Prediction of active ingredients in Salvia miltiorrhiza Bunge. based on soil elements and artificial neural network.
Prediction of active ingredients in Salvia miltiorrhiza Bunge. based on soil elements and artificial neural network.
复制标题
预测萨尔维亚米尔蒂奥尔兹·布吉（Salvia Miltiorrhiza Bunge）中的活性成分。基于土壤元素和人工神经网络。
作者：
Liu Y;Wang K;Yan ZY;Shen X;Yang X
丹参的根。常用于治疗心血管疾病，丹参酮和丹参酸是其主要活性成分。然而，S.丹参种植在不同地区的土壤环境也有较大差异，这给人工种植的规模化和标准化增加了新的困难。因此，在本研究中，我们测定了S的根中的活性成分。以我国8省25个产区的丹参根际土壤元素含量为研究对象，建立了基于BP神经网络的丹参根际土壤元素含量预测模型。结果表明，有效成分与土壤常量营养元素和微量元素具有不同程度的相关性，预测模型的预测效果最好（MSE = 0.0203，0.0164; R2 = 0.93，0.94）。人工神经网络模型可用于筛选适宜的栽培地点和合理施肥。它还可以用于优化特定地点的施肥。建议对药用植物进行测土配方施肥。因此，我们应该大力推广多种来源的丹参，而不是大规模推广使用“专用肥”。因此，该模型有助于高效、合理、科学地指导番茄栽培中的施肥管理。丹参。
The roots of Salvia miltiorrhiza Bunge. are commonly used in the treatment of cardiovascular diseases, and tanshinones and salvianolic acids are its main active ingredients. However, the composition and content of active ingredients of S. miltiorrhiza planted in different regions of the soil environment are also quite different, which adds new difficulties to the large-scale and standardization of artificial cultivation. Therefore, in this study, we measured the active ingredients in the roots of S. miltiorrhiza and the contents of rhizosphere soil elements from 25 production areas in eight provinces in China, and used the data to develop a prediction model based on BP (back propagation) neural network. The results showed that the active ingredients had different degrees of correlation with soil macronutrients and trace elements, the prediction model had the best performance (MSE = 0.0203, 0.0164; R2 = 0.93, 0.94). The artificial neural network model was shown to be a method that can be used to screen the suitable cultivation sites and proper fertilization. It can also be used to optimize the fertilizer application at specific sites. It also suggested that soil testing formula fertilization should be carried out for medicinal plants like S. miltiorrhiza, which is grown in multiple origins, rather than promoting the use of “special fertilizer” on a large scale. Therefore, the model is helpful for efficient, rational, and scientific guidance of fertilization management in the cultivation of S. miltiorrhiza.
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影响因子：
64.8
作者：
RUMELHART, DE;HINTON, GE;WILLIAMS, RJ
通讯作者：
WILLIAMS, RJ
影响因子：
4.3
作者：
Gago, J.;Martinez-Nunez, L.;Gallego, P. P.
通讯作者：
Gallego, P. P.
影响因子：
4.6
作者：
Su Chun-Yan;Ming Qian-Liang;Qin Lu-Ping
通讯作者：
Qin Lu-Ping
影响因子：
3.3
作者：
Huang, ZH;Shimeld, J;Katsube, J
通讯作者：
Katsube, J
DOI：
10.1007/978-3-030-24716-4_1
发表时间：
2019-01-01
期刊：
SALVIA MILTIORRHIZA GENOME
影响因子：
--
作者：
Lu, Shanfa
通讯作者：
Lu, Shanfa