Acta Chim. Sinica ›› 2016, Vol. 74 ›› Issue (3): 277-284.

Article

### 三线性分解方法用于光谱数据中背景漂移的扣除

1. a. 湖南人文科技学院 化学与材料科学系 娄底 417000;
b. 化学生物传感与计量学国家重点实验室 湖南大学化学化工学院 长沙 410082
• 投稿日期:2015-11-09 发布日期:2016-01-29
• 通讯作者: 吴海龙 E-mail:hlwu@hnu.edu.cn
• 基金资助:

项目受国家自然科学基金(Nos. 21221003, 21575039)和湖南人文科技学院高层次引进人才科研启动(No. 8250139)的资助.

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

Qing Xiangdonga,b, Wu Hailongb, Gu Huiwenb, Yin Xiaolib, Wen Jina, Shen Xiangzhonga, Yu Ruqinb

1. 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
• Received:2015-11-09 Published:2016-01-29
• Supported by:

Project supported by the National Natural Science Foundation of China (Nos. 21221003, 21575039) and theHigh-level Scientific Research Foundation for the Introduction of Talent of Hunan University of Humanities, Science and Technology (No. 8250139).

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 (Xraw) with the factor number of N, then three profile matrices A, B and C can be obtained. Vectors an, bn and cn that representing the signal of the background drift can be extracted from these matrices to construct a 3-D background drift data array (Xdrift ). After subtracting the Xdrift from the Xraw, 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.