Jiang Yuying, Ge Hongyi, Lian Feiyu, Zhang Yuan, Xia Shanhong
State Key Laboratory of Transducer Technology, Institute of Electronics, Chinese Academy of Sciences, Beijing 100080, China.
University of Chinese Academy of Sciences, Beijing 100080, China.
Sci Rep. 2016 Feb 19;6:21299. doi: 10.1038/srep21299.
In this paper, we propose a feasible tool that uses a terahertz (THz) imaging system for identifying wheat grains at different stages of germination. The THz spectra of the main changed components of wheat grains, maltose and starch, which were obtained by THz time spectroscopy, were distinctly different. Used for original data compression and feature extraction, principal component analysis (PCA) revealed the changes that occurred in the inner chemical structure during germination. Two thresholds, one indicating the start of the release of α-amylase and the second when it reaches the steady state, were obtained through the first five score images. Thus, the first five PCs were input for the partial least-squares regression (PLSR), least-squares support vector machine (LS-SVM), and back-propagation neural network (BPNN) models, which were used to classify seven different germination times between 0 and 48 h, with a prediction accuracy of 92.85%, 93.57%, and 90.71%, respectively. The experimental results indicated that the combination of THz imaging technology and chemometrics could be a new effective way to discriminate wheat grains at the early germination stage of approximately 6 h.
在本文中，我们提出了一种可行的工具，该工具使用太赫兹（THz）成像系统来识别处于不同发芽阶段的小麦籽粒。通过太赫兹时间光谱法获得的小麦籽粒主要变化成分麦芽糖和淀粉的太赫兹光谱明显不同。主成分分析（PCA）用于原始数据压缩和特征提取，揭示了发芽过程中内部化学结构发生的变化。通过前五张得分图像获得了两个阈值，一个表示α-淀粉酶开始释放的时间，另一个表示其达到稳态的时间。因此，将前五个主成分输入到偏最小二乘回归（PLSR）、最小二乘支持向量机（LS-SVM）和反向传播神经网络（BPNN）模型中，这些模型用于对0至48小时之间的七个不同发芽时间进行分类，预测准确率分别为92.85%、93.57%和90.71%。实验结果表明，太赫兹成像技术和化学计量学的结合可能是一种在大约6小时的早期发芽阶段鉴别小麦籽粒的新有效方法。
