School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China; Intelligent Control and Compute Vision Lab, Hefei University, Hefei 230601, China.
School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China.
Food Chem. 2016 Nov 1;210:415-21. doi: 10.1016/j.foodchem.2016.04.117. Epub 2016 Apr 26.
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.
对转基因生物的鉴别越来越多地受到世界范围内立法和消费者的要求。本研究采用太赫兹光谱成像系统结合化学计量学方法，探讨了从非转基因水稻种子中无损鉴别转基因水稻种子的可行性。基于基因型，利用主成分分析（PCA）、最小二乘支持向量机（LS-SVM）、主成分分析-反向传播神经网络（PCA-BPNN）和随机森林（RF）模型，并结合一阶和二阶导数以及标准正态变量变换（SNV）预处理，对水稻种子进行分类。结果表明，非转基因和转基因水稻种子之间存在差异，使用 RF 模型结合一阶导数预处理可以实现优异的分类（预测集准确率为 96.67%）。结果表明，太赫兹光谱成像结合化学计量学是一种高效、无需样品制备即可识别转基因水稻种子的有前途的技术。
