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

doi:10.1016/S1672-6308(14)60291-2

Abstract

Abstract:

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.

Key words: near infrared reflectance spectroscopy, genetically-modified food, regulation gene, protein gene, partial least squares regression discrimiant analysis

Long Zhang, Shan-shan Wang, Yan-fei Ding, Jia-rong Pan, Cheng Zhu. Discrimination of Transgenic Rice Based on Near Infrared Reflectance Spectroscopy and Partial Least Squares Regression Discriminant Analysis[J]. Rice Science, 2015, 22(5): 245-249.

Figures/Tables 7

Fig. 1. Standard normal variance pretreated spectra of rice grains.（ZH11, Zhonghua 11.）

Fig. 2. Variation explained by principal component (PC) in principal component analysis.

Fig. 3. Plots of the first and second principal components (PCs).

Table 1 Effects of different wavenumber range on classification results of partial least squares regression discrimiant analysis models between transgenic and wild type rice.

Wavenumber	Calibration					Validation
(cm^-1)	No. of PCs	RMSEC	r²_c	CR_c(%)		No. of PCs	RMSEV	r²_v	CR_v (%)
4 000-8 000	5	0.3555	0.8578	100	5		0.3543	0.8344	100
4 000-10 000	6	0.2695	0.9183	100	6		0.2878	0.8979	100

Table 2 Effects of different wavenumber range on classification results of partial least squares regression discrimiant analysis models between tctp and mi166.

Wavenumber	Calibration					Validation
(cm^-1)	No. of PCs	RMSEC	r²_c	CR_c(%)		No. of PCs	RMSEV	r²_v	CR_v (%)
4 000-8 000	5	0.3659	0.8661	97.5	5		0.3799	0.8123	98.3
4 000-10 000	5	0.2945	0.9133	97.5	5		0.3873	0.7537	100

Fig. 4. Regression plot of designed and prediction category variables of transgenic (TCTP and mi166) and wild type (Zhonghua 11) rice.

Fig. 5. Regression plot of reference and prediction category variables of TCTP and mi166.

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