Zhang Yini, Luan Qifu, Jiang Jingmin, Li Yanjie
Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Fuyang, China.
Front Plant Sci. 2021 Oct 18;12:735275. doi: 10.3389/fpls.2021.735275. eCollection 2021.
Drought is a major abiotic stress that adversely affects the growth and productivity of plants. Malondialdehyde (MDA), a substance produced by membrane lipids in response to reactive oxygen species (ROS), can be used as a drought indicator to evaluate the degree of plasma membrane damage and the ability of plants to drought stress tolerance. Still measuring MDA is usually a labor- and time-consuming task. In this study, near-infrared (NIR) spectroscopy combined with partial least squares (PLS) was used to obtain rapid and high-throughput measurements of MDA, and the application of this technique to plant drought stress experiments was also investigated. Two exotic conifer tree species, namely, slash pine () and loblolly pine (), were used as plant material exposed to drought stress; different types of spectral preprocessing methods and important feature-selection algorithms were applied to the PLS model to calibrate it and obtain the best MDA-predicting model. The results show that the best PLS model is established the combined treatment of detrended variable-significant multivariate correlation algorithm (DET-sMC), where latent variables (LVs) were 6. This model has a respectable predictive capability, with a correlation coefficient ( ) of 0.66, a root mean square error (RMSE) of 2.28%, and a residual prediction deviation (RPD) of 1.51, and it was successfully implemented in drought stress experiments as a reliable and non-destructive method to detect the MDA content in real time.
干旱是一种主要的非生物胁迫，对植物的生长和生产力产生不利影响。丙二醛（MDA）是膜脂响应活性氧（ROS）产生的一种物质，可作为干旱指标来评估质膜损伤程度和植物对干旱胁迫的耐受能力。然而，测量MDA通常是一项耗时费力的任务。在本研究中，近红外（NIR）光谱结合偏最小二乘法（PLS）用于快速高通量测量MDA，并研究了该技术在植物干旱胁迫实验中的应用。两种外来针叶树种，即湿地松（）和火炬松（），被用作遭受干旱胁迫的植物材料；将不同类型的光谱预处理方法和重要特征选择算法应用于PLS模型进行校准，以获得最佳的MDA预测模型。结果表明，采用去趋势变量显著多元相关算法（DET-sMC）联合处理建立了最佳PLS模型，其中潜在变量（LVs）为6。该模型具有良好的预测能力，相关系数（）为0.66，均方根误差（RMSE）为2.28%，残差预测偏差（RPD）为1.51，并且作为一种可靠的无损方法成功应用于干旱胁迫实验中，可实时检测MDA含量。
