Wang Wei, Man Zun, Li Xiaolong, Chen Rongqin, You Zhengkai, Pan Tiantian, Dai Xiaorong, Xiao Hang, Liu Fei
Key Laboratory of Urban Environment and Health, Ningbo Observation and Research Station, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China; Zhejiang Key Laboratory of Urban Environmental Processes and Pollution Control, CAS Haixi Industrial Technology Innovation Center in Beilun, Ningbo 315830, China.
College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.
J Hazard Mater. 2023 May 5;449:131010. doi: 10.1016/j.jhazmat.2023.131010. Epub 2023 Feb 15.
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=0.9958) for Cd, competitive adaptive reweighted sampling-extreme learning machine (CARS-ELM) (R=0.9161) for SP and CARS-ELM (R=0.9021) for MDA, all with R 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.
根系是影响谷物中镉积累的重要器官，但目前尚无关于镉胁迫下水稻根系表型的全面研究。为了评估镉对根系表型的影响，本文研究了包括镉积累、逆境生理、形态参数和微观结构特征等表型信息的响应机制，并探索了镉积累和逆境生理的快速检测方法。我们发现镉对根系表型具有“低促高抑”的作用。此外，基于光谱技术和化学计量学实现了对镉（Cd）、可溶性蛋白（SP）和丙二醛（MDA）的快速检测，其中最优预测模型分别为基于全光谱的最小二乘支持向量机（LS-SVM）（镉的R = 0.9958）、竞争性自适应重加权采样-极限学习机（CARS-ELM）（可溶性蛋白的R = 0.9161）和CARS-ELM（丙二醛的R = 0.9021），所有R值均高于0.9。令人惊讶的是，整个过程仅需约3分钟，与实验室分析相比检测时间减少了90%以上，证明了光谱技术在根系表型检测方面的卓越能力。这些结果揭示了对重金属的响应机制，并为表型信息提供了快速检测方法，这对作物重金属控制和食品安全监管具有重要意义。
