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Predicting Toxicity of Complex Mixtrues by Artificial Neural Networks

机译：通过人工神经网络预测复杂混音的毒性

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Industrial and municipal wastewaters constitute major sources of contamination of the aquatic compartment and represent a threat to the aquatic life. Artificial neural networks, based on three different learning paradigms, were sturied to predict the acute toxicity of trouts (5 days exposure to wastewaters) with the inputs of two simple microbiotests which requires only 5 and 15 min incubation time. These microbiotests wer 1) the chemoluminescent peroxidase (Cl-Per) assay which can detect radical scavengers and enzyme-inhibiting substances, and 2) the bacterial luminescent toxicity test (Microtox~TM) which is responsive to toxic substances affecting the vital function of bacteria. The responses obtained with the the trout bioassay, the Cl-Per and the Microtox~TM Microbiotests were analyzed for statistical correlations (Pearson-moment correlation), unsupervised learning by self-organizing network, and assisted learning by the backpropagation and the Hopfield (probabilistic) paradigms. The results showed that no significant correlation (p<0.05) was obtained between either the responses obtained with Cl-Per (p = 0.121) or the Microtox~TM (p = 0.061) microbiotests with the ones obtained with the trout bioassay. The self-organizing network was able to identify by itself a maximum number of 5 classes that were more or less related to fish toxicity: class 1 contained 2 samples that were toxic to fish, class 2 contained 2/3 samples that were toxic to fish, class 3 showed 6/8 samples that were not toxic, class 4 contained 5/6 samples that were non-toxic and class 5 identified one sample that was toxic. Supervised learning with backpropagation analysis yielded 2 kinds of networks that proved promising. The first one ,was able to predict the actual toxic wastewater concentration with an overall performance of 65
机译：工业和城市污水占水生环境中的污染的主要来源，并代表水生生物构成威胁。人工神经网络，基于三个不同的学习范例，被sturied预测鳟鱼的两个简单的microbiotests仅需要5至15分钟的温育时间的输入的急性毒性（5天暴露于废水）。这些microbiotests WER 1）化学发光的过氧化物酶（CL-PER）测定法，其可检测的自由基清除剂和酶抑制物质，和2）的细菌发光毒性试验（了Microtox〜TM），它是响应于影响细菌的生命机能的有毒物质。与鳟鱼生物分析得到的答复是，C-PER和了Microtox〜TM Microbiotests进行分析的统计相关性（皮尔逊矩相关），无监督学习的自组织网络，并协助通过反传化和Hopfield（概率学习）范例。结果表明要么有Cl-PER（P = 0.121）或了Microtox〜TM（p值= 0.061）和拥有鳟鱼生物测定所获得的那些microbiotests获得的响应之间没有获得显著相关（p <0.05）。自组织网络是能够通过自身来识别5类，或多或少相关的鱼类毒性的最大数目：1类包含2个样品均认为有毒的鱼，类2含有2/3样本那名对鱼有毒，类3显示6/8的样品这还有毒，类4含有5/6样本那名无毒和类5中确定这是有毒的一个样本。反向传播分析监督学习得到2-类型的网络被证明有前途的。第一种，是能够以65的总体性能来预测实际的有毒废水浓度

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