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An Algorithm for Three-way Data Analysis That Alternatively Minimizes Coupled Vector (COV) Resolution Error and PARAFAC Error

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Abstract

A novel algorithm, alternatively minimizing coupled vector (COV) resolution error and PARAFAC error algorithm, is proposed in this paper. This algorithm can overcome the problem of slow convergence and is insensitive to the estimation of component number, such problems are unavoidable while using the traditional parallel factors analysis (PARAFAC) algorithm. In other words, this algorithm is capable of improving the computing speed and providing accurate resolutions provided that the number of factors used in the computation is no less than that of the actual underlying ones. The characteristic performances were demonstrated with a novel fluorescence data array.

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Authors and Affiliations

  1. Key Laboratory of New Packaging Materials & Technology of China National Packaging Corporation, Zhuzhou Institute of Technology, Zhuzhou, 412008, China

    Yu-Zhen Cao & Hong Chen

  2. Environmental Monitoring Central Station of Guangzhou City, NO. 95, Jixiang Road of Guangzhou City, Guangzhou, 510030, China

    Yu-Zhen Cao

  3. College of Chemistry and Chemical Engineering, Hunan University, Changsha, 410082, China

    Hai-Long Wu & Ru-Qin Yu

Authors
  1. Yu-Zhen Cao
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  2. Hong Chen
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  3. Hai-Long Wu
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  4. Ru-Qin Yu
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Cao, YZ., Chen, H., Wu, HL. et al. An Algorithm for Three-way Data Analysis That Alternatively Minimizes Coupled Vector (COV) Resolution Error and PARAFAC Error. ANAL. SCI. 19, 753–756 (2003). https://doi.org/10.2116/analsci.19.753

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  • DOI: https://doi.org/10.2116/analsci.19.753

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