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Discrimination of Transgenic Rice Based on Near Infrared Reflectance Spectroscopy and Partial Least Squares Regression Discriminant Analysis 被引量:7

Discrimination of Transgenic Rice Based on Near Infrared Reflectance Spectroscopy and Partial Least Squares Regression Discriminant Analysis
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摘要 Near infrared reflectance spectroscopy (NIRS), a non-destructive measurement technique, was combined with partial least squares regression discrimiant analysis (PLS-DA) to discriminate the transgenic (TCTP and mi166) and wild type (Zhonghua 11) rice. Furthermore, rice lines transformed with protein gene (OsTCTP) and regulation gene (Osmi166) were also discriminated by the NIRS method. The performances of PLS-DA in spectral ranges of 4 000-8 000 cm-1 and 4 000-10 000 cm-1 were compared to obtain the optimal spectral range. As a result, the transgenic and wild type rice were distinguished from each other in the range of 4 000-10 000 cm-1, and the correct classification rate was 100.0% in the validation test. The transgenic rice TCTP and mi166 were also distinguished from each other in the range of 4 000-10 000 cm-1, and the correct classification rate was also 100.0%. In conclusion, NIRS combined with PLS-DA can be used for the discrimination of transgenic rice. Near infrared reflectance spectroscopy (NIRS), a non-destructive measurement technique, was combined with partial least squares regression discrimiant analysis (PLS-DA) to discriminate the transgenic (TCTP and mi166) and wild type (Zhonghua 11) rice. Furthermore, rice lines transformed with protein gene (OsTCTP) and regulation gene (Osmi166) were also discriminated by the NIRS method. The performances of PLS-DA in spectral ranges of 4 000-8 000 cm-1 and 4 000-10 000 cm-1 were compared to obtain the optimal spectral range. As a result, the transgenic and wild type rice were distinguished from each other in the range of 4 000-10 000 cm-1, and the correct classification rate was 100.0% in the validation test. The transgenic rice TCTP and mi166 were also distinguished from each other in the range of 4 000-10 000 cm-1, and the correct classification rate was also 100.0%. In conclusion, NIRS combined with PLS-DA can be used for the discrimination of transgenic rice.
出处 《Rice science》 SCIE CSCD 2015年第5期245-249,共5页 水稻科学(英文版)
基金 supported by the projects under the Innovation Team of the Safety Standards and Testing Technology for Agricultural Products of Zhejiang Province, China (Grant No.2010R50028) the National Key Technologies R&D Program of China during the 11th Five-Year Plan Period (Grant No.2006BAK02A18)
关键词 near infrared reflectance spectroscopy genetically-modified food regulation gene protein gene partial least squares regression discrimiant analysis near infrared reflectance spectroscopy genetically-modified food regulation gene protein gene partial least squares regression discrimiant analysis
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  • 1Ahmed F E. 2000. Molecular markers for early cancer detection. J Environ Sci Health Part C, 18(2): 75-125.
  • 2Akiyama H, Sasaki N, Sakata K, Ohmori K, Toyota A, Kikuchi Y, Watanabe T, Furui S, Kitta K, Maitani T. 2007. Indicated detection of two unapproved transgenic rice lines contaminating vermicelli products. JAgric Food Chem, 55(15): 5942-5947.
  • 3Alishahi A, Farahmand H, Pneto N, Cozzolino D. 2010. Identification of transgenic foods using NIR spectroscopy: A review. Spectrochimica Acta Part A, 75: 1-7.
  • 4Anklam E, Gadani F, Heinze P, Pijnenburg H, van den Eede G. 2002. Analytical methods for detection and determination of genetically modified organisms in agricultural crops and plant-derived food products. Eur Food Res Technol, 214: 3-26.
  • 5Broomhead D S, Lowe D. 1988. Multi-variable functional interpolation and adaptive networks. Comp Syst, 2(3): 269-303.
  • 6Campbell M R, Sykes J, Glover D V. 2000. Classification of single and double-mutant corn endosperm genotypes by near-infrared transmittance spectroscopy. Cereal Chem, 77: 774-778.
  • 7Ding Y F, Chen Z, Zhu C. 2011. Microarray-based analysis of cadmium-responsive microRNAs in rice (Oryza sativa). J Exp Bot, 62(10): 3563-3573.
  • 8Fagan J, Schoel B, Haegert A, Moore J, Beeby J. 2001. Performance assessment under field conditions of a rapid immunological test for transgenic soybeans. Int JFood Sci Technol, 36:1 11.
  • 9Grohmann L, Mide D. 2009. Detection of genetically modified rice: Collaborative validation study of a construct-specific real-time PCR method for detection of transgenic Bt rice. Eur FoodRes Technol, 228(3): 497- 500.
  • 10Heid C A, Stevens J, Livak K J, Williams P M. 1996. Real-time quantitative PCR. Genome Res, 6: 986-994.

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