DESK STUDY TO ASSESS THE AVAILABILITY OF METHODS FOR DETERMINING THE COUNTRY OF ORIGIN OF SELECTED FOODS TO SUPPORT EXISTING AND PLANNED LEGISLATION
Sandy Primrose Food Authenticity Programme Advisor
Simon Kelly and Céline Pye Food and Environment Research Agency, Sand Hutton, York
September 2013
Page 1 of 49
1 Executive summary This document is a review of the methods available for determining the country of origin of a variety of foodstuffs to support pending legislation. Methods are available for all the foodstuffs of interest except for meat that is used as an ingredient in sausages, burgers and kebabs. The principal method for determining country of origin is likely to be stable isotope analysis. Other methods (e.g. proteomics, metabolomics and metagenomics) could be useful, especially with certain foodstuffs, but require further experimental evaluation.
Although the suitability of stable isotope analysis for determining country of origin has been demonstrated clearly, the utility of the method currently is severely limited by the absence of a comprehensive and accessible database for all member states. For countries outside the EU the databases are very fragmented or totally absent. If sufficient funding were made available, e.g. via an ERA-NET, then a suitable European database could be generated. The absence of databases also is a problem with other techniques.
In the absence of comprehensive databases, it is possible to use stable isotope analysis to determine if a particular sample comes from the region or country stated on the product label. However, to do this it will be necessary to have authentic samples of the same material from the region of interest. If the product is claimed to be produced in Europe then it should be possible to obtain the requisite authentic samples, perhaps with some difficulty. Should the product be labelled as coming from a country outside of the EU then obtaining authentic samples could be extremely difficult. With a few exceptions, e.g. beef, currently it is not possible to identify the exact country of origin (within or without Europe) of a foodstuff without evidence of the likely place of manufacture or processing.
2 Rationale for this study The European Commission is planning to implement legislation for mandatory indication of country of origin or place of provenance for unprocessed meat of pigs, poultry, sheep and goats (beef is already covered by existing legislation). The possibility of extending the legislation to other foods currently is being discussed in the European legislature.
This desk review was commissioned to inform Defra of the existing experimental methodologies for determining the authenticity of country of origin labelling and to determine:
What additional work needs to be done to validate existing methodologies; What, if any, new methods need to be developed to support implementation of country of origin legislation.
3 Scope of study Following guidance from Defra labelling policy teams, this review covers:
Fresh, chilled or frozen meat from pigs, sheep, goats and poultry;
Page 2 of 49
Cooked and cured meats; Meat as an ingredient in hamburgers, sausages and kebabs; Milk; Fruit and vegetables. The over-riding interest of Defra is the labelling of food as coming from the UK or one of its constituent countries. However, should funding be required to develop and/or validate new methodologies there could be scope for cost sharing with other countries via ERA-NET (or any equivalent funding vehicle introduced in Horizon 20-20). Therefore, this review considers country of origin labelling (COOL) from a European perspective.
4 Technical limitations In reviewing the potential of any method to determine geographical provenance it should be noted that a geographical region may lie wholly within a country (e.g. ‘west country’ refers to south-west England) or across the border between two countries (e.g. the Welsh border country). In the latter case, it may be possible to determine that a foodstuff comes from a particular region but not which of the two countries that encompass that region. This will be an issue in certain parts of the EU, e.g. the Netherlands/ Belgium and Belgium/ France border areas.
It is theoretically possible for an animal to be born in one country, raised in another, slaughtered in a third country and processed into a finished product in a fourth country. Some of the methodologies reviewed below can determine the country or region in which an animal was reared or a crop was grown. However, we are not aware of any methodology that currently exists for determining the place of birth or slaughter of an animal. However, some mammalian tissues (e.g. parts of the cerebral cortex) have a significantly longer turn- over time and possibly could be used to determine the place of birth. Furthermore, very recently laid down protein, such as hair, could provide a resolution of days for origin determination. But for the purpose of verifying the declared origin of retail/catering cuts of meat such approaches are not practical.
According to European legislation, country of origin is defined as the country where the goods were wholly obtained or produced or, if produced in more than one country, where they last underwent substantial change. As noted above, it may be possible to determine where plant material was grown or an animal was raised. However, the possibility of determining where substantial change occurred will depend on what is involved in ‘substantial change’. For example, the artisanal nature of cured meat production results in the production of specific metabolites that are likely to be specific to a particular region (see section 6.3). However, it would not be possible to determine the country of origin of hamburgers or sausages that were produced in one country using ingredients from one or more other countries.
Page 3 of 49
5 Available methodologies 5.1 Introduction
The food authenticity programme, as managed originally by the Food Standards Agency and now by Defra, has supported the development of analytical methodologies (for reviews, see references 1 and 2) that could be of use in determining country or geographical region of origin. These methodologies are described below. However, for some of the products of interest (e.g. cured meats) the product definition encompasses not just geographical origin but also other factors such as breed of animal and mode of preparation. Methodologies that could be used for verifying these additional features also are described.
5.2 Stable isotope analysis
The potential to determine the geographical origin of animal or plant derived material using stable isotope signatures is well established in food authentication studies. The transfer of isotope signals from the bio-elements (H,C,N,O,S) present in local feed and water to animal and plant tissue is understood and forms the basis of the approach. Similarly, the geochemical isotope signature of a particular region can be traced through the use of strontium isotope analysis (and sulphur to a lesser extent).
A fundamental part of this approach to determine the geographical origin of a suspect food sample in an objective way is by statistical comparison of its stable isotopic composition to a database of samples of which the geographical origin is known. Because this approach requires a representative database of samples from all the relevant production areas, it is especially useful to discern between food commodities that are produced in a limited number of confined areas or by a limited number of producers. This is the case for food products that have a Protected Geographical Indication (PGI) or Protected Designation of Origin (PDO) status. This approach, however, quickly becomes too expensive for a food commodity produced in many different areas. For such cases, the food isotope map (Isoscape) approach (see next section) may provide a cost-effective extension to the database approach.
5.3 Isoscapes (Isotope landscapes or maps)
Isoscapes are spatially explicit predictions of elemental isotope ratios (δ) that are produced by executing process-level models of elemental isotope fractionation or distribution in a Geographic Information System (GIS). The isotopic composition of food is predicted for the relevant production areas by using additional information in the form of one or more ancillary variables (reference 3). Ancillary variables are those that are available at a higher spatial density than the original data which are used to improve the final interpolation results e.g. meteorological or geological data. Although isotope maps can in principle also be derived by direct spatial interpolation of the data, interpolation using ancillary variables reduces the need for an exhaustive sample database and furthermore puts constraints on
Page 4 of 49 the predicted isotope values in sparsely sampled areas. The food isotope map concept is based on the observation that local climatic and geological characteristics of a production area are often reflected in the isotopic composition of food produced in that area. Provided that there is a clear relation between both, the isotopic composition of food can be predicted for any area using ancillary spatial information about the local climate and geology.
Isoscape data usually is available in the form of high resolution digital maps. In general, the food isotope map can be used to answer two different but related questions about a suspected food sample. The first is ‘Is the data from stable isotope analysis on a sample consistent with the geographic origin claimed’? If the answer to the first question is negative, or if no geographic origin is stated, then what might be the provenance of the sample based on stable isotope analysis? As for all statistical techniques, the method does not provide unequivocal data on the provenance of a food sample but can confirm its possible origin at a specified level of confidence and spatial specificity of the method. Therefore this approach should be regarded as a screening method the results of which might suggest further investigation by enforcement authorities. The additional work could involve analysis of documentation or could involve the generation of much more supporting isotope data using samples of guaranteed provenance.
5.4 Trace element analysis
As with stable isotopes, element concentrations in plants and animals are mainly related to the geological and microclimatic characteristics of the site of cultivation and farming practices. For example, variations in the sodium concentrations of European cereals have been shown to be linked with the soil geology and the distance from the sea (“sea-spray effect”)
Appendix 1 summarises the published literature in the use of combined Stable Isotope and Trace Element (SITE) analysis to verify the geographical origin of food and demonstrates that it has been applied with varying degrees of success to meat, vegetables, fruits, cereals, wine and beverages.
5.4 DNA speciation
DNA-based methods are particularly suited to determining if meat from a particular species of animal is present in a product. Standard Operating Procedures (SOPs) are available for identifying beef, pork, lamb, goat, horse, donkey, chicken, turkey, duck and pheasant. The standard protocol is to extract DNA from the test sample, amplify a particular region of the genome using the polymerase chain reaction (PCR) and analyse the amplified DNA by gel electrophoresis. Confirmation of the presence of a particular species can be achieved by sequencing the amplified DNA. All of the relevant methodology has been transferred to UK public analysts and they have the capability of developing procedures for animal species not
Page 5 of 49 listed above. Whilst speciation on its own gives no information about country of origin, the absence of a declared species or the presence of an undeclared species should immediately arouse suspicion about the provenance of the sample.
5.5 Identification of animal breed
Certain cured meats are not only produced in particular geographic regions but are supposed to be made with meat from particular breeds of animal (usually pigs). In FSA project Q01130 and Defra project FA0112, a method was developed for determining if a piece of beef or pork came from any one of 12 breeds of cattle or 14 breeds of pigs that are considered as specialties in the UK. This method was based on analysis of a large number (96) of single nucleotide polymorphisms (SNPs) and the data generated can be used to answer two different but related questions. The easiest question to answer is ‘Is this meat from breed X?’ and the result obtained is unambiguous in most cases. The harder question to answer is ‘From which breed of cattle or pig has this meat come from?’ A positive answer will be obtained most of the time but only if the breed of interest was included in the project work. Thus, it will be possible to determine if the meat present is from Landrace pigs (e.g. Serrano ham) but not Iberico pigs (e.g. Iberico ham). Similarly, ‘Vitellone dell’ Appennino Centrale’ beef should be produced from only three cattle breeds: Chianina, Romagnola and Marchigiana. If a more complete SNP database was generated that included not just the specialty UK breeds but all the breeds farmed in the UK and the EU then it might be possible to use the method more generally for country of origin determination. However, this would depend on having information about which breeds are farmed in which countries.
The method described above has been shown to work in the hands of the public analyst who was a sub-contractor for the original project. However, the reagents used in the test have to be manufactured as a special order and are very expensive. Consequently, attempts are being made to simplify the assay (Defra project FA0125) and if successful will facilitate transfer of the method to official control laboratories.
Wild animals can exist in distinct populations with little or no inter-breeding between the different populations. When populations do not inter-breed they begin to drift genetically such that DNA sequence differences are detectable. For example, in Defra project FA0112 it was noticed that Hereford cattle in the UK were genetically different from Hereford cattle in the US, even though the two populations had common ancestors ~200 years ago. With animals there could be topographical barriers (e.g. mountains, major rivers) or extensive urbanisation that prevent different groups of animals mixing. Thus it is likely that deer and many game birds in Great Britain are genetically different from those in Ireland or mainland Europe. Within Europe there could well be genetic differences.
Page 6 of 49
5.6 Proteomics
Using mass spectrometry it is possible to unambiguously identify, and quantify, many of the peptides present in a sample. These peptides naturally may be present or may be generated by the analyst by treatment of the sample with proteolytic agents. Once the peptides have been identified it is relatively easy to determine the functional proteins from which they are derived. For example, in FSA project Q01104, analysis of peptides derived from myosin light chain 3 was used to detect 0.5% contaminating chicken in pork meat (reference 8). The method is amenable to highly processed foods, which can be highly problematic for analysis by other methods, and can be used with the types of mass spectrometer likely to be found in official control laboratories.
5.7 Metabolomics
Metabolomics is the systematic study of the unique chemical fingerprints that specific cellular processes leave behind. The metabolome represents the collection of all metabolites in a biological cell, tissue, organ or organism, which are the end products of cellular processes. There is no technique that will permit the extraction and separation of all the metabolites in a sample but it is possible to distinguish up to 1,000 different molecules using a combination of HPLC and mass spectrometry. The FSA funded six metabolomics projects as part of the G02 programme plus about another 6-8 projects via the G03, Q01 and nutrition programmes. For example, metabolomics has been used to identify markers of dietary intake (reference 4) and to identify mechanically-recovered meat (FSA project Q01102).
Work at Royal Holloway College, but not funded by Defra or FSA, has shown that it is possible to distinguish the same variety of tomato grown in glasshouses in different parts of England and to separate English and Spanish tomatoes of the same variety. One of the key differentiating groups of metabolites between English and Spanish tomatoes is the pigments, presumably reflecting different light levels. Although some varieties of tomatoes are grown in more than one country, the main varieties grown in each of the major producers are quite different. Therefore, it is likely that a combination of metabolomics coupled with pigment and genotype analysis would be very informative.
5.8 Metagenomics
Metagenomics is a technique whereby DNA is extracted from environmental samples and sequenced. Bioinformatics techniques then are used to identify the microbial species from which the DNA was derived, including species that never have been cultivated in the laboratory. Essentially, the technique allows one to get a profile of the total microbial flora in a sample. Since the microbial flora associated with a sample will reflect the environment from which it was derived, metagenomics may be a useful technique for verifying COOL. A pilot project funded by Defra is underway at Fera.
Page 7 of 49
5.9 Spectroscopic techniques
FT-IR spectroscopy has been used to discriminate Italian and non-Italian olive oils and Ligurian and non-Ligurian olive oils (reference 9). Similarly, FT-raman spectroscopy has been used to discriminate Corsican and non-Corsican honeys (reference 10). With the possible exception of milk, spectroscopy is unlikely to be of use for determining the country of origin of the foodstuffs listed in section 3.
6 Specific challenges 6.1 Fresh and Frozen uncooked meat
An existing isotope landscape or food origin map (isoscape) has been created for British Beef in the form of a decision making web tool with previous funding from the FSA and Defra seedcorn fund. However, the isoscape’s reliability is currently limited by a deficit of representative authentic samples from Scotland, East Anglia, the Midlands and the South/South East of England as shown in the Figure below. It would also be useful during any project to supplement the database to obtain authentic beef samples from Northern Ireland and the Republic of Ireland.
Low authentic beef sample numbers in Scotland, East Anglia, the Midlands and the South/South East of England
The isoscape technology is applicable to other meat types from grazing animals such as lamb. It is also applicable to pork, chicken, turkey and venison. In the case of monogastric animals, the relationship with the local environment is likely to be reduced because of the use of imported feed but the isotope signal from local water will still be present in muscle tissues. An alternative approach is used by the British Pig Executive (BPEX), which has
Page 8 of 49 established its own pork stable isotope database to verify labelling claims. This is used as an industry tool for self-regulation by BPEX. If a non-compliance is observed in a retail sample BPEX accesses their own archived samples from all pork producers in the UK. In this way they can exactly match the claimed origin from the retail pork supplier and verify the claim (reference 7).
There is some unpublished evidence that modified atmosphere packaging can affect the carbon isotope signature to some degree but this requires further investigation and peer review. Note also that the method described can be applied equally to fresh or frozen meat.
6.2 Meat as an ingredient
With existing methodology it is possible to determine which species of meat are present in a processed sample such as a sausage, burger or kebab. If more than one species is present it also is possible to quantify all species relative to one another. However, determining the geographical origin of meat in highly comminuted products such as sausages and burgers is extremely challenging. This is principally because other bulking ingredients (such as potato starch, wheat flour and rusk) or preservatives (nitrate and meta-bisulphite) could affect the HCNS isotope signatures of a bulk measurement. Furthermore, these products may contain meat from more than one animal of different geographical origin. Research would be required to establish if it would be possible to analyse the isotope signature of specific amino acids or fatty acids that are known to be solely derived from meat present as an ingredient.
6.3 Cooked and cured meats
Cured meats are meats that are preserved and/or flavoured by one or more of the following processes: drying, smoking or addition of one or more of salt, sugar, spices or herbs. Curing can be done with nitrates or nitrites but these are not added deliberately in traditional or regional cured meats. A non-comprehensive list of the different types of European cured meats is presented in Table 2. From this table it can be seen that key features of particular cured meats can include:
species (e.g. pig, cow, deer), breed of animal (e.g. Landrace pig for Serrano ham), animal feed (e.g. Modano ham), cut of animal, processing of the meat (e.g. air drying, smoking), geographic origin.
Page 9 of 49
Table 2: Examples of European cured meats
CURED MEAT COUNTRY OF KEY FEATURES ORIGIN
Coppa Italy Similar to prosciutto Capocollo Made from pork shoulder or neck Capicolla Cured whole Speck Italy Made from hind quarters of pig Cold smoked Cured with garlic, nutmeg, bay, juniper berries Prosciutto Italy Six regional varieties Made from hind leg of pig or wild boar Cured for 9-24 months Presunto Spain, Portugal, US Similar to prosciutto Serrano ham Spain Similar to prosciutto Must be made from Landrace pig Iberico ham Spain Similar to prosciutto Must be made from Iberico pigs Jambon cru France Similar to prosciutto Bayonne ham France Air dried ham from South-west France Must be made from one of eight breeds of pig Soppressata Italy Italian pressed sausage Some made from best cuts of pork but others (e.g. Tuscan) made from leftover cuts Bresaola Italy Made from beef (unlike other cured meats) Air-dried and salted and aged for several months Made from top round Tuscan PDO ham Italy Made from pigs raised in Tuscany Modena ham Italy Pigs must be reared on particular feeds Only certain breeds of white pig Lomo Spain Made from pork tenderloin embuchado Dry-cured Many versions depending on pig breed used Cecino Spain Made from beef, horse or other animals Best cecino is from Leon in NW Spain Venison bresaola UK As Italian bresaola but made from venison Cornish coppa England As Italian coppa but from Cornwall Wiltshire cure UK Wet cured bacon and ham Yorkshire ham England Traditional cure that is saltier but milder in flavour than European hams Made from local pigs Belfast ham N. Ireland Dry salted ham Smoked over peat
Validated methods exist for determining the species of animal used to make a cooked or cured meat. The usual method involves RFLP analysis of amplified DNA with or without
Page 10 of 49 confirmation of the result by DNA sequencing (section 5.4). However, there is an alternative proteomic approach (section 5.6). The feasibility of using SNP analysis to establish the breed of animal from which a piece of meat has been derived has been shown (section 5.5). Data might not be available for certain breeds of interest but the methodology could be expanded to generate the required data. In some instances, determining the breed of animal might give information regarding country of origin. For example, if meat from a breed of pig not farmed in the UK was found in a product labelled as British.
Many biochemical changes occur during the cooking or curing of meats and some of these changes include proteolysis of muscle proteins. A group at Royal Holloway College has been working with colleagues in Valencia to determine what peptides are present in Spanish cured hams. They were able to identify 58 peptides derived from creatine kinase and 14 peptides from myosin light chain I and titin (references 5 & 6). They also have identified numerous peptides with potential pharmacological effects. Given that the process of cooking or curing is standardised there is the possibility that the peptide content of these products is consistent from batch to batch and that this could give information about the manufacturing process and, indirectly, the geographical origin. Unpublished data has been produced by Fera, for Quality Meat Scotland, to assess the effects of cooking on the stable isotope composition of beef. Controlled pan frying to rare, medium and well done was undertaken to industry guidelines and the results showed that significant differences were observed after medium cooking compared to uncooked beef. Consequently, stable isotope databases of uncooked beef skeletal muscle could only be applied with caution to cooked samples taken from restaurants.
For dry-cured meats, stable isotope analysis will give information about the region or country where the source animal was raised. Significant amounts of stable isotope data have been produced by the Institute of San Michele working with a co-operative of Parma Ham producers but these data are not in the public domain. Similarly, where the meat has been wet-cured stable isotope analysis can provide information about region or country of origin. However, it is important to characterise and understand any effects that may arise from curing processes.
6.4 Fish
Preliminary work being done in project FA0118 suggests that it will be possible to determine the fishing area from which fish were caught by examining DNA polymorphisms. However, this methodology only will be possible with non-migratory fish.
6.5 Milk and cheese
Stable isotope analysis should be suitable for determining the origin of milk (presumably frozen or long-life milk). It certainly has been used to determine the origin of two PDO cheeses: Grana Padano and Parmigiano Reggiano (see appendix)
Page 11 of 49
6.6 Fruit and vegetables
The data in Table 1 show that stable isotope analysis has been successfully used to distinguish the geographical origin of cereals, orange juice and potatoes. Food Forensics is currently working with Fera to establish an isoscape for UK tomatoes with funding provided by the Technology Strategy Board. This approach would be amenable to other fruits and vegetables with declared geographical origin. The metabolomics approach developed at Royal Holloway College, with or without pigment and genotype analysis, should be suitable for determining the country of origin of tomatoes and almost certainly could be applied to peas, beans, sweetcorn, cherries, strawberries, citrus fruits and root crops.
6.7 Summary of what can be done
Table 3: Summary of suitability of existing methodologies
FOODSTUFF WHAT CAN BE DONE Fresh & frozen Stable isotope analysis can be used to identify the region of origin meat (i.e. more specific than country of origin) of farmed animals. The methodology is widely used and all laboratories with the technical capability routinely undertake in-house validation. However, the existing databases will need to be made much more comprehensive. It will be easier to answer the question ‘Did this sample come from region X’, i.e. comply with COOL rather than to determine the regional origin of a particular sample with no prior knowledge or origin claim. Work is needed to determine if the use of animal feed, either seasonally or all year round, affects the results. Meat as an Currently there is no suitable methodology and the identification of ingredient appropriate methodology is unlikely for the foreseeable future. Cooked and If a particular cured meat should be made from a particular animal cured meats breed then it will be possible to determine if that breed has been used in the preparation of a sample. This will involve SNP analysis. The methodology has been shown to work with particular breeds of UK cattle and pigs but it has not been validated. A database will need to be established for European breeds of pigs, sheep, cattle and deer.
Where curing or a particular cooking method is used in the preparation of the meat then proteomics and/ or metabolomics methodology should be able to show if a particular sample matches authentic samples. The method has been demonstrated for a few Spanish dry cured hams but has not been validated. A database will need to be established. Fish Should be able to determine fishing ground for non-migratory fish. Milk and cheese Stable isotope analysis with or without trace element analysis should be suitable for determining the origin of milk and cheese. The methodology is widely used and all laboratories with the technical capability routinely undertake in-house validation.
Page 12 of 49
However, the existing databases may need to be made more comprehensive. It will be easier to answer the question ‘Did this sample come from region X’, i.e. comply with COOL rather than to determine the regional origin of a particular sample with no prior knowledge or origin claim. Fruit and Stable isotope analysis with or without trace element analysis vegetables should be suitable for determining the origin of fruit and vegetables. The methodology is widely used and all laboratories with the technical capability routinely undertake in-house validation. However, the existing databases may need to be made much more comprehensive. It will be easier to answer the question ‘Did this sample come from region X’, i.e. comply with COOL rather than to determine the regional origin of a particular sample with no prior knowledge or origin claim.
Metabolomics, with or without pigment and genotype analysis, should be suitable for many fruit and vegetables but work is needed to confirm this and to begin building the necessary databases. As with stable isotope analysis, it will be much easier to determine if a sample came from a particular region or not than to determine which region it came from.
7 Novel Packaging Labels Barcodes are widely used on packaging and can detail many different kinds of information including manufacturing site, date of manufacture, batch number, etc. However, it is very easy to forge barcodes and many counterfeiters have no problem in copying all the graphics present on a package. However, a new generation of smart inks has been developed that cannot be copied because they are based on genetically-engineered proteins that are made in special ways that have not been disclosed. These inks absorb and emit light at very precise wavelengths and examination under laser light will instantly reveal if the right ink is present. By using only one or two specific inks it should be possible to print tens of billions of different barcodes. Individual producers could be supplied with labels with their own barcodes that could be used to track the goods from farm to fork.
8 Current Capabilities and Need for Method Development In terms of COOL there are two questions that can be asked. These are:
1. Based on experimental analysis, from which country did the product in question originate? 2. Is the data obtained by analysis of the product consistent with the country of origin specified on the label? In terms of the first question, Table 4 contains a summary of what can be done now and what work needs to be done to generate a toolbox of methods to enable the country of
Page 13 of 49 origin of a foodstuff to be determined by laboratory analysis. It should be clear from this table that, with one exception (meat as an ingredient), there is suitable methodology but that the requisite comprehensive reference data to identify the European country of origin are missing. If these databases are generated then it should be possible in most cases to identify the European country of origin. It should be noted that reference databases and expertise in geographical origin determination of meat do exist in other member states, e.g. France, Germany, Italy and Spain. What is lacking is a coherent framework to share these data to the best advantage of all EU member states. Sharing of these data could be facilitated via the ERA-NET funding process.
If a product from a non-European country were falsely labelled as being from a European country then it would not be possible to identify the real country of origin as the relevant reference data will not have been collected. Again, some expertise and databases do exist in non-EU countries and existing networks could be used to find a mechanism to share these data.
In terms of the second question, there is suitable methodology except for meat as an ingredient. However, for each product in question it will be necessary to gather representative authentic samples from a particular country or region and to analyse these authentic samples alongside test samples
Table 4: Current capabilities and need for further work
FOODSTUFF WHAT CAN BE DONE TODAY METHODS REQUIRING FURTHER WORK Meat and There is sufficient data to 1 All the existing data on stable isotope poultry enable the origin of meat and composition of European meat and poultry to be mapped to regions poultry needs to be collated and the within the UK using stable gaps identified. isotope analysis. There is some 2 Experimental data needs to be data for other European generated to fill gaps in the database. countries but not at sufficient density to be comprehensive. Stable isotope analysis already is used to determine the country of origin of beef and no extensive validation of the methodology is required. Venison No method available at present. 1 Desk study to confirm that deer and and game game birds exist as separate populations and hence should be amenable to SNP analysis. 2 Derivation of population-specific SNPs using same methodology as Defra project FA0112.
Page 14 of 49
Cured No universal method available 1 Compilation of database of all cooked meats today. and cured meats of interest along with details of production process and, where relevant, breed of animal. Some of this data exists already in the EU DOOR PDO/PGI database 2 Proteomics study to determine if each meat product has a characteristic peptide signature with/ without animal breed determination. 3 Extension of SNP database for selected animal breeds where necessary. 4 Generation of stable isotope, with/ without trace elements, for products of particular interest. Meat as an No suitable methods No methods that might be useful have ingredient been identified. This is a major gap. Milk Stable isotope analysis will 1 Generate stable isotope database for provide information but only in all the key European areas. certain instances because of incomplete database. Fruit and Stable isotope analysis can be 1 Stable isotope database needed for vegetables used to determine geographical fruit and vegetables other than origin of cereals and potatoes. potatoes. 2 Experimentally evaluate metabolomics, with or without genotyping, as a tool for determining country of origin. 3 Experimentally evaluate metagenomics.
A key issue in all experimental work associated with determination of country of origin is access to authentic samples for use as reference material. Many individual research groups will arrange for the collection of samples of known provenance to meet their needs for a particular project. It would facilitate work on COOL if these laboratories were to:
Generate GPS data when the material is collected.
Collect more material than they actually need for the project.
Properly curate their sample material
Make it available to other groups for a reasonable access fee.
Page 15 of 49
9 References 1. Woolfe M. & Primrose S. (2004) Food forensics: using DNA technology to combat misdescription and fraud. Trends in Biotechnology 22, 222-226.
2. Primrose S., Woolfe M. & Rollinson S. (2010) Food forensics: methods for determining the authenticity of foodstuffs. Trends in Food Science and Technology 21, 582-590.
3. West J.B. et al (2007) Geography and vintage predicted by a novel GIS model of wine δ18O. Journal of Agricultural and Food Chemistry 55, 7075-7083.
4. Primrose S. et al (2011) Metabolomics and human nutrition. British Journal of Nutrition 105, 1277-1283.
5. Mora L. et al (2009) Naturally generated small peptides derived from myofibrillar proteins in Serrano dry-cured ham. Journal of Agriculture and Food Chemistry 57, 3228-3234.
6. Mora L. et al (2009) Oligopeptides arising from the degradation of creatine kinase in Spanish dry-cured ham. Journal of Agriculture and Food Chemistry 57, 8982-8988.
7. http://www.bpex.org.uk/R-and-D/R-and-D/LonghandBPEXIsotopereport.pdf
8. Sentandreu M.A. et al (2010) A proteomic-based approach for detection of chicken in meat mixes. Journal of Proteome Research 9, 3374-3383.
9. Caetano S. et al (2007) Geographical classification of olive oils by the application of CART and SVM to their FT-IR. Journal of Chemometrics 21, 324-334.
10. Pierna J.F. et al (2011) Discrimination of Corsican honey by FT Raman spectroscopy and chemometrics. Biotechnology Agronomy Society Environment 5, 75-84.
11. Hegerding L. et al (2002) Oxygen isotope-ratio-analysis for the determination of the origin of beef. Fleischwirtschaft, 82(4), 95–100.
12. Hintze, K. J. et al (2002) Selenium accumulation in beef: Effect of dietary selenium and geographical area of animal origin. Journal of Agricultural and Food Chemistry, 50, 3938– 3942.
13. Boner, M. et al (2004). Stable isotope variation as a tool to trace the authenticity of beef. Analytical and Bioanalytical Chemistry, 378(2), 301–310.
14. Renou, J. P. et al (2004). Characterization of animal products according to geographic origin and feeding diet using nuclear magnetic resonance and isotope ratio mass spectrometry. Part II: Beef meat. Food Chemistry, 86(2), 251 256.
15. Schmidt O., Quilter J.M., Bahar B., Moloney A.P., Scrimgeour C.M., Begley I.S., Monahan F.J. (2005) Inferring the origin and dietary history of beef from C, N and S stable isotope ratio analysis. Food Chemistry, Volume 91, Issue 3, Pages 545-549
Page 16 of 49
16. Heaton, K., Kelly, S.D., Hoogewerff, J., Woolfe, M. (2008) Verifying the geographical origin of beef: The application of multi-element isotope and trace element analysis. Food Chemistry, Volume 107, Issue 1, Pages 506-515
17. Nakashita, R. Suzuki, Y.; Akamatsu, F. ; Iizumi, Y. ; Korenaga, T. ; Chikaraishi, Y .(2008) Stable carbon, nitrogen, and oxygen isotope analysis as a potential tool for verifying geographical origin of beef. ANALYTICA CHIMICA ACTA Volume: 617 Issue: 1-2 Pages: 148-152
18. Nakashita, R. et al (2008) Stable isotope ratios as a potential tool for verifying the geographical origin of domestic and imported beef in Japan. JOURNAL OF THE JAPANESE SOCIETY FOR FOOD SCIENCE AND TECHNOLOGY-NIPPON SHOKUHIN KAGAKU KOGAKU KAISHI Volume: 55 Issue: 4 Pages: 191-193
19. Guo B.L., Wei Y.M., Pan J.R., Li Y. (2008) Stable C and N isotope ratio analysis for regional geographical traceability of cattle in China. Food Chemistry, Volume: 118, Pages: 915–920
20. Guo B.L. Wei, Y.M., Kelly S.D., Pan .J.R.,Wei S.A. (2009) Application of Stable Hydrogen Isotope Analysis in Beef Geographical Origin Traceability. CHINESE JOURNAL OF ANALYTICAL CHEMISTRY Volume: 37 Issue: 9 Pages: 1333-1336
21. Nakashita, R. et al (2008) Stable Isotope Analysis for Tracing the Geographical Origin of Beef. BUNSEKI KAGAKU Volume: 58 Issue: 12 Pages: 1023-1028
22. Guo B.L.; Wei, Y. M.; Pan, J. R.; Li, Y.(2010) Stable C and N isotope ratio analysis for regional geographical traceability of cattle in China. FOOD CHEMISTRY Volume: 118 Issue: 4 Pages: 915-920
23. Horacek, M.; Min, J.S. (2010) Discrimination of Korean beef from beef of other origin by stable isotope measurements. FOOD CHEMISTRY Volume: 121 Issue: 2 Pages: 517-520
24. Bong Y.S.; Shin, W.J. ; Lee, A.R. ; Kim, Y.S. ; Kim, K. ; Lee, K.S. (2010) Tracing the geographical origin of beefs being circulated in Korean markets based on stable isotopes. RAPID COMMUNICATIONS IN MASS SPECTROMETRY Volume: 24 Issue: 1 Pages: 155-159
25. Osorio M.T.; Moloney, A.P.; Schmidt, O. ; Monahan, F.J. (2011) Multielement Isotope Analysis of Bovine Muscle for Determination of International Geographical Origin of Meat. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY Volume: 59 Issue: 7 Special Issue: SI Pages: 3285-3294
26. Osorio M.T.; Moloney, A.P.; Schmidt, O. ; Monahan, F.J. (2011) Beef Authentication and Retrospective Dietary Verification Using Stable Isotope Ratio Analysis of Bovine Muscle and Tail Hair. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY Volume: 59 Issue: 7 Special Issue: SI Pages: 3295-3305
Page 17 of 49
27. Yanagi Y.; Hirooka, H. ; Oishi, K. et al (2012) Stable carbon and nitrogen isotope analysis as a tool for inferring beef cattle feeding systems in Japan. FOOD CHEMISTRY Volume: 134 Issue: 1 Pages: 502-506
28. Rummel S., Dekant C.H., Hölzl S., Kelly S.D., Baxter M., Marigheto N., Quetel C.R., Larcher R., Nicolini G., Fröschl H., Ueckermann H., Hoogewerff J. (2012) Sr isotope measurements in beef-analytical challenge and first results. ANALYTICAL AND BIOANALYTICAL CHEMISTRY Volume: 402 Issue: 9 Pages: 2837-2848
29. Bong Y.S.; Gautam, M.K.; Lee, K.S. (2012) Origin Assessment of Domestic and Imported Beef Sold in the Korean Markets Using Stable Carbon and Oxygen Isotopes. FOOD SCIENCE AND BIOTECHNOLOGY Volume: 21 Issue: 1 Pages: 233-237
30. Zhao Y.; Zhang, B.; Chen, G.; Chen, A.L.; Yang, S.M.; Ye, Z.H.(2013) Tracing the Geographic Origin of Beef in China on the Basis of the Combination of Stable Isotopes and Multielement Analysis. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY Volume: 61 Issue: 29 Pages: 7055-7060
31. Liu X.L.; Guo, B.L.; Wei, Y.M.; Shi, J.L.; Sun, S.M. (2013) Stable isotope analysis of cattle tail hair: A potential tool for verifying the geographical origin of beef. FOOD CHEMISTRY Volume: 140 Issue: 1-2 Pages: 135-140
32. Piasentier, E. et al (2003). Stable isotope ratio analysis for authentication of lamb meat. Meat Science, 64(3), 239–247.
33. Sacco, D.; Brescia, M.A.; Buccolieri, A.; Jambrenghi, A.C. (2005) [1] Geographical origin and breed discrimination of Apulian lamb meat samples by means of analytical and spectroscopic determinations. Meat Science Volume: 71 Issue: 3 Pages: 542-548
34. Camin, F.; Bontempo, L.; Heinrich, K.; Horacek, M.; Kelly, S. D.; Schlicht, C.; Thomas, F.; Monahan, F. J.; Hoogewerff, J.; Rossmann, A. (2007) Multi-element (H,C,N,S) stable isotope characteristics of lamb meat from different European regions. ANALYTICAL AND BIOANALYTICAL CHEMISTRY Volume: 389 Issue: 1 Pages: 309-320
35. Moreno-Rojas, J.M.; Vasta V., Lanza A., Luciano G., Ladroue V., Guillou C.et al. (2008)Stable isotopes to discriminate lambs fed herbage or concentrate both obtained from C3 plants. Rapid Communications in Mass Spectrometry, pp. 3701–3705
36. Perini, M.; Camin, F.; Bontempo, L.; Rossmann, A.; Piasentier, E. (2009) Multielement (H, C, N, O, S) stable isotope characteristics of lamb meat from different Italian regions. RAPID COMMUNICATIONS IN MASS SPECTROMETRY Volume: 23 Issue: 16 Pages: 2573-2585
37. Moreno-Rojas, J.M.; Serra, F.; Giani, I.; Moretti, V.M.; Reniero, F.; Guillou, C. (2007) The use of stable isotope ratio analyses to discriminate wild and farmed gilthead sea bream
Page 18 of 49
(Spares aurata). RAPID COMMUNICATIONS IN MASS SPECTROMETRY Volume: 21 Issue: 2 Pages: 207-211
38. Sun, SM. Multi-element analysis for determining the geographical origin of mutton from different regions of China. FOOD CHEMISTRY Volume: 124 Issue: 3 Pages: 1151-1156
39. Kornexl, B. E., Werner, R., Rossmann, A., & Schmidt, H.-L. (1997). Measurement of stable isotope abundances in milk and milk ingredients—A possible tool for origin assignment and quality control. Zeitschrift fur Lebensmittel-Untersuchung und-Forschung A—Food Research and Technology, 205(1), 19–24.
40. Renou, J. P., Deponge, C., Gachon, P., Bonnefoy, J. C., Coulon, J. B., Garel, J. P., et al. (2004). Characterization of animal products according to geographic origin and feeding diet using nuclear magnetic resonance and isotope ratio mass spectrometry: Cow milk. Food Chemistry, 85(1), 63–66.
41. Ritz, P., Gachon, P., Garel, J. P., Bonnefoy, J. C., Coulon, J. B., & Renou, J. P. (2004). Milk characterization: Effect of the breed. Food Chemistry. Published on-line: 10.1016/j.foodchem.2004.06.041.
42. Crittenden, R.G., Andrew, A.S., LeFournour, M., Young, M.D.,Middleton, H., Stockmann, R. (2007). Determining the geographic origin of milk in Australasia using multi-element stable isotope ratio analysis. INTERNATIONAL DAIRY JOURNAL Volume: 17 Issue: 5 Pages: 421-428
43. Benincasa, C., Lewis, J., Sindona, G., Tagarelli, A. (2008). The use of multi element profiling to differentiate between cow and buffalo milk. FOOD CHEMISTRY Volume: 110 Issue: 1 Pages: 257-262
44. Sacco, D.,Brescia, M. A., Sgaramella, A., Casiello, G., Buccolieri, A., Ogrinc, N., Sacco, A. (2009). Discrimination between Southern Italy and foreign milk samples using spectroscopic and analytical data. FOOD CHEMISTRY Volume: 114 Issue: 4 Pages: 1559-1563
45. Sola-Larranaga, C., Navarro-Blasco, I., (2009). Chemometric analysis of minerals and trace elements in raw cow milk from the community of Navarra, Spain. FOOD CHEMISTRY Volume: 112 Issue: 1 Pages: 189-196
46. Scampicchio, M., Mimmo, T. et al (2012). Identification of Milk Origin and Process- Induced Changes in Milk by Stable Isotope Ratio Mass Spectrometry. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY Volume: 60 Issue: 45 Pages: 11268-11273
47. Bontempo, L., Lombardi, G., Paoletti, R.,Ziller, L., Camin, F. (2012). H, C, N and O stable isotope characteristics of alpine forage, milk and cheese. INTERNATIONAL DAIRY JOURNAL Volume: 23 Issue: 2 Pages: 99-104
Page 19 of 49
48. Rossmann, A., Haberhauer, G., Holzl, S., Horn, P., Pichlmayer, F., & Voerkelius, S. (2000). The potential of multielement stable isotope analysis for regional origin assignment of butter. European Food Research and Technology, 211(1), 32–40.
49. Balling, H. P., & Rossmann, A. (2004). Countering fraud via isotope analysis—Case report. Kriminalistik, 58(1), 44–47.
50. Manca, G., Camin, F., Coloru, G. C., Del Caro, A., Depentori, D., Franco, M. A., et al. (2001). Characterization of the geographical origin of Pecorino Sardo cheese by casein stable isotope (13C/12C and 15N/14N) ratios and free amino acid ratios. Journal of Agricultural and Food Chemistry, 49(3), 1404–1409.
51. Pillonel, L., Badertscher, R., Froidevaux, P., Haberhauer, G., Ho¨ lzl, S., Horn, P., et al. (2003). Stable isotope ratios, major, trace and radioactive elements in emmental cheeses of different origins. Lebensmittel-Wissenschaft und-Technologie—Food Science and Technology, 36(6), 615–623.
52. Pillonel, L., Bu¨ tikofer, U., Rossmann, A., Tabacchi, R., & Bosset, J. O. (2004). Analytical methods for the detection of adulteration and mislabeling of Raclette Suisse and Fontina PDO cheese. Mitteilungen aus Lebensmittel Untersuchung und Hygiene, 95, 489–502.
53. Fortunato, G., Mumic, K., Wunderli, S., Pillonel, L., Bosset, J. O., & Gremaud, G. (2004). Application of strontium isotope abundance ratios measured by MC-ICP-MS for food authentication. Journal of Analytical Atomic Spectrometry, 19(2), 227–234.
54. Camin, F., Wietzerbin, K., Blanch Cortes, A., Haberhauer, G., Lees, M., & Versini, G. (2004). Application of multielement stable isotope ratio analysis to the characterization of French, Italian, and Spanish cheeses. Journal of Agricultural and Food Chemistry, 52, 6592– 6601.
55. Brescia, M.A., Monfreda, M., Buccolieri, A., Carrino, C. (2005). Characterisation of the geographical origin of buffalo milk and mozzarella cheese by means of analytical and spectroscopic determinations. FOOD CHEMISTRY Volume: 89 Issue: 1 Pages: 139-147
56. Manca, G., Franco, M.A., Versini, G., Camin, F., Rossmann, A., Tola, A., (2006). Correlation between multielement stable isotope ratio and geographical origin in Peretta cows' milk cheese. JOURNAL OF DAIRY SCIENCE Volume: 89 Issue: 3 Pages: 831-839
57. Korenovska, M., Suhaj, M.(2007). Identification of Slovakian, Polish, and Romanian bryndza cheeses origin by factor analysis of some elemental data. EUROPEAN FOOD RESEARCH AND TECHNOLOGY Volume: 225 Issue: 5-6 Pages: 707-713
58. Suhaj, M., Korenovska, M. (2008). Correlation and distribution of elemental markers of origin in the production of Bryndza sheep cheese. FOOD CHEMISTRY Volume: 107 Issue: 1 Pages: 551-557
Page 20 of 49
59. Moreno-Rojas, R., Sanchez-Segarra, P. J., Camara-Martos, F., Amaro-Lopez, M. A. (2010). Multivariate analysis techniques as tools for categorization of Southern Spanish cheeses: nutritional composition and mineral content. EUROPEAN FOOD RESEARCH AND TECHNOLOGY Volume: 231 Issue: 6 Pages: 841-851
60. Bontempo, L., Larcher, R., Camin, F., Hoelzl, S., Rossmann, A., Horn, P., Nicolini, G. (2011). Elemental and isotopic characterisation of typical Italian alpine cheeses. INTERNATIONAL DAIRY JOURNAL Volume: 21 Issue: 6 Pages: 441-446
61. Camin, F., Wehrens, R., Bertoldi, D., Bontempo, L., Ziller, L., Perini, M., Nicolini, G., Nocetti, M., Larcher, R. (2012). H, C, N and S stable isotopes and mineral profiles to objectively guarantee the authenticity of grated hard cheeses. ANALYTICA CHIMICA ACTA Volume: 711 Pages: 54-59
62. Krivan, V., Barth, P., &Morales, A. F. (1993).Multielement analysis ofgreen coffee and its possible use for the determination of origin. Mikrochimica Acta, 110, 217–236.
63. Haswell, S. J., & Walmsley, A. D. (1998). Multivariate data visualisation methods based on multi-elemental analysis of wines and coffees using total reflection X-ray fluorescence analysis. Journal of Analytical Atomic Spectrometry, 13(2), 131–134.
64. Weckerle, B., Richling, E., Heinrich, S., & Schreier, P. (2002). Origin assessment of green coffee (Coffea arabica) by multi-element stable isotope analysis of caffeine. Analytical and Bioanalytical Chemistry, 374(5), 886–890.
65. Serra, F., Guillou, C.G., Reniero, F., Ballarin, L., Cantagallo, M.I., Wieser, M., Iyer, S.S., Heberger, K., Vanhaecke, F. (2005). Determination of the geographical origin of green coffee by principal component analysis of carbon, nitrogen and boron stable isotope ratios. RAPID COMMUNICATIONS IN MASS SPECTROMETRY Volume: 19 Issue: 15 Pages: 2111- 2115
66. Akamine, T., Otaka, A ., Hokura, A ., Ito, Y ., Nakai, I. (2010). Polarization Optics and Its Application to Identification of Their Production Area. BUNSEKI KAGAKU Volume: 59 Issue: 10 Pages: 863-871
67. Rodrigues, C., Maguas, C., Prohaska, T.(2011). Strontium and oxygen isotope fingerprinting of green coffee beans and its potential to proof authenticity of coffee. EUROPEAN FOOD RESEARCH AND TECHNOLOGY Volume: 232 Issue: 2 Pages: 361-373
68. Santato, A ., Bertoldi, D ., Perini, M ., Camin, F ., Larcher, R . (2012). Using elemental profiles and stable isotopes to trace the origin of green coffee beans on the global market. JOURNAL OF MASS SPECTROMETRY Volume: 47 Issue: 9 Special Issue: SI Pages: 1132- 1140
Page 21 of 49
69. Fernandez-Caceres, P. L.,Martin, M. J., Pablos, F., & Gonzalez, A. G. (2001). Differentiation of tea (Camellia sinensis) varieties and their geographical origin according to their metal content. Journal of Agricultural and Food Chemistry, 49(10), 4775–4779.
70. Moreda-Pineiro, A., Fisher, A., & Hill, S. J. (2003). The classification of tea according to region of origin using pattern recognition techniques and trace metal data. Journal of Food Composition and Analysis, 16(2), 195–211.
71. Moreda-Pineiro, A., Marcos, A., Fisher, A., & Hill, S. J. (2001). Evaluation of the effect of data pre-treatment procedures on classical pattern recognition and principal components analysis: A case study for the geographical classification of tea. Journal of Environmental Monitoring, 3(4), 352–360.
72. Chen, Y.X., Yu, M.G., Xu, J., Chen, X.C., Shi, J.Y. (2009). Differentiation of eight tea (Camellia sinensis) cultivars in China by elemental fingerprint of their leaves. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE Volume: 89 Issue: 14 Pages: 2350-2355
73. Pilgrim, T.S. Watling, R.J. Grice, K. (2010). Application of trace element and stable isotope signatures to determine the provenance of tea (Camellia sinensis) samples. FOOD CHEMISTRY Volume: 118 Issue: 4 Pages: 921-926
74. Zhang, L., Pan, J.R., Zhu, C. (2012). Determination of the geographical origin of Chinese teas based on stable carbon and nitrogen isotope ratios. JOURNAL OF ZHEJIANG UNIVERSITY-SCIENCE B Volume: 13 Issue: 10 Pages: 824-830
75. Lagad, R.A., Alamelu, D., Laskar, A.H., Rai, V.K., Singh, S.K., Aggarwal, S.K. (2013).Isotope signature study of the tea samples produced at four different regions in India. ANALYTICAL METHODS Volume: 5 Issue: 6 Pages: 1604-1611
76. Simpkins, W. A., Louie, H., Wu, M., Harrison, M., & Goldberg, D. (2000). Trace elements in Australian orange juice and other products. Food Chemistry, 71(4), 423–433.
77. Simpkins, W. A., Patel, G., Harrison, M., & Goldberg, D. (2000). Stable carbon isotope ratio analysis of Australian orange juices. Food Chemistry, 70(3), 385–390.
78. Rummel, S., Hoelzl, S., Horn, P., Rossmann, A., Schlicht, C. (2010). The combination of stable isotope abundance ratios of H, C, N and S with Sr-87/Sr-86 for geographical origin assignment of orange juices. FOOD CHEMISTRY Volume: 118 Issue: 4 Special Issue: SI Pages: 890-900
79. Ince, H., Coskun, N. (2008). Determination of heavy metals in fruit juices by flame atomic absorption spectrometry. ASIAN JOURNAL OF CHEMISTRY Volume: 20 Issue: 5 Pages: 3537-3542
80. Calderone, G., Guillou, C. (2008). Analysis of isotopic ratios for the detection of illegal watering of beverages. FOOD CHEMISTRY Volume: 106 Issue: 4 Pages: 1399-1405
Page 22 of 49
81. Magdas, D.A., Vedeanu, N.S., Puscas, R. (2012).The use of stable isotopes ratios for authentication of fruit juices. CHEMICAL PAPERS Volume: 66 Issue: 2 Pages: 152-155 DOI: 10.2478/s11696-011-0105-3
82. Magdas, D.A., Dehelean, A., Puscas, R., Cristea, G., Tusa, F., Voica, C. (2012). The Quality Control Of Fruit Juices By Using The Stable Isotope Ratios And Trace Metal Elements Concentrations. PROCESSES IN ISOTOPES AND MOLECULES (PIM 2011) Book Series: AIP Conference Proceedings Volume: 1425 Pages: 178-181
83. Brach-Papa, C., Van Bocxstaele, M., Ponzevera, E., Quetel, C.R. (2009). Fit for purpose validated method for the determination of the strontium isotopic signature in mineral water samples by multi-collector inductively coupled plasma mass spectrometry. SPECTROCHIMICA ACTA PART B-ATOMIC SPECTROSCOPY Volume: 64 Issue: 3 Pages: 229- 234
84. Voerkelius, S., Lorenz, G.D., Rummel, S., Quétel, C.R., Heiss, G., Baxter, M., Brach-Papa, C., Deters-Itzelsberger, P., Hoelzl, S., Hoogewerff, J., Ponzevera, E., Van Bocxstaele, E., Ueckermann, H. (2010). Strontium isotopic signatures of natural mineral waters, the reference to a simple geological map and its potential for authentication of food. Food Chemistry, Volume 118, Issue 4, 15 February 2010, Pages 933-940
85. Brencic, M., Vreca, P. (2010). The use of a finite mixture distribution model in bottled water characterisation and authentication with stable hydrogen, oxygen and carbon isotopes - Case study from Slovenia. JOURNAL OF GEOCHEMICAL EXPLORATION Volume: 107 Issue: 3 Special Issue: SI Pages: 391-399
86. Dotsika, E., Poutoukis, D., Raco, B., Psomiadis, D. (2010). Stable isotope composition of Hellenic bottled waters. JOURNAL OF GEOCHEMICAL EXPLORATION Volume: 107 Issue: 3 Special Issue: SI Pages: 299-304
87. Raco, B., Dotsika, E., Feroni, A. Cerrina, Battaglini, R., Poutoukis, D. (2013). Stable isotope composition of Italian bottled waters. JOURNAL OF GEOCHEMICAL EXPLORATION Volume: 124 Pages: 203-211
88. Calderone, G., Guillou, C., Reniero, F., Naulet, N. (2007). Helping to authenticate sparkling drinks with C-13/C-12 of CO2 by gas chromatography-isotope ratio mass spectrometry. FOOD RESEARCH INTERNATIONAL Volume: 40 Issue: 3 Pages: 324-331
89. Boeting, K., Aguilera de Benzo, Z., et al. (2010). Authentication of the protected designation of origin horchata de Valencia through the chemometric treatment of mineral content. ANALYTICAL METHODS Volume: 2 Issue: 11 Pages: 1723-1728
90. Yasui, A., & Shindoh, K. (2000). Determination of the geographic origin of brown-rice with trace-element composition. Bunseki Kagaku, 49(6), 405–410.
Page 23 of 49
91. Oda, H., Kawasaki, A., & Hirata, T. (2002). Determining the rice provenance using binary isotope signatures along with cadmium content. Proceedings of the 17thWorld Congress of soil science, 14th to 21st August, Thailand, symposium no. 59 2002 (pp. 2018-1 to 2018-10).
92. Kelly, S. D., Baxter, M., Chapman, S., Rhodes, C., Dennis, J., & Brereton, P. (2002). European Food Research and Technology, 214, 72–78.
93. Kawasaki, A., Oda, H., & Hirata, T. (2002). Determination of strontium isotope ratio of brown rice for estimating its provenance. Soil Science and Plant Nutrition, 48(5), 635–640.
94. Suzuki, Y., Chikaraishi, Y., Ogawa, N.O., Ohkouchi, N., Korenaga, T. (2008). Geographical origin of polished rice based on multiple element and stable isotope analyses. FOOD CHEMISTRY Volume: 109 Issue: 2 Pages: 470-475
95. Suzuki, Y., Nakashita, R., Akamatsu, F., Korenaga, T. (2008). Tracing the geographical origin of rice applied by stable isotope analysis. JOURNAL OF THE JAPANESE SOCIETY FOR FOOD SCIENCE AND TECHNOLOGY-NIPPON SHOKUHIN KAGAKU KOGAKU KAISHI Volume: 55 Issue: 5 Pages: 250-252
96. Suzuki, Y., Nakashita, R., Akamatsu, F., Korenaga, T. (2009). Multiple Stable Isotope Analyses for Verifying Geographical Origin and Agricultural Practice of Japanese Rice Samples. BUNSEKI KAGAKU Volume: 58 Issue: 12 Pages: 1053-1058
97. Korenaga, T., Musashi, M., Nakashita, R ., Suzuki, Y. (2010). Statistical Analysis of Rice Samples for Compositions of Multiple Light Elements (H, C, N, and O) and Their Stable Isotopes. ANALYTICAL SCIENCES Volume: 26 Issue: 8 Pages: 873-878
98. Gonzalvez, A., Armenta, S., de la Guardia, M. (2011). Geographical traceability of "Arros de Valencia" rice grain based on mineral element composition. FOOD CHEMISTRY Volume: 126 Issue: 3 Pages: 1254-1260
99. Ariyama, K., Shinozaki, M., Kawasaki, A ., Ishida, Y. (2011).Strontium and Lead Isotope Analyses for Determining the Geographic Origins of Grains. ANALYTICAL SCIENCES Volume: 27 Issue: 7 Pages: 709-713
100. Suzuki, Y., Akamatsu, F., Nakashita, R., Korenaga, T. (2013). Characterization of Japanese Polished Rice by Stable Hydrogen Isotope Analysis of Total Fatty Acids for Tracing Regional Origin. ANALYTICAL SCIENCES Volume: 29 Issue: 1 Special Issue: SI Pages: 143- 146
101. Li, G., Nunes, L., Wang, Y.J., Williams, P.N., Zheng, M.Z., Zhang, Q.F., Zhu, Y.G. (2013). Profiling the ionome of rice and its use in discriminating geographical origins at the regional scale, China. JOURNAL OF ENVIRONMENTAL SCIENCES-CHINA Volume: 25 Issue: 1 Pages: 144-154
Page 24 of 49
102. Brescia, M. A., Di Martino, G., Fares, C., Di Fonzo, N., Platani, C., Ghelli, S., et al. (2002). Characterization of Italian durum wheat semolina by means of chemical analytical and spectroscopic determinations. Cereal Chemistry, 79(2), 238–242.
103. Brescia, M. A., Di Martino, G., Guillou, C., Reniero, F., Sacco, A., & Serra, F. (2002). Differentiation of the geographical origin of durum wheat semolina samples on the basis of isotopic composition. Rapid Communications in Mass Spectrometry, 16(24), 2286–2290.
104. Otaka, A., Yanada, Y., Hokura, A., Matsuda, K., Nakai, I. (2009). Determination of Trace Elements in Wheat Flour by X-Ray Fluorescence Analysis and Its Application to Identification of Their Production Area. BUNSEKI KAGAKU Volume: 58 Issue: 12 Pages: 1011-1022
105. Zhao, H.Y., Guo, B.L., Wei, Y.M., Zhang, B., Sun, S.M., Zhang, L., Yan, J.H.(2011). Determining the Geographic Origin of Wheat Using Multielement Analysis and Multivariate Statistics. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY Volume: 59 Issue: 9 Pages: 4397-4402
106. Zhao, H.Y., Guo, B.L., Wei, Y.M., Zhang, B. (2012). Effects of Wheat Origin, Genotype, and Their Interaction on Multielement Fingerprints for Geographical Traceability. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY Volume: 60 Issue: 44 Pages: 10957-10962
107. Podio, N.S., Baroni, M.V. et al (2013). Elemental and Isotopic Fingerprint of Argentinean Wheat. Matching Soil, Water, and Crop Composition to Differentiate Provenance. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY Volume: 61 Issue: 16 Pages: 3763-3773
108. Zhao, H.Y., Guo, B.L., Wei, Y.M., Zhang, B. (2013). Multi-element composition of wheat grain and provenance soil and their potentialities as fingerprints of geographical origin. JOURNAL OF CEREAL SCIENCE Volume: 57 Issue: 3 Pages: 391-397
109. Husted, S., Mikkelsen, B.F., Jensen, J., Nielsen, N.E. (2004). Elemental fingerprint analysis of barley (Hordeum vulgare) using inductively coupled plasma mass spectrometry, isotope-ratio mass spectrometry, and multivariate statistics. ANALYTICAL AND BIOANALYTICAL CHEMISTRY Volume: 378 Issue: 1 Pages: 171-182
110. Asfaha, D.G., Quetel, C.R. et al (2011). Combining isotopic signatures of n(Sr-87)/n(Sr- 86) and light stable elements (C, N, O, S) with multi-elemental profiling for the authentication of provenance of European cereal samples. JOURNAL OF CEREAL SCIENCE Volume: 53 Issue: 2 Pages: 170-177
111. Horn, P., Schaaf, P., Holbach, B., Hoelxl, S., & Eschnauer, H. (1993). 87Sr and 86Sr from rock and soil into vine and wine. Zeitschrift fur
Page 25 of 49
Lebensmittel-Untersuchung und-Forschung, 196, 407–409.
112. Breas, O., Reniero, F., & Serrini, G. (1994). Isotope ratio massspectrometry— Analysis of wines from different European countries. Rapid Communications in Mass Spectrometry, 8(12), 967–970.
113. Day, M. P., Zhang, B. L., & Martin, G. J. (1994). The use of traceelement data to complement stable-isotope methods in the characterization of grapemusts. American Journal of Enology and Viticulture, 45, 79–85.
114. Day, M. P., Zhang, B., & Martin, G. J. (1995). Determination of the geographical origin of wine using joint analysis of elemental and isotopic composition. 2. Differentiation of the principal production zones in France for the 1990 vintage. Journal of the Science of Food and Agriculture, 67(1), 113–123.
115. Baxter, M. J., Crews, H. M., Dennis, M. J., Goodall, I., & Anderson, D. (1997). The determination of the authenticity of wine from its trace element composition. Food Chemistry, 60(3), 443–450.
116. Horn, P., Ho¨ lzl, S., Todt, W., & Matthies, D. (1998). Isotope abundance ratios of Sr in wine provenance determinations, in a tree-root activity study, and of Pb in a pollution study on treerings. Isotopes in Environmental and Health Studies, 34(1–2), 31–42.
117. Rossmann, A., Reniero, F., Moussa, I., Schmidt, H-L. , Versini, G., & Merle, M. H. (1999). Stable oxygen isotope content of water of EU data-bank wines from Italy, France and Germany. Zeitschrift fur Lebensmittel-Untersuchung und-Forschung A—Food Research and Technology, 208(5–6), 400–407.
118. Almeida, C. M., & Vasconcelos, M. T. S. D. (2001). ICP-MS determination of strontium isotope ratio in wine in order to be used as a fingerprint of its regional origin. Journal of Analytical Atomic Spectrometry, 16(6), 607–611.
119. Barbaste, M.,Halicz, L., Galy, A., Medina, B., Emteborg,H.,Adams, F. C., et al. (2001). Evaluation of the accuracy of the determination of lead sotope ratios in wine by ICP MS using quadrupole, multicollector magnetic sector and time-of-flight analyzers. Talanta, 54(2), 307–317.
120. Kosir, I.J., Kocjancic, M., Ogrinc, N. et al(2001). Use of SNIF-NMR and IRMS in combination with chemometric methods for the determination of chaptalisation and geographical origin of wines (the example of Slovenian wines). ANALYTICA CHIMICA ACTA Volume: 429 Issue: 2 Pages: 195-206
121. Barbaste, M., Robinson, K., Guilfoyle, S., Medina, B., & Lobinski, R. (2002). Precise determination of the strontium isotope ratios in wine by inductively coupled plasma sector
Page 26 of 49 field multicollector mass spectrometry (ICP-SF-MC-MS). Journal of Analytical Atomic Spectrometry, 17(2), 135–137.
122. Christoph, N., Rossmann, A., & Voerkelius, S. (2003). Possibilities and limitations of wine authentication using stable isotope and meteorological data, data banks and statistical tests. Part 1: Wines from Franconia and Lake Constance 1992 to 2001. Mitteilungen Klosterneuberg, 53, 23–40.
123. Christoph, N., Bara´ tossy, G., Kubanovic, V., Kozina, B., Rossmann, A., Schlicht, C., et al. (2004). Possibilities and limitations of wine authentication using stable isotope ratio analysis and traceability. Part 2: Wines from Hungary, Croatia and other European countries. Mitteilungen Klosterneuberg, 54, 155–169.
124. Brescia, M. A., Kosir, I. J., Caldarola, V., Kidric, J., & Sacco, A. (2003). Chemometric classification of Apulian and Slovenian wines using 1H NMR and ICP-OES together with HPICE data. Journal of Agricultural and Food Chemistry, 51(1), 21–26.
125. Marisa, C., Almeida, R., Teresa, M., & Vasconcelos, T. S. D. (2004). Does the winemaking process influence the wine Sr-87/Sr-86? A case study. Food Chemistry, 85, 7–12.
126. Gremaud, G., Quaile, S., Piantini, U., Pfammatter, E., & Corvi, C. (2004). Characterization of Swiss vineyards using isotopic data in combination with trace elements and classical parameters. European Food Research And Technology, 219(1), 97–104.
127. Coetzee, P.P., Vanhaecke, F. (2005). Classifying wine according to geographical origin via quadrupole-based ICP-mass spectrometry measurements of boron isotope ratios. ANALYTICAL AND BIOANALYTICAL CHEMISTRY Volume: 383 Issue: 6 Pages: 977-984
128. Suhaj, M., Korenovska, M. (2005).Application of elemental analysis for identification of wine origin - A review. ACTA ALIMENTARIA Volume: 34 Issue: 4 Pages: 393-401
129. Galgano, F., Favati,F., Camso, M. et al. ( 2008). Analysis of trace elements in southern Italian wines and their classification according to provenance. LWT-FOOD SCIENCE AND TECHNOLOGY Volume: 41 Issue: 10 Pages: 1808-1815
130. Serapinas, P.; Venskutonis, P. R.; Aninkevicius, V.; et al. (2008). Step by step approach to multi-element data analysis in testing the provenance of wines. FOOD CHEMISTRY Volume: 107 Issue: 4 Pages: 1652-1660
131. Gonzalvez, A.; Llorens, A.; Cervera, M. L.; et al. (2009). Elemental fingerprint of wines from the protected designation of origin Valencia. FOOD CHEMISTRY Volume: 112 Issue: 1 Pages: 26-34
Page 27 of 49
132. Hubert, Ph.; Perrot, F.; Gaye, J.; et al. (2009). Radioactivity measurements applied to the dating and authentication of old wines. COMPTES RENDUS PHYSIQUE Volume: 10 Issue: 7 Pages: 622-629
133. van der Linde, G., Fischer, J. L., Coetzee, P. P. (2010).Multi-element Analysis of South African Wines and their Provenance Soils by ICP-MS and their Classification according to Geographical Origin using Multivariate Statistics. SOUTH AFRICAN JOURNAL OF ENOLOGY AND VITICULTURE Volume: 31 Issue: 2 Pages: 143-153
134. Vorster, C., Greeff, L., Coetzee, P. P., (2010). The Determination of B-11/B-10 and Sr- 87/Sr-86 Isotope Ratios by Quadrupole-Based ICP-MS for the Fingerprinting of South African Wine. SOUTH AFRICAN JOURNAL OF CHEMISTRY-SUID-AFRIKAANSE TYDSKRIF VIR CHEMIE Volume: 63 Pages: 207-214
135 Fabani,M.P., Arrúa, R.C., Vázquez,F., Diaz,P.P., Baroni,M.V., Wunderlin, D.A. (2010). Evaluation of elemental profile coupled to chemometrics to assess the geographical origin of Argentinean wines. Food Chemistry, Volume 119, Issue 1, 1 March 2010, Pages 372-379
136. dos Santos, C.E.I., da Silva, L.R.M., Boufleur, L.A., Debastiani, R., Stefenon, C.A., Amaral, L., Yoneama, M.L., Dias, J.F. (2010). Elemental characterisation of Cabernet Sauvignon wines using Particle-Induced X-ray Emission (PIXE). FOOD CHEMISTRY Volume: 121 Issue: 1 Pages: 244-250
137. Paneque, P.; Álvarez-Sotomayor, M .T.; Clavijo, A.; Gómez I. A . (2010). Metal content in southern Spain wines and their classification according to origin and ageing. Microchemical Journal 2010 | 94 | 2 | 175-179
138. (Dutra, S. V., Adami, L., Marcon, A. R., Carnieli, G. J., Roani, C. A., Spinelli, F. R., Leonardelli, S., Ducatti, C., Moreira, M. Z., Vanderlinde, R. (2011). Determination of the geographical origin of Brazilian wines by isotope and mineral analysis. ANALYTICAL AND BIOANALYTICAL CHEMISTRY Volume: 401 Issue: 5 Pages: 1571-1576
139. Coetzee, P. P., Greeff, L., Vanhaecke, F. (2011).ICP-MS Measurement of B-11/B-10 Isotope Ratios in Grapevine Leaves and the Investigation of Possible Boron Isotope Fractionation in Grapevine Plants. SOUTH AFRICAN JOURNAL OF ENOLOGY AND VITICULTURE Volume: 32 Issue: 1 Pages: 28-34
140. Di Paola-Naranjo, R.D., Baroni, M.V., Podio, N.S., Rubinstein, H.R., Fabani, M.P., Badini, R.G., Inga, M., Ostera, H.A., Cagnoni, M., Gallegos, E., Gautier, E., Peral-Garcia, P., Hoogewerff, J., Wunderlin, D.A. (2011). Fingerprints for Main Varieties of Argentinean Wines: Terroir Differentiation by Inorganic, Organic, and Stable Isotopic Analyses Coupled to Chemometrics. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY Volume: 59 Issue: 14 Special Issue: SI Pages: 7854-7865
Page 28 of 49
141. Perez Trujillo, J.P.; Conde, J. E.; Perez Pont, M. L.; et al. (2011). Content in metallic ions of wines from the Madeira and Azores archipelagos. FOOD CHEMISTRY Volume: 124 Issue: 2 Pages: 533-537
142. Rodrigues, S.M.; Otero, M.; Alves, A. A.; et al. (2011). Elemental analysis for categorization of wines and authentication of their certified brand of origin. JOURNAL OF FOOD COMPOSITION AND ANALYSIS Volume: 24 Issue: 4-5 Special Issue: SI Pages: 548- 562
143. Magdas, D.A., Cuna, S., Cristea, G., Ionete, R.E., Costinel, D. (2012). Stable isotopes determination in some Romanian wines. ISOTOPES IN ENVIRONMENTAL AND HEALTH STUDIES Volume: 48 Issue: 2 Pages: 345-353
144. Martin A.E., Watling R.J.,Lee G.S. (2012). The multi-element determination and regional discrimination of Australian wines. Food Chemistry, Volume 133, Issue 3, 1 Pages 1081-1089
145. Geana, I., Iordache, A., Ionete, R., Marinescu, A., Ranca, A., Culea, M. (2013). Geographical origin identification of Romanian wines by ICP-MS elemental analysis. FOOD CHEMISTRY Volume: 138 Issue: 2-3 Pages: 1125-1134
146. Pirnau, A., Bogdan, M., Magdas, D.A., Statescu, D. (2013). Isotopic Analysis of some Romanian Wines by H-2 NMR and IRMS. FOOD BIOPHYSICS Volume: 8 Issue: 1 Pages: 24- 28
147. Perini, M., Camin, F. (2013). delta O-18 of Ethanol in Wine and Spirits for Authentication Purposes. JOURNAL OF FOOD SCIENCE Volume: 78 Issue: 6 Pages: C839- C844
148. Dutra, S.V., Adami, L., Marcon, A.R., Carnieli,G.J., Roani, C.A., .Spinelli, F.R., Leonardelli, S., Vanderlinde, R. (2013). Characterization of wines according the geographical origin by analysis of isotopes and minerals and the influence of harvest on the isotope values . Food Chemistry, Volume 141, Issue 3, Pages 2148-2153
149. Durante,C., Baschieri, C., Bertacchini, L., Cocchi, M., Sighinolfi, S., Silvestri, M., Marchetti, A. (2013). Geographical traceability based on 87Sr/86Sr indicator: A first approach for PDO Lambrusco wines from Modena. Food Chemistry, Volume 141, Issue 3, 1 Pages 2779-2787
150. Ishida-Fujii, K., Goto, S., Uemura, R., Yamada, K., Sato, M., Yoshida, N. (2005). Botanical and geographical origin identification of industrial ethanol by stable isotope analyses of C, H, and O. BIOSCIENCE BIOTECHNOLOGY AND BIOCHEMISTRY Volume: 69 Issue: 11 Pages: 2193-2199
151. Garcia-Ruiz, S., Moldovan, M., Fortunato, G., Wunderli, S., Alonso, J.I.G. (2007). Evaluation of strontium isotope abundance ratios in combination with multi-elemental
Page 29 of 49 analysis as a possible tool to study the geographical origin of ciders. ANALYTICA CHIMICA ACTA Volume: 590 Issue: 1 Pages: 55-66
152. Izu, H., Hashiguchi, T., Horii, S., Sudo, S., Matsumaru, K. (2012). Stable Isotope Analysis of Commercially Supplied Honkaku Shochu. BUNSEKI KAGAKU Volume: 61 Issue: 7 Pages: 643-647
153. Yanada, Y., Hokura, A., Matsuda, K., Mizuhira, M., Nakai, I. (2007). [8] High-sensitive determination of inorganic elements in spinach leaves by X-ray fluorescence analysis and its application to identification of their production area. BUNSEKI KAGAKU Volume: 56 Issue: 12 Pages: 1053-1061
154. Swoboda, S.; Brunner, M.; Boulyga, S. F.; et al.(2008). Identification of Marchfeld asparagus using Sr isotope ratio measurements by MC-ICP-MS. ANALYTICAL AND BIOANALYTICAL CHEMISTRY Volume: 390 Issue: 2 Pages: 487-494
155. Spalla, S.; Baffi, C.; Barbante, C.; et al. (2009).Determination of rare earth elements in tomato plants by inductively coupled plasma mass spectrometry techniques. RAPID COMMUNICATIONS IN MASS SPECTROMETRY Volume: 23 Issue: 20 Pages: 3285-3292
156. Lo Feudo, G., Naccarato, A., Sindona, G., Tagarelli, A. (2010). Investigating the Origin of Tomatoes and Triple Concentrated Tomato Pastes through Multielement Determination by Inductively Coupled Plasma Mass Spectrometry and Statistical Analysis. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY Volume: 58 Issue: 6 Pages: 3801-3807
157. Kelly, S.D.; Bateman, A.S.(2010). Comparison of mineral concentrations in commercially grown organic and conventional crops - Tomatoes (Lycopersicon esculentum) and lettuces (Lactuca sativa). FOOD CHEMISTRY Volume: 119 Issue: 2 Pages: 738-745
158. Bontempo, L., Camin, F., Manzocco, L., Nicolini, G., Wehrens, R., Ziller, L., Larcher, R. (2011). Traceability along the production chain of Italian tomato products on the basis of stable isotopes and mineral composition. RAPID COMMUNICATIONS IN MASS SPECTROMETRY Volume: 25 Issue: 7 Pages: 899-909
159. Zampella, M., Quetel, C.R., Paredes, E., Asfaha, D.G., Vingiani, S., Adamo, P. (2011). Soil properties, strontium isotopic signatures and multi-element profiles to authenticate the origin of vegetables from small-scale regions: illustration with early potatoes from southern Italy. RAPID COMMUNICATIONS IN MASS SPECTROMETRY Volume: 25 Issue: 19 Pages: 2721-2731
160. Bong, Y.S., Shin, W.J., Gautam, M.K., Jeong, Y.J., Lee, A.R., Jang, C.S., Lim, Y.P., Chung, G.S., Lee, K.S. (2012). Determining the geographical origin of Chinese cabbages using multielement composition and strontium isotope ratio analyses. FOOD CHEMISTRY Volume: 135 Issue: 4 Pages: 2666-2674
Page 30 of 49
161. Bong, Y.S., Song, B.Y., Gautam, M.K., Jang, C.S., An, H.J., Lee, K.S. (2013). Discrimination of the geographic origin of cabbages. FOOD CONTROL Volume: 30 Issue: 2 Pages: 626-630
162. Kim, J., Jung, Y., Song, B., Bong, Y.S., Ryu, D.H., Lee, K.S., Hwang, G.S. (2013). Discrimination of cabbage (Brassica rapa ssp pekinensis) cultivars grown in different geographical areas using H-1 NMR-based metabolomics. FOOD CHEMISTRY Volume: 137 Issue: 1-4 Pages: 68-75
163. Furia, E.; Naccarato, A.; Sindona, G.; et al. (2011). Multielement Fingerprinting as a Tool in Origin Authentication of PGI Food Products: Tropea Red Onion. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY Volume: 59 Issue: 15 Pages: 8450-8457
164. Imai, A., Otaka, A., Nakamura, S., Kadokura, M., Suzuki, T., Nakai, I. (2012). Determination of Trace Elements in Pumpkin Seeds by X-Ray Fluorescence Analysis and Its Application to Identifying the Geographic Origin of Individual Pumpkins. JOURNAL OF THE JAPANESE SOCIETY FOR FOOD SCIENCE AND TECHNOLOGY-NIPPON SHOKUHIN KAGAKU KOGAKU KAISHI Volume: 59 Issue: 7 Pages: 338-347
165. Zampella, M.; Quetel, C.R.; Paredes, E.; et al. (2010). Soil properties, strontium isotopic signatures and multi-element profiles to authenticate the origin of vegetables from small- scale regions: illustration with early potatoes from southern Italy. Conference: 6th Congress of the French-Society-of-Stables-Isotopes Location: ENSAT Sch, Ecolab Lab, Toulouse, FRANCE Date: OCT 26-29, 2010
166. Longobardi, F.; Casiello, G.; Sacco, D.; et al. (2011). Characterisation of the geographical origin of Italian potatoes, based on stable isotope and volatile compound analyses. FOOD CHEMISTRY Volume: 124 Issue: 4 Pages: 1708-1713
167. Laursen, K.H., Schjoerring, J.K., Olesen, J.E., Askegaard, M., Halekoh, U., Husted, S. (2011). Multielemental Fingerprinting as a Tool for Authentication of Organic Wheat, Barley, Faba Bean, and Potato. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY Volume: 59 Issue: 9 Pages: 4385-4396
168. Perez, A.L.; Smith, B.W.; Anderson, K.A. (2006). Stable isotope and trace element profiling combined with classification models to differentiate geographic growing origin for three fruits: Effects of subregion and variety. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY Volume: 54 Issue: 13 Pages: 4506-4516
169. Camin, F., Perini, M., Bontempo, L., Giongo, L. (2009). Multi-Element (H, C, N, O) Stable Isotope Characterization of Blueberries. IX INTERNATIONAL VACCINIUM SYMPOSIUM Book Series: Acta Horticulturae Volume: 810 Pages: 697-703
170. Camin, F.; Perini, M.; Bontempo, L.; et al. (2011). Potential isotopic and chemical markers for characterising organic fruits. FOOD CHEMISTRY Volume: 125 Issue: 3 Pages: 1072-1082
Page 31 of 49
171. Li, G.C., Wu, Z.J., Wang, Y.H., Dong, X.C. ,Li, B., He, W.D., Wang, S.C., Cui, J.H. (2011). Identification of geographical origins of Schisandra fruits in China based on stable carbon isotope ratio analysis. EUROPEAN FOOD RESEARCH AND TECHNOLOGY Volume: 232 Issue: 5 Pages: 797-802
172. Benabdelkamel, H., Di Donna, L., Mazzotti, F., Naccarato, A., Sindona, G., Tagarelli, A., Taverna, D. (2012). Authenticity of PGI "Clementine of Calabria" by Multielement Fingerprint. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY Volume: 60 Issue: 14 Pages: 3717-3726
173. Bat, K.B., Vidrih, R., Necemer, M., Vodopivec, B.M., Mulic, I., Kump, P., Ogrinc, N. (2012). Characterization of Slovenian Apples with Respect to Their Botanical and Geographical Origin and Agricultural Production Practice. FOOD TECHNOLOGY AND BIOTECHNOLOGY Volume: 50 Issue: 1 Pages: 107-116
174. Li, Q., Chen, L.S., Ding, Q.B., Lin, G.H. (2013). The stable isotope signatures of blackcurrant (Ribes nigrum L.) in main cultivation regions of China: implications for tracing geographic origin. EUROPEAN FOOD RESEARCH AND TECHNOLOGY Volume: 237 Issue: 2 Pages: 109-116
175. Anklam, E. (1998). A review of the analytical methods to determine the geographical and botanical origin of honey. Food Chemistry, 63(4), 549–562.
176. Fernandez-Torres, R., Perez-Bernal, J.L., Bello-Lopez, M.A., Callejon-Mochon, M., Jimenez-Sanchez, J.C., Guiraum-Perez, A. (2005). Mineral content and botanical origin of Spanish honeys. TALANTA Volume: 65 Issue: 3 Pages: 686-691
177. Nalda, M.J.N., Yague, J.L.B., Calva, J.C.D., Gomez, M.T.M. (2005). Classifying honeys from the Soria Province of Spain via multivariate analysis. ANALYTICAL AND BIOANALYTICAL CHEMISTRY Volume: 382 Issue: 2 Pages: 311-319
178. Golob, T., Dobersek, U., Kump, P., Necemer, M. (2005). Determination of trace and minor elements in Slovenian honey by total reflection X-ray fluorescence spectroscopy. FOOD CHEMISTRY Volume: 91 Issue: 4 Pages: 593-600
179. Pisani, A.; Protano, G.; Riccobono, F. (2008). Minor and trace elements in different honey types produced in Siena County (Italy). FOOD CHEMISTRY Volume: 107 Issue: 4 Pages: 1553-1560
180. Madejczyk, M., Baralkiewicz, D., (2008). Characterisation of honey from different areas of Poland by their physico-chemical parametres and trace elements. PROCEEDINGS OF ECOPOLE 2008, VOL 2 NO 1 Book Series: Proceedings of ECOpole Volume: 2 Issue: 1 Pages: 59-63
Page 32 of 49
181. Camina, J.M., Cantarelli, M.A., Lozano, V.A., Boeris, M.S., Irimia, M.E., Gil, R.A., Marchevsky, E.J. (2008). Chemometric tools for the characterisation of honey produced in La Pampa, Argentina, from their elemental content, using inductively coupled plasma optical emission spectrometry (ICP-OES). JOURNAL OF APICULTURAL RESEARCH Volume: 47 Issue: 2 Pages: 102-107
182. Czipa, N., Borbely, M., Kovacs, B. (2008). THE EFFECT OF GEOGRAPHICAL ORIGIN ON THE COMPOSITION OF HONEY. CEREAL RESEARCH COMMUNICATIONS Volume: 36 Supplement: S Pages: 1435-1438
183. Chudzinska M. , Baralkiewicz D. (2009). Estimation of honey authenticity by multielements characteristics using inductively coupled plasma-mass spectrometry (ICP-MS) combined with chemometrics. Food Chem Toxicol. 2010 Jan;48(1):284-90
184. Schellenberg, A.; Chmielus, S.; Schlicht, C.; et al. (2010).Multielement stable isotope ratios (H, C, N, S) of honey from different European regions. FOOD CHEMISTRY Volume: 121 Issue: 3 Pages: 770-777
185. Kropf, U.; Korosec, M.; Bertoncelj, J.; et al. (2010). Determination of the geographical origin of Slovenian black locust, lime and chestnut honey. FOOD CHEMISTRY Volume: 121 Issue: 3 Pages: 839-846
186. Kropf, U.; Golob, T.; Necemer, M.; et al. (2010). Carbon and Nitrogen Natural Stable Isotopes in Slovene Honey: Adulteration and Botanical and Geographical Aspects. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY Volume: 58 Issue: 24 Pages: 12794-12803
187. Kropf, U.; Golob, T.; Necemer, M.; Kump, P.; Korosec, M.; Bertoncelj, J.; Ogrinc, N. (2010). Carbon and Nitrogen Natural Stable Isotopes in Slovene Honey: Adulteration and Botanical and Geographical Aspects. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY Volume: 58 Issue: 24 Pages: 12794-12803
188. Chudzinska, M.; Baralkiewicz, D. (2011). Application of ICP-MS method of determination of 15 elements in honey with chemometric approach for the verification of their authenticity. FOOD AND CHEMICAL TOXICOLOGY Volume: 49 Issue: 11 Pages: 2741- 2749
189.Bilandžić,N., Marija Sedak, M.D., Kolanović,B.S, Varenina, I., Končurat, A., Rudan N. (2011). Determination of trace elements in Croatian floral honey originating from different regions. Food Chemistry - FOOD CHEM 01/2011; 128(4):1160-1164
190. Chudzinska, M.; Baralkiewicz, D. (2011). Application of ICP-MS method of determination of 15 elements in honey with chemometric approach for the verification of their authenticity. FOOD AND CHEMICAL TOXICOLOGY Volume: 49 Issue: 11 Pages: 2741- 2749
Page 33 of 49
191. Simsek, A.; Bilsel, M.; Goren, A. C. (2012). C-13/C-12 pattern of honey from Turkey and determination of adulteration in commercially available honey samples using EA-IRMS. FOOD CHEMISTRY Volume: 130 Issue: 4 Pages: 1115-1121
192. Pellerano, R.G., Unates, M.A., Cantarelli, M.A., Camina, J.M., Marchevsky, E.J. (2012). Analysis of trace elements in multifloral Argentine honeys and their classification according to provenance. FOOD CHEMISTRY Volume: 134 Issue: 1 Pages: 578-582
193. Pohl, P., Stecka, H., Sergiel, I., Jamroz, P. (2012). Different Aspects of the Elemental Analysis of Honey by Flame Atomic Absorption and Emission Spectrometry: A Review. FOOD ANALYTICAL METHODS Volume: 5 Issue: 4 Pages: 737-751
194. de Alda-Garcilope, C., Gallego-Pico, A., Bravo-Yaguee, J. C., Garcinuno-Martinez, R. M., Fernandez-Hernando, P. (2012). Characterization of Spanish honeys with protected designation of origin "Miel de Granada" according to their mineral content. FOOD CHEMISTRY Volume: 135 Issue: 3 Pages: 1785-1788
195. Morgano, M.A., Martins, M.C.T., Rabonato, L.C., Milani, R.F., Yotsuyanagi, K., Rodriguez-Amaya, D.B. (2012). A Comprehensive Investigation of the Mineral Composition of Brazilian Bee Pollen: Geographic and Seasonal Variations and Contribution to Human Diet. JOURNAL OF THE BRAZILIAN CHEMICAL SOCIETY Volume: 23 Issue: 4 Pages: 727-736
196. Grembecka, M.; Szefer, P. (2013). Evaluation of honeys and bee products quality based on their mineral composition using multivariate techniques. ENVIRONMENTAL MONITORING AND ASSESSMENT Volume: 185 Issue: 5 Pages: 4033-4047
197. Breas, O., Guillou, C., Reniero, F., Sada, E., & Angerosa, F. (1998). Oxygen-18 measurement by continuous flow pyrolysis/isotope ratio mass spectrometry of vegetable oils. Rapid Communications in Mass Spectrometry, 12, 188–192.
198. Angerosa, F., Breas, O., Contento, S., Guillou, C., Reniero, F., & Sada, E. J. (1999). Application of stable isotope ratio analysis to the characterization of the geographical origin of olive oils. Journal of Agricultural and Food Chemistry, 47, 1013–1017.
199. Benincasa, C., Lewis, J., Perri, E., Sindona, G., Tagarelli, A. (2007). Determination of trace element in Italian virgin olive oils and their characterization according to geographical origin by statistical analysis. ANALYTICA CHIMICA ACTA Volume: 585 Issue: 2 Pages: 366- 370
200. Bontempo, L.; Camin, F.; Larcher, R.; et al. (2009). Coast and year effect on H, O and C stable isotope ratios of Tyrrhenian and Adriatic italian olive oils. RAPID COMMUNICATIONS IN MASS SPECTROMETRY Volume: 23 Issue: 7 Pages: 1043-1048
Page 34 of 49
201. Camin, F.; Larcher, R.; Perini, M.; et al. (2010). Characterisation of authentic Italian extra-virgin olive oils by stable isotope ratios of C, O and H and mineral composition. FOOD CHEMISTRY Volume: 118 Issue: 4 Pages: 901-909
202. Camin, F.; Larcher, R.; Nicolini, G.; et al. (2013). Isotopic and Elemental Data for Tracing the Origin of European Olive Oils. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY Volume: 58 Issue: 1 Pages: 570-577
203. Farmaki, E.G.; Thomaidis, N. S.; Minioti, K. S.; et al. (2012). GEOGRAPHICAL CHARACTERIZATION OF GREEK OLIVE OILS USING RARE EARTH ELEMENTS CONTENT AND SUPERVISED CHEMOMETRIC TECHNIQUES. ANALYTICAL LETTERS Volume: 45 Issue: 8 Pages: 920-932
204. Cabrera-Vique, C.; Bouzas, P. R.; Oliveras-Lopez, M. J. (2012). Determination of trace elements in extra virgin olive oils: A pilot study on the geographical characterisation. FOOD CHEMISTRY Volume: 134 Issue: 1 Pages: 434-439
205. Hrastar, R., Petrisic, M.G., Ogrinc, N., Kosir, I.J. (2009). Fatty Acid and Stable Carbon Isotope Characterization of Camelina sativa Oil: Implications for Authentication. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY Volume: 57 Issue: 2 Pages: 579-585
206. Joebstl, D.; Bandoniene, D.; Meisel, T.; et al. (2010). Identification of the geographical origin of pumpkin seed oil by the use of rare earth elements and discriminant analysis. FOOD CHEMISTRY Volume: 123 Issue: 4 Pages: 1303-1309
207. Rubiniene, E., Gruzdiene, D., Bandoniene, D., Zettl, D., Meisel, T.C. (2011). Lithuanian Rapeseed: Quality and Geographical Origin by the Use of Rare Earth Elements. RURAL DEVELOPMENT IN GLOBAL CHANGES, VOL 5, BOOK 2 Book Series: Rural Development Volume: 5 Issue: 2 Pages: 221-226
208. Bandoniene, D., Zettl, D., Meisel, T., Maneiko, M. (2013). Suitability of elemental fingerprinting for assessing the geographic origin of pumpkin (Cucurbita pepo var. styriaca) seed oil. FOOD CHEMISTRY Volume: 136 Issue: 3-4 Pages: 1533-1542
209. Anderson, K.A.; Smith, B.W. (2005). Use of chemical profiling to differentiate geographic growing origin of raw Pistachios. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY Volume: 53 Issue: 2 Pages: 410-418
210. Brescia, M.A., Sacco, D., Sgaramella, A., Pasqualone, A., Simeone, R., Peri, G., Sacco, A. (2007). Characterisation of different typical Italian breads by means of traditional, spectroscopic and image analyses. FOOD CHEMISTRY Volume: 104 Issue: 1 Pages: 429- 438
211. Brunner, M., Katona, R., Stefanka, Z., Prohaska, T. (2010). Determination of the geographical origin of processed spice using multielement and isotopic pattern on the
Page 35 of 49 example of Szegedi paprika. EUROPEAN FOOD RESEARCH AND TECHNOLOGY Volume: 231 Issue: 4 Pages: 623-634
212. Lee, A.R., Gautam, M., Kim, J., Shin, W.J., Choi, M.S., Bong, Y.S., Hwang, G.S., Lee, K.S. (2011).A Multianalytical Approach for Determining the Geographical Origin of Ginseng Using Strontium Isotopes, Multielements, and H-1 NMR Analysis. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY Volume: 59 Issue: 16 Pages: 8560-8567
213. Maggi, L., Carmona, M., Kelly, S.D., Marigheto, N., Alonso, G.L. (2011). Geographical origin differentiation of saffron spice (Crocus sativus L. stigmas) - Preliminary investigation using chemical and multi-element (H, C, N) stable isotope analysis. FOOD CHEMISTRY Volume: 128 Issue: 2 Pages: 543-548
214. Hondrogiannis, E. Peterson, K. Zapf, C. M. Roy, W. Blackney, B. Dailey, K. (2012). The use of wavelength dispersive X-ray fluorescence and discriminant analysis in the identification of the elemental composition of cumin samples and the determination of the country of origin. FOOD CHEMISTRY Volume: 135 Issue: 4 Pages: 2825-2831
215. Hondrogiannis, E., Rotta, K., Zapf, C.M. (2013). The Use of Wavelength Dispersive X-ray Fluorescence in the Identification of the Elemental Composition of Vanilla Samples and the Determination of the Geographic Origin by Discriminant Function Analysis. JOURNAL OF FOOD SCIENCE Volume: 78 Issue: 3 Pages: C395-C401
Page 36 of 49
Appendix 1: Tables and Figures summarising the use of Stable isotope and Trace Element (SITE) analysis for the determination of the geographical origin of food and drink - published in peer reviewed scientific literature.
'SITE' publications on the geographical Number of SITE Papers Commodity origin of food since 1993 - % by commodity published [1993-2013] Others commodities [n=7] Oils [n= 12] 3% Meat [n=28] 28 6%
Dairy products [n=23] 23 Meat [n=28] Dairy products Beverages [n=28] 28 Honey [n=22] 14% [n=23] 11% 11% Cereals crops [n=21] 21 Vegetables/ Alcoholic beverages [n=42] 42 fruits [n=23] 11% Vegetables/ fruits [n=23] 23 Beverages [n=28] 14% Honey [n=22] 22 Alcoholic Cereals crops Oils [n= 12] 12 beverages [n=42] [n=21] 20% Others commodities [n=7] 7 10% TOTAL 206
Page 37 of 49
Number of 'SITE' publications per year since 1993 on the geographical origin of food 30
25
20
15
10 Numberpublications 5
0 1993 1998 2003 2008 2013 Year
Page 38 of 49
Key to abbreviations 2H/1H - The ratio of the isotope of hydrogen with atomic mass 2 to the isotope AAS – Atomic Absorption Spectrometry of hydrogen with atomic mass 1 EA – Elemental Analysis (Dumas Combustion) 13C/12C - The ratio of the isotope of carbon with atomic mass 13 to the isotope GF – Graphite Furnace of carbon with atomic mass 12 HPLC – High Performance Liquid Chromatography 15N/14N - The ratio of the isotope of nitrogen with atomic mass 15 to the HPIC – High Performance Ion Chromatography isotope of nitrogen with atomic mass 14. ICP-AES – Inductively Coupled Plasma-Atomic Emission Spectrometry 18O/16O - The ratio of the isotope of oxygen with atomic mass 18 to the isotope ICP-OES – Inductively Coupled Plasma-Optical Emission Spectrometry of oxygen with atomic mass 16. INAA – Instrumental Neutron Activation Analysis 34S/32S - The ratio of the isotope of sulfur with atomic mass 34 to the isotope of IRMS – Isotope Ratio Mass Spectrometry sulfur with atomic mass 32 MC-ICP-MS – Multiple Collector-Inductively Coupled Plasma–Mass 87Sr/86Sr - The ratio of the isotope of strontium with atomic mass 87 to the Spectrometry isotope of strontium with atomic mass 86 NMR – Nuclear Magnetic Resonance spectrometry 87Rb/85Rb - The ratio of the isotope of strontium with atomic mass 87 to the Q-ICP-MS – Quadrupole- Inductively Coupled Plasma–Mass Spectrometry isotope of strontium with atomic mass 85 TIMS – Thermal Ionisation Mass Spectrometry 11B/10B - The ratio of the isotope of strontium with atomic mass 11 to the TOF – Time-Of-Flight isotope of strontium with atomic mass 10 SNIF-NMR - Site-specific Natural Isotope Fractionation - Nuclear Magnetic ME – Multielement Analysis Resonance REE – Rare-earth composition XRF – total reflection X-Ray Fluorescence analysis ANOVA – Analysis of Variance; ANN – Artificial Neural Network CA – Cluster Analysis; CART – Classification and Regression Tree Analysis C(DA) – Canonical (Discriminant Analysis); HCA – Hierarchical Cluster Analysis LDA – Linear Disciminant Analysis; PCA – Principal Component Analysis;
Page 39 of 49
Table 5: Meat Commodity Parameters measured Instrumental techniques Data interpretation Reference
Beef O# IRMS univariate, box & whisker 11. Hegerding et al., 2002 Beef Se AAS - hydride generation univariate 12. Hintze et al., 2002 Beef C#, N#, S#, H# IRMS box & whisker, PCA 13. Boner et al., 2004 Beef C#, N#, H# IRMS (1H, 2H & 13C-NMR) DA 14. Renou et al., 2004a Beef C#, N#,S# IRMS ANOVA 15. Schmidt O. et al ,2005 Beef C#, N#,O#, H# IRMS, ICP-MS CDA 16. Heaton, K. et al, 2008 Beef C#, N#,O# IRMS Not available 17. Nakashita, R. et al , 2008 Beef C#, N#,O# IRMS - 18. Nakashita, R. et al , 2008 ANOVA, Duncan's test, CA, Beef C#, N# IRMS DA 19. Guo B.L. et al , 2008 Beef H# IRMS Not available 20. Guo B.L. et al , 2009 Beef C#, N#,O# IRMS Not available 21. Nakashita, R. et al, 2008 ANOVA, Duncun's LSD test, Beef C#, N# IRMS CA, DA 22. Guo B.L. et al, 2010 Beef C#, N#, H# IRMS DA 23. Horacek, M. et al, 2010 Beef C#, N#,O# IRMS ANOVA, LSD 24. Bong Y.S. et al , 2010 Beef C#, N#,S#, H# IRMS DA 25. Osorio M.T. et al, 2011 Beef C#, N#, S#, H# IRMS CDA 26. Osorio M.T. et al, 2011 Beef C#, N# IRMS Tukey-Kramer, CA 27. Yanagi Y. et al, 2012 Beef Sr# TIMS - 28. Rummel S. et al, 2012 Beef C#, O# IRMS ANOVA 29. Bong Y.S. et al, 2012 Beef C#, N#, ME PCA, DA 30. Zhao Y. et al, 2013 Beef C#, N#, H# IRMS LDA 31. Liu X.L. et al, 2013 Lamb C#, N# IRMS CDA 32. Piasentier et al., 2003 ICP-AES, IRMS, HPIC, 33. Sacco, D. et al, 2005 Lamb C#, N#, ME HR-MAS NMR Multivariate statistics Lamb C#, N#, S#, H# IRMS Multivariate method 34. Camin, F. et al, 2007 Lamb C#, N# IRMS ANOVA 35. Moreno-Rojas, J.M. et al, 2008 Lamb C#, N#, S#, H#, O# IRMS ANOVA, PCA, CA 36. Perini, M. et al al, 2009 Bream C#, N# IRMS - 37. Moreno-Rojas, J.M. et al, 2007 Mutton ME ICP-MS LDA 38. Sun, SM. et al, 2011
Page 40 of 49
Table 6: Dairy products Commodity Parameters measured Instrumental techniques Data interpretation Reference
Dairy Products Milk C#, N#,O# IRMS Univariate 39. Kornexl et al., 2000 Milk O# IRMS (1H-NMR) DA 40. Renou et al., 2004b Milk O# IRMS univariate 41. Ritz et al., 2004 Milk ME, C#, N#,O#, S#, Sr# ICP-MS, IRMS 42. Crittenden, R.G. et al, 2007 Milk PCA, LDA ME ICP-MS 43. Benincasa, C et al, 2008 Milk C#, N#,H#, ME IRMS, 1H NMR, ICP- AES Chemometric methods 44. Sacco, D. et al, 2009 Milk ME ICP-AES, AAS, FAAS PCA, LDA, FA 45. Sola-Larranaga, C.et al, 2009 Milk C#, N# IRMS LDA, MLR, PCR, PLS 46. Scampicchio, M.et al 2012 CDA, Turkey's test, Kruskal-Wallis test, Milk, cheese O# , C#, H#, N# EA-IRMS, TC/EA-IRMS Persons's correlation 47. Bontempo, L. et al 2012 Butter C#, N#, S#, Sr# IRMS, TIMS DA 48. Rossmann et al., 2000 Butter C#, N#,O#, S#, Sr# IRMS, TIMS DA 49. Balling & Rossmann, 2004 Cheese C#, N# IRMS (HPLC) PCA, CA, LDA 50. Manca et al., 2001 Cheese C#, N#, S#, Sr#, ME IRMS. Q-ICP-MS, TIMS, α-spectrometry ANOVA, PCA 51. Pillonel et al., 2003 Cheese C#, N#, S#, IRMS, AAS (GC, HPLC, NIR, electronic nose) PCA 52. Pillonel et al., 2004 Cheese Sr# MC-ICP-MS, TIMS univariate 53. Fortunato et al., 2004 Cheese C#, N#,O#, S# IRMS (HPLC) Box & whisker, CDA 54. Camin et al., 2004 Cheese C#, N#,H#, ME IRMS, 1H NMR, ICP-AES Multivariate statistical 55. Brescia, M.A. et al, 2005 Cheese C#, N#,O#, S#, H# IRMS ANOVA, Turkey's test 56. Manca, G. et al, 2006 Cheese ME AAS PCF, CDA, FA 57. Korenovska, M. et al, 2007 Cheese ME AAS, XRF CDA 58. Suhaj, M. et al, 2008 Pearson_s correlation, Cheese ME AAS Turkey_s test, PCA, MCDA 59. Moreno-Rojas, R. et al, 2010 ANOVA, Turkey's test, Kruskal-Wallis test, ICP-AES, ICPMS, EA-IRMS, Persons's correlation, Cheese O# , C#, H#, N#, Sr#, ME TC/EA-IRMS, TIMS MCDA 60. Bontempo, L. et al, 2011 Cheese C#, N#, S#, H#, ME IRMS, ICPMS PCA 61. Camin, F. et al, 2012
Page 41 of 49
Table 7: Beverages Commodity Parameters measured Instrumental techniques Data interpretation Reference
Coffee ME AAS, GF-AAS, EA, NAA t-test 62. Krivan et al, 1993 Coffee ME XRF PCA 63. Haswell et al., 1998 Coffee C#, N#, H# IRMS LDA, CART 64. Weckerle et al., 2002 Coffee C#, N#, B# IRMS PCA 65. Serra et al, 2005 Coffee ME XRF PCA 66. Akamine et al, 2010 Coffee Sr#, O# MC-ICPMS, IRMS PCA 67. Rodrigues et al, 2011 Coffee C#, N#, H#, O#, ME IRMS, ICPMS CDA 68. Santato et al, 2012 Tea ME ICP-AES PCA, LDA, ANN 69. Fernandez-Caceres et al., 2001 Tea ME ICP-AES, Q-ICP-MS PCA, LDA 70. Moreda-Pineiro et al., 2001 Tea ME ICP-AES, Q-ICP-MS PCA, LDA 71. Moreda-Pineiro et al., 2003 Tea Me ICP-AES PCA, HCA, LDA, BPNN 72. Chen et al, 2009 Tea C#,ME IRMS, ICPMS LDA 73. Pilgrim et al, 2010 Tea C#, N# IRMS ANOVA 74. Zhang et al, 2012 Tea Sr#, C# MC-ICPMS, IRMS PCA 75. Lagad et al, 2013 Orange juice 13C/12C IRMS univariate 76. Simpkins et al., 2000 Orange juice ME ICP-AES, Q-ICP-MS PCA 77. Simpkins et al., 2000 Orange juice C#, N#, S#,H#,Sr# IRMS, TIMS DA 78. Rummel et al, 2010 Fruit juices ME FAAS PCA, CA 79. Ince et al, 2008 Fruit juices O# , C#, H# IRMS - 80. Calderone et al, 2008 Fruit juices O# , C#, H# TC/EA-IRMS, - 81. Magdas et al, 2012 Fruit juices C#, H#, O#, ME ICP-MS - 82. Magdas et al, 2012 Bottled waters Sr# MC-ICPMS Univariate 83. Brach-Papa et al, 2009 Bottled waters Sr# TIMS, MC-ICPMS Univariate 84. Voerkelius et al, 2010 Bottled waters O# , C#, H# EA-IRMS ANOVA, Turkey's test 85. Brencic et al, 2010 Bottled waters O# , H# CF-IRMS - 86. Dotsika et al, 2010 Bottled waters O# , C#, H# IRMS - 87.Raco et al, 2013 Sparkling drinks C# GC-C-IRMS - 88. Calderone et al, 2007 Horchata ME ICP-AES DA, PCA, CARTs 89. Boeting et al, 2010
Page 42 of 49
Table 8: Cereals crops Commodity Parameters measured Instrumental techniques Data interpretation Reference
Rice ME ICP-AES, Q-ICP-MS PCA, CA 90. Yasui & Shindoh, 2000 Rice B#, Sr#, Cd concentration Q-ICP-MS, MC-ICP-MS Binary plots 91. Oda et al., 2002 Rice O# , C#, ME IRMS. Q-ICP-MS CDA 92. Kelly et al., 2002 Rice Sr# MC-ICP-MS Univariate? 93. Kawasaki et al., 2002 Rice C#, N#, O# IRMS - 94. Suzuki et al,2008 Rice C#, N#, O# IRMS - 95. Suzuki et al, 2008 Rice C#, N#, O# IRMS - 96.Suzuki et al, 2009 Rice C#, N#, O#, H# IRMS PCA 97. Korenaga et al, 2010 Rice ME ICP-OES LDA 98.Gonzalvez et al, 2011 Rice, Barley, Wheat Sr#, Pb# ICP-MS - 99. Ariyama et al, 2011 Rice H# IRMS - 100. Suzuki et al, 2013 Rice ME ICP-MS PCA, DFA, FIA 101. Li et al, 2013 Wheat 13C/12C, 15N/14N IRMS, (HR-MAS-NMR) PCA 102. Brescia et al., 2002 Wheat 13C/12C, 15N/14N, ME Q-ICP-MS, IRMS Box & whisker, CDA 103. Branch et al., 2003 Wheat ME XRF Multivariate statistical 104. Otaka et al, 2009 Wheat ME ICP-Ms Multivariate statistical 105. Zhao et al, 2011 Wheat ME ICP-MS ANOVA 106. Zhao et al, 2012 Wheat C#, N#, Sr#, ME IRMS, TIMS, ICP-MS CA 107. Podio et al, 2013 Wheat ME HR-ICP-MS, XRF PCA, LDA 108. Zhao et al, 2013 Barley C#, N#, ME IRMS, ICP-MS Multivariate statistics 109. Husted et al, 2004 Cereals C#, N#, O#, S#, Sr# IRMS Multivariate statistics 110. Asfaha et al. 2011
Page 43 of 49
Table 9: Alcoholic beverages Commodity Parameters measured Instrumental techniques Data interpretation Reference Wine Sr# TIMS univariate 111. Horn et al., 1993 Wine O# , C# IRMS univariate 112. Breas et al., 1994 Wine ME, (D/H)1, (D/H)2 AAS, SNIF-NMR ANOVA, PCA 113. Day et al., 1994 Wine ME, (D/H)1, (D/H)2 AAS, SNIF-NMR PCA, CDA 114. Day et al., 1995 Wine ME Q-ICP-MS CDA, PCA 115. Baxter et al., 1997 Wine Sr#, O# IRMS, TIMS Univariate 116. Horn et al., 1998 Wine O# , C#, (D/H)1, (D/H)2 IRMS, SNIF-NMR Univariate 117. Rossmann et al., 1999 Wine Sr# MC-ICP-MS Univariate 118. Almeida &, Vasconcelos, 2001 Wine Pb# Q-ICP-MS, TOF-ICP-MS, MC-ICP-MS Univariate 119. Barbaste et al., 2001 Wine O# , C#, (D/H)1, (D/H)2 IRMS, SNIF-NMR PCA, ANN 120. Kosir et al., 2001 Wine Sr# MC-ICP-MS Univariate 121. Barbaste et al., 2002 Wine O# , C#, (D/H)1, (D/H)2 IRMS, SNIF-NMR t-test 122. Christoph et al., 2003 Wine O# , C#, (D/H)1, (D/H)2 IRMS, SNIF-NMR Univariate 123. Christoph et al., 2004 Wine ME ICP-AES, HPIC, (1H-NMR) PCA, HCA, RDA 124. Brescia et al., 2003 Wine Sr# Q-ICP-MS univariate 125. Marisa et al., 2004 Wine 2H/1H, 18O/16O, ME IRMS, ICP-MS?, (classical parameters) CDA 126. Gremaud et al., 2004 Wine B# ICP-MS ANOVA 127. Coetzee et al, 2005 Wine Review - 128.Suhaj et al , 2005 ANOVA, Person's correlation, Wine ME HR-ICPMS Bonferroni test, PCA 129.Galgano et al, 2008 Wine Cs# Gamma spectoscopy - 130.Serapinas et al, 2008 Wine ME ICPMS ANOVA, FA, DA 131.Gonzalvez et al, 2009 Wine ME AAS, AES CA, PCA, LDA 132.Hubert et al, 2009 Wine ME ICP-MS Multivariate statistics 133. van der Linde et al, 2010 Wine B#, Sr# ICP-MS 134.Vorster et al, 2010 Wine ME FAAS, AAS LDA, CC 135.Fabani et al,2010 Wine ME PIXE ANOVA, Turkey's test 136. dos Santos et al, 2010 Wine ME ICP-AES HCA, PCA, CARTs, DA 137. Paneque et al, 2010 Wine C#, O# IRMS DA 138. Dutra et al, 2011 Grapevine B# ICP-MS, IRMS ANOVA 139. Coetzee et al, 2011 Wine Sr#, C#, ME TIMS, IRMS, ICP-MS Chemometrics analysis 140. Di Paola-Naranjo et al, 2011 ANOVA, Person's correlation, Wine ME AAS, DRC-ICPMS CA, LDA, PCA 141. Perez Trujillo et al, 2011 Wine ME AAS, AES ANOVA, PCA, LDA 142. Rodrigues et al, 2011 Wine C#, O# IRMS - 143. Magdas et al, 2012 Wine ME ICP-MS, ICP-AES LDA 144. Martin et al, 2012 Wine ME ICP-MS CA, PCA 145. Geana et al, 2013 Wine C#, O#, H# IRMS, H2-NMR - 146. Pirnau et al, 2013 Wine O# TC/EA-IRMS 147. Perini et al, 2013 Wine C#, O# IRMS ANOVA 148. Dutra et al, 2013 Wine Sr# ICP-MS PCA 149.Durante et al, 2013
Page 44 of 49
Ethanol C#, O#, H# CR-IRMS Statistical anlysis 150. Ishida-Fujii et al, 2005 Cider Sr#, ME MC-ICP-MS, ICP-AES, ICP-MS LDA, PCA 151. Garcia-Ruiz et al, 2007 Honkaku Shochu C#, O# IRMS 152. Izu et al, 2012 Table 10: Fruits and vegetables Commodity Parameters measured Instrumental techniques Data interpretation Reference
Spinach ME XRF PCA 153. Yanada et al, 2007 Asparagus Sr# , Rb# DRC-ICPMS, MC-ICPMS Univariate 154. Swoboda et al, 2008 Tomatoes REE ICP-MS, ICP-SFMS Univariate 155. Spalla et al, 2009 Tomatoes ME ICP-MS LDA, SIMCA, KNN 156. Lo Feudo et al, 2010 Tomatoes, lettuce N#, ME EA/CF-IRMS, ICP-MS CDA 157. Kelly et al, 2010 Tomatoes C#, N#, O#, H#, S#, ME IRMS, ICP-MS LDA 158. Bontempo et al, 2011 Vegetables Sr#, ME ICP-MS PLS-DA 159. Zampella et al, 2011 Cabbages ME, Sr# ICP-MS DA 160. Bong et al, 2012 Cabbages Me, Sr# ICP-Ms DA 161. Bong et al, 2013 Cabbages H# 1H-NMR PCA 162. Kim et al, 2013 Red onions ME DRC-ICPMS LDA, SIMCA, BP-ANN 163. Furia et al, 2011 Pumpkin seed ME XRF LDA 164. Imai et al, 2012 Potatoes Sr#, ME MC-ICPMS PCA, PLS-DA 165. Zampella et al, 2010 Potatoes O# , C#, N# EA-IRMS ANOVA, DFA, PCA 166. Longobardi et al, 2011 Potatoes C#, N#, ME ICP-AES, ICP-MS, EA-IRMS PCA 167. Laursen et al, 2011 Fruits ME ICP-MS LDA 168. Perez et al, 2006 Blueberry C#, N#, O#, H# IRMS, SNIF-NMR Kruskall-Wallis 169. Camin et al, 2009 Orange, clementine, Peach, strawberry C#, N#, O#, H#, S# TC/EA-IRMS Box plots, CDA 170. Camin et al, 2011 Schisandra fruits C# EA-IRMS Kruskal-Wallis 171. Li et al, 2011 PCA, LDA, SIMCA, Clementine ME ICP-MS PLS-DA 172. Benabdelkamel et al, 2012 Apple C#, N#, O#, H#, ME IRMS, TXRF LDA, Kruskal-Wallis test 173. Bat et al, 2012 Blackcurrant C#, N#,H#, O# IRMS ANOVA, DA, CA 174. Li et al, 2013
Page 45 of 49
Table 11: honey Commodity Parameters measured Instrumental techniques Data interpretation Reference
Honey Review 175. Anklam, 1998 Honey ME ICP-MS PCA, LDA 176. Fernandez-Torres et al, 2005 Honey ME ICP-AES PCA 177. Nalda et al, 2005 Honey ME XRF Statistical treatment 178. Golob et al, 2005 Honey ME ICP-AES, ICP-MS ANOVA, PCA 179. Pisani et al, 2008 Honey ME ORS-ICP-MS, FAAS ANOVA, turkey's test, CA 180. Madejczyk et al, 2008 Honey ME ICP-OES PCA, CA, LDA 181. Camina et al, 2008 Honey ME ICP-OES LDA 182. Czipa et al, 2008 Honey ME ORS-ICP-MS PCA, CA 183. Chudzinska et al, 2009 Honey C#, N#, S#, O# TC/EA-IRMS Turkey's test 184. Schellenberg et al, 2010 ANOVA, Kruskal- Wallis test, Honey C#, N# CF-IRMS LDA 185. Kropf et al, 2010 ANOVA, Kruskal- Wallis test, Honey C#, N#, ME CF-IRMS, XRF LDA, PCA, CA 186. Kropf et al, 2010 Honey C#, N# IRMS LDA 187. Kropf et al, 2010 Honey ME DRC-ICP-MS CA, PCA, LDA, CARTs 188. Chudzinska et al, 2011 Honey ME AAS ANOVA 189. Bilandžić et al, 2011 Honey ME ICP-MS LDA 190. Chudzinska et al, 2011 Honey C# TC/EA-IRMS - 191. Simsek et al, 2012 Honey ME ICP-MS LDA 192. Pellerano et al, 2012 Honey Review 193. Pohl et al, 2012 Honey ME AAS ANOVA, cluster analysis 194. de Alda-Garcilope et al, 2012 Honey ME ICP-OES PCA 195. Morgano et al, 2012 Honey ME AAS ANOVA 196. Grembecka et al, 2013
Page 46 of 49
Table 12: oils Commodity Parameters measured Instrumental techniques Data interpretation Reference
Vegetable oils O#, C# IRMS univariate 197. Breas et al., 1998 Vegetable oils O#, C# IRMS CA, PCA 198. Angerosa et al., 1999 Olive oil ME ICP-MS LDA 199. Benincasa et al, 2007 ANOVA, Pearson's correlation, Olive oils O#, C# , H# EA-IRMS, TC/EA-IRMS Turkey's test 200. Bontempo et al, 2009 Kruskal - Wallis test, multiple bilateral Olive oils O#, C# , H#, ME EA-IRMS, ORS-ICPMS comparison 201. Camin et al, 2010 ANOVA, Pearson's correlation, Olive oils O#, C# , H#, ME TC/EA-IRMS, ORS-ICPMS Turkey's test, CDA 202. Camin et al, 2010 Olive oils REE ICP-MS ANN, CARTs, DA 203. Farmaki et al, 2012 Extra virgin olive Oils ME ETAAS DA 204. Cabrera-Vique et al, 2012 Camelina sativa Oil C# GC-C-IRMS - 205. Hrastar et al, 2009 Pumpkin oils REE ICP-MS Grubbs test, LDA 206. Joebstl et al, 2010 Rapeseed oil REE, ME ICP-MS LDA 207. Rubiniene et al, 2011 Pumpkin seed oil REE ICP-MS Multivariate statistics 208. Bandoniene et al, 2013
Page 47 of 49
Table 13: other commodities Commodity Parameters measured Instrumental techniques Data interpretation Reference
Multivariate and Chemical profiling Pistachios C#, N#, ME IRMS, ICP-AES methods 209. Anderson et al, 2005 Bread C#, N#, H# IRMS, 1-H NMR PCA, DA 210. Brescia et al, 2007 Paprika Sr#, ME ICP-MS PCA, CDA 211. Brunner et al, 2010 ICP-AES, ICP-MS Multivariate statistical, Ginseng Sr#, ME, REE, H-1 NMR TIMS PCA 212. Lee et al, 2011 Saffron H#, C#, N#, IRMS Multivariate analysis 213. Maggi et al, 2011 Cumin ME WDXRF ANOVA, DA 214. Hondrogiannis et al, 2012 Vanilla ME WDXRF DA 215. Hondrogiannis et al, 2013
Page 48 of 49
Appendix 2: Authentication of Italian cheeses
Grana Padano cheese (Protected Denomination of Origin) consortium, Italy
Dr Federica Camin, Fondazione Edmundo Mach-Istituto Agrario di San Michele all'Adige (FEM- IASMA) has been working with the consortium for the protection of Grana Padano cheese 1. The control of authenticity in grated cheeses sampled by the consortium from the retail market was a serious concern. In fact the biggest fraud for these kinds of PDO cheese is in the grated product, because the genuine pieces of cheese have the rind stamped with the mark of Grana Padano, whereas the grated products are without rind and therefore without this characteristic mark. Grana Padano has several cheaper competitor cheeses, coming from Lithuania, the Czech Republic and Germany that are not PDO Italian cheeses. Therefore the opportunity for fraud in grated products exists. The contents of the isotopic databank are propriety information of the consortium of Grana Padano. In 2005 the consortium requested the EC to include the stable isotope analysis as a Quality Control analysis in the production description of the Grana Padano PDO cheese and this happened in August 2009.
Parmigiano Reggiano cheese (Protected Denomination of Origin) consortium, Italy
Since 2008 the consortium for the protection of Parmigiano Reggiano Cheese2 has also been working with FEM-IASMA to isotopically characterise this world renowned cheese. The authenticity issue is the same as for Grana Padano cheese i.e. how to protect from against frauds with respect to substitution of the grated cheese with inferior products. In this FEM-IASMA have performed both isotopic (H, C, N, S) and elemental analysis (by ICP-MS). The combination of the 2 analytical approaches permits 100% discrimination of Parmigiano Reggiano cheese from the competitor cheeses. The model was also validated with 14 blind samples sent by the consortium. FEM-IASMA is now conducting surveillance of the authenticity of commercial samples. The Consortium of Parmigiano Reggiano wants to publish the results in a peer reviewed scientific journal.
1 Official Journal C199 25.08.2009 “COUNCIL REGULATION (EC) No 510/2006; Amendment application pursuant to Article 9 ‘GRANA PADANO’; EC No: IT-PDO-0217-0011-26.07.2006 2 Official Journal C87 16.04.2009; “COUNCIL REGULATION (EC) No 510/2006; Amendment application pursuant to Article 9; ‘PARMIGIANO REGGIANO’; EC No: IT-PDO-0317-0016-26.7.2007
Page 49 of 49