A flexible and novel strategy of alternating trilinear decomposition method coupled with two-dimensional linear discriminant analysis for three-way chemical data analysis: Characterization and classification

This paper proposes a flexible and novel strategy that alternating trilinear decomposition (ATLD) method combines with two-dimensional linear discriminant analysis (2D-LDA). The developed strategy was applied to three-way chemical data for the characterization and classification of samples. In order...

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Bibliographic Details
Published in:Analytica chimica acta 2018-08, Vol.1021, p.28-40
Main Authors: Hu, Yong, Wu, Hai-Long, Yin, Xiao-Li, Gu, Hui-Wen, Liu, Zhi, Xiao, Rong, Xie, Li-Xia, Fang, Huan, Yu, Ru-Qin
Format: Article
Language:English
Subjects:
Alternating trilinear decomposition
Classification
Computer simulation
Data analysis
Data processing
Decomposition
Discriminant analysis
Linear algebra
Methodology
Multi-way classification
Organic chemistry
Pattern recognition
Strategy
Tea
Tea classification
Two dimensional analysis
Two-dimensional linear discriminant analysis
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Summary:This paper proposes a flexible and novel strategy that alternating trilinear decomposition (ATLD) method combines with two-dimensional linear discriminant analysis (2D-LDA). The developed strategy was applied to three-way chemical data for the characterization and classification of samples. In order to confirm the methodology performances of characterization and classification, a series of simulated three-way data arrays and a real-life EEMs data set involving the characterization and classification of tea samples according to the tea varieties were subjected to ATLD-2DLDA analysis. Further, the obtained results were compared with those obtained by using LDA based on relative concentrations of ATLD (ATLD-LDA), discriminant analysis by N-way partial least square (N-PLS-DA) and 2D-LDA method. For the simulated data sets with respect to different levels of noise and class overlap as well as number of groups, the ATLD-2DLDA always obtains superior classification performances than the ATLD-LDA, 2D-LDA and N-PLS-DA methods. Regarding the real EEMs data set of tea samples, the proposed methodology not only could provide a chemically meaningful model of the data for characterizing the different tea varieties, but also achieved the best correct classification rate (100%) for the test samples, compared with the results of ATLD-LDA (83.9%), 2D-LDA (90.3%) and N-PLS-DA (90.3%). These results demonstrated that the proposed methodology was indeed a feasible and reliable tool for characterization and classification of three-way chemical data arrays in a flexible and accurate manner. [Display omitted] •A novel strategy ATLD-2DLDA for characterization and classification was proposed.•ATLD was applied to chemically characterize and decompose the three-way data array.•2D-LDA was applied to reconstructed data matrices of ATLD for accurate classification.•Case studies involving a series of simulated data and a real-life EEMs data set.•Better results compared with ATLD-LDA, 2D-LDA and N-PLS-DA methods.
ISSN:0003-2670
1873-4324
DOI:10.1016/j.aca.2018.03.050