Stable Isotope Analysis to detect Food Adulteration and Fraud

Transcription

1 Stable Isotope Analysis to detect Food Adulteration and Fraud Simon KELLY, Aiman ABRAHIM, Marivil ISLAM, Zora JANDRIC, Britt MAESTRONI and Andrew CANNAVAN Food and Environmental Protection Laboratory & Section Joint FAO/IAEA Division of Nuclear Techniques in Food and Agriculture

2 Can you connect the adulterants on the top row to the foods and beverages on the bottom row? vodka

3 Organizational Chart Agriculture and Consumer Protection Department Department of Nuclear Sciences and Application Joint FAO/IAEA Division of Nuclear Techniques in Food and Agriculture Plant Breeding and Genetics Section Animal Production and Health Section Insect Pest Control Section Food and Environment Protection Section Soil and Water Management & Crop Nutrition Section Plant Breeding and Genetics Laboratory Soil and Water Animal Production and Insect Pest Control Food and Environment Animal Production Unit Entomology Unit Agrochemicals Unit Management Soil Science & Unit Crop Health Laboratory Laboratory Protection Laboratory Nutrition Laboratory The FAO/IAEA Agriculture & Biotechnology Laboratories Seibersdorf, Austria

4 Food Fraud The EU agri-food chain legislation does not contain a definition of food fraud. However, this category broadly overlaps with the fraudulent and deceptive practices referred to in Article 8 of Regulation (EC) No 178/2002 (the General Food Law ). The UK Food Standards Agency states food fraud has occurred..when food is deliberately placed on the market, for financial gain, with the intention of deceiving the consumer.

5 Types of food fraud UK FSA Food Crime Annual Strategic Assessment, baseline report 2016

6 Bio-element Stable Isotope analysis in food authentication 13 C 1 2 C 15 N 14 N 18 O 1 6 O 34 S 32 S 2 H 1 H CN OS Chemical Element.com Apr. 21, 2004 <

7 Fruit juice adulteration issues addressed by isotope testing addition of water addition of sugar addition of pectins cane maize (HFCS) beet (BMIS) addition of organic acids addition of flavours

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9 Common challenges in food authentication methods 1. Methods are comparative and require a database of authentic samples 2. Authentic samples must reflect variability in: a) geographical origin b) seasonal variability in production c) permissible technological processing d) varietal differences in raw materials

10 Fruit juice adulteration issues addressed by isotope testing addition of water addition of sugar addition of pectins cane maize (HFCS) beet (BMIS) addition of organic acids addition of flavours

11 Overcoming the database challenges -Internal isotopic correlations Variation in meteoric input water at different growing locations leads to wide variation in δ 18 O values. However generally fruit juice water is significantly enriched in 18 O compared to ground water due to leaf evapotranspiration. Applying a single cut-off for all juice from a particular fruit limits sensitivity of ground water detection. However, internal oxygen isotopic correlation between sugar and water δ 18 O values was demonstrated for SSOJ NFC permitting improved detection limits. Houerou et al., RCM, (1999) δ 18 O [ ] OJ water δ 18 O [ ] OJ sugars

12 Multi-component fingerprinting for added water in Orange juice δ 18 O water ( ) δ 18 O ethanol ( ) Water addition Authentic oranges collected in Costa RIca Lab-squeezed samples Market NFC juices Market FC juices Max (95% c.i.) Min (95% c.i.) AIJN limit E. Jamin et al. J. Agric. Food Chem. (2003) 51, 18,

13 Fruit juice adulteration issues addressed by isotope testing addition of water addition of sugar addition of pectins cane maize (HFCS) beet (BMIS) addition of organic acids addition of flavours

14 δ 13 C Ranges of C 3 and C 4 plants After Tipple and Pagani, 2007

15 AOAC methods recommendedby CODEX δ 13 C ( ) Cane, maize AOAC Beet Orange -28 -> -25 limits(aijn CoP) 103 -> AOAC (D/H) 2 I (ppm) H-NMR Courtesy of Freddy Thomas, Eurofins, SFIS, Toulouse, 2010

16 δ 2 H measurements of non-exchangeable H in sugars (CH 2 ) 6 N 4, hexamethylenetetramine EA or GC-IRMS NH 3 Kelly et al., J. Agric. Food Chem. (2003) H 2 CO HPLC H 5 IO 6 c. HNO 3, c. H 2 SO 4 H Nitrate-ester DI-IRMS Dunbar & Schmidt, Fresenius z. Anal Chem (1984) Ce(NH 4 ) 2 (NO 3 ) 6, HClO 4 Fermentation (H 2 COO) 2 Ca DI-IRMS Krueger., ISBN (1995) CH 3 CH 2 OH K 2 CrO 3, c. H 2 SO 4 (CH 3 COO) 2 Ca DI-IRMS Roβmann & Schmidt, Z Lebensm Unters Forsch (1989) Cadiot distillation CH 3 CH 2 OH 2 H-NMR Martin et al., J. Am. Chem. Soc., (1986)

17 HMT method RESULTS (apples n = 100) HMT δ 13 C V-PDB BEET APPLE CANE HMT δ 2 H V-SMOW England W. Europe Germany Ukraine Australia & NZ S. Africa USA & Canada Brazil & Chile E. Europe BEET CANE Kelly et al., J. Agric. Food Chem. (2003)

18 δ 2 H measurements of non-exchangeable H in sugars (CH 2 ) 6 N 4, hexamethylenetetramine EA or GC-IRMS NH 3 Kelly et al., J. Agric. Food Chem. (2003) H 2 CO HPLC H 5 IO 6 c. HNO 3, c. H 2 SO 4 H Nitrate-ester DI-IRMS Dunbar & Schmidt, Fresenius z. Anal Chem (1984) Ce(NH 4 ) 2 (NO 3 ) 6, HClO 4 Fermentation (H 2 COO) 2 Ca DI-IRMS Krueger., ISBN (1995) CH 3 CH 2 OH K 2 CrO 3, c. H 2 SO 4 (CH 3 COO) 2 Ca DI-IRMS Roβmann & Schmidt, Z Lebensm Unters Forsch (1989) Cadiot distillation CH 3 CH 2 OH 2 H-NMR Martin et al., J. Am. Chem. Soc., (1986)

19 Fluorinated compounds

20 Feasibility study One step reaction Direct injection of reaction mixture into GC-IRMS system after 1 hour Separation of sugar derivatives Measurement of non-exchangeable hydrogen

21 Typical 2 H ion chromatogram for sucrose-octa-trifluroacetateand USGS70 (Methyl-Icosanoate) syringe standard USGS70

22 Preliminary results Sugar source Range δ 2 H [ ] Reference Sugar beet (sucrose nitrate ester) Sugar beet (sucrose nitrate ester) -130 to -108 Dunbar et al. (1983) -178 to -108 Doner et al. (1987) Sugar beet (sucrose nitrate ester) -163 to -109 Bricout & Koziet (1987) Retail Austrian beet sugar (sucrose TFA) Cane sugar (sucrose nitrate ester) Retail cane sugar (sucrose TFA) to unpublished measured by GC-Cr-IRMS (2017) Dunbar et al. (1983) unpublished measured by GC-Cr-IRMS (2017)

23 Counterfeit wine sold in the UK (2011)

24 SNIF-NMR and 13 C analysis of counterfeit wine ethanol Counterfeit wine Cane sugar ethanol EA-IRMS δ 13 C[ PDB ] Beet sugar ethanol Grape ethanol reference zone Genuine wine Unpublished data SNIF-NMR (D/H) I [ppm]

25 SNIF-NMR and 18 O analysis of counterfeit wine water Genuine wine 5.00 IRMS δ 18 O VSMOW Counterfeit wine (ground water) Unpublished data SNIF-NMR (D/H) I [ppm]

26 Counterfeit wine sold in the UK Misspelling of Australia

27 Production origin claims Is it Organic (Biological)? Can nitrogen & oxygen stable isotope analysis help us detect conventional crops that have been labelled as Organic?

28 Rationale for measuring nitrogen isotopes in Organic vegetables CONVENTIONAL ORGANIC N 2 from air 2(air) δ 15 N = 0 Haber process - NH 4 + Animal manure NO 3- or NH NOFertiliser 3 fertiliser NO 2 - O 2 (soil air) δ 15 N -2 to +2 NO 3 - on average δ 15 N = O

29 Tomato total nitrogen isotope data Organic Conventional Number of individuals δ 15 N ( ) air

30 Tomatoes 15 N normal distribution Probability Conventional n=43 Mean = -0.1 σ n-1 = 2.1 Organic n=61 Mean = +8.1 σ n-1 = δ 15 N( ) air

31 Organic Tomato 15 N test Mean ± 2.0σ n-1 Probability Unlikely < % δ 15 N( ) air

32 Cut-off value of 0 includes 50% of conventional Tomatoes leading to false negatives Mean ± 2.66σ n-1 Probability Highly unlikely < % δ 15 N( ) air

33 Univariate v s bivariate Courtesy of Pietro Franceschi(IsoFood, Slovenia, Dec 2016)

34 Univariate v s bivariate Courtesy of Pietro Franceschi(IsoFood, Slovenia, Dec 2016)

35 Rationale for measuring oxygen isotopes in extracted plant nitrate

36 The denitrifiermethod* using denitrifying bacteria Pseudomonas aureofaciens for conversion of plant NO 3- (hot water liquid extract ) to N 2 O (gas) * Sigman et al., 2001; Casciotti et al., 2002

37 Potatoes grown under conventional and Organic systems (OrgTrace project) CONVNETIONAL SYNTHETIC FERTILISER ORGANIC GREEN MANURE ORGANIC SLURRY APPLICATION

38 Potatoes grown under conventional and Organic systems (OrgTrace project) CONVENTIONAL - SYNTHETIC FERTILISER ORGANIC SLURRY & GREEN MANURE RAFA 4-6 Nov 2009;

39 Why do we need to verify the geographical origin of food? Brand protection PDO/PGI legislation [(EC) No 510/2006] Beef Labelling regs [(EC) No 2772/1999] Specific legislation REGULATION (EU) No 1169/2011 food information for consumers Targeted recalls & compartmentalised recalls Consumer awareness Consumer confidence Ethical production Patriotism (Ethnocentrism) Food miles

40 Geographical origin of some beef using carbon stable isotope analysis?

41 δ 13 C as a geographical proxy for UK beef

42 Distribution of C 3 and C 4 plants across the world C4 grasslands C3 grasslands Forests (also C3) Mato Grosso do Sul The map is credited to Professor Ian Woodward (f.i.woodward@sheffield.ac.uk) and Dr Mark Lomas at the Department of Animal & Plant Sciences, University of Sheffield

43 δ 13 C & δ 15 N values from authentic beef defatted dry mass (n = 430) [ ] C 3 pasture diet C 3 /C 4 mixed diet C 4 diet C 3 to C 4 switch ~ T 1/2 167 days* [ ] * Baharet al (2006) All

44 Saffron H, C & N data δ 2 H AIR -60,00-65,00-70,00-75,00-80,00-85,00 Italy Authentic Saffron 13 C & 2 H values Italy Spain Greece Iran Greece -90,00-27,50-27,00-26,50-26,00-25,50-25,00-24,50-24,00-23,50 δ 13 C PDB Authentic Saffron 15 N & 2 H values Iran Italy Spain Greece Iran Spain -60,00-65,00 Iran Italy δ 2 H AIR -70,00-75,00-80,00-85,00 Greece Spain -90,00 0,00 1,00 2,00 3,00 4,00 5,00 6,00 7,00 δ 15 N PDB

45 Analysis of Chicken HCNS stable isotope data by CDA (n = 500) y S. America Thailand 2 H China z Europe 13 C & 15 N Rees et al., Food Control 67 (2016) x

46 Chicken HCNS stable isotope data Classification Results c Predicted Group Membership Origin2 S. America Europe China Thailand Total Cross-validated a Count % S. America Europe China Thailand S. America Europe China Thailand a. Cross validation is done only for those cases in the analysis. In cross validation, each case is classified by the functions derived from all cases other than that case. c. 81.4% of cross-validated grouped cases correctly classified.

47 Summary Stable isotope analysis provides us with a set of parameters outside chemical and biochemical composition that is difficult for food adulterators to subvert. There are challenges when developing a reference database of authentic samples to ensure all natural variability and permitted processing effects are incorporated. The use of internal isotopic referencing can remove the need for extensive databases, but the ground work on correlations still needs to be done. Stable isotope analysis is arguably the best hypothesis driven technique to determine the geographical origin of food and is now incorporated into some PDO descriptions such as Grana Padano cheese. Stable isotope data can be combined with chemical and trace element data to provide powerful multivariate authentication models.

48 What's in the pipeline? NEW CRP for 2018 Planned new Coordinated Research Project (2018) Implementation of Nuclear and related techniques to confirm the authenticity of foods with high value production chains and high value food property labelling claims. Organic, wild/farmed, free range, Halal, Kosher, natural/synthetic Consultant s meeting early st RCM 2019

49 Opportunities to support our activities in developing countries? Active projects Coordinated research activities Hosting Fellowships (3-months) Hosting Scientific Visitors (1-2 weeks) Nuclear Techniques in Food and Agriculture

50 Thank You! CuSO 4 α-cellulose Sudan 1 dye formaldehyde vodka

51 Thank You! Food and Environmental Protection Subprogramme