分析化学2025，Vol.53Issue(6)：944-954,中插16-中插21,17.DOI:10.19756/j.issn.0253-3820.251080
基于宽度学习系统和理化指标的复杂样品鉴别方法研究
Identification of Complex Samples Based on Broad Learning System and Physicochemical Indicators
摘要
Abstract
Compared to traditional machine learning algorithms,which often suffer from low feature extraction efficiency,insufficient nonlinear pattern recognition capabilities and slow training speeds,the broad learning system(BLS)enhances the learning ability and efficiency by horizontally expanding the network structure.BLS offers advantages such as a simple structure,fast training speed,and strong generalization capabilities.While BLS has demonstrated potential in various fields,but its application in identification of complex samples has not been fully explored.This research investigated the feasibility of using BLS algorithm for identification of complex samples based on physicochemical indicators.Using the iris,wine,and breast cancer datasets,the length and width of petals and sepals of iris flowers,the physicochemical properties of wine,and the nuclear characteristics of breast cancer cells were used as input variables to establish BLS models for identifying iris species,wine varieties,and benign versus malignant nucleus.The model performance was evaluated by confusion matrices,accuracy,and runtime.Compared with partial least squares-discriminant analysis(PLS-DA),soft independent modeling of class analogies(SIMCA),and artificial neural networks(ANN),the results indicated that BLS demonstrated significant advantages in computational efficiency and recognition accuracy.Thus,BLS was an efficient and reliable method for identification of complex samples.关键词
宽度学习/理化指标/复杂样品/鉴别/混淆矩阵Key words
Broad learning system/Physicochemical indicators/Complex sample/Identification/Confusion matrix引用本文复制引用
谢佳琦,张强,刘培然,杨亚非,卞希慧..基于宽度学习系统和理化指标的复杂样品鉴别方法研究[J].分析化学,2025,53(6):944-954,中插16-中插21,17.基金项目
药物制剂技术研究与评价国家药品监督管理局重点实验室开放课题项目(No.2023TREDP01)和2024年国家级大学生创新创业训练计划项目(No.202410058025)资助. Supported by the Open Projects Fund of National Medical Products Administration Key Laboratory for Technology Research and Evaluation of Drug Products(No.2023TREDP01)and the 2024 National Undergraduate Training Program for Innovation and Entrepreneurship(No.202410058025). （No.2023TREDP01）
