三线性分解方法用于光谱数据中背景漂移的扣除
收稿日期: 2015-11-09
网络出版日期: 2016-01-29
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
Received date: 2015-11-09
Online 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).
在光谱测量中, 通常会发生光谱背景漂移现象. 引起光谱背景漂移的因素有很多, 如仪器的背景噪声变化, 测量时环境温度的变化, 光源如氙灯的使用时间等等. 针对三维光谱数阵, 发展了一种基于交替三线性分解(ATLD)算法的化学计量学方法用来处理光谱背景漂移问题. 该方法在进行三线性分解时, 对待光谱背景漂移与感兴趣组分一样, 将其单独当作一个组分或因子来考虑, 并将其从分解得到的相应矩阵中提取出来, 构建一个光谱背景漂移阵, 然后从三维原始响应数阵中将其减掉, 从而达到成功扣除光谱背景漂移的目的. 采用发展的方法处理2组模拟数据和2组实验数据, 都获得了满意的结果. 另外, 对于非线性较严重的背景漂移, 通过再进行一次扣除, 即“二次扣除”, 也达到了理想的效果. 该方法有望发展成为一种很有潜力的光谱预处理技术.
卿湘东 , 吴海龙 , 谷惠文 , 尹小丽 , 文瑾 , 申湘忠 , 俞汝勤 . 三线性分解方法用于光谱数据中背景漂移的扣除[J]. 化学学报, 2016 , 74(3) : 277 -284 . DOI: 10.6023/A15110704
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.
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