应用红外光谱和化学计量学进行疾病诊断及甘草指标成分含量测定的研究
发布时间：2018-08-18 14:43
【摘要】：本论文主要研究建立基于衰减全反射傅里叶红外光谱(Fourier Transform Infrared Spectroscopy-Attenuated Total Refraction,FTIR-ATR)和近红外光谱的定性和定量模型,用于快速筛查新生儿苯丙酮尿症(Phenylketonuria,PKU)及同时测定甘草饮片中甘草苷和甘草酸两种指标成分的含量。研究采用了平滑、求导、主成分分析、无信息变量消除、归一化等预处理方法,并以偏最小二乘法、多模型共识偏最小二乘法(consensus Partial Least Squares,cPLS)、核偏最小二乘法(Kernel Partial Least Squares)、多模型共识核偏最小二乘法(consensus Kernel Partial Least Squares)建立了目标成分含量的定量校正模型,经多种评价指标对模型性能进行考察。本论文为红外光谱用于疾病筛查和中药饮片所含指标成分的同时测定等研究提供了方法学参考。研究内容一:采用FTIR/ATR光谱建立不同的校准模型,并应用于新生儿的苯丙酮尿症筛查。在本课题组过往研究的基础上,通过串联质谱法测定69例干血斑样品中苯丙氨酸(Phe)和酪氨酸(Tyr)的浓度,并使用FTIR/ATR采集样品的光谱。通过平滑、求导、矢量归一化、凹式Rubberband等方法对光谱进行预处理,比较偏最小二乘法(PLS)、核偏最小二乘法(KPLS)、多模型共识偏最小二乘法(cPLS)及cKPLS四种模型构建样品中Phe浓度的校准模型,以决定定系数(R2),均方根误差(RMSE),平均相对误差(MRE)等来评估所获得的模型。结果发现,经过比较,cKPLS引入核方法和多模型共识,cKPLS模型表现最好,产生的结果更准确和稳定,为建立稳健的FTIR/ATR光谱模型及解决FTIR/ATR光谱中其他复杂的校准提供了新方法,并成功地应用于预测新生儿干血片中Phe浓度,为苯丙酮尿尿症筛查提供参考。研究内容二:利用近红外光谱法结合主成分分析和聚类分析对甘草进行产地鉴别。对原始光谱进行预处理后,分别用SPSS或MATLAB程序进行主成分分析和聚类分析,对比两种方法的聚类结果,发现SPSS聚类的效果优于MATLAB程序。可以发现,当SPSS的类间距离为5.0时,40份实验样品可分为四大类:Ⅰ类为所有内蒙古样品;Ⅱ类为5份甘肃样品、8份内蒙古样品、2份新疆,1份宁夏聚在一起;Ⅲ类为1份甘肃,1份新疆,1份内蒙古;Ⅳ类为10份甘肃样品同6份内蒙古样品、2份新疆、1份宁夏聚为一类;而用MATLAB程序做聚类分析时,其类间距离为50时,40份实验样品被分成了八大类,而且没有一个产地聚类的比较集中。研究内容三:利用近红外光谱和偏最小二乘法建立一种能够同时快速测定甘草中甘草苷和甘草酸含量的定量校正模型。以HPLC所测样品中甘草苷和甘草酸的含量为参考值,利用近红外分析技术,将参考值与样品的近红外光谱进行关联,采用主成分(PCA)结合偏最小二乘法(PLS)建立甘草中甘草苷和甘草酸的定量分析模型。甘草苷的最优模型结果为:校正决定系数(R2),验证集的决定系数,校正均方差(RMSEC)和验证均方差(RMSEP)分别为0.9522,0.9305,0.0004和0.0017;甘草酸的最优模型结果为:校正集的决定系数,验证集的决定系数(R2)、校正均方差(RMSEC)和验证均方差(RMSEP)为0.9766,0.9591,0.0006和0.0021。结果发现,利用近红外光谱技术建立的定量分析模型,能够对甘草中甘草苷和甘草酸的含量进行快速无损的测定。
[Abstract]:In this paper, a qualitative and quantitative model based on Fourier Transform Infrared Spectroscopy-Attenuated Total Refraction (FTIR-ATR) and near infrared spectroscopy was developed for rapid screening of neonatal phenylketonuria (PKU) and simultaneous determination of glycyrrhizin and glycyrrhizin in licorice slices. The content of oxalic acid was determined by smoothing, derivative, principal component analysis, non-information variable elimination, normalization and other pretreatment methods. Partial least squares (PLS), consensus Partial Least Squares (cPLS), kernel partial least squares (Kernel Partial Least Squares) and multi-model consensus kernel bias were used. Quantitative calibration model of target component content was established by consensus Kernel Partial Least Squares, and the performance of the model was evaluated by various evaluation indexes. FTIR/ATR spectroscopy was used to establish different calibration models for neonatal phenylketonuria screening. Based on the previous studies of our group, the concentrations of phenylalanine (Phe) and tyrosine (Tyr) in 69 dry blood spot samples were determined by tandem mass spectrometry, and the spectra were collected by FTIR/ATR. Concave Rubberband and other methods were used to pre-process the spectra. The calibration models of Phe concentration in samples were constructed by comparing partial least squares (PLS), kernel partial least squares (KPLS), multi-model consensus partial least squares (cPLS) and cKPLS. The calibration models were used to determine the constant coefficients (R2), root mean square error (RMSE) and mean relative error (MRE). The results show that the cKPLS model performs best by introducing the nuclear method and multi-model consensus. The results are more accurate and stable. It provides a new method for establishing a robust FTIR/ATR spectral model and solving other complex calibration problems in FTIR/ATR spectroscopy. It has been successfully applied to predict the Phe concentration in the neonatal dry blood tablets and converting it into benzene. Content 2: Using near infrared spectroscopy combined with principal component analysis and clustering analysis to identify the origin of Glycyrrhiza uralensis. After pretreatment of the original spectrum, principal component analysis and clustering analysis were carried out by SPSS or MATLAB program, and the clustering results of the two methods were compared. It was found that the effect of SPSS clustering was better. In MATLAB program, it can be found that when the SPSS class spacing is 5.0, 40 experimental samples can be divided into four categories: I for all samples in Inner Mongolia; II for 5 samples in Gansu, 8 samples in Inner Mongolia, 2 samples in Xinjiang, 1 sample in Ningxia together; III for Gansu, 1 sample in Xinjiang, 1 sample in Inner Mongolia; IV for 10 samples in Gansu and 6 samples in Inner Mongolia. Two samples from Xinjiang and one sample from Ningxia were clustered into one group, while 40 samples were classified into eight groups when the distance between classes was 50 by MATLAB, and there was no clustering in any place of origin. Quantitative calibration model of glycyrrhizin content was established. The contents of glycyrrhizin and glycyrrhizin in the samples determined by HPLC were taken as reference values. The near infrared spectroscopy was used to correlate the reference values with the near infrared spectra of the samples. The quantitative analysis model of glycyrrhizin and glycyrrhizin in the samples was established by principal component analysis (PCA) combined with partial least squares (PLS). The optimal model results are: Corrected Decision Coefficient (R2), Verification Set Decision Coefficient (RMSEC), Corrected Mean Variance (RMSEC) and Verification Mean Variance (RMSEP) are 0.9522, 0.9305, 0.0004 and 0.0017 respectively; The optimal model results of glycyrrhizic acid are: Corrected Decision Coefficient (R2), Corrected Mean Variance (RMSEC) and Verification Mean Variance (RMSEP) are 0.9522, 0.9305, 0.0004 and 0.0017 respectively. 9766,0.9591,0.0006 and 0.0021. The results showed that the quantitative analysis model established by near infrared spectroscopy could be used to determine the content of glycyrrhizin and glycyrrhizic acid in licorice rapidly and nondestructively.
【学位授予单位】：广东药科大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：R284.1;O657.33
本文编号：2189797
[Abstract]:In this paper, a qualitative and quantitative model based on Fourier Transform Infrared Spectroscopy-Attenuated Total Refraction (FTIR-ATR) and near infrared spectroscopy was developed for rapid screening of neonatal phenylketonuria (PKU) and simultaneous determination of glycyrrhizin and glycyrrhizin in licorice slices. The content of oxalic acid was determined by smoothing, derivative, principal component analysis, non-information variable elimination, normalization and other pretreatment methods. Partial least squares (PLS), consensus Partial Least Squares (cPLS), kernel partial least squares (Kernel Partial Least Squares) and multi-model consensus kernel bias were used. Quantitative calibration model of target component content was established by consensus Kernel Partial Least Squares, and the performance of the model was evaluated by various evaluation indexes. FTIR/ATR spectroscopy was used to establish different calibration models for neonatal phenylketonuria screening. Based on the previous studies of our group, the concentrations of phenylalanine (Phe) and tyrosine (Tyr) in 69 dry blood spot samples were determined by tandem mass spectrometry, and the spectra were collected by FTIR/ATR. Concave Rubberband and other methods were used to pre-process the spectra. The calibration models of Phe concentration in samples were constructed by comparing partial least squares (PLS), kernel partial least squares (KPLS), multi-model consensus partial least squares (cPLS) and cKPLS. The calibration models were used to determine the constant coefficients (R2), root mean square error (RMSE) and mean relative error (MRE). The results show that the cKPLS model performs best by introducing the nuclear method and multi-model consensus. The results are more accurate and stable. It provides a new method for establishing a robust FTIR/ATR spectral model and solving other complex calibration problems in FTIR/ATR spectroscopy. It has been successfully applied to predict the Phe concentration in the neonatal dry blood tablets and converting it into benzene. Content 2: Using near infrared spectroscopy combined with principal component analysis and clustering analysis to identify the origin of Glycyrrhiza uralensis. After pretreatment of the original spectrum, principal component analysis and clustering analysis were carried out by SPSS or MATLAB program, and the clustering results of the two methods were compared. It was found that the effect of SPSS clustering was better. In MATLAB program, it can be found that when the SPSS class spacing is 5.0, 40 experimental samples can be divided into four categories: I for all samples in Inner Mongolia; II for 5 samples in Gansu, 8 samples in Inner Mongolia, 2 samples in Xinjiang, 1 sample in Ningxia together; III for Gansu, 1 sample in Xinjiang, 1 sample in Inner Mongolia; IV for 10 samples in Gansu and 6 samples in Inner Mongolia. Two samples from Xinjiang and one sample from Ningxia were clustered into one group, while 40 samples were classified into eight groups when the distance between classes was 50 by MATLAB, and there was no clustering in any place of origin. Quantitative calibration model of glycyrrhizin content was established. The contents of glycyrrhizin and glycyrrhizin in the samples determined by HPLC were taken as reference values. The near infrared spectroscopy was used to correlate the reference values with the near infrared spectra of the samples. The quantitative analysis model of glycyrrhizin and glycyrrhizin in the samples was established by principal component analysis (PCA) combined with partial least squares (PLS). The optimal model results are: Corrected Decision Coefficient (R2), Verification Set Decision Coefficient (RMSEC), Corrected Mean Variance (RMSEC) and Verification Mean Variance (RMSEP) are 0.9522, 0.9305, 0.0004 and 0.0017 respectively; The optimal model results of glycyrrhizic acid are: Corrected Decision Coefficient (R2), Corrected Mean Variance (RMSEC) and Verification Mean Variance (RMSEP) are 0.9522, 0.9305, 0.0004 and 0.0017 respectively. 9766,0.9591,0.0006 and 0.0021. The results showed that the quantitative analysis model established by near infrared spectroscopy could be used to determine the content of glycyrrhizin and glycyrrhizic acid in licorice rapidly and nondestructively.
【学位授予单位】：广东药科大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：R284.1;O657.33
【参考文献】
相关期刊论文 前10条
1 吴敏;张伟涛;田沛荣;凌晓锋;徐智;;正常人体甲状腺体表傅里叶红外光谱图的特征分析[J];光谱学与光谱分析;2016年10期
2 侯湘梅;张磊;岳洪水;鞠爱春;叶正良;;基于近红外光谱分析技术的丹参多酚酸大孔吸附树脂柱色谱过程监测方法[J];中国中药杂志;2016年13期
3 李云;毕宇安;王振中;萧伟;;近红外光谱技术在热毒宁注射液栀子提取液浓缩过程中的应用[J];中国实验方剂学杂志;2016年12期
4 毛佩芝;杨凯;金叶;刘雪松;王龙虎;;近红外光谱法快速测定野菊花药材中水分及蒙花苷含量[J];中国现代应用药学;2015年12期
5 杨佒雯;张锦水;朱秀芳;谢登峰;袁周米琪;;随机森林在高光谱遥感数据中降维与分类的应用[J];北京师范大学学报(自然科学版);2015年S1期
6 闫蔚;曾柏淋;王淑美;孟江;梁生旺;;6种硫酸盐类矿物药中红外鉴别[J];中国实验方剂学杂志;2015年20期
7 吴红梅;王祥培;杨烨;徐锋;;液相色谱指纹图谱技术在中药鉴定学教学中的应用探讨[J];贵阳中医学院学报;2015年05期
8 马丹;顾志荣;甘玉伟;赵克加;郭玫;;唐古特大黄及其不同炮制品的近红外光谱分析[J];中药材;2015年09期
9 周文婷;林萍;王海霞;姬生国;;巴戟天药材中耐斯糖含量近红外光谱测定方法的建立[J];井冈山大学学报(自然科学版);2015年05期
10 杨天鸣;张璐;付海燕;李鹤东;姜杜;周蓉;;不同产地甘草的近红外指纹图谱模式识别鉴别方法[J];亚太传统医药;2015年14期
相关硕士学位论文 前1条
1 孔庆明;神经网络在食用油质量近红外光谱分析中的应用研究[D];哈尔滨商业大学;2012年
，本文编号：2189797
本文链接：https://www.wllwen.com/kejilunwen/huaxue/2189797.html
教材专著
