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摘要
Recently, there has been growing interest in the decomposition of multilinear component models in the field of multi-way calibration. In this work, a novel quadrilinear decomposition method, i.e. four-way algorithm combination method (FACM), is proposed for four-way calibration. The FACM skillfully integrates the alternating quadrilinear decomposition (AQLD) algorithm with the four-way parallel factor analysis (FPARAFAC) algorithm and gives full play to their advantages. The performance of the FACM and the existing five algorithms are compared by using two simulated data sets. Moreover, a published four-way data array and a new four-way data array, which record a series of fluorescence spectra information of biomarkers in biological samples, are investigated by the proposed method respectively. The results demonstrate that the novel method can accurately and effectively extract the qualitative and quantitative information of analytes of interest even in the presence of unknown interferents and varying background. Besides, the FACM has attractive properties, such as very fast convergence, insensitive to initial values and excess number of components, and suitable for high noise level as well as severely collinear data. In addition, the proposed method can also be extended to combine other iterative algorithms, providing a feasible idea for the exploration and development of new algorithms.
- 出版日期2019-2-15
- 单位湖南大学; 化学生物传感与计量学国家重点实验室
