A flavour of omics approaches for the detection of food fraud

Current Opinion in Food Science

Volumn 10, Issue , 2016, Pages 7-15

  Ellis, David I ^a   Muhamadali, Howbeer ^a,b   Allen, David P ^a   Elliott, Christopher T ^b   Goodacre, Royston ^a  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

^b QUEEN'S UNIVERSITY BELFAST   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84989931752     PISSN: 22147993     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cofs.2016.07.002     Document Type: Review

References (69)

1. 1 Interpol: Largest-ever seizures of fake food and drink in INTERPOL-Europol operation. 2016; vol. 2016.
2. 2 NFCU., Unit NFC, (eds.) Food Crime Annual Strategic Assessment, 2016, Food Standards Agency and Food Standards Scotland, 58.
3. 3 Balzan, J., Malta at the centre of organic food fraud. Malta Today, 2016.
4. 4 Burns, M., Wiseman, G., Knight, A., Bramley, P., Foster, L., Rollinson, S., Damant, A., Primrose, S., Measurement issues associated with quantitative molecular biology analysis of complex food matrices for the detection of food fraud. Analyst 141 (2016), 45–61.
5. 5 Galimberti, A., De Mattia, F., Losa, A., Bruni, I., Federici, S., Casiraghi, M., Martellos, S., Labra, M., DNA barcoding as a new tool for food traceability. Food Res Int 50 (2013), 55–63.
6. 6 Carvalho, D.C., Palhares, R.M., Drummond, M.G., Frigo, T.B., DNA Barcoding identification of commercialized seafood in South Brazil: a governmental regulatory forensic program. Food Control 50 (2015), 784–788.
7. 7 Kim, H., Kumar, K.S., Hwang, S.Y., Kang, B.C., Moon, H.B., Shin, K.N.H., Utility of stable isotope and cytochrome oxidase I gene sequencing analyses in inferring origin and authentication of hairtail fish and shrimp. J Agric Food Chem 63 (2015), 5548–5556.
8. 8 Chuah, L.O., He, X.B., Effarizah, M.E., Syahariza, Z.A., Shamila-Syuhada, A.K., Rusul, G., Mislabelling of beef and poultry products sold in Malaysia. Food Control 62 (2016), 157–164.
9. 9 Kane, D.E., Hellberg, R.S., Identification of species in ground meat products sold on the US commercial market using DNA-based methods. Food Control 59 (2016), 158–163.
10. 10•• Nixon, G.J., Wilkes, T.M., Burns, M.J., Development of a real-time PCR approach for the relative quantitation of horse DNA. Anal Methods 7 (2015), 8590–8596 A designated hot research article. It being stated that the development of this non-proprietary novel real-time PCR method for the quantitation of horse DNA will inform and strengthen the decision making processes of food companies and regulators.
11. 11 Bloch, M.S., Paunescu, D., Stoessel, P.R., Mora, C.A., Stark, W.J., Grass, R.N., Labeling milk along its production chain with DNA encapsulated in silica. J Agric Food Chem 62 (2014), 10615–10620.
12. 12 Chen, S.L., Pang, X.H., Song, J.Y., Shi, L.C., Yao, H., Han, J.P., Leon, C., A renaissance in herbal medicine identification: from morphology to DNA. Biotechnol Adv 32 (2014), 1237–1244.
13. 13 Parvathy, V.A., Swetha, V.P., Sheeja, T.E., Leela, N.K., Chempakam, B., Sasikumar, B., DNA barcoding to detect chilli adulteration in traded black pepper powder. Food Biotechnol 28 (2014), 25–40.
14. 14 Puddu, M., Paunescu, D., Stark, W.J., Grass, R.N., Magnetically recoverable, thermostable, hydrophobic DNA/silica encapsulates and their application as invisible oil tags. ACS Nano 8 (2014), 2677–2685.
15. 15 Shokralla, S., Gibson, J.F., Nikbakht, H., Janzen, D.H., Hallwachs, W., Hajibabaei, M., Next-generation DNA barcoding: using next-generation sequencing to enhance and accelerate DNA barcode capture from single specimens. Mol Ecol Res 14 (2014), 892–901.
16. 16 Moraes, D.F.C., Still, D.W., Lum, M.R., Hirsch, A.M., DNA-based authentication of botanicals and plant-derived dietary supplements: where have we been and where are we going?. Planta Med 81 (2015), 687–695.
17. 17 Schneider, G.F., Dekker, C., DNA sequencing with nanopores. Nat Biotechnol 30 (2012), 326–328.
18. 18 Claydon, A.J., Grundy, H.H., Charlton, A.J., Romero, M.R., Identification of novel peptides for horse meat speciation in highly processed foodstuffs. Food Addict Contam Part A-Chem Anal Control Exp Risk Assess 32 (2015), 1718–1729.
19. 19 Chen, Y.N., Kong, D.Z., Liu, L.Q., Song, S.S., Kuang, H., Xu, C.L., Development of an enzyme-linked immunosorbent assay (ELISA) for natamycin residues in foods based on a specific monoclonal antibody. Anal Methods 7 (2015), 3559–3565.
20. 20 Wu, X.L., Wang, W.B., Liu, L.Q., Kuang, H., Xu, C.L., Monoclonal antibody-based cross-reactive sandwich ELISA for the detection of Salmonella spp. in milk samples. Anal Methods 7 (2015), 9047–9053.
21. 21 von Bargen, C., Dojahn, J., Waidelich, D., Humpf, H.U., Brockmeyer, J., New sensitive high-performance liquid chromatography tandem mass spectrometry method for the detection of horse and pork in halal beef. J Agric Food Chem 61 (2013), 11986–11994.
22. 22 Ohana, D., Dalebout, H., Marissen, R.J., Wulff, T., Bergquist, J., Deelder, A.M., Palmblad, M., Identification of meat products by shotgun spectral matching. Food Chem 203 (2016), 28–34.
23. 23 Orduna, A.R., Husby, E., Yang, C.T., Ghosh, D., Beaudry, F., Assessment of meat authenticity using bioinformatics, targeted peptide biomarkers and high-resolution mass spectrometry. Food Addit Contam Part A-Chem Anal Control Exp Risk Assess 32 (2015), 1709–1717.
24. 24 Sassi, M., Arena, S., Scaloni, A., MALDI-TOF-MS platform for integrated proteomic and peptidomic profiling of milk samples allows rapid detection of food adulterations. J Agric Food Chem 63 (2015), 6157–6171.
25. 25 Agrawal, G.K., Timperio, A.M., Zolla, L., Bansal, V., Shukla, R., Rakwal, R., Biomarker discovery and applications for foods and beverages: proteomics to nanoproteomics. J Proteomics 93 (2013), 74–92.
26. 26 Colgrave, M.L., Goswami, H., Howitt, C.A., Tanner, G.J., Proteomics as a tool to understand the complexity of beer. Food Res Int 54 (2013), 1001–1012.
27. 27 Fasoli, E., Righetti, P.G., Proteomics of fruits and beverages. Curr Opin Food Sci 4 (2015), 76–85.
28. 28 Paredi, G., Sentandreu, M.A., Mozzarelli, A., Fadda, S., Hollung, K., de Almeida, A.M., Muscle and meat: new horizons and applications for proteomics on a farm to fork perspective. J Proteomics 88 (2013), 58–82.
29. 29 Herrero, M., Simo, C., Garcia-Canas, V., Ibanez, E., Cifuentes, A., Foodomics: MS-based strategies in modern food science and nutrition. Mass Spectrom Rev 31 (2012), 49–69.
30. 30 Goodacre, R., Vaidyanathan, S., Bianchi, G., Kell, D.B., Metabolic profiling using direct infusion electrospray ionisation mass spectrometry for the characterisation of olive oils. Analyst 127 (2002), 1457–1462.
31. 31 Vidal, N.P., Goicoechea, E., Manzanos, M.J., Guillen, M.D., Fourier transform infrared spectroscopy as a tool to study farmed and wild sea bass lipid composition. J Sci Food Agric 94 (2014), 1340–1348.
32. 32 Arana, V.A., Medina, J., Alarcon, R., Moreno, E., Heintz, L., Schafer, H., Wist, J., Coffee's country of origin determined by NMR: the Colombian case. Food Chem 175 (2015), 500–506.
33. 33 Ellis, D.I., Brewster, V.L., Dunn, W.B., Allwood, J.W., Golovanov, A.P., Goodacre, R., Fingerprinting food: current technologies for the detection of food adulteration and contamination. Chem Soc Rev 41 (2012), 5706–5727.
34. 34 Jumhawan, U., Putri, S.P., Yusianto, Marwani, E., Bamba, T., Fukusaki, E., Selection of discriminant markers for authentication of asian palm civet coffee (Kopi Luwak): a metabolomics approach. J Agric Food Chem 61 (2013), 7994–8001.
35. 35• Petrakis, E.A., Cagliani, L.R., Polissiou, M.G., Consonni, R., Evaluation of saffron (Crocus sativus L.) adulteration with plant adulterants by H-1 NMR metabolite fingerprinting. Food Chem 173 (2015), 890–896 A well cited NMR metabolite fingerprinting study on the adulteration of the highly prized and expensive saffron with other plant adulterants and bulking agents. Chemometric modelling approaches were used to authenticate and predict saffron adulteration which was said to compare well to the stated deficiencies in currently established methodologies.
36. 36• Black, C., Haughey, S.A., Chevallier, O.P., Galvin-King, P., Elliott, C.T., A comprehensive strategy to detect the fraudulent adulteration of herbs: the oregano approach. Food Chem 210 (2016), 551–557 An article which received a great deal of media attention even prior to its official publication. Using a metabolic profiling and metabolic fingerprinting approach, it alarmingly showed that 24% of the oregano samples analysed had some form of adulterant present, ranging from 30 to 70% adulteration.
37. 37 Cicero, N., Corsaro, C., Salvo, A., Vasi, S., Giofre, S.V., Ferrantelli, V., Di Stefano, V., Mallamace, D., Dugo, G., The metabolic profile of lemon juice by proton HR-MAS NMR: the case of the PGI Interdonato Lemon of Messina. Nat Prod Res 29 (2015), 1894–1902.
38. 38 Jandric, Z., Roberts, D., Rathor, M.N., Abrahim, A., Islam, M., Cannavan, A., Assessment of fruit juice authenticity using UPLC-QToF MS: a metabolomics approach. Food Chem 148 (2014), 7–17.
39. 39 Lang, T.T., Pang, S., He, L.L., Integration of colorimetric and SERS detection for rapid screening and validation of melamine in milk. Anal Methods 7 (2015), 6426–6431.
40. 40 Inoue, K., Tanada, C., Sakamoto, T., Tsutsui, H., Akiba, T., Min, J.Z., Todoroki, K., Yamano, Y., Toyo'oka, T., Metabolomics approach of infant formula for the evaluation of contamination and degradation using hydrophilic interaction liquid chromatography coupled with mass spectrometry. Food Chem 181 (2015), 318–324.
41. 41 Spraul, M., Link, M., Schaefer, H., Fang, F., Schuetz, B., Wine analysis to check quality and authenticity by fully-automated H-1-NMR. JeanMarie, A., (eds.) 38th World Congress of Vine and Wine, 2015, E D P Sciences [BIO Web of Conferences, vol 5].
42. 42 Jamroz, M.K., Paradowska, K., Zawada, K., Makarova, K., Kazmierski, S., Wawer, I., H-1 and C-13 NMR-based sugar profiling with chemometric analysis and antioxidant activity of herbhoneys and honeys. J Sci Food Agric 94 (2014), 246–255.
43. 43 Cajka, T., Danhelova, H., Zachariasova, M., Riddellova, K., Hajslova, J., Application of direct analysis in real time ionization-mass spectrometry (DART-MS) in chicken meat metabolomics aiming at the retrospective control of feed fraud. Metabolomics 9 (2013), 545–557.
44. 44•• Trivedi, D.K., Hollywood, K.A., Rattray, N.J.W., Ward, H., Greenwood, J., Ellis, D.I., Goodacre, R., Meat, the metabolites: an integrated metabolite profiling and lipidomics approach for the detection of the adulteration of beef with pork. Analyst 141 (2016), 2155–2164 A designated hot original research article which uses an integrated omics approach for the detection of beef adulterated with pork. Which not only demonstrated an excellent example of the integrated omics approach but also provided a panel of metabolites specific to this form of adulteration and interesting additional information on lipids.
45. 45• Ellis, D.I., Goodacre, R., Detecting food authenticity and integrity. Anal Methods 8 (2016), 3281–3283 Could be considered as a useful fingerpost, pointing to most, though not all, of the articles and reviews within a very recent Royal Society of Chemistry themed collection on detecting food authenticity and integrity.
46. 46 Cozzolino, D., Porker, K., Laws, M., An overview on the use of infrared sensors for in field, proximal and at harvest monitoring of cereal crops. Agric Basel 5 (2015), 713–722.
47. 47• Cubero-Leon, E., Penalver, R., Maquet, A., Review on metabolomics for food authentication. Food Res Int 60 (2014), 95–107 Another very well received review which describes metabolomics workflows for food authentication, recent applications of metabolomics methods, and again, their potential advantages and limitations.
48. 48• Do, T.K.T., Hadji-Minaglou, F., Antoniotti, S., Fernandez, X., Authenticity of essential oils. Trends Anal Chem 66 (2015), 146–157 A comprehensive overview of known adulterants and analytical methods for detecting adulterants in essential oils. Includes an overview of fundamental principles and applications of analytical techniques as well as a comparison of their advantages and limitations.
49. 49 Ibanez, C., Garcia-Canas, V., Valdes, A., Simo, C., Novel MS-based approaches and applications in food metabolomics. Trends Anal Chem 52 (2013), 100–111.
50. 50 Senyuva, H.Z., Gokmen, V., Sarikaya, E.A., Future perspectives in Orbitrap (TM)-high-resolution mass spectrometry in food analysis: a review. Food Addit Contam Part A-Chem Anal Control Exp Risk Assess 32 (2015), 1568–1606.
51. 51 Trimigno, A., Marincola, F.C., Dellarosa, N., Picone, G., Laghi, L., Definition of food quality by NMR-based foodomics. Curr Opin Food Sci 4 (2015), 99–104.
52. 52 Li, Z.Z., Li, Z.L., Li, D.M., Gao, H.L., Chen, X.J., Cao, L., Hou, Y.Z., Li, S.B., Molecularly imprinted polymer-based chemiluminescence imaging assay for the determination of ethopabate residues in chicken muscle. Anal Methods 7 (2015), 9295–9303.
53. 53 Ucles, A., Garcia, A.V., Garcia, M.D.G., del Real, A.M.A., Fernandez-Alba, A.R., Benzimidazole and imidazole fungicide analysis in grape and wine samples using a competitive enzyme-linked immunosorbent assay. Anal Methods 7 (2015), 9158–9165.
54. 54 Wang, Q., Zheng, Y.J., Zhang, X.L., Han, X.X., Wang, T., Zhang, Z.P., A silica coated paper substrate: development and its application in paper spray mass spectrometry for rapid analysis of pesticides in milk. Analyst 140 (2015), 8048–8056.
55. 55 Uchida, K., Konishi, Y., Harada, K., Okihashi, M., Yamaguchi, T., Do, M.H.N., Bui, L.T., Nguyen, T.D., Khong, N.D.T., Tran, H.T., et al. Monitoring of antibiotic residues in aquatic products in urban and rural areas of Vietnam. J Agric Food Chem, 2016, 10.1021/acs.jafc.6b00091 in press.
56. 56 Hou, R.Y., Pang, S., He, L.L., In situ SERS detection of multi-class insecticides on plant surfaces. Anal Methods 7 (2015), 6325–6330.
57. 57 Kouassi Nzoughet, J., Dervilly-Pinel, G., Chereau, S., Biancotto, G., Monteau, F., Elliott, C., Le Bizec, B., First insights into serum metabolomics of trenbolone/estradiol implanted bovines; screening model to predict hormone-treated and control animals’ status. Metabolomics 11 (2015), 1184–1196.
58. 58 Sayers, R.L., Johnson, P.E., Marsh, J.T., Barran, P., Brown, H., Mills, E.N.C., The effect of thermal processing on the behaviour of peanut allergen peptide targets used in multiple reaction monitoring mass spectrometry experiments. Analyst 141 (2016), 4130–4141.
59. 59•• Walker, M.J., Burns, D.T., Elliott, C.T., Gowland, M.H., Mills, E.N.C., Is food allergen analysis flawed?. Health and supply chain risks and a proposed framework to address urgent analytical needs. Analyst 141 (2016), 24–35 A timely and important article by a range of recognised food allergy experts discussing the urgent and serious issues relating to the ability to measure food allergens reproducibly, their traceability, and the identification of substantial gaps within the international analytical community.
60. 60 Muhamadali, H., Weaver, D., Subaihi, A., AlMasoud, N., Trivedi, D.K., Ellis, D.I., Linton, D., Goodacre, R., Chicken, beams, and Campylobacter: rapid differentiation of foodborne bacteria via vibrational spectroscopy and MALDI-mass spectrometry. Analyst 141 (2016), 111–122.
61. 61•• Ellis, D.I., Muhamadali, H., Haughey, S.A., Elliott, C.T., Goodacre, R., Point-and-shoot: rapid quantitative detection methods for on-site food fraud analysis — moving out of the laboratory and into the food supply chain. Anal Methods 7 (2015), 9401–9414 A well received and designated hot article discussing a range of spectrometry/spectroscopy based approaches, as well as other methods currently under development. Forwards a future perspective of how this range of detection methods, along with developments in predictive computational and information sciences, could reduce the areas of vulnerability to food crime within food supply chains.
62. 62 Sharma, T.K., Ramanathan, R., Rakwal, R., Agrawal, G.K., Bansal, V., Moving forward in plant food safety and security through NanoBioSensors: adopt or adapt biomedical technologies?. Proteomics 15 (2015), 1680–1692.
63. 63 Zou, X.B., Huang, X.W., Povey, M., Non-invasive sensing for food reassurance. Analyst 141 (2016), 1587–1610.
64. 64 Hollis-Peel, M.E., Welsh, B.C., What makes a guardian capable?. A test of guardianship in action. Security J 27 (2014), 320–337.
65. 65 Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M., Internet of Things (IoT): a vision, architectural elements, and future directions. Fut Gen Comput Syst — Int J Grid Comput Esci 29 (2013), 1645–1660.
66. 66 Barabasi, A.L., Scale-free networks: a decade and beyond. Science 325 (2009), 412–413.
67. 67 Macfadyen, S., Tylianakis, J.M., Letourneau, D.K., Benton, T.G., Tittonell, P., Perring, M.P., Gomez-Creutzberg, C., Baldi, A., Holland, J.M., Broadhurst, L., et al. The role of food retailers in improving resilience in global food supply. Global Food Security-Agric Policy Econ Environ 7 (2015), 1–8.
68. 68 Cohen, L.E., Felson, M., Social change and crime rate trends — a routine activity approach. Am Soc Rev 44 (1979), 588–608.
69. 69 Willems, S.P., Real-world multiechelon supply chains used for inventory optimization. Manufact Serv Oper Manage 10 (2008), 19–23.