化学计量学基础
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九品
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作者梁逸曾、易伦朝 著
出版社华东理工大学出版社
出版时间2010-10
版次1
装帧平装
货号东017
上书时间2023-07-21
商品详情
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图书标准信息
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作者
梁逸曾、易伦朝 著
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出版社
华东理工大学出版社
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出版时间
2010-10
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版次
1
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ISBN
9787562828716
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定价
38.00元
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装帧
平装
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开本
16开
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纸张
胶版纸
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页数
196页
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字数
340千字
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正文语种
简体中文,英语
- 【内容简介】
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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。
- 【目录】
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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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