Early detection of germinated wheat grains using terahertz image and chemometrics.
Early detection of germinated wheat grains using terahertz image and chemometrics.
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DOI：
10.1038/srep21299
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发表时间：
2016-02-19
影响因子：
4.6
通讯作者：
Xia S
中科院分区：
文献类型：
--
作者：
Jiang Y;Ge H;Lian F;Zhang Y;Xia S
在本文中，我们提出了一种可行的工具，使用太赫兹（THz）成像系统来识别不同发芽阶段的小麦籽粒。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存在明显差异。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。通过前五张评分图像获得两个阈值，一个表示 α-淀粉酶释放的开始，第二个表示其达到稳定状态。因此，前 5 个 PC 输入偏最小二乘回归（PLSR）、最小二乘支持向量机（LS-SVM）和反向传播神经网络（BPNN）模型，用于对 0 至 48 h 之间的 7 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，太赫兹成像技术与化学计量学的结合可能成为在发芽初期（约6小时）区分小麦籽粒的一种新的有效方法。
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.