Outlier Identification Method Based on Multi-Model Weighted Consensus in Conjunction With Monte Carlo Cross-Validation

作者：Yujing Wang, Zhengguang Chen, Jinming Liu, He Wang · 发表于：Journal of AOAC International · 年份：2025 · DOI：10.1093/jaoacint/qsaf061 · 被引用次数：7 · 研究领域：Spectroscopy and Chemometric Analyses、Advanced Statistical Methods and Models、Soil Geostatistics and Mapping

BACKGROUND: The accurate identification and removal of outliers are fundamental to the development of a robust model. OBJECTIVE: Exclusively relying on a single model for outlier detection may be insufficient for the proper identification of all outliers. This study examines the identification of anomalous data using the multi-model consensus technique to address issues of false positives, false negatives, and model reliance inherent in the identification process using a singular model. METHODS: This study introduces a method called Monte Carlo cross-validation in conjunction with multiple models of Weighted Consensus for outlier identification (MCWC, Monte Carlo Weighted Consensus). The proposed method integrates Monte Carlo random sampling with three distinct modeling methods: Partial Least-Squares Regression (PLSR), Gaussian process regression (GPR), and support vector regression (SVR). This integration allows for the amalgamation of predictions from each model, facilitating the identification of outliers effectively. RESULTS: This study employed a dataset comprising 305 sorghum samples as the experimental foundation. The predictive model for sorghum protein was built using the data after removing outliers using the single model method and the MCWC method, respectively. The experimental results indicate that the dataset, which was obtained by removing outliers using a single modeling method, is appropriate for further modeling with the same method. However, it is not suita...