基于BP神经网络的虾蛄捕捞海域溯源方法

虾蛄是重要的经济水产品,对环境中重金属元素具有富集作用。不同海域捕捞的虾蛄体内重金属含量差异较大,因此,可根据虾蛄体内重金属含量推断捕捞海域的污染状况,即基于虾蛄体内重金属状况对其来源进行溯源。以渤海、东海和南海三大海域虾蛄中的3种重金属含量数据作为输入,建立BP神经网络判别分析模型,并对模型进行优化,通过模型判断虾蛄样本的来源海域。结果显示:经过网络训练后,总计90个样本中,86个分类正确,模型的判别准确率为95.6%,其中训练集判别准确率为98.1%,验证集准确率为94.4%,测试集准确率为88.9%。研究表明:基于BP神经网络建立的判别分析模型能够解析非线性复杂体系中各元素的内在关联,以区分样品的来源,并可据此进行有效的追溯。

A method for tracing the original fishing sea areas for the corresponding mantis shrimps based on BP neural network

Mantis shrimp is one of the important economic aquatic products in China, which enriches the heavy metal elements in the environment.There is a significant difference in contents of heavy metal elements among mantis shrimps in different sea areas.And thus that the pollution status of fishing sea areas could be inferred according to the contents of heavy metal elements in the corresponding mantis shrimps.That is to say, the origin sea area of shrimp mantis can be traced by the contents of heavy metal elements in it.The data of the contents of the three kinds of heavy metals in mantis shrimps from the corresponding three sea areas were used as the input to create a BP neural network discriminant analysis model, which can discriminate the origin sea area of the corresponding mantis shrimp samples after the well optimization of the models.The result showed, using the trained neural network, 86 samples among a total of 90 can be discriminated correctly, the accuracy rate is 95.6%, of which, the accuracy rate by using the training sets is 98.1%, the accuracy rate by using the validation set is 94.4%, and the accuracy rate by using the test set is 88.9%.The results showed that the discriminatory analysis model based on BP neural network model can be used to analyses the relationship among the different elements with the nonlinear complex systems, so as to discriminate the samples and trace them effectively.