以掺假山茶油样为气相离子迁移谱(gas chromatography-ion mobility spectrometry,GC-IMS)检测对象,利用多维主成分分析(multi-way principal component analysis,MPCA)法和偏最小二乘(partial least squares,PLS)回归分析处理二维谱图...以掺假山茶油样为气相离子迁移谱(gas chromatography-ion mobility spectrometry,GC-IMS)检测对象,利用多维主成分分析(multi-way principal component analysis,MPCA)法和偏最小二乘(partial least squares,PLS)回归分析处理二维谱图数据,探索并建立一种山茶油纯度检测方法。对配制的不同比例3种食用植物油的掺假油样进行GC-IMS检测,采用MPCA压缩并提取矩阵中的得分矩阵进行主成分分析,将提取的得分矩阵进行PLS分析,建立掺假量的定量预测模型。结果表明,MPCA处理后的主成分图可以明显区分山茶油样和掺入不同种类食用油的掺假山茶油样,且不同掺入比例组有其明显的归属区域;采用PLS对MPCA的得分矩阵进行回归分析,可实现对山茶油掺假比例的准确定量测定。该方法具有快速、准确、无损的特点,可应用推广到其他联用仪器的数据分析处理中,在食用油品质控制与评价方法中具有很大的应用前景。展开更多
Multi-way principal component analysis（MPCA）has received considerable attention and been widely used in process monitoring.A traditional MPCA algorithm unfolds multiple batches of historical data into a two-dimensio...Multi-way principal component analysis（MPCA）has received considerable attention and been widely used in process monitoring.A traditional MPCA algorithm unfolds multiple batches of historical data into a two-dimensional matrix and cut the matrix along the time axis to form subspaces.However,low efficiency of subspaces and difficult fault isolation are the common disadvantages for the principal component model.This paper presents a new subspace construction method based on kernel density estimation function that can effectively reduce the storage amount of the subspace information.The MPCA model and the knowledge base are built based on the new subspace.Then,fault detection and isolation with the squared prediction error（SPE）statistic and the Hotelling（T2）statistic are also realized in process monitoring.When a fault occurs,fault isolation based on the SPE statistic is achieved by residual contribution analysis of different variables.For fault isolation of subspace based on the T2 statistic,the relationship between the statistic indicator and state variables is constructed,and the constraint conditions are presented to check the validity of fault isolation.Then,to improve the robustness of fault isolation to unexpected disturbances,the statistic method is adopted to set the relation between single subspace and multiple subspaces to increase the corrective rate of fault isolation.Finally fault detection and isolation based on the improved MPCA is used to monitor the automatic shift control system（ASCS）to prove the correctness and effectiveness of the algorithm.The research proposes a new subspace construction method to reduce the required storage capacity and to prove the robustness of the principal component model,and sets the relationship between the state variables and fault detection indicators for fault isolation.展开更多
文摘以掺假山茶油样为气相离子迁移谱(gas chromatography-ion mobility spectrometry,GC-IMS)检测对象,利用多维主成分分析(multi-way principal component analysis,MPCA)法和偏最小二乘(partial least squares,PLS)回归分析处理二维谱图数据,探索并建立一种山茶油纯度检测方法。对配制的不同比例3种食用植物油的掺假油样进行GC-IMS检测,采用MPCA压缩并提取矩阵中的得分矩阵进行主成分分析,将提取的得分矩阵进行PLS分析,建立掺假量的定量预测模型。结果表明,MPCA处理后的主成分图可以明显区分山茶油样和掺入不同种类食用油的掺假山茶油样,且不同掺入比例组有其明显的归属区域;采用PLS对MPCA的得分矩阵进行回归分析,可实现对山茶油掺假比例的准确定量测定。该方法具有快速、准确、无损的特点,可应用推广到其他联用仪器的数据分析处理中,在食用油品质控制与评价方法中具有很大的应用前景。
基金Supported by National Hi-tech Research and Development Program of China(863 Program,Grant No.2011AA11A223)
文摘Multi-way principal component analysis（MPCA）has received considerable attention and been widely used in process monitoring.A traditional MPCA algorithm unfolds multiple batches of historical data into a two-dimensional matrix and cut the matrix along the time axis to form subspaces.However,low efficiency of subspaces and difficult fault isolation are the common disadvantages for the principal component model.This paper presents a new subspace construction method based on kernel density estimation function that can effectively reduce the storage amount of the subspace information.The MPCA model and the knowledge base are built based on the new subspace.Then,fault detection and isolation with the squared prediction error（SPE）statistic and the Hotelling（T2）statistic are also realized in process monitoring.When a fault occurs,fault isolation based on the SPE statistic is achieved by residual contribution analysis of different variables.For fault isolation of subspace based on the T2 statistic,the relationship between the statistic indicator and state variables is constructed,and the constraint conditions are presented to check the validity of fault isolation.Then,to improve the robustness of fault isolation to unexpected disturbances,the statistic method is adopted to set the relation between single subspace and multiple subspaces to increase the corrective rate of fault isolation.Finally fault detection and isolation based on the improved MPCA is used to monitor the automatic shift control system（ASCS）to prove the correctness and effectiveness of the algorithm.The research proposes a new subspace construction method to reduce the required storage capacity and to prove the robustness of the principal component model,and sets the relationship between the state variables and fault detection indicators for fault isolation.
