Komt eerst het eten en dan de moraal? Prof.dr.ir. Saskia van Ruth Wageningen University and Research Centre The Netherlands

Transcription

1 Komt eerst het eten en dan de moraal? Prof.dr.ir. Saskia van Ruth Wageningen University and Research Centre The Netherlands

2 Wageningen University & Research Centre The Netherlands Two pillars: 1. Wageningen University Real research university (>2000 PhD students) students from >110 countries 2. Specialized research institutes

3 Trickery in our trolley?

4 Economically driven Food Fraud Intentional Food Quality Food Defence Unintentional/ Accidental Food Safety Ideologically driven From: Petra Wissenburg, Danone Corporate Quality & Food Safety

5 Characteristics of food fraud Addition of replacements of products/ingredients/constituents On purpose For economic gain May result in serious economic losses, brand damage, loss of consumer confidence and food safety issues

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7 Food fraud issues Composition: species/varieties, addition of productforeign material or product-own material, addition of water, protein, fat, other constituents, counterfeiting Processing/storage: freezing, heating, artisanal production, storage (sell-by-date) Production system: organic, animal welfare considering, sustainable, wild/farmed, fair trade Geographical origin

8 How easy is it? McDonalds

9 Fraud vulnerability

10 Fraud ingredients Suitable target Motivated offender Lack of guardianship

11 Global Food Safety Initiative 2014 Position paper on mitigating the risk of food fraud Food fraud vulnerability assessment Food fraud vulnerability control plan SSAFE: Development of a standard for self-assessment of food fraud vulnerability 11

12 Opportunityrelated fraud risk factors Motivationsrelated fraud risk factors Fraud control measures Actual fraud vulnerability

13 Impact analysis decision tree 50 questions Series of spider web diagrams Identified vulnerabilities as basis for control plan

14 Hard controls: Analytical testing Advanced, confirmatory tests in the laboratory Rapid, on site, red/green traffic light

15 Differences and similarities

16 Analytical techniques Chromatography Mass spectrometry DNA-based techniques Immunology Physical tests, e.g. DSC Wet chemistry Microscopy Sensor-based technologies Sensory analysis

17 Single marker approach Presence/concentration of a particular compound

18 Fingerprint analysis

19 Examples Broad anomaly testing, e.g. milk powders, spices and oils/fats Processing history: thawed meat and heat treated milk in Farmers cheese Organic produce, e.g. eggs, milk, pork meat, wheat Distinguishing geographical origin, e.g. juices from South America and the EU Linking geographical origin to sustainability: palm oil, cocoa/chocolate, bananas Brand typicality: coffee, chocolate, alcoholic beverages

20 Is it possible to chemically prove if wheat was organically produced or conventionally? 20

21 Sampling design Danish origin Locations Growing year Growing systems Repetition Flakkebjerg Conventional, Organic A Organic B Foulum Conventional, Organic A Organic B Jyndevad Conventional, Organic A Organic B Locations 2 growing years 3 growing systems 2 repetitions = 36 samples

22 1. Liquid chromatography phenolics 280 nm Conventional Organic A Organic B? 1. Align your data! 2. Start the chemometrics Column: Zorbax 300Extend-C18 4.6x250mm 5µm; Precolumn: LiChrosorb C18 4.6x7.5mm 5µm 2

23 Area A280 (AU) Specific phenolic compound differs sufficiently: single marker Conventional Organic A Organic B 2 3

24 Current trend in food analysis: Miniaturisation and physical characteristics MicroNIRs: USB powered 60 grams (45 x 42 mm) Spectral range of nm

25 Predicted value Absorbance Pilot experiment Measurements with JDSU MicroNIR Samples Fresh Thawed Freezing (-24 o C) for 24 h MicroNIR measurements 2. Samples Fresh Gelatin injected Brine injected De-frosting for 24 h. 24 h Wavelength (nm) Chemometric analysis 3. Reference methods for: Moisture content (NEN-ISO 1442:1997) Protein content (NEN-ISO 937:1992) Reference value

26 Next level: retail chicken fillets Discrimination between chilled versus thawed product Moisture and protein content

27 Building the database

28 Results for Albert Heijn chicken fillets 95% correct prediction of chilled products and 80% for thawed products Data consistent with data acquired with reference method Moisture and protein regression promising, some more reference data in progress After finalisation of these models => on-site measurements

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31 The sound of chocolate

32 The sound of chocolate Valentina Acierno, PhD student Microphone Texture analyser TA.XT plus 32

33 Force (N) The chocolate snap: force required Peru Ghana Indonesia/ Ghana Aligned breaking point 33

34 Do you have good ears? Peru Ghana Indonesia/ Ghana 34

35 PCA showing differences according to the sound energy produced by the different origin chocolates Factor 3 (1.0%) 2Ghana 3 2Ghana 1 2Djakarta 1 Factor 2 (4.2%) 2Djakarta 4 Factor 1 (91.3%) 2Perù 3 2Ghana 4 2Djakarta 2 2Perù 2 2Perù 4 2Perù 1 35

36 Conclusions Developments in Fraud vulnerability risk profiles High tech laboratory solutions combining analytics and chemometrics Portable, on-site measurements Empower the consumer in the future? 36

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38 Thank you 3 8