Analysis of paclitaxel concentration in rat plasma by Raman spectrums combined with partial least square.
10.7507/1001-5515.201607051
- Author:
Meiyu TENG
1
,
2
;
Jia SONG
3
,
4
;
Yi ZHAO
5
;
Chengyu LU
2
;
Gaoyang XING
2
;
Lanzhou LI
2
;
Guodong YAN
2
;
Di WANG
6
Author Information
1. Jilin JiCe Detective Technical Co.LTD, Changchun 130012, P.R.China
2. College of Life Science, Jilin University, Changchun 130012, P.R.China.
3. College of Life Science, Jilin University, Changchun 130012, P.R.China
4. College of Biotechnology, Tianjin University of Science and Technology, Tianjin 300457, P.R.China.
5. Trauma Department of Orthopedics, The First Hospital of Jilin University, Changchun 130012, P.R.China.
6. College of Life Science, Jilin University, Changchun 130012, P.R.China.jluwangdi@jlu.edu.cn.
- Publication Type:Journal Article
- Keywords:
Raman spectroscopy;
paclitaxel;
partial least square
- From:
Journal of Biomedical Engineering
2018;35(4):578-582
- CountryChina
- Language:Chinese
-
Abstract:
Partial least square (PLS) combining with Raman spectroscopy was applied to develop predictive models for plasma paclitaxel concentration detection. In this experiment, 312 samples were scanned by Raman spectroscopy. High performance liquid chromatography (HPLC) was applied to determine the paclitaxel concentration in 312 rat plasma samples. Monte Carlo partial least square (MCPLS) method was successfully performed to identify the outliers and the numbers of calibration set. Based on the values of degree of approach ( ), moving window partial least square (MWPLS) was used to choose the suitable preprocessing method, optimum wavelength variables and the number of latent variables. The correlation coefficients between reference values and predictive values in both calibration set ( ) and validation set ( ) of optimum PLS model were 0.933 1 and 0.926 4, respectively. Furthermore, an independent verification test was performed on the prediction model. The results showed that the correlation error of the 20 validation samples was 9.36%±2.03%, which confirmed the well predictive ability of established PLS quantitative analysis model.
