中国年鉴信息网--中国规模最大、实力最强的研究报告提供商!您好,欢迎来到中国年鉴信息网! [登录] [注册] 我的购物车 我的订单
首 页
中国年鉴
研究报告
省市统计
中国名录
省市年鉴
地方志
社会经济
古籍文献
行业数据
复杂体系仪器分析--白、灰、黑分析体系及其多变量解析方法
2012-05-22文字显示[ ]
[图书名称]:复杂体系仪器分析--白、灰、黑分析体系及其多变量解析方法
[关键文字]:复杂,体系,仪器,分析--白、灰、黑,分析,体系,及其,多变量,解析,方法
[书刊号]:
[图书作者]:化学工业出版社
[出版日期]:2016年6月
[图书价格]:¥198.00
[优 惠 价]:¥198.00
[传真订购]:010-58697500
[咨询电话]:010-58697871 58697872
[值班手机]:13801017046
本书相关年份
     查看详细..
           
详细内容:  

    全书分为理论方法篇与应用篇两个部分,主要讨论了复杂多组分体系的直接仪器分析的化学计量学方法及其实际应用问题。内容主要包括如何采用不同的化学计量学方法,分别对分析化学中频繁碰到的多种不同性质的白、灰、黑复杂多组分体系所得的不同分析仪器数据,进行解析和各种有用化学信息提取的理论与实际应用问题。在应用方面,主要是对中药复杂分析体系的色谱指纹图谱与质量控制,近红外光谱应用于广义灰色复杂体系的分析,代谢组学高通量分析及其生物标记物的确定,蛋白组学的仪器分析及其生物信息学解析方法的讨论。另外还简要介绍了统计学和应用数学的相关基础知识。

复杂体系仪器分析
——白、灰、黑分析体系及其多变量解析方法
第一部分理论方法篇(Part Ⅰ:Theory and Methodology)1Chapter 1
第一章概论 (Introduction)/2
第一节现代分析化学面临的机遇与挑战(Opportunity and Challenge Faced by Modern Analytical Chemistry)/2
第二节化学计量学的多变量解析思路(Thoughts on Chemometric Multivariate Analysis)/4
第三节化学计量学中的软模型和硬模型 (Soft Modeling and Solid Modeling in Chemometrics)/9
参考文献/11Chapter 2
第二章分析化学中的几个理论思考问题(Several Theoretic Problems in Analytical Chemistry)/12
第一节分析化学的信息理论(Information Theory on Analytical Chemistry)/13
第二节张量校正理论(Theory of Tensor Calibration)/14
第三节黑、白、灰多组分体系及仪器分析策略(White,Grey and Black Analytical Systems and Strategy of Instrumental Analysis)/16
参考文献/18Chapter 3
第三章白色分析体系的多元校正方法(Multivariate Calibration Methods for White Analytical Systems)/19
第一节直接校正方法(Direct Calibration Method)/20
一、多元线性回归方法(Multiple Linear Regression)/20
二、Kalman滤波法(Kalman Filtering)/24
三、加权最小二乘回归法(Weighted Least Squares)/27
第二节间接校正方法(Indirect Calibration Method)/29
一、K矩阵法(Kmatrix Method)/29
二、P矩阵法(Pmatrix Method)/30
三、主成分回归法(Principal Component Regression)/31
四、偏最小二乘法(Partial Least Squares)/32
第三节通用标准加入法(Generalized Standard Additive Method)/35
第四节广义内标法(Generalized Internal Reference Method)/36
第五节非线性体系的人工神经网络校正方法 (Calibration Method Based on Artificial Neurol Network for Nonlinear Systems)/41
第六节病态体系的岭回归法估计方法 (Illconditioned System and Ridge Regression Estimating Method)/46
第七节多元校正的分析化学品质因数和可靠性分析(Figures of Merit of Analytical Chemistry and Reliable Analysis for Multivariate Calibration)/49
参考文献/52Chapter 4
第四章灰色分析体系的多元校正方法(Multivariate Calibration Methods for Grey Analytical Systems)/54
第一节矢量校正方法(Vectorial Calibration Methods)/54
一、投影算法和多元校正模型的检验(Projection Algorithm and Testing of Multicalibration Method)/55
二、标准加入迭代目标转换因子分析法(Additional Itarative Target Transformation Factor Analysis)/56
三、自适应Kalman滤波法(Adaptive Kalman Filtering Method)/58
四、局部曲线拟合法(Local Curve Fitting Method)/61
第二节矩阵校正方法 (Matrix Calibration Methods)/63
一、秩消失因子分析法(Rank Annihilation Factor Analysis)/64
二、广义秩消失因子分析法(Generalized Rank Annihilation Factor Analysis)/67
三、残差双线性分解法(Residual Bilinearization Method) /72
四、约束背景双线性分解法(Constrained Background Bilinearization Method)/74
参考文献/79Chapter 5
第五章黑色分析体系的多元分辨方法(Multivariate Resolution Methods for Black Analytical Systems)/81
第一节基于主成分分析的体系组分数确定方法(Methods Based on Principal Component Analysis for Estimating Number of Chemical Components in Systems)/82
一、误差扰动下的协方差阵特征值变化限制(Varying Limits of Eigenvalues of Covariance Matix under Error Disturbance)/83
二、因子分析的误差理论(Error Theory of Factor Analysis)/84
三、主因子数确定的几种方法(Several Methods for determining Number of Principal Factors)/87
第二节矩阵分辨方法(Matrix Resolution Methods)/93
一、自模式曲线分辨法(Selfmodeling Curve Resolution Method)/93
二、迭代目标转换因子分析法(Iterative Target Transformation Factor Analysis)/107
三、渐进因子分析法及其相关方法(Evolving Factor Analysis and Related Methods)/109
四、窗口因子分析法(Window Factor Analysis)/112
五、直观推导式演进特征投影法(Heuristic Evolving Latent Projections)/114
六、正交投影分辨法(Orthogonal Projection Resolution Method)/131
七、子窗口因子分析法(Subwindow Factor Analysis)/133
八、二维色谱的一阶微分矩阵顺序秩分析方法(Sequential Rank Analysis of FirstOrder Differentiated Matrix of Twoway Chromatographic Data)/134
第三节张量分辨方法(Tensor Resolution Method)/140
一、投影旋转因子分析法(Projection Rotation Factor Analysis)/141
二、广义秩消失因子分析法(Generalized Rank Annihilation Factor Analysis)/142
参考文献/150Chapter 6
第六章广义灰色分析体系的多元校正模型(Multivariate Calibration Methods for Generalized Grey Analytical Systems)/152
第一节近红外光谱与广义灰色分析体系 (Near Infrared Spectroscopy and Generalized Grey Analytical Systems)/153
第二节广义灰色分析体系的模型校验方法(Model Validation for Generalized Grey Analytical Systems)/155
一、模型过拟合与潜变量回归模型(Overfitting and Latent Variable Regression Modeling)/155
二、模型复杂度与预测标准的提出(Model Complexity and Preditive Criterion)/157
三、检验集的构造与模型交叉校验(Contruction of Test Dataset and Modeling Crossvalidation)/158
四、交叉校验的几种方法(Several Methods for Crossvalidation)/160
第三节广义灰色分析体系的常用多元校正方法(Common Multivariate Calibration Methods for Generalized Grey Analytical Systems)/165
一、主成分与偏最小二乘回归(Principal Component and Partial Least Squares Regression)/166
二、人工神经网络(Artificial Neural Network)/168
第四节回归建模中的稳健方法(Robust Methods for Regression Modeling)/170
一、回归诊断方法(Regression Diagnostic Methods)/171
二、稳健回归方法(Robust Regression Methods)/176
参考文献/181Chapter 7
第七章复杂分析体系多变量数据的模式分析与模式识别(Pattern Analysis and Pattern Recognition Upon Multivariate Data of Complex Analytical Systems)/186
第一节概论(Introduction)/187
一、模式空间的几种距离与相似性度量(Several Measures of Distance and Similarity in Pattern Space)/187
二、特征抽取方法(Feature Extraction Methods) /189
三、常见数据预处理方法(Commonly Used Pretreatment Methods for Multivariate Data)/190
第二节多变量数据的模式识别及模式分析(Pattern Recognition and Pattern Analysis for Multivariate Data)/191
一、有监督的模式识别方法——判别分析法(Supervised Pattern Recognition Methods—Discriminant Analysis Methods)/191
二、无监督的模式识别方法——聚类分析法(Unsupervised Pattern Recognition Methods—Clustering Analysis Methods)/200
三、基于特征投影的降维显示方法(Visual Dimensional Reduction Based on Latent Projection)/217
四、基于机器学习的分类回归方法(Classification and Regression Methods Based on Machine Learning)/227
参考文献/239Chapter 8
第八章模型集群分析及新型化学计量学算法的开发研究(Model Population Analysis and Research on Developing New Chemometric Algorithms)/241
第一节现代仪器分析数据的特点及其挑战性(Characteristics and Challenges of Modern Instrumental Analytical Data)/241
第二节一次性建模思路的数据分析方法的缺陷(Drawback of Single Data Modeling Analysis)/242
第三节模型集群分析——一种新算法开发的一般框架(Model Population Analysis—A General Framework for Developing New Algorithms)/245
一、有关模型集群分析的几个实例(Several Examples of Model Population Analysis)/247
二、蒙特卡洛采样与机器学习和化学计量学算法(MonteCarlo Sampling Machine Learning and Chemometric Algorithm)/251
三、模型集群分析的主要思路(Major Idea of Model Population Analysis)/252
第四节基于模型集群分析的新算法开发(Developing New Types of Algorithms Based on Model Population Analysis)/253
一、基于蒙特卡洛采样的奇异样本的回归诊断与筛选(Regression Diagnosis and Screening of Outliers Based on MonteCarlo Sampling)/253
二、子窗口重排分析(Subwindow Permutation Analysis,SPA)/259
三、边界影响分析(Margin Influence Analysis,MIA)/263
第五节关于算法研究的一些展望(Some Perspectives on Developing New Algorithms)/265
参考文献/265Chapter 9
第九章复杂分析体系的气相色谱质谱联用仪器数据的定性及结构解析初探 (Primary Analysis Based on Chromatographic Retention Indices and Mass Spectral Elucidation)/267
第一节气相色谱的保留指数及其定性分析应用(Retention Index of Gas Chromatography and Its Application for Qualitative Analysis)/267
一、Kovats保留指数(Kovats Retention Index)/270
二、程序升温保留指数(Programmedtemperature Retention Index,PTRI)/271
三、程序升温保留指数标准化(Standarization of Programmedtemperature Retention Index)/271
四、不同色谱条件下程序升温保留指数的相互转换(Conversion between Programmedtemperature Retention Indices Under Different Chromatographic Conditions)/273
第二节质谱图解析基本原理(Elements for Elucidation of Mass Spectrum)/291
一、主要离子类型及其在质谱解析中的作用(Main Types of Ions and Their Application in Interpretation of Mass Spectra)/291
二、中性丢失(Neutral Losses)/297
三、质谱解析的步骤(Classic Procedures of Interpretation of Mass Spectra)/297
第三节离子裂解基本原理 (Elements for Fragmentation)/298
一、σ断裂(σ Cleavage)/299
二、自由基中心引发的α断裂反应 (α Cleavage Induced by Radical Site)/300
三、电荷中心诱导的i裂解(i Cleavage Induced by Charge Sites)/303
四、环的裂解 (Cleavage of Ring)/304
五、自由基诱导的重排反应 (Radical Site Rearrangements)/307
六、电荷诱导的重排反应(Charge Site Rearrangements)/309
第四节质谱特征挖掘与保留指数结合用于化合物结构鉴定(Identification of Compound Structure by Data Mining of Mass Spectra and Retention Indices)/310
一、质谱特征结合保留指数用于质谱数据的定性分析(Mass Spectral Characteristics,Retention Indices Rules and Their Application in Identification)/312
二、专用数据库的建立(Establishment of Customized Library)/320
三、仪器间质谱差异的消除及其在定性顺反异构中的应用(Elimination of Mass Spectral Instrumental Difference and Its Application in Identification of cis/trans Isomers)/321
第五节质谱解析的量子化学解释初探(Primanery Study on Mass Spectral Eluciation Based on Quantum Chemistry)/326
一、EI质谱解析中初始电离位点的确定方法(Determinination of Initial Ionization Position for EI Mass Spectral Eluciation)/326
二、电喷雾离子源(ESI)质谱解析的量化计算研究 (Study on Quantum Chemistry Calcalation for ESI Mass Spectral Eluciation)/340
参考文献/350
第二部分应用篇(Part Ⅱ: Applications)355Chapter 10
第十章中药分析体系的色谱指纹图谱技术及其质量控制 (Chromatographic Fingerprinting Techniques of Traditional Chinese Medicines and their Quality Control)/356
第一节概论(Introduction)/356
一、中药色谱指纹图谱——中药高通量分析的化学表征(Chromatographic Fingerprinting—High Throughput Technique for Chemical Characteristics of Traditional Chinese Medicines)/357
二、中药色谱指纹图谱在中药现代化研究中的核心地位(The Core Status of Chromatographic Fingerprinting Technique in the Research on Modernization of Traditional Chinese Medicines)/360
第二节中药色谱指纹图谱的预处理方法(Preprocessing Methods for Chromatographic Fingerprints of Traditional Chinese Medicines)/362
一、中药色谱指纹图谱的信息特征(Informative Features of Chromatographic Fingerprints of Traditional Chinese Medicines)/363
二、中药色谱指纹图谱的漂移背景扣除 (Elimination of the Shift Background of Chromatographic Fingerprints of Traditional Chinese Medicines)/365
三、中药色谱指纹图谱的谱峰漂移校准(Peak Alignment for Chromatographic Fingerprints of Traditional Chinese Medicines)/369
第三节中药色谱指纹图谱整体性与中草药的质量控制(Chromatographic Fingerprints and Quality Control of Traditional Chinese Medicines)/384
一、中药色谱指纹图谱的基本特征(Basic Features of Chromatographic Fingerprints of Chinese Medicines)/385
二、中药色谱指纹图谱的相似度量与质量控制(Similarity Measures of Chromatographic Fingerprints of Chinese Medicines and their Quality Control)/386
三、中药色谱指纹图谱的模式识别与质量控制(Pattern Recognition of Chromatographic Fingerprints of Chinese Medicines and their Quality Control)/398
第四节中药色谱指纹图谱的定性定量及不同样本间的比较分析(Comparative Analysis for Chromatographic Fingerprints of Traditional Chinese Medicines)/419
一、中药色谱指纹图谱的定性定量分析(Simultaneously Qualitative and Quantitative Analysis for Chromatographic Fingerprints of Traditional Chinese Medicines)/420
二、中药色谱指纹图谱的比较分析(Comparative Analysis for Chromatographic Fingerprints of Traditional Chinese Medicines)/434
第五节中药色谱指纹图谱用于质量控制的几个实例(Several Examples of Qualitative and Quantitative Analysis of Chromatographic Fingerprints of Traditional Chinese Medicines)/458
一、含诃子属中药产品的质量控制(Quality Control of Products Containing Tchebula)/458
二、硫黄熏制白芷的色谱指纹图谱分析(Chromatographic Fingerprinting Analysis for Baizhi with Sulfur Fumigation)/460
三、淫羊藿种属色谱指纹图谱的模式分析(Pattern Analysis of Epimedium herb Based on Chromatographic Fingerprints)/462
四、干鲜鱼腥草的色谱指纹图谱分析(Chromatographic Fingerprinting Analysis for Dry and Fresh Houttuynia cordata)/463
参考文献/468Chapter 11
第十一章近红外光谱应用于广义灰色复杂体系的分析(Near Infrared Spectroscopy Applied to Quantitative Analysis of Generalized Grey Analytical Systems)/473
第一节近红外光谱的预处理方法(Pretreatment Methods for Near Infrared Spectrum)/474
一、平滑与微分(Smoothing and Derivative Methods)/474
二、多元散射校正与标准正态变换(Multiplicative Scatter Correction and Standard Normal Variate Methods)/476
三、近红外光谱的小波预处理(Pretreatment Methods Based on Wavelet for Near Infrared Spectrum)/478
四、正交投影方法(Orthogonal Projection for Pretreatment of Near Infrared Spectrum)/479
第二节近红外光谱整体定性分析与模式识别(Pattern Recognition and Integral Analysis of Near Infrared Spectrum)/480
一、近红外光谱的整体性特征(Integral Feature of Near Infrared Spectrum)/480
二、化学模式识别与近红外光谱的整体定性分析(Chemical Pattern Recognition and Integral Qualitative Analysis of Near Infrared Spectrum)/481
第三节近红外光谱的定量分析与多元校正(Quantitative Analysis of Near Infrared Spectra and Multivariate Calibration)/483
一、近红外光谱定量分析的多变量线性校正方法(Linear Calibration Methods for Quantitative Analysis of Near Infrared Spectra)/484
二、近红外光谱定量分析的多变量非线性校正方法(Nonlinear Calibration Methods for Quantitative Analysis of Near Infrared Spectra)/484
第四节近红外光谱定量分析建模中的几个问题(Several Problems in Quantitative Modeling in Near Infrared Spectroscopy)/487
一、奇异样本的去除方法(Methods for Outliers Detection and Deleting)/489
二、光谱波长的选择与模型优化(Wavelength Selection and Optimization of Calibration Model)/494三、近红外光谱定量分析模型转换方法(Model Transformation Methods in Near Infrared Spectroscopic Quantitative Analysis)/502
四、近红外光谱分析的模型效验(Model Validation of Near Infrared Spectroscopic Quantitative Analysis)/512
第五节近红外光谱分析一个实例(An Example of Quantitative Analysis of Infrared Spectra)/514
参考文献/520Chapter 12
第十二章代谢组学高通量分析及模式识别解析(High Throughput Analysis and Pattern Recognition for Metabolomics)/523
第一节概论(Introduction)/523
第二节代谢组学高通量分析数据的模式分析与识别(High Throughput Analysis and Pattern Recognition for Metabolomics)/524
一、主成分分析及偏最小二乘线性判别法(Principal Component Analysis and Partial Least SquaresLinear Discriminant Method)/524
二、基于判别分析的不相关变量投影分析(Uncorrelated Projection Variable Analysis Based on Discriminant Analysis)/525
第三节代谢组学高通量数据的定性定量分析及其生物标记物发现(Qualitative and Quantitative Analysis and Biomarker Discovery Based on High Throughput Analytic Data in Metabonomics)/537
一、代谢组学研究的仪器分析技术(Analytical Technology of Instruments in Metabonomics)/537
二、代谢组学中GCMS数据的定性定量分析(Qualitative and Quantitative Analysis for GCMS Data in Metabolomics)/540
三、代谢组学生物标记物的发现及其在医学诊断中的应用(Metabonomic Biomarker Discovery and its Application in Medicinal Diagnosis)/554
第四节代谢组学方法应用于中药现代化研究初探(Metabolomics Applied to Primary Research on Modernization of Chinese Medicines)/567
一、基于代谢组分析的中药药代动力学新方法初探(Primary Research on New Method of Kinetics of Pharmaceutical Metabolization of Traditional Chinese Medicines Based on Metabonomics)/568
二、基于代谢组学的中药抗菌作用模式探索(Exploring the Antibacterial Mode of Traditional Chinese Medicines Based on Metabonomics)/572
三、基于血浆代谢谱的2型糖尿病判别模型及药效评价平台的构建(Construction of Research Platform of Evaluting Drug Effect Based on Discriminant Model of Plasma Metaboloc Profile of Type 2 Diabetes Mellitus)/578
参考文献/581Chapter 13
第十三章蛋白组学的仪器分析及其生物信息学解析方法(Instrumental Analysis for Proteomics and Related Bioinformatic Methods)/588
第一节蛋白组学高通量分析方法及其数据的信号特征(Methods in High Throughput Proteomics and Their Signal Features)/588
一、蛋白组学的凝胶电泳分析方法(Analytical Method of Gel Electrophoresis in Proteomics)/589
二、蛋白组学的HPLCMS/MS分析方法(Analytical Methods of HPLCMS/MS Method in Proteomics)/591
第二节蛋白组学高通量LCMS数据的预处理方法(Pretreatment Methods in High Throughput LCMS Data Analysis of Proteome)/594
一、LCMS数据预处理方法(Preprocessing Methods of LCMS Data)/594
二、LCMS/MS数据预处理方法(Preprocessing Methods of LCMS/MS Data)/597
第三节蛋白组学高通量分析数据用于蛋白质定性分析(Identification of Proteins by High Throughput Analytical Data of Proteomics)/604
一、蛋白质序列测定中蛋白质序列库搜索算法(Protein Sequence Database Search Algorithms to Identify Protein Sequence)/604
二、从头测序法与多肽指纹用于多肽序列测定(de novo Methods and Peptide Mass Fingerprinting to Identify Peptide Sequence)/609
三、多肽质谱库搜索用于定性多肽(Identification of Peptides by Searching Peptide Library)/612
第四节蛋白质库搜索结果评估方法(Methods of Evaluating Protein Sequence Database Search Results)/613
一、蛋白质序列库搜索结果评价方法(Protein Sequence Database Search Results Validation Methods)/613
二、翻转蛋白质序列库用于估计定性结果(Evaluation of Database Search Results by Decoy Protein Database)/618
三、蛋白质定性(Protein Identification)/621
第五节定量蛋白质组研究方法及蛋白质组新研究策略(Methods in Quantitative Proteome Research and New Strategies)/623
一、蛋白质定量分析方法(Methods in Quantitative Proteomics)/623
二、蛋白质组研究中的新策略(New Strategies in Proteome Research)/628
参考文献/631Chapter 14
第十四章统计学和应用数学基础知识(Necessary Fundamental Knowledge of Statistics and Linear Algebra)/647
第一节必要统计学基础知识(Necessary Fundamental Knowledge of Statistics)/647
一、随机事件的概率公式(Probability Formula of Random Events)/647
二、随机变量及其分布(Random Variable and its Distribution) /649
三、随机变量的数值特征(Numerical Feature of Random Variable)/653
第二节必要应用数学基础知识(Necessary Fundamental Knowledge of Linear Algebra)/654
一、矢量及其运算(Vector and its Calculation)/654
二、矩阵及其运算(Matrix and its Calculation)/655
三、独立性、正交性和子空间(Independence,Orthogonality and Subspace)/660
四、矢量范数和矩阵范数(Fronenius Number of Vector and Matrix)/661
五、张量的概念(Concept of Tensor)/662
第三节最优化方法基础(Elements for Optimization Methods)/662
一、优化理论简介(Introduction to Optimization Theory)/662
二、优化问题的一般形式与基本概念(General Forms and Basic Conceptions of Optimization)/663
三、优化问题的一般求解思路及最速下降算法(General Train of Thought for Optimization and Steepest Descent Algorithm)/665
四、拉格朗日乘子法(Lagrange Multiplier Method)/666
参考文献/666

中国年鉴
研究报告
省市统计
中国名录
省市年鉴
地方志
社会经济
行业数据
古籍文献
友情链接:中国统计年鉴2013|中国2010年人口普查资料

中国年鉴信息网 版权所有 @1997-2013

电话:010-58697871 58697872 传真:010-58697500

Mail:chinayearbook@vip.sina.com 地址:北京朝阳区东三环中路39号建外SOHO12号楼2006室