The Spiced project: Seasonable doubt - BfR · The Spiced project: Seasonable doubt... Saskia van...
Transcript of The Spiced project: Seasonable doubt - BfR · The Spiced project: Seasonable doubt... Saskia van...
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The Spiced project: Seasonable doubt...
Saskia van Ruth ([email protected])
Partners Spiced WP4: BfR, BIOR, Fuchs, DLO-RIKILT
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Souls for sale?
Food product adulteration and counterfeiting is a thriving multi-billion euro global industry. It is highly profitable and the risks of significant legal consequences are low.
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Fraud reports in three databases
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Herbs and spices
Olive oil
Fish and fish products
Milk and milk products
Meat and edible offal
Vegetable fats and oils…
Nuts, nut products and…
Honey and royal jelly
Other seafood…
Fraud cases 2008 - 2013
Food Fraud Food Shield RASFF
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Food Fraud
Food Defence
Food Safety
Food Quality
Ideologically driven Motivation is ‘HARM’
Food borne illness
Economically driven
Motivation is ‘GAIN’ Intentional Adulteration
Unintentional / Accidental Contamination
Petra Wissenburg, Danone Corporate Quality & Food Safety
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EU project SPICED fraud detection methods
The aim?
• To develop rapid and cost-efficient methodologies for detection of contamination (adulteration) of spices/herbs with chemical agents
• Aiming at early detection of anomalies in view of food integrity, i.e. signalling/flagging for unusual samples (authentic vs adulterated)
By which means?
• By exploring spectroscopic and spectrometric fingerprinting techniques
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Development of broad anomaly fingerprint tests
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Overview of activities
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Method set-up
for spices
Data genuine samples
Data self-adul-
terated samples
Data
bases spices
Broad Anomaly models/ compa-
rison
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Herbs
• Basil
• Thyme
• Oregano
Development of techniques for broad anomaly testing of spices
Spices
• Pepper
• Chili paprika
• Nutmeg
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Oregano and product foreign material: leaves
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Nutmeg and product own material/removal of valuable fractions
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Paprika powder and unapproved ‘enhancements’: colourants
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Selected analytical techniques
Spectroscopy
• FTIR
• NMR
Mass spectrometry
• PTR-MS
• DIMS
• (LC)-HRMS
• ICP-MS
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Approach: one class classification model
Normal variation, Database samples (50)
Samples with deviations from what is considered normal
Building the best statistical model for discrimination
Future samples to be tested
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Approach
50 Authentic baseline samples of each spice were analysed with each method
One class classification models were built based on those authentic samples
Inferior/adulterant material, and mixtures of authentic and inferior/adulterant material were analysed
The models were challenged with the authentic and modified sample material
Optimised classification models of the various methods were compared to select most promising approaches
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Will the
real nutmeg please stand up?
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Proton Transfer Reaction Mass Spectrometry
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Nutmeg
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Inte
nsi
ty`[
pp
bv]
Mass [amu]
Nutmeg spectrum Terpenes: pinenes, carenes, limonene Myrcene, cymene, sabinene, camphene
1,2
-cin
eole
safr
ole
meth
yle
ugenol
asaro
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myristicin
acetic a
cid
1-b
uta
nol
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One class classification result: Nutmeg
Baseline
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Adultera
tions
Challenge
bla
nks
Cla
ss d
ista
nce
• SIMCA model, 7 factors • Normalised, 10log scaled
data
Classification results:
Prediction Authentic Adulterated % correct
Baseline samples 47 3 94% Challenge samples:
Authentic 5 0 100%
Adulterated 4 38 90%
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Best models: discrimination 100% real nutmeg and own material mixed samples
Volatiles PTR-MS: • kNN model
• 100% real nutmeg and 100% correct prediction for own material mixed samples
• Estimated limit of detection 7%
Non-volatiles FI-ESI-MS: • PCA Q values
• Similar success rate predictions
• Limit of detection 30%
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Successful classification PTR-MS - nutmeg
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Adulterantchallenge
Mixed adulterantchallenge
[%]
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Conclusions
Broad anomaly testing approaches have been developed for a variety of spices and herbs and various fraud issues in Spiced
For each spice/herb at least a few approaches were able to distinguish well between authentic and adulterated materials
Because of the one class classifier approach, the methods are expected to be able to detect a variety of other deviations from normal, authentic sample material
To increase robustness the training sets of authentic material could be expanded to cover more “special but natural” variation of the spice and herb materials
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More presentations on the topic to follow
Bettina Horn (BfR): More fingerprinting methods, especially on paprika powders
Vadims Bartkevičs: The targeted approach
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The partners
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Thank you for your attention