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梁逸曾 、 易伦朝 著 / 华东理工大学出版社 / 2010-10 / 平装
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化学计量学基础
《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。
Chapter1IntroductionandNecessaryFundamentalKnowledgeofMathematics1.1Chemometrics:DefinitionandItsBriefHistory/31.2TheRelationshipbetweenAnalyticalChemistryandChemometrics/41.3TheRelationshipbetweenChemometrics,ChemoinformaticsandBioinformatics/71.4NecessaryKnowledgeofMathematics/91.4.1VectorandItsCalculation/101.4.2MatrixandItsCalculation/19Chapter2ChemicalExperimentDesign2.1Introduction/392.2FactorialDesignandItsRationalAnalysis/412.2.1ComputationofEffectsUsingSignTables/442.2.2NormalPlotofEffectsandResiduals/452.3FractionalFactorialDesign/472.4OrthogonalDesignandOrthogonalArray/522.4.1DefinitionofOrthogonalDesignTable/532.4.2OrthogonalArraysandTheirInter-effectTables/542.4.3LinearGraphsofOrthogonalArrayandItsApplications/552.5UniformExperimentalDesignandUniformDesignTable/552.5.1UniformDesignTableandItsConstruction/562.5.2UniformityCriterionandAccessoryTablesforUniformDesign/592.5.3UniformDesignforPseudo-level/602.5.4AnExampleforOptimizationofElectropheroticSeparationUsingUniformDesign/612.6D-OptimalExperimentDesign/652.7OptimizationBasedonSimplexandExperimentDesign/682.7.1ConstructinganInitialSimplextoStarttheExperimentDesign/692.7.2SimplexSearchingandOptimization/70Chapter3ProcessingofAnalyticSignals3.1SmoothingMethodsofAnalyticalSignals/773.1.1Moving-WindowAverageSmoothingMethod/773.1.2Savitsky-GolayFilter/773.2DerivativeMethodsofAnalyticalSignals/833.2.1SimpleDifferenceMethod/833.2.2Moving-WindowPolynomialLeast-SquaresFittingMethod/843.3BackgroundCorrectionMethodofAnalyticalSignals/893.3.1PenalizedLeastSquaresAlgorithm/893.3.2AdaptiveIterativelyReweightedProcedure/903.3.3SomeExamplesforCorrectingtheBaselinefromDifferentInstruments/923.4TransformationMethodsofAnalyticalSignals/943.4.1PhysicalMeaningoftheConvolutionAlgorithm/943.4.2MultichannelAdvantageinSpectroscopyandHadamardTransformation/963.4.3FourierTransformation/99Appendix1.AMatlabProgramforSmoothingtheAnalyticalSignals/108Appendix2:AMatlabProgramforDemonstrationofFTAppliedtoSmoothing/112Chapter4MultivariateCalibrationandMultivariateResolution4.1MultivariateCalibrationMethodsforWhiteAnalyticalSystems/1164.1.1DirectCalibrationMethods/1164.1.2IndirectCalibrationMethods/1214.2MultivariateCalibrationMethodsforGreyAnalyticalSystems/1264.2.1VectoralCalibrationMethods/1274.2.2MatrixCalibrationMethods/1274.3MultivariateResolutionMethodsforBlackAnalyticalSystems/1294.3.1Self-modelingCurveResolutionMethod/1314.3.2IterativeTargetTransformationFactorAnalysis/1344.3.3EvolvingFactorAnalysisandRelatedMethods/1374.3.4WindowFactorAnalysis/1414.3.5HeuristicEvolvingLatentProjections/1454.3.6SubwindowFactorAnalysis/1524.4MultivariateCalibrationMethodsforGeneralizedGreyAnalyticalSystems/1544.4.1PrincipalComponentRegression(PCR)/1564.4.2PartialLeastSquares(PLS)/1574.4.3Leave-one-outCross-validation/159Chapter5PatternRecognitionandPatternAnalysisforChemicalAnalyticalData5.1Introduction/1695.1.1ChemicalPatternSpace/1695.1.2DistanceinPatternSpaceandMeasuresofSimilarity/1715.1.3FeatureExtractionMethods/1735.1.4PretreatmentMethodsforPatternRecognition/1735.2SupervisedPatternRecognitionMethods:DiscriminantAnalysisMethods/1745.2.1DiscriminationMethodBasedonEuclideanDistance/1755.2.2DiscriminationMethodBasedonMahaianobisDistance/1755.2.3LinearLearningMachine/1765.2.4k-NearestNeighborsDiscriminationMethod/1775.3UnsupervisedPatternRecognitionMethods:ClusteringAnalysisMethods/1795.3.1MinimumSpanningTreeMethod/1795.3.2k-meansClusteringMethod/1815.4VisualDimensionalReductionBasedonLatentProjections/1835.4.1ProjectionDiscriminationMethodBasedonPrincipalComponentAnalysis/1835.4.2SMICAMethodBasedonPrincipalComponentAnalysis/1865.4.3ClassificationMethodBasedonPartialLeastSquares/193
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