Identification of transgenic foods using NIR spectroscopy: A review

Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy

Volumn 75, Issue 1, 2010, Pages 1-7

  Alishahi, A ^a   Farahmand, H ^a   Prieto, N ^b   Cozzolino, D ^c  

^a UNIVERSITY OF TEHRAN   (Iran)

^b CSIC   (Spain)

^c SCOTTISH AGRICULTURAL COLLEGE   (United Kingdom)

Author keywords

Discrimination; Food; NIRS; Transgenic

Indexed keywords

CHEMOMETRIC METHOD; LOW COSTS; NIR SPECTROSCOPY; NON DESTRUCTIVE; QUALITATIVE ANALYSIS; TRANSGENIC FOODS; TRANSGENICS;

INFRARED DEVICES; INFRARED TRANSMISSION; NEAR INFRARED SPECTROSCOPY;

INFRARED SPECTROSCOPY;

AGRICULTURE; ANIMAL; FOOD ANALYSIS; GENETICALLY MODIFIED FOOD; METHODOLOGY; NEAR INFRARED SPECTROSCOPY; REVIEW; TRANSGENIC ORGANISM;

AGRICULTURE; ANIMALS; FOOD ANALYSIS; FOOD, GENETICALLY MODIFIED; ORGANISMS, GENETICALLY MODIFIED; SPECTROSCOPY, NEAR-INFRARED;

EID: 73049102599     PISSN: 13861425     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.saa.2009.10.001     Document Type: Review

References (57)

1. Ahmed F.E. Detection of genetically modified organisms in foods. Trends in Biotechnology 20 (2002) 215-223
2. Liu Y., Lyon B.G., Windham W.R., Lyon C.E., and Savage E.M. Prediction of physical, color, and sensory characteristics of broiler breasts by visible/near infrared reflectance spectroscopy. Poultry Science 83 (2004) 1467-1474
3. Munck L., Møller B., Jacobsen S., and Søndergaard S. Near infrared spectra indicate specific mutant endosperm genes and reveal a new mechanism for substituting starch with (1/3, 1/4)-b-glucan in barley. Journal of Cereal Science 40 (2004) 213-222
4. Small G.W. Chemometrics and near-infrared spectroscopy: avoiding the pitfalls. Trends in Analytical Chemistry 11 (2006) 1057-1066
5. Raamsdonk L.W.D., Holst C., Baeten V., Berben G., Boix A., and Jong J. New developments in the detection and identification of processed animal proteins in feeds. Animal Feed Science and Technology 133 (2007) 63-83
6. Cen H., and He Y. Theory and application of near infrared reflectance spectroscopy in determination of food quality. Trends in Food Science und Technology 18 (2007) 72-83
7. Workman J. Review of process and non-invasive near-infrared and infrared spectroscopy. Applied Spectroscopy Review 34 (1999) 1-89
8. Isaksson T., and Naes T. The effect of multiplicative scatter correction (MSC) and linearity improvement in NIR spectroscopy. Applied Spectroscopy 42 (1988) 1273-1284
9. Lammertyn J., Nicolai B., Ooms K., de Smedt V., and de Baerdemaeker J. Non-destructive measurement of acidity, soluble solids, and firmness of jongold apples using NIR-spectroscopy. Transactions of the ASAE 41 (1998) 1089-1094
10. Barnes R., Dhanoa M., and Lister J. Standard normal variable transformation and detrending of near infrared diffuse reflectance spectra. Applied Spectroscopy 43 (1989) 772-777
11. Bro R. Multivariate data analysis as a tool in advanced quality monitoring in the food chain production. Trend in Food & Technology 13 (2002) 235-244
12. Bro R., Berg F., Thybo A., Anderson C.M., Jorgenson M., and Anderson H. Multivariate data analysis as a tool in advanced quality monitoring in the food production chain. Trends in Food Science und Technology 13 (2002) 235-244
13. Bro R. Multivariate calibration what is in chemometrics for the analytical chemist. Analytica Chimica Acta 500 (2003) 185-194
14. Fertig C.C., Podczeck F., Jee R.D., and Smith M.R. Feasibility study for the rapid determination of the amylase content in starch by near-infrared spectroscopy. European Journal of Pharmaceutical Sciences 21 (2003) 155-159
15. Osborne G. Near infrared spectroscopy in food analysis. Journal of Food Science 95 (2003) 568-582
16. Wang L., Lee F.S.C., Wang X.R., and He Y. Feasibility study of quantifying and discriminating soybean oil adulteration in camellia oils by attenuated total reflectance MIR and fiber optic diffuse reflectance NIR. Food Chemistry 95 (2006) 529-536
17. Ortiz-Somovilla V., Espana-Espana F., Gaitan-Jurado A.J., Perez-Aparicio-Pérez J., and Pedro-Sanz De E.J. Proximate analysis of homogenized and minced mass of pork sausages by NIRS. Food Chemistry 101 (2007) 1031-1040
18. Sirisomboon P., Tanaka M., Fujita S., and Kojima T. Evaluation of pectin constituents of Japanese pear by near infrared spectroscopy. Journal of Food Engineering 78 (2007) 701-707
19. Uddin M., Okazaki E., Ahmad M., Fukuda Y., and Tanaka M. NIR spectroscopy: a non-destructive fast technique to verify heat treatment of fish-meat gel. Food Control 17 (2006) 660-664
20. Blanco M., Coello J., Iturriaga H., Maspoch S., and Pages J. Calibration in nonlinear infrared reflectance spectroscopy: a comparison of several methods. Analytica Chimica Acta 384 (1999) 207-214
21. Aske N., Kallevik H., and Sjoblom J. Determination of saturate, aromatic, resin, and asphaltenic (SARA) components in crude oils by means of infrared and near-infrared spectroscopy. Energy Fuels 15 (2001) 1304-1312
22. Baranski R., Baranska M., and Schulz H. Changes in carotenoid content and distribution in living plant tissue can be observed and mapped in situ using NIR-FT-Raman spectroscopy. Planta 222 (2002) 448-457
23. Geesink G.H., Schreutelkamp F.H., Frankhuizen R., Vedder H.W., Faber N.M., and Kranen R.W. Prediction of pork quality attributes from near infrared reflectance spectra. Meat Science 65 (2003) 661-668
24. Lin M., Cavinato A.G., Mayes D.M., Smiley S., Huang Y., Holy M., and Rasco B.A. Bruise detection in pacific pink salmon (Oncorhynchus gorbuscha) by visible and short-wavelength near-infrared (SW-NIR) spectroscopy (600-1100 nm). Journal of Agricultural and Food Chemistry 51 (2003) 6404-6408
25. Lin M., Cavinato A.G., Huang Y., and Rasco B.A. Predicting sodium chloride content in commercial king (Oncorhynchus tshawytscha) and chum (O. keta) hot smoked salmon fillet portions by short-wavelength near-infrared (SW-NIR) spectroscopy. Food Research International 36 (2003) 761-766
26. Cozzolino D., and Murray I. Identification of animal meat muscles by visible and near infrared reflectance spectroscopy. Lebensmittel-Wissenschaft und Technologie 37 (2004) 447-452
27. Zhang M.H., Luypaert J., Pierna J.A.F., Xu Q.S., and Massart D.L. Determination of total antioxidant capacity in green tea by near-infrared spectroscopy and multivariate calibration. Talanta 62 (2004) 25-35
28. Ni Y.N., Zhang G.W., and Kokot S. Simultaneous spectrophotometric determination of maltol, ethylmaltol, vanillin and ethyl vanillin in foods by multivariate calibration and artificial neural networks. Food Chemistry 89 (2005) 465-473
29. Pedro A.M.K., and Ferreira M.M.C. Nondestructive determination of solids and carotenoids in tomato products by near-infrared spectroscopy and multivariate calibration. Analytical Chemistry 77 (2005) 2505-2511
30. Cozzolino D., Murraya I., Chreeb A., and Scaife J.R. Multivariate determination of free fatty acids and moisture in fish oils by partial least-squares regression and near-infrared spectroscopy. Lebensmittel-Wissenschaft und Technologie 38 (2006) 821-828
31. Gomez A.H., He Y., and Pereira A.G. Non-destructive measurement of acidity, soluble solids and firmness of Satsuma mandarin using vis/NIR-spectroscopy techniques. Journal of Food Engineering 77 (2006) 313-319
32. Prieto N., Andrés S., Giráldez F.J., Mantecón A.R., and Lavín P. Potential use of near infrared reflectance spectroscopy (NIRS) for the estimation of chemical composition of oxen meat samples. Meat Science 74 (2006) 487-496
33. Rodríguez-Nogales J.M. Approach to the quantification of milk mixtures by partial least-squares, principal component and multiple linear regression techniques. Food Chemistry 98 (2006) 782-789
34. Khodabux K., Omellete M.S., Laullo S.J., Ramasami P., and Rondeau P. Chemical and near-infrared determination of moisture, fat and protein in tuna fishes. Food Chemistry 102 (2007) 669-675
35. Moros J., Inon F.A., Garrigues S., and Guardia M. Near-infrared diffuse reflectance spectroscopy and neural networks for measuring nutritional parameters in chocolate samples. Analytica Chimica Acta 584 (2007) 215-222
36. Rossel S.A., Hardy C.L., Hurburgh C.R., and Rippke G.R. Application of near-infrared diffuse reflectance spectroscopy to the detection and identification of transgenic corn. Applied Spectroscopy 55 (2001) 1425-1432
37. Hurburgh C.R., Rippke G.R., Heithoff C., Roussel S.A., and Hardy C.L. Detection of genetically modified grains by near-infrared spectroscopy. Proceedings PITTCON 2000-Science for the 21st Century, 12-17 March. New Orleans, USA (2000) 1431
38. Munck L., Pram Nielsen J., Møller B., Jacobsen S., Søndergaard I., Engelsen S.B., Nørgaard L., and Bro R. Exploring the phenotypic expression of a regulatory proteome-altering gene by spectroscopy and chemometrics. Analytica Chimica Acta 446 (2001) 171-186
39. Campbell M.R., Sykes J., and Glover D.V. Classification of single and double-mutant corn endosperm genotypes by near-infrared transmittance spectroscopy. Cereal Chemistry 77 (2000) 774-778
40. Murray I., Aucott L.S., and Pike I.H. Use of discriminant analysis on visible and near infrared reflectance spectra to detect adulteration of fish meal with meat and bone meal. Journal of Near Infrared Spectroscopy 9 (2001) 297-311
41. Myhr A.I., and Dalmo R.A. Introduction of genetic engineering in aquaculture: ecological and ethical implications for science and governance. Aquaculture 250 (2005) 542-554
42. Piferrer F., Blázquez M., Navarro L., and González A. Genetic, endocrine, and environmental components of sex determination and diVerentiation in the European sea bass (Dicentrarchus labrax L.). General and Comparative Endocrinology 142 (2005) 102-110
43. Swatland H.J. On line Evaluation of Meat (1995), Technomic Publishing Co., Lancaster, USA
44. Cozzolino D., Murray I., Paterson R., and Scaife J.R. Visible and near infrared reflectance spectroscopy for the determination of moisture, fat and protein in chicken breast and thigh muscle. Journal of Near Infrared Spectroscopy 4 (1996) 213-223
45. Cozzolino D., Murray I., Scaife J.R., and Paterson R. Study of dissected lamb muscles by visible and near infrared reflectance spectroscopy for composition assessment. Animal Science 70 (2000) 417-425
46. Cozzolino D., De Mattos D., and Vaz Martins D. Visible/near infrared reflectance spectroscopy for predicting composition and tracing system production of beef muscle. Animal Science 74 (2002) 477-484
47. Cozzolino D., and Murray I. Effect of sample presentation and animal muscle species on the analysis of meat by near infrared reflectance spectroscopy. Journal of Near Infrared Spectroscopy 10 (2002) 37-44
48. Pérez-Marín M.D., Garrido-Varo A., Guerrero J.E., Murray I., Puigdomenech A., Dardenne P., Baeten V., and Zegers J. Detection and quantification of mammalian meat and bone meal in compound feeding stuffs using NIR. Near infrared spectroscopy. In: Davies A.M.C., and Garrido-Varo A. (Eds). Proceedings of the 11th International Conference. Norfolk, UK (2004), NIR Publications 667-671
49. Rui Y., Luo Y., Haung K., Wang W., and Zhang L. Discrimination of transgenic corns using NIR diffuse reflectance spectroscopy and back propagation (BP). Spectroscopy and Spectral Analysis 25 (2005) 1581-1592
50. Osorio M.T., Zumalacarregui M., Prieto N., Giráldez F.J., Andrés S., and Mateo J. Differentiation between carcasses from suckling lambs reared with ewe milk or milk replacers by near infrared reflectance spectroscopy of perirenal fat. Small Ruminant Research 72 (2007) 221-226
51. Garrido-Varo A., Pérez-Marín M.D., Guerrero J.E., Gómez-Cabrera A., de la Haba M.J., Bautista J., Soldado A., Vicente F., Martínez A., de la Roza-Delgado B., and Termes S. Near infrared spectroscopy for enforcement of European legislation concerning the use of animal by-products in animal feeds. Biotechnology, Agronomy, Society and Environment 9 (2005) 3-9
52. Yamada T., Yeh T.F., Chang H.M., Li L., Kadla J.F., and Chiang V.C. Rapid analysis of transgenic trees using transmittance near-infrared spectroscopy (NIR). Holzforschung 60 (2006) 24-28
53. Chen Q., Zhao J., Fang C.H., and Wang D. Feasibility study on identification of green, black and Oolong teas using near-infrared reflectance spectroscopy based on support vector machine (SVM). Spectrochimica Acta Part A 66 (2007) 568-574
54. Xie L., Ying Y., Ying T., Yu H., and Fu X. Discrimination of transgenic tomatoes based on visible/near-infrared spectra. Analytica Chimica Acta 584 (2007) 379-384
55. Esteban-Diez I., González-Saiz J.M., Saenz-González C., and Pizarro C. Coffee varietal differentiation based on near infrared spectroscopy. Talanta 71 (2007) 221-229
56. Martn I.G., Álvarez-García N., González-Pérez C., and Villaescusa-García V. Determination of inorganic elements in animal feeds by NIRS technology and a fibre-optic probe. Talanta 69 (2006) 711-715
57. A. Garrido-Varo, V. Fernandez, NIRS technology for the quantitative prediction of the percentage of meat and bone meal added to a feed compound. A feasibility study, in: Jorgensen, J.S. (ed.), Dans Report of the Workshop Identification of animal ingredients in compound feeds, CEMA-DG VI-SMT Program, (1998) May 26, Lyngby, Denmark.