Screening GC-MS data for carbamate pesticides with temperature-constrained-cascade correlation neural networks
Screening GC-MS data for carbamate pesticides with temperature-constrained-cascade correlation neural networks
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DOI：
10.1016/s0003-2670(99)00865-x
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发表时间：
2000-03-09
影响因子：
6.2
通讯作者：
Harrington, PD
中科院分区：
文献类型：
--
作者：
Wan, CH;Harrington, PD
芳香氨基甲酸酯类农药是重要的农用化学品。利用温度约束级联相关网络（TC-CCNs）建立了氨基甲酸酯及其子结构的质谱分类模型。氨基甲酸酯分类器应用于GC-MS运行的质谱扫描。根据参考质谱和实验质谱建立了分类模型。比较了单输出的多网络模型和多输出的单网络模型的不同网络配置。通过使用拉丁分区方法，将随机划分训练集和预测集引起的主要变异源降低了一个数量级。该方法也为比较分类方法提供了精度尺度。具有单个输出的多个网络通常比具有多个输出的单个网络预测更好。在一项研究中，分层单输出网络的分类准确率超过98%。TC-CCN模型优于k近邻（KNN）和判别偏最小二乘（DPLS）参考方法。(C) 2000 Elsevier Science B.V.版权所有
Aromatic carbamate pesticides are important agrochemicals. Mass spectral classification models were built for carbamates and their substructures using temperature-constrained-cascade correlation networks (TC-CCNs). The carbamate classifier was applied to the mass spectral scans of a GC-MS run. The classification models were built from reference and experimental mass spectra. Different network configurations were compared that used multiple network models with single outputs and single networks with multiple outputs. A major source of variation caused by randomly partitioning the training and prediction sets was reduced by an order of magnitude by using a method of Latin-partitions. This method also furnished a precision measure for comparing classification methods. Multiple networks with single outputs generally predicted better than single networks with multiple outputs. Hierarchical single output networks achieved better than 98% classification accuracy in one study. The TC-CCN models compared favorably to the K-nearest neighbors (KNN) and discriminant partial least squares (DPLS) reference methods. (C) 2000 Elsevier Science B.V. All rights reserved.