Novel Sexalinear Decomposition Algorithm for Analyzing the Chemical Sexalinear Data Array

Abstract

With the development of analytical instrument towards more and more high-way and complex, it is very important and meaningful work to obtain ultra-high-way chemical data and explore its analytical methods. In this paper, a novel and excellent six-way algorithm combination method (six-way ACM) was proposed. In addition, a real chemically meaningful ultra-high-way sexalinear data array was obtained and constructed for the first time. The proposed six-way data array has highly collinearity, which puts forward higher requirements for parsing this data array to a certain extent. To verify the feasibility of the proposed algorithm, it was used to analyze the above real sexalinear six-way data array and a series of simulated six-way data arrays with different noise levels. The results of real data and simulated data demonstrate that the proposed method can be well used in the analysis of six-way data arrays and shows fascinating performance, including insensitive to excessive number of components, fast convergence speed, and suitable for high collinearity and high noise data. Compared with three-way, four-way, and five-way calibration methods, the six-way ACM provides higher sensitivity, a lower limit of detection, a lower limit of quantification, and more stable and accurate results, showing an outstanding “higher-order advantages” and better ability to handle collinearity problems. This work provides not only data analysis method for high-order instruments that may emerge in the future but also real data support and methodological reference for theoretical research on high-order tensor algebra.