化学计量学基础
出版时间:
2010-10
版次:
1
ISBN:
9787562828716
定价:
38.00
装帧:
平装
开本:
16开
纸张:
胶版纸
页数:
196页
字数:
340千字
正文语种:
简体中文，英语
25人买过
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《化学计量学基础》以化学计量学的基础知识为其主线，在讲述数学基础时就试图与其化学应用直接相连，始终注意到讲解这些知识可为化学家们提供了什么样的新思路，可以解决什么样的化学问题。《化学计量学基础》虽用英文编写，但文中出现的一些非常用英文单词皆给出中文提示，以节省学生查阅字典的时间；凡是在书中出现重要知识点的地方，本书尽量佐以问题进行提示，以引起学生的足够注意；另外，本书在必要时还尽量给出中文注释和评述，对所授知识进一步进行解释和阐述，以提高学生的认识和降低阅读的难度。 Chapter1IntroductionandNecessaryFundamentalKnowledgeofMathematics
1.1Chemometrics：DefinitionandItsBriefHistory/3
1.2TheRelationshipbetweenAnalyticalChemistryandChemometrics/4
1.3TheRelationshipbetweenChemometrics，ChemoinformaticsandBioinformatics/7
1.4NecessaryKnowledgeofMathematics/9
1.4.1VectorandItsCalculation/10
1.4.2MatrixandItsCalculation/19
Chapter2ChemicalExperimentDesign
2.1Introduction/39
2.2FactorialDesignandItsRationalAnalysis/41
2.2.1ComputationofEffectsUsingSignTables/44
2.2.2NormalPlotofEffectsandResiduals/45
2.3FractionalFactorialDesign/47
2.4OrthogonalDesignandOrthogonalArray/52
2.4.1DefinitionofOrthogonalDesignTable/53
2.4.2OrthogonalArraysandTheirInter-effectTables/54
2.4.3LinearGraphsofOrthogonalArrayandItsApplications/55
2.5UniformExperimentalDesignandUniformDesignTable/55
2.5.1UniformDesignTableandItsConstruction/56
2.5.2UniformityCriterionandAccessoryTablesforUniformDesign/59
2.5.3UniformDesignforPseudo-level/60
2.5.4AnExampleforOptimizationofElectropheroticSeparationUsingUniformDesign/61
2.6D-OptimalExperimentDesign/65
2.7OptimizationBasedonSimplexandExperimentDesign/68
2.7.1ConstructinganInitialSimplextoStarttheExperimentDesign/69
2.7.2SimplexSearchingandOptimization/70
Chapter3ProcessingofAnalyticSignals
3.1SmoothingMethodsofAnalyticalSignals/77
3.1.1Moving-WindowAverageSmoothingMethod/77
3.1.2Savitsky-GolayFilter/77
3.2DerivativeMethodsofAnalyticalSignals/83
3.2.1SimpleDifferenceMethod/83
3.2.2Moving-WindowPolynomialLeast-SquaresFittingMethod/84
3.3BackgroundCorrectionMethodofAnalyticalSignals/89
3.3.1PenalizedLeastSquaresAlgorithm/89
3.3.2AdaptiveIterativelyReweightedProcedure/90
3.3.3SomeExamplesforCorrectingtheBaselinefromDifferentInstruments/92
3.4TransformationMethodsofAnalyticalSignals/94
3.4.1PhysicalMeaningoftheConvolutionAlgorithm/94
3.4.2MultichannelAdvantageinSpectroscopyandHadamardTransformation/96
3.4.3FourierTransformation/99
Appendix1.AMatlabProgramforSmoothingtheAnalyticalSignals/108
Appendix2：AMatlabProgramforDemonstrationofFTAppliedtoSmoothing/112
Chapter4MultivariateCalibrationandMultivariateResolution
4.1MultivariateCalibrationMethodsforWhiteAnalyticalSystems/116
4.1.1DirectCalibrationMethods/116
4.1.2IndirectCalibrationMethods/121
4.2MultivariateCalibrationMethodsforGreyAnalyticalSystems/126
4.2.1VectoralCalibrationMethods/127
4.2.2MatrixCalibrationMethods/127
4.3MultivariateResolutionMethodsforBlackAnalyticalSystems/129
4.3.1Self-modelingCurveResolutionMethod/131
4.3.2IterativeTargetTransformationFactorAnalysis/134
4.3.3EvolvingFactorAnalysisandRelatedMethods/137
4.3.4WindowFactorAnalysis/141
4.3.5HeuristicEvolvingLatentProjections/145
4.3.6SubwindowFactorAnalysis/152
4.4MultivariateCalibrationMethodsforGeneralizedGreyAnalyticalSystems/154
4.4.1PrincipalComponentRegression（PCR）/156
4.4.2PartialLeastSquares（PLS）/157
4.4.3Leave-one-outCross-validation/159
Chapter5PatternRecognitionandPatternAnalysisforChemicalAnalyticalData
5.1Introduction/169
5.1.1ChemicalPatternSpace/169
5.1.2DistanceinPatternSpaceandMeasuresofSimilarity/171
5.1.3FeatureExtractionMethods/173
5.1.4PretreatmentMethodsforPatternRecognition/173
5.2SupervisedPatternRecognitionMethods：DiscriminantAnalysisMethods/174
5.2.1DiscriminationMethodBasedonEuclideanDistance/175
5.2.2DiscriminationMethodBasedonMahaianobisDistance/175
5.2.3LinearLearningMachine/176
5.2.4k-NearestNeighborsDiscriminationMethod/177
5.3UnsupervisedPatternRecognitionMethods：ClusteringAnalysisMethods/179
5.3.1MinimumSpanningTreeMethod/179
5.3.2k-meansClusteringMethod/181
5.4VisualDimensionalReductionBasedonLatentProjections/183
5.4.1ProjectionDiscriminationMethodBasedonPrincipalComponentAnalysis/183
5.4.2SMICAMethodBasedonPrincipalComponentAnalysis/186
5.4.3ClassificationMethodBasedonPartialLeastSquares/193
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内容简介:
《化学计量学基础》以化学计量学的基础知识为其主线，在讲述数学基础时就试图与其化学应用直接相连，始终注意到讲解这些知识可为化学家们提供了什么样的新思路，可以解决什么样的化学问题。《化学计量学基础》虽用英文编写，但文中出现的一些非常用英文单词皆给出中文提示，以节省学生查阅字典的时间；凡是在书中出现重要知识点的地方，本书尽量佐以问题进行提示，以引起学生的足够注意；另外，本书在必要时还尽量给出中文注释和评述，对所授知识进一步进行解释和阐述，以提高学生的认识和降低阅读的难度。
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目录:
Chapter1IntroductionandNecessaryFundamentalKnowledgeofMathematics
1.1Chemometrics：DefinitionandItsBriefHistory/3
1.2TheRelationshipbetweenAnalyticalChemistryandChemometrics/4
1.3TheRelationshipbetweenChemometrics，ChemoinformaticsandBioinformatics/7
1.4NecessaryKnowledgeofMathematics/9
1.4.1VectorandItsCalculation/10
1.4.2MatrixandItsCalculation/19
Chapter2ChemicalExperimentDesign
2.1Introduction/39
2.2FactorialDesignandItsRationalAnalysis/41
2.2.1ComputationofEffectsUsingSignTables/44
2.2.2NormalPlotofEffectsandResiduals/45
2.3FractionalFactorialDesign/47
2.4OrthogonalDesignandOrthogonalArray/52
2.4.1DefinitionofOrthogonalDesignTable/53
2.4.2OrthogonalArraysandTheirInter-effectTables/54
2.4.3LinearGraphsofOrthogonalArrayandItsApplications/55
2.5UniformExperimentalDesignandUniformDesignTable/55
2.5.1UniformDesignTableandItsConstruction/56
2.5.2UniformityCriterionandAccessoryTablesforUniformDesign/59
2.5.3UniformDesignforPseudo-level/60
2.5.4AnExampleforOptimizationofElectropheroticSeparationUsingUniformDesign/61
2.6D-OptimalExperimentDesign/65
2.7OptimizationBasedonSimplexandExperimentDesign/68
2.7.1ConstructinganInitialSimplextoStarttheExperimentDesign/69
2.7.2SimplexSearchingandOptimization/70
Chapter3ProcessingofAnalyticSignals
3.1SmoothingMethodsofAnalyticalSignals/77
3.1.1Moving-WindowAverageSmoothingMethod/77
3.1.2Savitsky-GolayFilter/77
3.2DerivativeMethodsofAnalyticalSignals/83
3.2.1SimpleDifferenceMethod/83
3.2.2Moving-WindowPolynomialLeast-SquaresFittingMethod/84
3.3BackgroundCorrectionMethodofAnalyticalSignals/89
3.3.1PenalizedLeastSquaresAlgorithm/89
3.3.2AdaptiveIterativelyReweightedProcedure/90
3.3.3SomeExamplesforCorrectingtheBaselinefromDifferentInstruments/92
3.4TransformationMethodsofAnalyticalSignals/94
3.4.1PhysicalMeaningoftheConvolutionAlgorithm/94
3.4.2MultichannelAdvantageinSpectroscopyandHadamardTransformation/96
3.4.3FourierTransformation/99
Appendix1.AMatlabProgramforSmoothingtheAnalyticalSignals/108
Appendix2：AMatlabProgramforDemonstrationofFTAppliedtoSmoothing/112
Chapter4MultivariateCalibrationandMultivariateResolution
4.1MultivariateCalibrationMethodsforWhiteAnalyticalSystems/116
4.1.1DirectCalibrationMethods/116
4.1.2IndirectCalibrationMethods/121
4.2MultivariateCalibrationMethodsforGreyAnalyticalSystems/126
4.2.1VectoralCalibrationMethods/127
4.2.2MatrixCalibrationMethods/127
4.3MultivariateResolutionMethodsforBlackAnalyticalSystems/129
4.3.1Self-modelingCurveResolutionMethod/131
4.3.2IterativeTargetTransformationFactorAnalysis/134
4.3.3EvolvingFactorAnalysisandRelatedMethods/137
4.3.4WindowFactorAnalysis/141
4.3.5HeuristicEvolvingLatentProjections/145
4.3.6SubwindowFactorAnalysis/152
4.4MultivariateCalibrationMethodsforGeneralizedGreyAnalyticalSystems/154
4.4.1PrincipalComponentRegression（PCR）/156
4.4.2PartialLeastSquares（PLS）/157
4.4.3Leave-one-outCross-validation/159
Chapter5PatternRecognitionandPatternAnalysisforChemicalAnalyticalData
5.1Introduction/169
5.1.1ChemicalPatternSpace/169
5.1.2DistanceinPatternSpaceandMeasuresofSimilarity/171
5.1.3FeatureExtractionMethods/173
5.1.4PretreatmentMethodsforPatternRecognition/173
5.2SupervisedPatternRecognitionMethods：DiscriminantAnalysisMethods/174
5.2.1DiscriminationMethodBasedonEuclideanDistance/175
5.2.2DiscriminationMethodBasedonMahaianobisDistance/175
5.2.3LinearLearningMachine/176
5.2.4k-NearestNeighborsDiscriminationMethod/177
5.3UnsupervisedPatternRecognitionMethods：ClusteringAnalysisMethods/179
5.3.1MinimumSpanningTreeMethod/179
5.3.2k-meansClusteringMethod/181
5.4VisualDimensionalReductionBasedonLatentProjections/183
5.4.1ProjectionDiscriminationMethodBasedonPrincipalComponentAnalysis/183
5.4.2SMICAMethodBasedonPrincipalComponentAnalysis/186
5.4.3ClassificationMethodBasedonPartialLeastSquares/193
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