spectrum

met

ho

ds

Plenty of information

Little information

Am

ou

nt

of

info

rmat

ion

29.9.2019

10

Example of use of DNA barcoding• European Commission coordinated control plans 2015

• Fish substitution

• White fish mislabeled in regard to its declared species

• DNA-barcoding as detection method

19

Gadus morhua, cod, turska

Pollachius virens, saithe, seiti

C, T, A, G

Cod or saithe?

DNA-barcoding

20

PCR is used to multiply a specific area from genome, which is sequenced and compared to information in genome databanks.

Degenerative primers bind to the start and end of the barcoding area. As little variation between species as possible.

Genomic region should have as much variation between species as possible and within species as little as possible.

648 bp

29.9.2019

11

We started DNA barcoding with fish samples, but it has worked with other species as well :

21

HIRVI

RUSAKKO

SIKA

NAUTA

KANA

KARHU

LAMMASKOTISIRKKA

ANKKA

PORO

KALKKUNA

LAMMAS

Metabarcoding

22

• Complex food

• Uses both DNA barcodes and minibarcodes (shorter DNA sequences)

• Several barcode areas

• Areas are multiplied and sequenced using WGS

• Bioinformatics is planned as automated as possible

COI 16S COI 16SCytB CytB rbcL rbcLMatK trnL trnLITS2

Animal barcodes Animal minibarcodes Plant barcodes Plant minibarcodes

Example of method developepment in DECATHLON project to identify CITES species. GigaScience 2017, 6: 1–18

29.9.2019

12

Strawberry project

Alkuperältään aidot – project for strawberry authentication

• Research project between Finnish Food Authority and Natural Resources institute Finland (Luke)

• Financed by Makera

• Aim to create an analytical tool to identify Finnish strawberries

• Creation of reference database from Finnish strawberries

• Databases contains profile of relative values of stable isotopes and profile of chemical elements

• In collaboration with Kehitysyhtiö SavoGrow Oy/Marjaosaamiskeskus, Hedelmän - ja Marjanviljelijäin liitto ry HML, Suonenjoen Seudun Marjanviljelijäin yhdistys ry SSMY, Pakkasmarja Oy ja Kasvishovi Oy, Mansikantuottajat, Länstyrelsen i Jönköpings län, Ruotsi

29.9.2019

13

Analysis of stable isotopes

• Isotopes used in the study are stable (non-radioactive).

• Number of protons in the chemical element is stable but number of neutrons varies

• The relative proportion of isotopes can be measured with specially sensitive equipment by using differences in isotope mass.

• δX = [(Rsample / Rstandard) – 1] x 103

heavy light Pro-portionof light(%)

2H 1H 98,8918O 16O 99,75915N 14N 99,6 13C 12C 98,8934S 32S 94,93

Reasons for different ratios of light and heavy isotopes

heavy light Pro-portion of light (%)

2H 1H 98,89

18O 16O 99,759

15N 14N 99,6

13C 12C 98,89

34S 32S 94,93

water, local thermal environment, height, latitudeand continentality

Organic fertilizers enrich plants δ15N (fertlizers produced from atmospheric nitrogen δ15N=0)

C4 ja C3 plants, high δ13C refers to C4 plants (maize, sugar cane)

Local geological and chemical circulation

29.9.2019

14

The ratios of heavy isotopes 2H ja 18O in rainwater decreases towards north

δ18O in rainwater decreases toward north

Terzer et al. 2013δ2H in rainwater decreases toward north

Terzer et al. 2013

Complex process determines the accumulation of isotopes in plants

28

Moisture in atmosphereH2

18O, H16O, H216O Carbon dioxide in atmosphere

C18O16O, C16O2, 13CO2, 12CO2

rainH2

18O, H16O, H216O

Water in the groundH2

18O, H16O, H216O

Water in the leavesH2

18O, H16O, H216O

Photosynthesis,Sugars C6H2O6

evaporation

Dawson et al. 2002 Annu Rev Ecol Syst mukaan

Kuva: Anu Villberg

29.9.2019

15

Isotope library for analysis of geographical origin

Determination of the relative amount of different isotopes

• Certain berries at the certain degree of ripeness (ripen strawberry)

• Certain geographical area (Finland) compared to other countries

• Resolution (it is not known in beforehand which isotopes are important for resolution to determine the origin, so several isotopes needs to be analysed

• The number of the sample should be adequate and the whole production area should be covered sufficiently

• Samples collected for the reference database have to be authentic –reliable collection of samples

Reliable and adequate collection of samples

• Finland was divided 50 km x 50 km squares, at least one farm from each square was selected for collection of samples

• 222 samples from 74 farms in 2017

• 117 samples from 39 farms in 2018, most of the farms were the same as previous year, Åland was a new area

• 100 samples from 40 farms in 2019, different methods of production, greenhouses etc.

• Researches from Luke were collecting the samples Strawberry farms from which the

samples were collected in 2017

29.9.2019

16

Reliable and adequate collection of samples

Sample collection from field plot

Isotope analysis

Chemical element analysis

1.

2.

3.

Photo: Jorma Hellsten

Analysis of samples

The ratios of different stable isotopes• In AgroIsolab in Germany

• Technique EA-IRMS (Element Analyser – Isotope Ratio Mass Spectrometry) ja HT-EA-IRMS (High Temperature Element Analyser – Isotope Ratio Mass Spectrometry)

• 2H/1H, 13C/12C, 15N/14N, 18O/16O, 34S/32S were determined

• Matrix that was used in the analyses were strawberry tissue water and proteins

29.9.2019

17

Analysis of samples

Chemical Element Analyses• In Ruokavirasto, Chemistry Unit

• ICP-OES (inductively coupled plasma optical emission spectrometry ):

B, Ca, K, Mg, Na, P, S

• ICP-MS (inductively coupled plasma – mass spectrometry):As, Ba, Cd, Co, Cu, Li, Mn, Mo, Ni, Pb, Rb, Sr, Zn

• Samples were ionized with inductively coupled plasma (temperature 10000°C)

• OES: measures electromagnetic radiation from excited atoms that is typical for each chemical element.

• MS: Samples are separated and measured according to their mass/ charge ratio.

• Whole strawberries were used as matrix

• Method has been validated and accredited

Farm D/H [‰] v.s. vsmow

18O / 16O [‰] v.s. vsmow

D/Horg [‰]v.s. vsmow

13C / 12C [‰] v.s. PDP

15N / 14N [‰] v.s. Air

34N / 32N [‰] v.s. CDT

X

Creation of stable isotope reference library

• Isotope analysis results from one farm does not tell much about the geographical origin of the sample:

29.9.2019

18

• After stable isotope ratios from Finnish strawberries is measured, data is analysed with multivariate analysis

• Principal component analysis (PCA)• gradual: first is measured the principal component 1

(PC1), that is variable which explains most of the total variation. Next is chosen PC2 which explains most of the residual variation that is not explained by PC1, and that does not correlate with PC1 etc.

• Usually model of 2-5 PC is achieved to which the results of the analytical samples is compared.

PC1

PC2

PC3

Creation of stable isotope reference library

Strawberries form different countries are divided to different PC

Preliminary results from isotope-analysis

• Finnish strawberries differ from foreign strawberries according to the ratio of their stable isotopes

• Some variation can be seen between the years

China

SwedenPoland

Finland2017

Finland2018

76,16 %

92,55 %

16

,80

%

29.9.2019

19

15N / 14N [‰] relative amount nitrogen is higher in berries collected from organic farms

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

Organic Conventional

Mansikan d15N, näytteenotot 2017 - 2018, luomu vs perinteinen lannoitus

δ15N

Luomu tavanomainen

What are our future plans for the analysis of food fraud:

• Strawberry project:• Strawberry project continues with the analysis of the results from 2019• Statistical analyses of results from chemical element analyses. • We hope to get official control samples each summer• We will start establish isotope method in Finnish Food Authority• New targets such as bilberries

• MetabarcodingPlans to establish the method

• Analysis of samples from annual monitoring plans gives plenty of information. With Q-TOF even more

→ ”profiles for normal samples”When we get enough data, deviations are more easy to notice

38

29.9.2019

20

KIITOSAnnikki Welling

yksikönjohtaja

Kemian yksikkö Laboratorio- ja tutkimuslinja