Zhang Tingting, Fan Shuxiang, Xiang Yingying, Zhang Shujie, Wang Jianhua, Sun Qun
Department of Plant Genetics and Breeding, College of Agriculture, China Agricultural University/Beijing Key Laboratory of Crop Genetic Improvement/The Innovation Center (Beijing) of Crop Seed Sciences Ministry of Agriculture, Beijing 100193, China.
Beijing Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2020 Oct 5;239:118488. doi: 10.1016/j.saa.2020.118488. Epub 2020 May 16.
Two hyperspectral imaging (HSI) systems, visible/near infrared (Vis/NIR, 304-1082 nm) and short wave infrared (SWIR, 930-2548 nm), were used for the first time to comprehensively predict the changes in quality of wheat seeds based on three vigour parameters: germination percentage (GP, reflecting the number of germinated seedling), germination energy (GE, reflecting the speed and uniformity of seedling emergence), and simple vigour index (SVI, reflecting germination percentage and seedling weight). Each sample contained a small number of wheat seeds, which were obtained by high temperature and humidity-accelerated aging (0, 2, and 3 days) to simulate storage. The spectra of these samples were collected using HSI systems. After collection, each seed sample underwent a standard germination test to determine their GP, GE, and SVI. Then, several pretreatment methods and the partial least-squares regression algorithm (PLS-R) were used to establish quantitative models. The models for the Vis/NIR region obtained excellent performance, and most effective wavelengths (EWs) were selected in the Vis/NIR region by the successive projections algorithm (SPA) and regression coefficients (RC). Subsequently, PLS-R-RC models using selected wavebands (sixteen wavebands for GP, 14 wavebands for GE, and 16 wavebands for SVI) exhibited similar performance to the PLS-R models based on the full wavebands. The best R results obtained in the simplified models' prediction sets were 0.921, 0.907, and 0.886, with RMSE values of 4.113%, 5.137%, and 0.024, for GP, GE, and SVI, respectively. Distribution maps of GP, GE, and SVI were produced by applying these simplified PLS models. By interpreting the EWs and building prediction models, soluble protein and sugar content were demonstrated to have a relationship with spectral information. In summary, the present results lay a foundation towards the development of a significantly simpler, more comprehensive, and non-destructive hyperspectral-based sorting system for determining the vigour of wheat seeds.
首次使用两个高光谱成像（HSI）系统，即可见/近红外（Vis/NIR，304 - 1082纳米）和短波红外（SWIR，930 - 2548纳米），基于三个活力参数全面预测小麦种子质量的变化：发芽率（GP，反映发芽幼苗的数量）、发芽势（GE，反映幼苗出土的速度和均匀性）和简单活力指数（SVI，反映发芽率和幼苗重量）。每个样本包含少量小麦种子，这些种子通过高温高湿加速老化（0、2和3天）获得以模拟储存。使用HSI系统收集这些样本的光谱。收集后，每个种子样本进行标准发芽试验以确定其GP、GE和SVI。然后，使用几种预处理方法和偏最小二乘回归算法（PLS - R）建立定量模型。Vis/NIR区域的模型表现出色，通过连续投影算法（SPA）和回归系数（RC）在Vis/NIR区域选择了最有效的波长（EWs）。随后，使用选定波段（GP为16个波段，GE为14个波段，SVI为16个波段）的PLS - R - RC模型表现出与基于全波段的PLS - R模型相似的性能。简化模型预测集中获得的最佳R结果分别为0.921、0.907和0.886，GP、GE和SVI的RMSE值分别为4.113%、5.137%和0.024。通过应用这些简化的PLS模型生成了GP、GE和SVI的分布图。通过解释EWs并建立预测模型，证明可溶性蛋白质和糖含量与光谱信息有关。总之，目前的结果为开发一种显著更简单、更全面且无损的基于高光谱的分选系统以确定小麦种子活力奠定了基础。
