October 2024
This is a a fundamental rewrite of the IFST’s Food Authenticity Testing Information Statement. It has been split into two parts. This statement now covers the role of analytical testing within the context of an overall supply chain assurance strategy. It describes where testing can and cannot be used and highlights generic issues relating to interpreting food authenticity testing results.
The description of specific analytical techniques, their applications, strengths and weaknesses have been moved to a separate information statement entitled ‘'Food Authenticity Testing: Analytical Techniques.'
Executive Summary
Analytical testing is a valuable tool in the armoury to assure food authenticity, but cannot be used to identify every type of food fraud. It is only one part of an overall strategy to mitigate fraud risk.
Many modern tests are based upon comparing a pattern of measured values in the test sample with patterns from a database of authentic samples. Interpretation is highly dependent on the robustness of the database, and whether it includes all possible authentic variables and sample types. This information may not be released by the laboratory. Interpretation of results is rarely clear-cut, and analytical results are often used to inform and target further investigation (such as unannounced audits or mass balance checks) rather than for making a compliance decision.
There are multiple organisations working on an internationally accepted, and legally enforceable, definition of food fraud. The 2018 working draft of the Codex Committee on Food Import and Export Inspection and Certification Systems is typical: ‘food fraud includes adulteration, deliberate and intentional substitution, dilution, simulation, tampering, counterfeiting, or misrepresentation of food, food ingredients, or food packaging; or false or misleading statements made about a product for economic gain’. There is debate whether the legal scope should be expanded wider than ‘ … deliberate and intentional… ‘ and than ‘ ... for economic gain’.
Adulteration and mislabelling of food have been known since biblical times. Fraud goes back as far as when food started to be traded. Ancient Greeks had laws on adulteration of cereals and fats. As an example, whenever there is a price premium between two ostensibly similar products, or a downward pressure on prices, then there is scope for criminality. Other external factors can also drive food fraud. The risk and potential scale have increased in recent years with the complexity of modern supply chains, high value ingredients and the proliferation of premium-labelled variants of food and drink types. A recent analysis estimated that fraud accounted for 5 to 25% of all globally reported food safety incidents1.
In the simplest case, one (safe and legal) food is misrepresented as a more expensive variety. There is an economic and reputational risk to any defrauded trader, but no direct risk to the safety of the consumer. Nobody will be harmed by eating conventional carrots mis-sold as organically produced, or a ready meal containing a different variety of potato than the manufacturer believed they were buying. At the other end of a spectrum, the health risk can be severe if a non-food grade ingredient is added to disguise the quality or variety of a product. Examples are adding melamine to increase the apparent protein content of milk powder, Sudan dyes to disguise vegetable oils as palm oil and fake vodka containing methanol2. Even where there is no direct health risk, it is unlikely that such an unscrupulous trader will diligently be following all other food safety and hygiene rules. The UK’s stated regulatory approach is therefore to take a zero tolerance approach to any cases of mis-selling3, otherwise food safety incidents will inevitably follow.
Food manufacturers and retailers should assess their vulnerability to fraud as part of their routine risk assessments4 and put risk mitigation steps in place as appropriate.
The only way for the food industry to guard against being the victim of fraud is robust supply chain defence policies, short and transparent supply chains, financial audits, mass balance checks and effective whistleblower procedures. This is often supported by an analytical testing programme, whether designed to directly detect issues, to target further investigation and audit, or purely as a deterrent. Analytical testing is targeted with the help of data sharing and early warning systems. Testing is used as a spot check to verify that control systems and certification are effective and trustworthy; it is not in itself a control or certification system. Typically, food manufacturers will use risk-based prioritisation to conduct unannounced analytical spot tests on their raw materials, to check that they are exactly what they purport to be.
It is important that manufacturers and retailers then have a procedure in place for acting upon analytical results. Unlike testing for chemical contaminants, the interpretation of results from food authenticity tests will often be ambiguous or have a high degree of uncertainty. There is little point in a manufacturer commissioning a test if they do not know how they will deal with ‘suggestive’, but not ‘conclusive’, results.
Detecting food fraud or verifying whether the information and voluntary claims accompanying the food are correct, involves analytical tests to examine the composition both qualitatively and quantitatively, processing conditions, geographical origin and compliance with certification systems. This means that a wide range of methods have been developed for this purpose, which are often collectively termed ‘food forensics’.
Many such methods rely upon comparing the test sample with a reference database that contains ‘normal’ or ‘authentic’ samples. These give an indicative or probabilistic, rather than a definitive, result. If the test sample differs from the reference database, then there may be a range of hypotheses as to why (both fraudulent and innocent reasons). For example, the 14N/15N ratio in a vegetable may differ from the reference database because an organically certified grower has used a novel, but permitted, seaweed-based fertiliser, or it may differ because they have illegally used a synthetic (mineral) fertiliser. Thus, analytical testing will not always give a definitive result upon which an accept/reject verdict can be made (see figure 1).
Figure 1: Certainty in Interpreting Analytical Test Results
See IFST Information Statement ‘Food Authenticity Testing: Analytical Techniques’ for further details.
Descriptions such as ‘organic’ refer to a legally defined system, for which the actual specification can vary in different parts of the world, and it is not realistic to have one or several methods of analysis which cover this whole system; only inspection and audit can do this. The best that can be achieved analytically is to verify one aspect of the system, e.g. soil fertilisation, antibiotic use or plant metabolites as one marker for verifying the use of the term ‘organic’.
This visualisation echoes the ‘seven deadly sins’ of food fraud defined by Spink and Moyer5. Not all of these sins can be detected by analysis, and of those that can, there is not always the luxury of an unambiguous verdict.
Figure 2: Seven Deadly Sins of Food Fraud: Applicability of Analytical Testing
Most analytical research and development is focused upon increasing the certainty of the probabilistic tests, developing analytical techniques, building reference databases of both legitimate and fraudulent products, and developing statistical pattern-recognition software. This area encompasses most of the ‘up-labelling’ fraud risks of current concern, such as mislabelling of product grade, species or variety, organically grown produce, country of origin, or artisan production method.
Irrespective of the analytical technique, there are some fundamental ways of classifying the analytical approach. Many of the techniques can be used for multiple approaches, depending on how they are configured. Typical ways of classifying the approach are:
-
Targeted vs. untargeted analysis
-
Specific analyte(s) vs. Multi-Variate Analysis (MVA), or
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Laboratory vs. point-of-use testing.
Traditionally, analysts have needed to predefine what they are seeking to measure. This is ‘targeted’ analysis. In the field of food fraud, examples are, testing for:
-
a specific adulterant, e.g. melamine in milk powder; chicory in soluble coffee powder
-
a known marker that is characteristic of a particular grade of product, e.g. UV absorbance ratios and fat ratios, to characterise Extra Virgin Olive Oil, or
-
a predefined section of DNA, e.g. use of specific primers to amplify DNA from a specific species of meat.
Targeted analysis also includes tests where ratios of known ‘markers’ are used in a more probabilistic manner, when there are no fixed tolerances or thresholds, but there is a degree of knowledge about what is ‘typical’ for certain product types, e.g. measuring the 14N/15N isotope ratio in vegetables labelled as organically, grown to try and infer the misuse of a synthetic (mineral) fertiliser.
Targeted analysis tends to be much more sensitive than untargeted, because instruments and techniques can be tuned and optimised for the specific analyte(s) sought. The obvious limitation with it is that if an issue is not sought then it will not be found. It is always reactive.
‘Untargeted’ can be used to describe situations where there is still a predefined list of parameters, but it is so large that, for all intents and purposes, the application is universal. An example is the use of universal primers for DNA amplification, sequencing and matching against a comprehensive reference database for species identification.
In its purest form, untargeted analysis has no predefined list of test parameters. All that is known is the pattern of results. Multiple data points are collected from the sample. It may not be known which individual parameters or analytes are being measured or what they indicate. Examples are measuring changes in the complex patterns of proteins, metabolites or genes in a sample, not all of which may be identified, often referred to as ‘-omics’ techniques (e.g. genomics, proteomics, metabolomics). But there are many other examples, such as measuring complex fat profiles in fish or meat, or the intensity of each (unidentified) peak in the complex Nuclear Magnetic Resonance (NMR) spectrum of an alcoholic drink, or the intensity of each (unidentified) peak in the complex mass spectrum of a dried herb. In all cases, data assessment involves Multi-Variate Analysis (see Section 2.2) and comparison with extensive reference databases. For example, an MVA reference database of the fat profile of cod might reveal a pattern of statistical clusters, each associated with fish of the same species but from a different catch area. If the test sample fell within one of these clusters, then it might be inferred that it was also from this catch area.
It is important, in untargeted analysis, to keep a clear distinction between the analytical result and the interpretation of the result. This is particularly critical for laboratory accreditation. Accreditation for ‘testing’ and for ‘opinions and interpretations’ are very different and separate processes. Typically, a laboratory will be accredited for ‘testing’ but only a named expert can be accredited for ‘opinions and interpretations’. Even a named expert can have his interpretation challenged in a Court of Law, so it is not necessarily unequivocal. Laboratories will take care, on their written reports, not to stray beyond their accredited scope by commenting on interpretation, and this can mean that customers are unaware of caveats and are left with the false impression that the interpretation of the result is clear-cut. Measurement uncertainty, repeatability and sampling factors should always be considered during any assessment.
Untargeted analysis lends itself to spectral techniques where data over an entire signal range is collected, where there is no pre-selection of data. Techniques such as mass spectrometry (in full scan mode), NMR and spectral imaging using any or all of the infra-red, near infra-red, visible or ultra-violet light ranges.
MVA is the basis behind all non-targeted approaches6, but may also be used for targeted analysis if multiple predefined parameters are measured, where no individual parameter or ratio is a marker for the result, but the overall pattern gives an indication of the result. An example of targeted MVA is stable isotope ratio mass spectrometry where the isotopic ratios of four or five different natural elements are plotted to give an indication of geographic origin.
The prerequisite for .VA is the construction of a database of results from a large number of authenticated and well characterised reference samples. For each reference sample a multi-dimensional point is plotted that corresponds to the value of every component or parameter that was measured. This is analogous to plotting a point for just two parameters on an x-y graph. Statistical pattern-recognition techniques, such as Principal Component Analysis (PCA), are then used to see whether the reference samples fall into clusters, depending upon their provenance. An example is PCA of mass spectra from reference samples of different species of ground dried herbs (see figure 37).
Figure 3: Example of Reference Data Sets used for Principle Component Analysis
The test sample is then measured and plotted in a similar way. If it purported, in this example, to be oregano but the PCA plot did not fall within the reference cluster of oregano samples so suspicions would be raised. In this example an expert microscopist might be able to confirm if the product had been diluted with olive leaves, for example.
Generally, the authentic database approach is better at confirming, or otherwise, the claimed origin or authenticity of a sample, rather than determining the origin or authenticity of an unknown sample.
The strength of MVA is that there is no preconception about what the fraudulent activity or problem might be, just that the sample is different than the reference set. If results can be plotted on a visual graph, then this gives the advantage of an instinctive appreciation of how wide a difference between the test sample and the reference cluster. Such a crude probabilistic interpretation is invaluable for prioritising resources to follow up audit or investigation.
The limitation of all MVA approaches is the strength of the reference database. Is it representative of all-natural variation within genuine examples, in terms of provenance of the food in question? It is difficult to predict the effect of seemingly minor variations on the position of an MVA data point in a pattern, particularly when the parameters being measured are uncharacterised with no cause-and-effect theories underpinning their variation. For example, the MVA pattern of fats in beef, intended to diagnose the cattle breed, might be profoundly affected by a change in the composition of cattle feed.
Reference datasets are often built in-house by laboratories, with the risk that they do not appreciate the full nuances and variety of the genuine food on the market, and so unwittingly exclude some variations related to provenance. The best reference datasets are constructed in collaboration with the appropriate food industry. To ensure effectiveness, reference databases can need continuous updating, particularly where the measurement principle may be affected by seasonal or yearly changes, e.g. temperature and rainfall. The UK Food Authenticity Network (FAN) has a repository of known databases8.
Reference databases are expensive to construct; a big commercial investment for any laboratory. Due to both practical and cost limitations the number of individual data points in a reference database can be limited. There are valid technical reasons why some datasets cannot be transferred between different instruments in different laboratories, but there are also intellectual property protections on some reference datasets. This can make it difficult to challenge or to gain a second opinion on test results and interpretations. It also means that different laboratories specialise in different applications, and even different food types. There are programmes to coordinate different laboratory offerings to provide virtual networks of expertise, for example FAN9 in the UK.
The traditional analytical model is for samples to be sent to a laboratory, with results returned in a few days or weeks but this is beginning to change. There are clear advantages to the food industry in tests that can be conducted at point-of-use and give a real time result. Some such tests are now in routine use. One example, used in the processed fish industry, is online NMR to tell the species of frozen white block fish ingredients. Another is the use of Near Infrared (NIR) scanners for authenticity testing of milk coming into large dairy collection centres used by milk powder manufacturers. NIR and Ultraviolet/Visible light (UV-Vis) are particularly suited for handheld scanners and continuous flow applications (see IFST Information Statement ‘Food Authenticity Testing: Analytical Techniques’) and have widespread use in the pharmaceutical industry for similar on-line verification testing of raw materials. A realtime warning flag is raised if there is anything abnormal about the raw material intake. The key to this type of test is that a food company should set up this testing for their own product lines using authentic samples. The system should also be set up to monitor how products change over time, with suitable flags to alert when something significant has changed in the manufacturing process or supply chain. This change may not always be the result of fraud, but simply due to a known swap, such as the supplier or variety of ingredient. The actual samples size used by these ‘point-of use’ methods is often small, therefore averaged data from replicate analyses, or good sample homogenisation, may be required before analysis. This is especially true for non-liquid product types.
The next predicted paradigm shift is towards tests that can be conducted by the general public at supermarket shelves or in their own homes, and the concept of Citizen Science popularised by astronomy. Verification of ’free-from’ claims is a particular area of interest, using small immunoassay test kits (analogous to home pregnancy test kits) linked to smartphones to upload results onto public databases. There is at least one kit already on the market10 and although currently sold, these cannot be relied on for valid results. There are major publicly funded research projects11 to develop and validate home allergen test kits or miniaturised NIR scanners linked to smartphones that will pass scientific acceptance.
The majority of testing will continue to be conducted in specialist laboratories in the foreseeable future, due to the inherent capital cost of equipment, need for purchase and disposal of specialist reagents, a highly controlled environment, the need for expert interpretation, or due to simple economies of scale. But the use of certain tests in limited applications, within food production line environments, has provided a step change in the effectiveness of fraud detection measures in recent years.
Once it is known that a particular food fraud can be detected, and that testing is in routine use, fraudsters will move on to something else. Therefore, whilst the established valdiated methods are needed for continued due diligence, development of new methods tends to be a rapidly moving field. There can be a necessary compromise between speed of development, publication, offering to market, and the robustness and scope of the method validation. Many of the established, validated and documented methods were developed under publiucly funded programmes, such as the UK Food Authenticity Programme (methods now curated online7) or the EU Food Integrity Programme.
Table 1 gives some examples of the many publications on different test methods and applications. Other examples can be found in a 2024 review paper12, to give a flavour of what is available. It is not a comprehensive list of the thousands of scientific papers that have been published on specific test methods and applications in recent years, nor an IFST endorsement of a particular method or researcher. It is also important to remember the role of lower technology testing in food analysis. Some claims, e.g. ‘Premium muesli, with 50% fruit’ can be verified simply by separating and weighing the ingredients, and despite the best advances of science there is no substitute for an expert taste panel to verify the provenance of premium whiskies, wines or olive oils. Some of the most powerful analytical applications use a combination of different test techniques and perform multivariate analysis on the total data set.
Table 1: Example Applications of Analytical Testing
|
Food |
Issue |
Technique |
Targeted? |
Ref. |
|
Almonds (ground) |
Peanut addition |
ICP-OES |
Untargeted |
13 |
|
Butter |
Palm/coconut oil addition |
Fluorescence spectroscopy |
Untargeted |
14 |
|
Cheese |
Plant oil addition |
Fluorescence spectroscopy |
Untargeted |
15 |
|
Coffee |
Arabica vs Robusta |
NMR |
Untargeted |
16 |
|
Cooking oils |
Variety substitution |
FTIR |
Untargeted |
17 |
|
Fish |
Catch area |
DNA PCR-RAPD |
Untargeted |
18 |
|
Fish |
Species substitution |
MALDI-ToF-MS with proteomics |
Untargeted |
19 |
|
Fruit juice |
Apple juice addition |
HPLC |
Targeted |
20 |
|
Fruit (plums) |
Organic production & cultivar verification |
Mass spectrometry (of volatiles) |
Untargeted |
21 |
|
Grains |
Authenticity |
NIR & UV-Vis spectroscopy |
Untargeted |
22 |
|
Herbs (dried) |
Authenticity |
FTIR & mass spectrometry |
Untargeted |
23 |
|
Honey |
Sugar addition |
NMR |
Targeted |
24 |
|
Honey |
Sugar addition |
Isotope ratio MS |
Targeted |
25 |
|
Honey |
Floral origin |
Mass spectrometry & Isotope ratio MS & Raman & NIR spectroscopy & ICP-MS |
Untargeted |
26 |
|
Margarine |
Fat profile |
Raman & NIR spectroscopy |
Untargeted |
27 |
|
Meat |
Species adulteration |
NIR & UV-Vis spectroscopy |
Untargeted |
28 |
|
Meat |
Adulteration with offal |
IR spectroscopy |
Untargeted |
29 |
|
Meat |
Species adulteration |
DNA hybridisation probes |
Targeted |
30 |
|
Meat |
Species adulteration |
DNA PCR amplification |
Targeted |
31 |
|
Milk powder |
Melamine |
NIR spectroscopy |
Targeted |
32 |
|
Milk |
Nitrogen enrichment |
Mass spectrometry |
Targeted |
33 |
|
Milk |
Additives for shelf-life extension, dilution |
IR spectroscopy |
Targeted |
34 |
|
Olive oil |
Geographic origin |
Mass spectrometry |
Untargeted |
35 |
|
Parmigiano Reggiano |
Fatty acid profile |
Mass spectrometry |
Targeted |
36 |
|
Rice |
Variety mislabelling |
DNA PCR-RAPD |
Untargeted |
37 |
|
Salt |
Premium origin |
NIR spectroscopy |
Untargeted |
38 |
|
Shellfish |
Geographic origin |
ICP-AES |
Untargeted |
39 |
|
Vegetables |
Organic production |
Isotope ratio MS |
Targeted |
40 |
|
Vinegar |
Wine vinegar authenticity |
Isotope ratio MS |
Targeted |
41 |
See IFST Information Statement “Food Authenticity Testing: Analytical Techniques” for further details on specific techniques.
There is a conceptual divide between analytical techniques traditionally used for food contaminants or nutritional parameters and those used for many food authenticity tests. Rather than measure a specific component against a fixed limit, they often rely on a probabilistic match of a result or a pattern of results against a reference database of authentic samples. This means that the interpretation of modern authenticity test results rarely meets the burden of proof that would be required in a court of law. There is inevitable uncertainty over both the fitness of the probability match and whether the reference database is truly representative of the test sample. Provided that these caveats are appreciated, authenticity testing has a valuable place in the supply chain assurance programmes of food businesses. Test results can be used to target and inform follow-up investigations and audits. And testing programmes are a deterrent to potential fraudsters.
The analytical techniques and references databases used for authenticity testing are rapidly evolving. Whatever the authenticity question there is likely a research group, somewhere, working on it. Amongst the plethora of scientific publications and advertised laboratory services, it is important to differentiate between proof of concept studies using narrowly controlled conditions and approaches that have been applied to real world situations.
Rather than a transactional customer-client relationship for analytical testing, laboratories are increasingly working with food industry clients to understand and tailor analytical approaches to address their specific authenticity risks and ingredient or product types. This collaboration and communication are often essential for the successful interpretation of results.
Before commissioning analyses, food companies should ensure that they:
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understand the scope of the reference database used,
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understand the likely outputs/information which will be obtained from a particular food authenticity test, and
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document the next steps to be taken in case of a suspicious result.
Glossary
|
AIJN |
The representative association of the fruit juice industry in the European Union |
|
DNA |
Used generically to describe test methods that identify based on protein sequences within nucleic acids |
|
ELISA |
Enzyme Linked ImmunoSorbent Assay |
|
GC-MS |
Gas Chromatography-Mass Spectrometry |
|
HADH |
An oxidoreductase enzyme, 3-hydroxyacyl-CoA dehydrogenase |
|
HPLC |
High Performance Liquid Chromatography |
|
HMF |
Hydroxymethyl formamide, formed when honey is overheated |
|
LC-MS |
Liquid Chromatography-Mass Spectrometry |
|
MVA |
Multivariate Analysis, a statistical treatment of data clusters |
|
NIR |
Near Infra-Red spectroscopy |
|
NMR |
Nuclear Magnetic Resonance spectroscopy |
|
PDO |
Protected Designation of Origin |
|
UV |
Ultraviolet light absorbance spectroscopy |
1 J Points, in ‘Horizonscan occasional articles 4: Food and feed authenticity – recent trends’, published by Fera Science Ltd, York, UK, 2016.
2 Food fraud: about 27 000 tonnes off the shelves, https://www.europol.europa.eu/media-press/newsroom/news/food-fraud-about-27-000-tonnes-shelves
3 Elliot review into the integrity and assurance of food supply networks – final report”, HM Government PB 14192, July 2014
4 “Global Standard Food Safety Issue 9”, British Retail Consortium, London, UK,2022
5 John Spink and Douglas Moyer, Defining the Public Health Threat of Food Fraud, Journal of Food Science, 76 (2011) R157
6 MP Calloa and I Ruisanchez, “An overview of multivariate qualitative methods for food fraud analysis”, Food Control 86 (2018) 283
7 SA Haughey et al, “A comprehensive strategy to detect the fraudulent adulteration of herbs: The oregano approach”, Food Chemistry 2010 (2016) 551
8 Food Authenticity Databases, https://documents.foodauthenticity.global/index.php/auth-dbs
9 Food Authenticity Network, http://www.foodauthenticity.uk/, accessed 4 January 2018
10 GlutenTox test kit, https://emportllc.com/one-stop-information-center/, accessed 12 June 2024
11 Food Smart Phone, http://www.foodsmartphone.eu/, accessed 19 December 2017
12 Vinothkan6(2024) 138893
13 M Esteki et al “Qualitative and quantitative analysis of peanut adulteration in almond powder samples using multi-element fingerprinting combined with multivariate data analysis” Food Control 82(2017) 31
14 A Dankowska et al, “Application of synchronous fluorescence spectroscopy with multivariate data analysis for determination of butter adulteration”, Int J Food Sci Tech, 49 (2014) 2628
15 A Dankowska et al, “Dectection of plant oil addition to cheese by synchronous fluorescence spectroscopy”, Dairy Sci and Tech 95 (2015) 413
16 DW Lachenmeier et al, “Rapid approach to identify the presence of Arabica and Rustica species in coffee using 1H NMR spectroscopy”, Food Chemistry 182 (2015) 178
17 MZ Durak et al, “Rapid detection of adulteration of cold pressed sesame oil adulterated with hazelnut, canola and sunflower oils using ATR-FTIR spectroscopy combined with chemometric” Food Control 82 (2017) 212
18 M Fischer et al “Applying population genetics for authentication of marine fish: the case of saithe (pollachius virens)” J Agric Food Chem 63 (2015) 802
19 A Stahl and U Schroder, “Development of a MALDI-ToF MS based protein fingerprint database of common food fish allowing fast and reliable identification of fraud and substitution”, J Agric Food Chem, 65 (2017) 7519
20 R Vanderlinde et al, “Detection of addition of apple juice in purple grape juice”, Food Control 69 (2016) 1
21 J Moreno-Rojas et al “Effect of management (organic vs conventional) on volatile profiles of six plum cultivars a chemometric approach for varietal classification and determination of potential markers” Food Chemistry 199 (2016) 479
22 M Burns et al “Feasibility study for applying spectral imaging for wheat grain authenticity testing in pasta” Food Nutrit.Sci. 7 (2016) 355
23 SA Haughey et al, “A comprehensive strategy to detect the fraudulent adulteration of herbs: The oregano approach”, Food Chemistry 2010 (2016) 551
24 E Jamin et al, “Fast and global authenticity screening of honey using 1H-NMR profiling”, Food Chemistry 189 (2015) 60
25 M Tosun, “Detection of adulteration in honey samples added various sugar syrups with 13C/12C isotope ratio analysis method” Food Chemistry 138 (2013) 1629
26 Z Jandric et al, “Discrimination of honeys of different floral origins by a combination of various chemical parameters”, Food Chemistry 189 (2015) 52
27 D Ucuncuoglu et al, “Rapid detection of adulteration in bakery products using Raman and near infrared spectroscopies”, Eur Food Res Technol 237 (2013) 703
28 JE Nychas et al, “Multispectral imaging: a promising method for the detection of minced beef adulteration with horsemeat” Food Control 73 (2017) 57
29 Y Hu et al, “Detection and quantification of offal content in ground beef meat using vibrational spectroscopic-based chemometric analysis” Scientific Reports 9 November 2017 DOI:10.1038/s41598-017-15389-3
30 S Rahmati et al, “Identification of meat origin in food products: a review”, Food Control 68 (2016) 379
31 S Rahmati et al, “Identification of meat origin in food products: a review”, Food Control 68 (2016) 379
32 PF Scholl et al, “Effects of the adulteration technique on the near-infrared detection of melamine in milk powder” J Agric Food Chem 65 (2017) 5799
33 N Frank et al “Development of a quantitative multi-compound method for the detection of 14 nitrogen-rich adulterants by LC-MS/MS in food materials” Food Add Contam A (2017) DOI:0.1080/19440049.2017.1372640
34 M Sena et al “Development and analytical validation of a screening method for simultaneous detection of five adulterants in raw milk using mid-infrared spectroscopy and PLS-DA” Food Chemistry 181 (2015) 31
35 R Gil-Solsona et al, “Metabolomic approach for extra virgin olive oil origin discrimination making use of ultra-high performance liquid chromatography with quadrupole time-of-flight mass spectrometry” Food Control 70 (2016) 350
36 A Caligiani et al, “Development of a quantitative GC-MS method for the detection of cyclopropane fatty acids in cheese as new molecular markers for Parmigiano Reggiano authentication”, J Agric Food Chem, 64 (2016) 4158
37 M Arlorio et al “Chemometrical characterisation of four Italian rice varieties based on genetic and chemical analysis” J Agric Food Chem 54 (2006) 9985
38 A Rangel et al, “Fourier transform near infra-red spectroscopy application for sea salt quality evaluation” J Agric Food Chem, 59 (2011) 11109
39 L Li et al “Assessment of elemental profiling for distinguishing geographic origin of aquacultured shrimp from India, Thailand and Vietnam” Food Control 80 (2017) 162
40 S Kelly et al “Nitrogen isotope composition of organically and conventionally grown crops” J Agric Food Chem 55 (2007) 2664
41 F Camin et al “Control of wine vinegar authenticity through ɖ18O analysis” Food Control 29 (2013) 107
Institute of Food Science & Technology has authorised the publication of the following updated Information Statement entitled 'Food authenticity testing part 1: The role of analysis' dated October 2024, replacing that of February 2019.
This updated Information Statement has been prepared and peer reviewed by professional members of IFST and approved by the IFST Scientific Committee.
The Institute takes every possible care in compiling, preparing and issuing the information contained in IFST Information Statements, but can accept no liability whatsoever in connection with them. Nothing in them should be construed as absolving anyone from complying with legal requirements. They are provided for general information and guidance and to express expert professional interpretation and opinion, on important food-related issues.