jana hajšlová, milena zachariasova and monika...
TRANSCRIPT
Jana Hajšlová, Milena Zachariasova and Monika Tomaniova
II International Congress
Food Technology, Quality and Safety
October 28 – 29, 2014, Novi Sad, Serbia
Food scandals, fraud
Traceability needs
Food integrity concept
Authentication
Metabolomics, laboratory tools
Case study: saffron
Conclusions
TALK SUMMARY
Recent food fraud incidents
2008 Melamine in Chinese milk products • 54,000 babies hospitalised, 6 deaths • High volume low price • Long term fraud due to deficient analytical methods
2012 Czech Republic methanol in spirits • 42 deaths in Eastern Europe • Short term “crude” fraud- easy to detect but high profits
2013 Horsemeat in Europe • No food safety issues, relatively easy to detect • High volume low price, • Long term fraud(?) due to lack of intelligence/surveillance?
Establishing FOOD FRAUD system
Annual report 2013
Traceability required by EU legislation
‘Traceability’ means the ability to trace and follow a food,
feed, food-producing animal or substance intended to be,
or expected to be incorporated into a food or feed,
through all stages of production, processing and distribution
Artile 3, paragraph 15
Is the sample typical of the material which it claims to be (compliance with label / certificate?)
Is the material typical for the batch of product from which it came?
From which farming system is the product coming and
what is its processing history?
What was the distribution / location of the product after delivery?
Tracing food commodities in Europe
TRACEABILITY - questions to
be answered:
1. General considerations
2. Choice of objectives
3. Products to achieve the objectives
4. Identify suppliers and customers
5. Flow of materials under its control
6. Information requirements
7. Establish procedures documenting the flow of products and
information
8. Establish documentation needed to achieve the objectives
Design of traceability system
Primary Production
Storage Processing Packaging Distribution Retail Product
Consumption
Supply Chain Management, Risk management procedures
Verification procedures
+
FOOD INTEGRITY
FOOD INTEGRITY: a comprehensive concept
Safety issues of concern (EFSA, 2013)
marine and non-marine bio-toxins,
novel food ingredients,
pesticides,
hydrocarbons,
persistent organic pollutants,
plasticisers,
residues of medical products,
Heavy metals
….and cocktails thereof
New hazard
Known hazard
Known hazard High exposure
Newly identified
high exposure
Increased
sensitivity
High exposure +
NEW
RISKS
+ +
+
(i)
(iii)
(ii)
Emerging
risks definition (EFSA, 2007)
FOOD AUTHENTICITY AND FRAUD
Important food quality parameter
Most of valued food commodities are subject to fraud
Substitution or extension by cheaper product / ingredient / raw material
Geographic origin misdeclaration
Use of undeclared technology / processing
ECONOMIC ASPECTS
CONSUMERS
DECEPTION THREAT TO
CONSUMERS’ HEALTH
RESEARCH INTERESTS
Journal of Food Science Vol. 77, Nr. 4, 2012
RESEARCH INTERESTS
The top 7 ingredients represented more than 50 % of the scholarly records in the database and included:
• olive oil
• milk
• honey
• SAFFRON
• orange juice
• coffee
• apple juice
METABOLOMICS:
analytical
definition
METABOLOMICS is a comprehensive analysis of the metabolome, focused on the broadest possible range of small molecules (<1200 Da) without a particular bias to specific groups of metabolites:
METABOLOMIC FINGERPRINTING: NON-TARGET analysis with minimum sample preparation. (Example: wine).
Metabolomic profiling: analysis of a SPECIFIC GROUP of metabolites. (Example: polyphenols in wine).
This review focuses on the recent
trends and potential applications of metabolomics
in four areas of food science and technology:
(1) food component analysis;
(2) Food quality / authenticity assessment
(3) food consumption monitoring
(4) physiological monitoring in food
intervention or diet challenge studies.
2008
Application scope of metabolomics
2013
A new keyword in food characterization
ICP–MS
LC–HRMS
NMR
IR / Raman
GC-HRMS
METABOLOME
AMS-HRMS
Instrumental platforms for metabolomics
Dried stigma of Crocus sativus
The most expensive spice in the world (10–15 €/g)
Unique properties → characteristic color, taste & alluring aroma
What is saffron?
Crocus sativus (ULCM)
Parts of saffron flower (ULCM)
Dried stigma
(~20 mm long)
Spanish Paella
Cooking
Swedish Saffron buns
Anticarcinogenic
Antimutagenitc
Immunomodulation
Antioxidant
u
Buddhist monks Indian Biryani French Bouillabaisse
Italian Milanese Risotto
Persian dessert
Sholezard
Traditional medicine
Coloring
Perfumery
Saffron production
Iran India
Spain
Italy Greece
Morocco
PRODUCTION 2004
Worldwide = 170 tones
Europe = 6800 kg = 4% WW
1. Harvesting
2. Separation
3. Drying
SAFFRON TREATMENT
5. Packaging
4. Cleaning
Fraud practices on saffron
The use of other parts: petals or leaves.
Diluted saffron using other materials.
The use of other products containing crocetin (Gardenia jasminoides).
The adulteration using dyes (synthetic).
False information about the country of origin (most valuable saffron is produced in PDO e.g. La Mancha, less valuable saffron from Iran or India).
Saffron fraud in newspaper This article described the real situation in Spain.
ONLY 1% of saffron, which was sold in the Spanish market was cultivated in Spain.
…So, where is this saffron coming from?
Saffron authentication tools: past and current
Current trends
Comprehensive information
on sample composition
Physico-chemical &
Biochemical measurements
Former trends
Single or only a few markers
TARGET analysis of specific
metabolites misses a large part of
molecular information
NONTARGET
metabolome fingerprinting methods
overcome these limitations
FINGERPRINTING
PROFILING
Case study: classification of 44 saffron samples set
Saffron Packed in Spain
PDO, La Mancha & Aragon
PDO, Greek
India
Turkey
Sample preparation (final method)
Homogenization Crushed by pressure in paper envelope
E
X
T
R
A
C
T
Extraction
50 mg of sample +
5 ml EtOH/H2O (70/30; v/v)
1 h ultrasonic bath
Centrifugation
UHPLC-ESI(+)/(-)
QTOFMS
Metabolomic fingerprinting workflow
MarkerView (AB sciex)
CONFIRMATION
OF STRUCTURE
ELEMENTAL COMPOSITION, IDENTIFICATION
DATA PROCESSING
INTERPRETATION
PCA OPLS-DA
Cross-Validation
HRMS MS/MS
PeakView
Peak picking Filtering
Alignment Normalization
PeakView (Formula Finder) ONLINE DATABASES
DATA PRE-TREATMENT
Pareto scaling
MULTIVARIATE ANALISIS (SUPERVISED & UNSUPERVISED ANALISIS)
MarkerView & SIMCA
MARKER
IDENTIFICATION
IDA Explorer
MarkerView (AB sciex) DATA
PROCESSING DATA
PRE-TREATMENT
MarkerView®
MarkerView software can process data acquired from non-classified workflows using Principal Components Analysis (PCA) or Principal Component Analysis-Discriminant Analysis (PCA-DA) (“PLS-DA”).
DATA PROCESSING:
Select samples (44 samples)
Peak picking (saffron metabolome (0.4-14 min))
Filters and Alignment (RT and m/z values)
PCA (unsupervised pattern) 1. The first step of the data analysis in order to detect
patterns in the measured data. 2. Positive data, PC1 describes 35.6% of the variability
in the data and PC2 11.9% of variability, therefore 47.5% of variability.
Spain PDO
La Mancha &
Aragon
Multivariate Data Analysis of Saffron: PCA (ESI+)
Data dimensionality reduction
DATA DIMENCIONALITY: 2317 molecular features (MF)69
Positive Data Matrix
2317 MF
Monoisotopic Peaks
1620 MF
Frequency of occurrence in the
samples (50%)
563 MF
t-test
69 MF
Frequency of occurrence in the
samples (50%) (Noise values)
Multivariate data analysis of saffron: PCA (ESI+)
Multivariate data analysis of saffron: PCA (ESI+)
Frequency of occurrence in the
samples (50%) EXCLUDED
m/z = 798.6
Spain PDO
La Mancha & Aragon
Multivariate data analysis of saffron: PCA (ESI+)
These samples were removed. Their
characteristic markers did not correspond to saffron
metabolome.
The statistical model (OPLS-
DA, supervised model) was
performed using Spanish samples and La Mancha-
Aragon (PDO) samples
The quality of the model, saffron origin, was evaluated by the goodness-of-Fit parameter (R2X=0.96), the proportion of the variance of the response variable that is explained by the model (R2Y=0.89) and the predictive ability parameter (Q2=0.86). Seven variables were used.
Multivariate data analysis of saffron: SIMCA, OPLS-DA, ESI+
PDO Spain
Multivariate analysis of saffron: PCA (ESI+)
OPLS-DA Score plot
Variable trend plot m/z 798.5719
RT 11.7 Marker
PDO
Spain
Average
Identification of markers: workflow
Selection of markers using MarkerView®
PeakView®:
• Its mass (m/z), retention time (RT) and MS/MS
• Formula Finder (Molecular formula)
• IDA Explorer (MS/MS Pathway)
Libraries: MassBank, METLIN, MMCD, CSFMetabolome, DrugBank, LMSD, PubChem,
KEGG, BioCyc, MetaCyc, HumanCyc, Reactome
Marker identification: saffron (PDO)
47 CandidatesC44H80NO9P OXIDIZED GLYCEROPHOSPHOLIPIDS
FORMULA FINDER: 1. MS (accurate mass) 2. Isotopic pattern (Theoretical vs. Experimental) 3. MS/MS data, fragment ions
Marker identification: Saffron (PDO)
PC 36:4
ESI+ ESI-
PC 18:2/18:2
ESI+ ESI-
Marker identification: Saffron (PDO)
PCA Loading plot
Oxidized glycerophospholipids
C44H80NO9P
Glycerophospholipids C44H80NO8P
Oxidations a consequence of “drying process” It is the most important and most delicate task during which the
stigmas lose 20% of their initial weight and turn into the saffron spice.
The drying methods vary slightly between regions: • In Sardinia, a process called “feidatura” (Olive oil) and a constant
temperature of almost 45°C.
• In Western Macedonia (Greece), the fresh stigmas are spread out in thin layers, placed on rectangular silk sieves and stored for 12 to 24 hours in a room with controlled temperature of approximately 25 and 30°C.
• In La Mancha, thin layers (2cm) of fresh stigmas are placed on silk or metal sieves and are exposed to higher temperatures such as butane gas fire, or vine coals and heaters or coal operated stoves. As far as drying time is concerned, they prefer the shortest time of about half an hour and a highest temperature of 70°C.
Tentative marker identification m/z RT Molecular Formula Observation
798.5674 11.7 C44H80NO9P PDO
Oxidized PC 36:4 PC 18:2/18:2
812.6164 12.6 C46H86NO8P PDO
PC 38:3
810.6057 12.4 C46H84NO8P PDO
PC 38:4 PC 18:1/20:3
838.6363 12.7 C48H88O8NP PDO
PC 40:4
820.5855 12.4
C47H82O8NP
PDO PC 39:6
796.5504 11.6 C44H78NO9P PDO
Oxidized PC 36:5
353.2311 7.8 Unknown Spain
Screening records of saffron samples analysisi
for dyes and addition of other spices
AIM OF
THIS STUDY
Dyes Plants/Spices Adulteration
Fraud
SLE RETROSPECTIVE DATA
HRMS(/MS)
SEPARATION (U)HPLC
IONIZATION ESI+/ESI-
MS ANALYSIS
Scan HRMS MS/MS
Fraud? (case No.1: Saffron from Czech e-shop)
Target screening to artificial colorants
N
NN
N
OH
S
O
OOH
OOH
S
O
O
OH
Tartrazine (E102)
NNH
S
O
OH
O
O
S OO
OH
Azorubine
(E122) N
O
S
S
O
O
OH
O O
OHNH
SO O
OH Ponceau 4R
(E124)
In the most suspicious sample three artificial colorants were found
Fraud? (case No.2: „Saffron“ from Turkish market)
Characteristic marker: m/z 611.1616 / 2.86 min
Molecular formula (from exact mass):
C27H32O16 FORMULA FINDER
Theoretical vs. measured isotopic profile
BPC MS MS/MS
Searching in databases:
Hydroxysafflor yellow A
Yellow colorant of safflower (Carthamus tinctorius), also
called „bastard saffron“.
Identification of the turkish sample
Saffron Safflower ≠
CONCLUSIONS Metabolomic fingerprinting using LC-ESI-QTOFMS is a suitable tool for fast quality assessment of saffron.
UHPLC-HRMS(/MS) fingerprinting analysis provides sufficient discrimination power and information to discriminate saffron origin.
Metabolomic fingerprinting (ESI+), OPLS-DA, allowed real Spanish saffron to be distinguished between saffron cultivated in Spain and packaged in Spain.
Glycerophospolipids and their lipid oxidation are the most significant markers.
Relatively high number (around 15%) of samples seem to be adulterated, as well as label “Spanish samples” were from unknown origin.
Sample preparation
HOMOGENIZATION & SAMPLE PREPARATION
Grinding Weighting
50 mg
SOLID PHASE MICROEXTRACTION (SPME)
SPME Fiber: 100 µm PDMS
50/30 µm DVB/CAR/PDMS
60 µm PDMS/DVB
Incubation: 40°C (10 min), 50°C (5 min)
Extraction: 40°C (20 min), 50°C (5 min)
Desorption: 250°C (1 min) in injection port
GC–QTOFMS (Quadrupole-Time of Flight)
Agilent 7200 GC-QTOFMS
TOF (MS)
Full spectral information → mass spectra library (NIST) search
High resolution & mass accuracy → low mass error
→ identification of unknowns
QTOF (MS/MS)
Accurate mass product ion spectra → identity confirmation
High selectivity & sensitivity → (ultra)trace analysis
MODES OF OPERATION
TOF → single MS
QTOF → tandem MS/MS
Electron ionisation (EI)
Chemical ionisation (CI)
- Positive (PCI)
- Negative (NCI)
PCA: country of origin (packaging)
Compounds influencing the distribution
PCA: country of origin (packaging)
23
Iran India
Spain
Italy Greece
Morocco
25
Significance analysis: PDO
others
Entities are filtered based on their p-values from statistical analysis.
2,5,5-Trimethylcyclohex-2-enon Ethanone, 2-(formyloxy)-1-phenyl-
5-Heptenal, 2,6-dimethyl-
Cyclopentane, 1,1-dimethyl-
2-Hydroxy-3,5,5-trimethyl-cyclohex-2-enone
Compounds influencing the distribution
Saffron adulteration
28
VV11383 – Powdered material
Unknown origin
Bought in 2013
7 x10
0
0.2
0.4
0.6
0.8
1
Time (min) 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36
7 x10
0
0.2
0.4
0.6
0.8
1
1.2
Time (min) 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36
EI; TIC m/z 30–400 VV5/14 – Saffron (whole stigma) PDO
La Mancha Spain
Bought in 2014
Adulteration by curcuma
IUPAC name: (6S)-2-Methyl-6-[(1S)-4-methylene-2-cyclohexen-1-yl]-2-hepten-4-one
Molecular formula: C9H14O
Natural occurrence: Curcuma
CURLONE
Ar-TUMERONE
Molecular formula: C12H22O
IUPAC name: (1′R, 6S)-2-methyl-6-(4-methylcyclohexa-2,4-dienyl)hept-2-en-4-one
Formation: Dehydrogenation of curlone
Properties: Insecticidal and repellent effects
29
Identification of curlone
VV11383 - Powdered material, country of origin Turkey, bought in 2013
7 x10
0
0.2
0.4
0.6
0.8
1
Time (min)
2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36
EI; TIC m/z 30–400 Pure integration → 130 peaks
1. DECONVOLUTION (EI) Deconvolution → 191 peaks
6 x10
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
Time (min) 22.2 22.4 22.6 22.8 23
Cpd 170
30
CONCLUSIONS
35
Unknown compound identification:
Full spectral information → Mass spectral library search
Exact mass
Molecular ion (PCI)
Product ion spectra → Identity confirmation
GC –QTOF MS
SAFRON AUTENTICATION
Agilent 7200 GC-QTOFMS
Metabolomics fingerprinting → Differentiation according to
Country of origin
Protected destination of origin
Form of material
Country of purchase
AccuTOF LP (Jeol) Time-of-flight mass spectrometer
~ 5000 – 7000 fwhm
DART–TOFMS
Exactive (Thermo Scientific) Orbitrap mass spectrometer
~ 10,000 – 100,000 fwhm
DART–orbitrapMS
Direct Analysis in Real Time
Ambient mass spectrometry employing
DART ion source
DART MS PRINCIPLE
excited He 200- 400°C
THERMO DESORPTION, APCI ionization
VV_9047-1_9052-2_pos #287-337 RT: 1.02-1.19 AV: 51 SB: 38 0.92-0.98 , 1.23-1.29 NL: 1.12E6T: FTMS + p NSI Full ms [50.00-1000.00]
100 150 200 250 300 350 400 450 500 550 600
m/z
0
10
20
30
40
50
60
70
80
90
100
Re
lativ
e A
bu
nd
an
ce
133.0645
354.3357205.1946
382.3669
337.3091
502.4603298.2732221.1894
426.3932109.1011 240.2316
81.0700 409.3767149.1321 478.4605
179.1426
518.4553439.3558 593.4913
MASS SPECTRUM
of entire sample
Is it really organic? BioFach 2014
Does contain thIs food supplement healthy
sea buckthorn oil?
SAMPLES: 1. Sea Buckthorn Oil (Reference material) 2. Sunflower Oil (Reference material) 3. Sea Buckthorn Oil Pills (Commercial
sample)
SEA BUCKTHORN PILLS
Sea Buckthorn oil
Sunflower oil
DART–HRFMS mass spectra of the methanol–water
extracts at 250
C (+)
Sunflower oil
SEA BUCKTHORN PILLS
Sea Buckthorn oil
Sunflower oil
Sea Buckthorn oil
DART–TOFMS mass spectra of oils diluted with
toluene 1:50 (v/v) at 450
C (+)
SEA BUCKTHORN PILLS
Sea Buckthorn oil
Sunflower oil
DART- HRMS FINGERPRINTS
DART – m/z: 537,4455 β-carotene identification
78,2 % of matches were found
between theoretical and observed pathway.
.
MS/MS
MS SEA BUCKTHORN PILLS
Conclusion
SUNFLOWER OIL + monoglycerolss (emulsifiers)
+ beeta-carotene (provitamin A mix)
Join us for discussion
of future challenges!
7th FP EU project
Ensuring the Integrity of the European food chain
Overall strategy
WP11: Dissemination &
Knowledge Transfer
WP leader: Jana Hajslova, ICT Prague
Kick-off meeting “FoodIntegrity”
25-26 February 2014, FERA, York, UK
Training activities (4)
TRAINERS: Well established experts´ institutes will act as key
trainers´ centers and also contribute to preparation
of other training materials:
• IAEA/FAO, Vienna, Austria
• CRA-W, Gembloux, Belgium
• RIKILT, Wageningen UR, The Netherlands
• QUB, Belfast, UK
• VSCHT, Prague, Czech Republic
• FiBL, Switzerland
• FERA, York, UK
• BfR, Berlin, Germany
• NOFIMA, Norway
• Barrila, Italy
Approach for development of appropriate training program
To identify needs / priorities of industry and other stakeholders
To exploit both existing intelligence and knowledge
generated by the project
For transfer of achieved outcomes / new generated knowledge to end-users several concepts will be applied
Commodity based concept
Analytical methodologies based concept
Other concept(s) (consumer issues,
traceability, chemometrics)
To develop training program for interested stakeholders to fulfill
their expectations and needs
A network of competent laboratories / intelligence owners (European network of competence for analytical techniques in food authentication OR Food Authenticity /
Fraud European Training Labs Network) will be established to provide training in specific technologies enabling food quality assessment and authentication
