在这项研究中,目标是开发一种检测和分类水体中有机磷农药(OPP)的方法。为每种有机磷农药制备了65个不同浓度的样品,即毒死蜱,乙酰甲胺磷,甲基对硫磷,敌百虫,敌敌畏,profenofos,马拉硫磷,乐果,fenthion,和辛硫磷,分别。首先,所有样品的光谱数据均使用紫外可见光谱仪获得。其次,五种预处理方法,六种流形学习方法,并利用五种机器学习算法建立了识别水体中OPP的检测模型。研究结果表明,在使用卷积平滑+一阶导数(SG+FD)预处理的数据上训练的机器学习模型的准确性优于在使用其他方法预处理的数据上训练的模型。反向传播神经网络(BPNN)模型的准确率最高,达到99.95%,其次是支持向量机(SVM)和卷积神经网络(CNN)模型,均为99.92%。极限学习机(ELM)和K最近邻(KNN)模型的准确率分别为99.84%和99.81%。分别。为了降维的目的,将流形学习算法应用于全波长数据集之后,然后将数据在前三个维度中可视化。结果表明,t-分布式域嵌入(t-SNE)算法具有良好的性能,表现出相似簇的密集聚类和不同簇的清晰分类。SGFD-t-SNE-SVM在性能方面在特征提取模型中排名最高。特征提取维数为4,平均分类准确率为99.98%,与全波长模型相比,这略微提高了预测性能。如这项研究所示,紫外-可见(UV-visible)光谱系统结合t-SNE和SVM算法可以有效地识别和分类水体中的OPP。
In this study, the goal was to develop a method for detecting and classifying organophosphorus pesticides (OPPs) in bodies of water. Sixty-five samples with different concentrations were prepared for each of the organophosphorus pesticides, namely chlorpyrifos, acephate, parathion-methyl, trichlorphon, dichlorvos, profenofos, malathion, dimethoate, fenthion, and phoxim, respectively. Firstly, the spectral data of all the samples was obtained using a UV-visible spectrometer. Secondly, five preprocessing methods, six manifold learning methods, and five machine learning algorithms were utilized to build detection models for identifying OPPs in water bodies. The findings indicate that the accuracy of machine learning models trained on data preprocessed using convolutional smoothing + first-order derivatives (SG + FD) outperforms that of models trained on data preprocessed using other methods. The backpropagation neural network (BPNN) model exhibited the highest accuracy rate at 99.95%, followed by the support vector machine (SVM) and convolutional neural network (CNN) models, both at 99.92%. The extreme learning machine (ELM) and K-nearest neighbors (KNN) models demonstrated accuracy rates of 99.84% and 99.81%, respectively. Following the application of a manifold learning algorithm to the full-wavelength data set for the purpose of dimensionality reduction, the data was then visualized in the first three dimensions. The results demonstrate that the t-distributed domain embedding (t-SNE) algorithm is superior, exhibiting dense clustering of similar clusters and clear classification of dissimilar ones. SG + FD-t-SNE-SVM ranks highest among the feature extraction models in terms of performance. The feature extraction dimension was set to 4, and the average classification accuracy was 99.98%, which slightly improved the prediction performance over the full-wavelength model. As shown in this study, the ultraviolet-visible (UV-visible) spectroscopy system combined with the t-SNE and SVM algorithms can effectively identify and classify OPPs in waterbodies.