基于主成分分析的神经网络用于新污染物环境影响预测

Emerging contaminants have structural diversity, environmental persistence, and biological toxicity, which pose potential risks to ecosystems and human health. Life Cycle Assessment (LCA) is an important method for evaluating their environmental impacts, but it has problems such as a lack of basic data and difficulty in accurate quantification. To address these issues, this paper proposed an Artificial Neural Network (ANN) model based on Principal Component Analysis (PCA) to optimize input features. By performing PCA dimensionality reduction on a large number of molecular descriptors, main features were extracted to reduce data redundancy and dimensional burden for achieving the prediction of environmental impacts of emerging contaminants. Four models with different feature retention ratios were constructed, namely NOPCA (all descriptors), PCA95 (retaining principal components with 95% cumulative variance contribution rate), PCA85 (retaining principal components with 85% cumulative variance contribution rate), and PCA75 (retaining principal components with 75% cumulative variance contribution rate), and the differences in prediction performance were evaluated and compared. The results show that moderate feature extraction can improve the generalization ability of the model; in the prediction tasks of global warming potential (GWP) and human toxicity (HTP), the model constructed using PCA85-preprocessed descriptors performs the best, with test-set R² of 0. 64 and 0. 73, respectively; in the prediction tasks of fossil energy depletion (FDP) and terrestrial acidification (TAP), the model achieves optimal performance under PCA95, with test-set R² of 0. 77 and 0. 76, respectively. The ANN model based on PCA feature extraction can effectively mitigate the problem of incomplete LCA data and provides an efficient and reliable method for predicting the environmental impacts of emerging contaminants.