文献类型：期刊文献

英文题名：Prediction and Utilization of Malondialdehyde in Exotic Pine Under Drought Stress Using Near-Infrared Spectroscopy

作者：Zhang, Yini[1] Luan, Qifu[1] Jiang, Jingmin[1] Li, Yanjie[1]

第一作者：Zhang, Yini

通信作者：Luan, QF[1];Li, YJ[1]

机构：[1]Chinese Acad Forestry, Res Inst Subtrop Forestry, Fuyang, Peoples R China

年份：2021

卷号：12

外文期刊名：FRONTIERS IN PLANT SCIENCE

收录：;Scopus(收录号：2-s2.0-85118646482);WOS:【SCI-EXPANDED(收录号:WOS:000715245000001)】；

基金：Funding This study was supported by the Fundamental Research Funds of CAF (CAFYBB2020SY008), the National Natural Science Foundation of China (31901323), Fundamental Research Funds of Chinese Forestry Academy (CAFYBB2017ZA001-2-1), and Zhejiang Science and Technology Major Program on Agricultural New Variety Breeding (2021C02070-8).

语种：英文

外文关键词：model calibration; abiotic stress; NIR spectroscopy; non-destructive; pine tree

摘要：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 (Pinus elliottii) and loblolly pine (Pinus taeda), 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 via 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 (R-2) 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.