Citation: Qing Xiangdong, Wu Hailong, Gu Huiwen, Yin Xiaoli, Wen Jin, Shen Xiangzhong, Yu Ruqin. Removal of Background Drift Nonlinear Interference in 3-D Spectral Arrays for Multi-Way Calibration Using Trilinear Decomposition Methods[J]. Acta Chimica Sinica, ;2016, 74(3): 277-284. doi: 10.6023/A15110704 shu

Removal of Background Drift Nonlinear Interference in 3-D Spectral Arrays for Multi-Way Calibration Using Trilinear Decomposition Methods

Qing Xiangdong ^a,b ,
Wu Hailong ^b,*,, ,
Gu Huiwen ^b ,
Yin Xiaoli ^b ,
Wen Jin ^a ,
Shen Xiangzhong ^a ,
Yu Ruqin ^b

a.
Department of Chemistry and Materials Science, Hunan University of Humanities, Science and Technology, Loudi 417000
b.
State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082

Corresponding author: Wu Hailong, hlwu@hnu.edu.cn
Received Date: 9 November 2015

Fund Project: the High-level Scientific Research Foundation for the Introduction of Talent of Hunan University of Humanities, Science and Technology 8250139the National Natural Science Foundation of China 21221003, 21575039

Figures(8)

Fluorescent and phosphorescent techniques have been widely used in the fields of food, biology, environment, chemistry, medicine, life science and so on. However, the spectral background drift often occurs in the spectral excitation dimension, creating the need for new methods to process this phenomenon. There are many different factors which may lead to this common phenomenon, such as the changes in background noise of instrument or temperature. In the work, a new technique for removal of background drift in three-dimensional spectral arrays is proposed. The basic idea is to perform trilinear decomposition based on the alternating trilinear decomposition (ATLD) algorithm on the instrumental response data. In model building, the background drift is modeled as an additional component or factor as well as the analytes of interest and the interferents. As the optimum number of factors (N) is provided by the core consistency diagnostic (CORCONDIA), the ATLD algorithm is applied to decompose the raw data (X_raw) with the factor number of N, then three profile matrices A, B and C can be obtained. Vectors a_n, b_n and c_n that representing the signal of the background drift can be extracted from these matrices to construct a 3-D background drift data array (X_drift ). After subtracting the X_drift from the X_raw, the background drift is removed, leaving the new data on a flat baseline. Two simulated data sets were firstly employed to demonstrate the reasonability of the new method. The same and different levels of background drifts along the excitation dimension are added into the two simulated data sets, respectively. Then, it is successfully used to analyze two experimental data sets in which significant background drift are present. These results highlight the fact that this technique yields a good removal of background drift. In addition, the good result is obtained by secondary removal for serious background drift. The proposed method can be viewed as a good spectral pretreatment technique.
- chemometrics,
- alternating trilinear decomposition,
- three-dimensional spectral array,
- background drift,
- spectral pretreatment
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