AUTHOR=Zhang Yini , Luan Qifu , Jiang Jingmin , Li Yanjie 

TITLE=Prediction and Utilization of Malondialdehyde in Exotic Pine Under Drought Stress Using Near-Infrared Spectroscopy

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 12 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2021.735275

DOI=10.3389/fpls.2021.735275

ISSN=1664-462X

ABSTRACT=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, can be used as a drought indicator to evaluate the degree of plasma membrane damage and the ability of plants to tolerance drought stress. Yet measuring malondialdehyde is usually a labor- and time-consuming task. Here near-infrared spectroscopy (NIRS) combined with partial least squares (PLS) was used to obtain rapid and high-throughput measurements of malondialdehyde and the application of this technique to plant drought stress experiments also investigated. Two exotic conifer tree species, slash pine (Pinus elliottii) and loblolly pine (P. taeda) were used as plant material exposed to drought stress; different types of spectra pre-processing methods and important feature-selection algorithms were applied to the PLS model to calibrate it and obtain the best malondialdehyde-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). This model has a respectable predictive capability, with an R² of 0.6565 and an RMSE of 2.2754%, and it was successfully implemented in drought stress experiments as a reliable and non-destructive method to detect the malondialdehyde content in real time.