Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7588341 | Food Chemistry | 2016 | 7 Pages |
Abstract
Discrimination of genetically modified organisms is increasingly demanded by legislation and consumers worldwide. The feasibility of a non-destructive discrimination of transgenic rice seeds from its non-transgenic counterparts was examined by terahertz spectroscopy imaging system combined with chemometrics. Principal component analysis (PCA), least squares support vector machines (LS-SVM), PCA-back propagation neural network (PCA-BPNN), and random forest (RF) models with the first and second derivative and standard normal variate transformation (SNV) pre-treatments were applied to classify rice seeds based on genotype. The results demonstrated that differences between non-transgenic and transgenic rice seeds did exist, and an excellent classification (accuracy was 96.67% in the prediction set) could be achieved using the RF model combined with the first derivative pre-treatment. The results indicated that THz spectroscopy imaging together with chemometrics would be a promising technique to identify transgenic rice seeds with high efficiency and without any sample preparation.
Related Topics
Physical Sciences and Engineering
Chemistry
Analytical Chemistry
Authors
Wei Liu, Changhong Liu, Xiaohua Hu, Jianbo Yang, Lei Zheng,