Beijing Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China; Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture, Beijing 100097, China.
Beijing Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China; Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture, Beijing 100097, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2018 Jul 5;200:186-194. doi: 10.1016/j.saa.2018.04.026. Epub 2018 Apr 13.
Rapid and visual detection of the chemical compositions of plant seeds is important but difficult for a traditional seed quality analysis system. In this study, a custom-designed line-scan Raman hyperspectral imaging system was applied for detecting and displaying the main chemical compositions in a heterogeneous maize seed. Raman hyperspectral images collected from the endosperm and embryo of maize seed were acquired and preprocessed by Savitzky-Golay (SG) filter and adaptive iteratively reweighted Penalized Least Squares (airPLS). Three varieties of maize seeds were analyzed, and the characteristics of the spectral and spatial information were extracted from each hyperspectral image. The Raman characteristic peaks, identified at 477, 1443, 1522, 1596 and 1654 cm from 380 to 1800 cm Raman spectra, were related to corn starch, mixture of oil and starch, zeaxanthin, lignin and oil in maize seeds, respectively. Each single-band image corresponding to the characteristic band characterized the spatial distribution of the chemical composition in a seed successfully. The embryo was distinguished from the endosperm by band operation of the single-band images at 477, 1443, and 1596 cm for each variety. Results showed that Raman hyperspectral imaging system could be used for on-line quality control of maize seeds based on the rapid and visual detection of the chemical compositions in maize seeds.
快速、可视化地检测植物种子的化学成分对于传统的种子质量分析系统来说是一项重要但具有挑战性的任务。在本研究中，我们应用了一个定制的线扫描拉曼高光谱成像系统，用于检测和显示异质玉米种子中的主要化学成分。从玉米种子的胚乳和胚中采集拉曼高光谱图像，并通过 Savitzky-Golay（SG）滤波器和自适应迭代重加权惩罚最小二乘法（airPLS）进行预处理。分析了三个品种的玉米种子，并从每个高光谱图像中提取了光谱和空间信息的特征。在 380 到 1800 cm 的拉曼光谱中，从 477、1443、1522、1596 和 1654 cm 处识别出的拉曼特征峰分别与玉米淀粉、油和淀粉混合物、玉米黄质、木质素和油有关。对应于特征带的每个单波段图像成功地描述了种子中化学成分的空间分布。通过对每个品种的 477、1443 和 1596 cm 处的单波段图像进行波段操作，将胚与胚乳区分开来。结果表明，拉曼高光谱成像系统可用于基于玉米种子化学成分的快速可视化检测对玉米种子进行在线质量控制。
