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近红外光谱快速检测丙氨酸氨基转移酶
作者姓名:Huang FR  Zhang J  Luo YH  Li SP  Zheng SF  Chen XD
作者单位:1. "重大工程灾害与控制"教育部重点实验室(暨南大学),广东,广州,510632
2. 暨南大学第一附属医院临床检验中心,广东,广州,510632
3. "重大工程灾害与控制"教育部重点实验室(暨南大学),广东,广州,510632;中国科学院长春光学精密机械与物理研究所,吉林,长春,130033
基金项目:国家(863计划)项目,国家青年基金项目,广东省科技计划项目,广东省自然科学基金项目 
摘    要:在探讨近红外光谱快速检测丙氨酸氨基转移酶的可行性过程中,首先对不同厚度(0.5,1,2和4mm)血液样品的近红外透射光谱进行了分析。发现全血样品0.5 mm厚时的近红外透射光谱更适合于进行光谱分析。进而采集了176个全血样品0.5 mm厚时的近红外光谱。对采集的光谱进行多元散射校正、二阶微分法光谱预处理后,采用逐步多元线性回归和偏最小二乘回归方法建立定量分析模型,预测了全血丙氨酸氨基转移酶的含量。结果表明:利用近红外光谱法测定丙氨酸氨基转移酶时,采用偏最小二乘回归方法建立的定标模型预测效果最好,定标相关系数、定标标准差和预测标准差的值分别为:0.98,2.42和7.22。

关 键 词:近红外光谱  全血  丙氨酸氨基转移酶  偏最小二乘法

Rapid detection of alanine aminotransferase with near-infrared spectroscopy
Huang FR,Zhang J,Luo YH,Li SP,Zheng SF,Chen XD.Rapid detection of alanine aminotransferase with near-infrared spectroscopy[J].Spectroscopy and Spectral Analysis,2010,30(10):2620-2623.
Authors:Huang Fu-rong  Zhang Jun  Luo Yun-han  Li Shi-ping  Zheng Shi-fu  Chen Xing-dan
Institution:Key Laboratory of Disaster Forecast and Control in Engineering, Ministry of Education (Jinan University), Guangzhou 510632, China. furong_huang@163.com
Abstract:Near infrared transmission spectroscopy of Whole blood are investigated with different thickness (0.5, 1, 2, 4 mm) in order to explore the feasibility of detecting alanine aminotransferase rapidly by near-infrared spectra. The results show that the whole blood sample with 0.5 mm thickness is more suitable for spectral analysis. And then Near infrared spectroscopy of 176 samples were collected. Multiplicative scatter correction and second-order differential method have been used to spectral pretreatment. Stepwise multiple linear regression method and partial least squares regression method have been employed to establish quantitative detection model to predict content of alanine aminotransferase in whole blood. The alanine aminotransferase measured presents best result in calibration and prediction by Near-Infrared Spectroscopy with partial least squares regression calibration model, and the calibration correlation coefficient, the standard error of calibration and the standard error of prediction are 0.98, 2.42 and 7.22 respectively.
Keywords:
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