Unverified paper record
Response mechanism and rapid detection of phenotypic information in rice root under heavy metal stress.
Journal of hazardous materials · 15 Feb 2023 · 10.1016/j.jhazmat.2023.131010
Abstract
The root is an important organ affecting cadmium accumulation in grains, but there is no comprehensive research involving rice root phenotype under cadmium stress yet. To assess the effect of cadmium on root phenotypes, this paper investigated the response mechanism of phenotypic information including cadmium accumulation, adversity physiology, morphological parameters, and microstructure characteristics, and explored rapid detection methods of cadmium accumulation and adversity physiology. We found that cadmium had the effect of "low-promotion and high-inhibition" on root phenotypes. In addition, the rapid detection of cadmium (Cd), soluble protein (SP), and malondialdehyde (MDA) were achieved based on spectroscopic technology and chemometrics, where the optimal prediction model was least squares support vector machine (LS-SVM) based on the full spectrum (R p =0.9958) for Cd, competitive adaptive reweighted sampling-extreme learning machine (CARS-ELM) (R p =0.9161) for SP and CARS-ELM (R p =0.9021) for MDA, all with R p higher than 0.9. Surprisingly, it took only about 3 min, which was more than 90% reduction in detection time compared with laboratory analysis, demonstrating the excellent ability of spectroscopy for root phenotype detection. These results reveal response mechanism to heavy metal and provide rapid detection method for phenotypic information, which can substantially contribute to crop heavy metal control and food safety supervision.
Plant phenotyping relevance
根のストレス状態に関する表現型情報を、分光計測とケモメトリクスで迅速推定する手法を開発・評価しており、方法論が研究の中心である。
abstractthe rapid detection of cadmium (Cd), soluble protein (SP), and malondialdehyde (MDA) were achieved based on spectroscopic technology and chemometrics
abstractthe optimal prediction model was least squares support vector machine (LS-SVM) based on the full spectrum (R p =0.9958) for Cd, competitive adaptive reweighted sampling-extreme learning machine (CARS-ELM) (R p =0.9161) for SP and CARS-ELM (R p =0.9021) for MDA
abstractdemonstrating the excellent ability of spectroscopy for root phenotype detection
Code and data availability
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