Applied chemometrics for scientists:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Hoboken, NJ
John Wiley & Sons
c2007
|
| Schlagworte: | |
| Online-Zugang: | Table of contents only Contributor biographical information Publisher description Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | XV, 379 S. graph. Darst. |
| ISBN: | 0470016868 9780470016862 |
Internformat
MARC
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| 007 | t | ||
| 008 | 080107s2007 xxud||| |||| 00||| eng d | ||
| 010 | |a 2006030737 | ||
| 020 | |a 0470016868 |c cloth : alk. paper |9 0-470-01686-8 | ||
| 020 | |a 9780470016862 |9 978-0-470-01686-2 | ||
| 035 | |a (OCoLC)85828378 | ||
| 035 | |a (DE-599)BVBBV023066101 | ||
| 040 | |a DE-604 |b ger |e aacr | ||
| 041 | 0 | |a eng | |
| 044 | |a xxu |c US | ||
| 049 | |a DE-703 |a DE-634 | ||
| 050 | 0 | |a QD75.4.S8 | |
| 082 | 0 | |a 543.01/5195 | |
| 082 | 0 | |a 543.015195 |2 22 | |
| 084 | |a VC 6050 |0 (DE-625)147081:253 |2 rvk | ||
| 100 | 1 | |a Brereton, Richard G. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Applied chemometrics for scientists |c Richard G. Brereton |
| 264 | 1 | |a Hoboken, NJ |b John Wiley & Sons |c c2007 | |
| 300 | |a XV, 379 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Includes bibliographical references and index | ||
| 650 | 4 | |a Chemometrics | |
| 650 | 0 | 7 | |a Chemometrie |0 (DE-588)4299578-4 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Chemometrie |0 (DE-588)4299578-4 |D s |
| 689 | 0 | |5 DE-604 | |
| 856 | 4 | |u http://www.loc.gov/catdir/toc/ecip071/2006030737.html |3 Table of contents only | |
| 856 | 4 | |u http://www.loc.gov/catdir/enhancements/fy0740/2006030737-b.html |3 Contributor biographical information | |
| 856 | 4 | |u http://www.loc.gov/catdir/enhancements/fy0740/2006030737-d.html |3 Publisher description | |
| 856 | 4 | 2 | |m OEBV Datenaustausch |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016269302&sequence=000007&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-016269302 | ||
Datensatz im Suchindex
| _version_ | 1804137306759626752 |
|---|---|
| adam_text | C O N T E N T S PREFACE? XIII? I N T R O D U C T I O N * 1 *
1.1 D E V E L O P M E N T O F C H E M O M E T R I C S * 1 *
1.1.1 E A R L Y D E V E L O P M E N T S * I * 1.1.2 1 9 8 0 S
A N D THE B O R D E R L I N E S B E T W E E N O T H E R D
I S C I P L I N E S * 1 * 1.1.3 1 9 9 0 S A N D P R O B L E
M S O F I N T E R M E D I A T E C O M P L E X I T Y * 2 *
1 . 1 . 4 C U R R E N T D E V E L O P M E N T S IN C O M P L E
X P R O B L E M S O L V I N G * 2 * 1.2* A P P L I C A T I O
N A R E A S 3 * 1.3* H O W TO USE T H I S B O O K 4 *
1.4* L I T E R A T U R E A N D O T H E R S O U R C E S O
F I N F O R M A T I O N 5 * R E F E R E N C E S 7 * 2
E X P E R I M E N T A L D E S I G N * 9 * 2.1 W H Y D E S I
G N E X P E R I M E N T S IN C H E M I S T R Y ? * 9 * 2 . 2
D E G R E E S O F F R E E D O M A N D S O U R C E S O
F E R R O R * 12* 2 . 3 A N A L Y S I S O F V A R I A N C
E A N D I N T E R P R E T A T I O N O F E R R O R S * 16*
2 . 4 M A T R I C E S , V E C T O R S A N D THE P S E U D O
I N V E R S E * 2 0 * 2.5 D E S I G N M A T R I C E S * 22*
2 . 6 F A C T O R I A L D E S I G N S * 25* 2.6.1 E X T E N D I
N G T H E N U M B E R O F F A C T O R S * 28* 2 . 6 . 2
E X T E N D I N G THE N U M B E R O F L E V E L S * 28* 2
. 7 AN E X A M P L E O F A F A C T O R I A L D E S I G N
* 2 9 * 2.8 F R A C T I O N A L F A C T O R I A L D E S I G
N S * 32* 2 . 9 P L A C K E T T - B U R M A N A N D T A G U
C H I D E S I G N S * 35* 2 . 1 0 T H E A P P L I C A T I O
N O F A P L A C K E T T - B U R M A N D E S I G N TO T H
E S C R E E N I N G * O F F A C T O R S I N F L U E N C I N
G A C H E M I C A L R E A C T I O N 37* 2. I I C E N T R
A L C O M P O S I T E D E S I G N S * 39* 2 . 1 2 M I X T U
R E D E S I G N S * 44* 2 . 1 2 . 1 S I M P L E X C E N T R
O I D D E S I G N S * 45* 2 . 1 2 . 2 S I M P L E X L A T T
I C E D E S I G N S * 47* 2 . 1 2 . 3 C O N S T R A I N E D
M I X T U R E D E S I G N S * 47* V I CONTENTS 2 . 1 3 A *
F O U R C O M P O N E N T M I X T U R E D E S I G N U S E
D T O S T U D Y B L E N D I N G * O F O L I V E O I L
S 4 9 * 2 . 1 4 S I M P L E X O P T I M I Z A T I O N *
51* 2 . 1 5 * L E V E R A G E A N D C O N F I D E N C E IN M
O D E L S 5 3 * 2 . 1 6 D E S I G N S F O R M U L T I V A
R I A T E C A L I B R A T I O N * 58* R E F E R E N C E S 6 2 *
3 S T A T I S T I C A L C O N C E P T S * 63* 3.1 S T A T I
S T I C S F O R C H E M I S T S * 6 3 * 3 . 2 E R R O R S
* 6 4 * 3.2.1* S A M P L I N G E R R O R S 65* 3 . 2 . 2 *
S A M P I E P R E P A R A T I O N E R R O R S 6 6 * 3 . 2
. 3 I N S T R U M E N T A L N O I S E * 67* 3 . 2 . 4 S O U
R C E S O F E R R O R * 67* 3 . 3 D E S C R I B I N G D A
T A * 67* 3 . 3 . 1 * D E S C R I P T I V E S T A T I S T I C S
68* 3 . 3 . 2 G R A P H I C A L P R E S E N T A T I O N *
69* 3 . 3 . 3 C O V A R I A N C E A N D C O R R E L A T I O N
C O E F F I C I E N T * 7 2 * 3 . 4 T H E N O R M A L
D I S T R I B U T I O N * 73* 3 . 4 . 1 * E R R O R D I S T R I
B U T I O N S 73* 3 . 4 . 2 * N O R M A L D I S T R I B U T I O
N F U N C T I O N S A N D T A B L E S 7 4 * 3 . 4 . 3
A P P L I C A T I O N S * 75* 3 . 5 I S A D I S T R I B U T
I O N N O R M A L ? * 76* 3.5.1* C U M U L A T I V E F R E Q U
E N C Y 76* 3 . 5 . 2 * K O L M O G O R O V - S M I R N O V T E
S T 78* 3 . 5 . 3 C O N S E Q U E N C E S * 7 9 * 3 . 6 H
Y P O T H E S I S T E S T S * 80* 3 . 7 C O M P A R I S O N
O F M E A N S : T H E T - T E S T * 81* 3.8 F - T E S T F
O R C O M P A R I S O N O F V A R I A N C E S * 85* 3 . 9
C O N F I D E N C E I N L I N E A R R E G R E S S I O N *
89* 3 . 9 . 1 * L I N E A R C A L I B R A T I O N 9 0 * 3 .
9 . 2 E X A M P L E * 9 0 * 3 . 9 . 3 C O N F I D E N C E
O F P R E D I C T I O N O F P A R A M E T E R S * 9 2 * 3
. 1 0 M O R E A B O U T C O N F I D E N C E * 93* 3 . 1 0 .
1 C O N F I D E N C E IN T H E M E A N * 93* 3 . 1 0 . 2
C O N F I D E N C E IN T H E S T A N D A R D D E V I A T I O N
* 95* 3.11 C O N S E Q U E N C E S O F O U T L I E R S A N D
H O W TO D E A L W I T H T H E M * 96* 3 . 1 2 D E T E
C T I O N O F O U TL I E R S * 100* 3 . 1 2 . 1 N O R M A
L D I S T R I B U T I O N S * 100* 3 . 1 2 . 2 L I N E A R R
E G R E S S I O N * 101* 3 . 1 2 . 3 M U L T I V A R I A T E C
A L I B R A T I O N * 103* 3 . 1 3 S H E W H A R T C H A R T S
* 104* 3. 14 M O R E A B O U T C O N T R O L C H A R T S *
106* 3 . 1 4 . 1 C U S U M C H A R T * 106* 3 . 1 4 . 2 R
A N G E C H A R T * 108* 3 . 1 4 . 3 M U L T I V A R I A T E
S T A T I S T I C A L P R O C E S S C O N T R O ! 108* R E F E
R E N C E S 109* V I I CONTENTS 4 SEQUENTIAL METHODS* 111* 4.1* S
E Q U E N T I A L D A T A 111* 4 . 2 C O R R E L O G R A M S *
112* 4 . 2 . 1 A U T O - C O R R E L O G R A M S * 113* 4 . 2 .
2 C R O S S - C O R R E L O G R A M S * 115* 4 . 2 . 3 M U L T
I V A R I A T E C O R R E L O G R A M S * 115* 4 . 3 L I N E A
R S M O O T H I N G F U N C T I O N S A N D F I L T E R S *
1 1 6 * 4 . 4 F O U R I E R T R A N S F O R M S * 1 2 0 *
4 . 5 M A X I M U M E N T R O P Y A N D B A Y E S I A N
M E T H O D S * 124* 4.5.1 B A Y E S T H E O R E M * 124*
4 . 5 . 2 M A X I M U M E N T R O P Y * 125* 4 . 5 . 3 M A X
I M U M E N T R O P Y A N D M O D E L L I N G * 1 2 6 * 4
. 6 F O U R I E R F I L T E R S * 128* 4 . 7 P E A K S H A P
E S IN C H R O M A T O G R A P H Y A N D S P E C T R O S C O P
Y * 134* 4.7.1 P R I N C I P A L F E A T U R E S * 135* 4 . 7 .
2 G A U S S I A N S * 1 3 6 * 4 . 7 . 3 L O R E N T Z I A N
S * 1 3 6 * 4 . 7 . 4 A S Y M M E T R I E P E A K S H A P
E S * 137* 4 . 7 . 5 U S E O F P E A K S H A P E I N F
O R M A T I O N * 138* 4 . 8 D E R I V A T I V E S IN S P E C T
R O S C O P Y A N D C H R O M A T O G R A P H Y * 138* 4 . 9
W A V E L E T S * 1 4 2 * R E F E R E N C E S 143* 5 PATTERN
RECOGNITION* 145* 5.1* I N T R O D U C T I O N 145* 5.1.1 E X P L O R
A T O R Y D A T A A N A L Y S I S * 145* 5 . 1 . 2 U N S U P
E R V I S E D P A T T E R N R E C O G N I T I O N * 1 4 6 *
5 . 1 . 3 S U P E R V I S E D P A T T E R N R E C O G N I T I O
N * 1 4 6 * 5 . 2 P R I N C I P A L C O M P O N E N T S A
N A L Y S I S * 147* 5.2.1 B A S I C I D E A S * 147* 5 . 2 . 2
M E T H O D * 1 5 0 * 5 . 3 G R A P H I C A L R E P R E S
E N T A T I O N O F S C O R E S A N D L O A D I N G S *
154* 5.3.1 C A S E S T U D Y I * 1 5 4 * 5 . 3 . 2 C A S
E S T U D Y 2 * 154* 5 . 3 . 3 S C O R E S P L O T S *
156* 5 . 3 . 4 L O A D I N G S P L O T S * 157* 5 . 3 . 5 E
X T E N S I O N S * 159* 5 . 4 C O M P A R I N G M U L T I V A
R I A T E P A T T E R N S * 159* 5 . 5 P R E P R O C E S S I N
G * 1 6 0 * 5 . 6 U N S U P E R V I S E D P A T T E R N R
E C O G N I T I O N : C L U S T E R A N A L Y S I S * 167* 5 .
7 S U P E R V I S E D P A T T E R N R E C O G N I T I O N *
171* 5.7.1 M O D E L L I N G THE T R A I N I N G S E T * 171* 5
. 7 . 2 T E S T S E T S , C R O S S - V A L I D A T I O N A
N D THE B O O T S T R A P * 172* 5 . 7 . 3 A P P L Y I N G T
H E M O D E L * 174* 5 . 8 S T A T I S T I C A L C L A S S I
F I C A T I O N T E C H N I Q U E S * 174* 5.8.1 U N I V A R I A T
E C L A S S I F I C A T I O N * 175* 5 . 8 . 2 B I V A R I A T
E A N D M U L T I V A R I A T E D I S C R I M I N A N T M O
D E L S * 175* V I I I CONTENTS 5.8.3 SIMCA 178* 5.8.4 STATISTICAL
OUTPUT 182* 5.9 K NEAREST NEIGHBOUR METHOD 182* 5.10 HOW MANY COMPONENTS
CHARACTERIZE A DATASET? 185* 5.11 MULTIWAY PATTERN RECOGNITION 187*
5.11.1 TUCKER3 MODELS 188* 5.11.2 PARAFAC 189* 5.11.3 UNFOLDING 190*
REFERENCES 190* 6 C A L I B R A T I O N 193* 6.1 INTRODUCTION 193*
6.2 UNIVARIATE CALIBRATION 195* 6.2.1 CLASSICAL CALIBRATION 195* 6.2.2
LNVERSE CALIBRATION 196* 6.2.3 CALIBRATION EQUATIONS 198* 6.2.4
INCLUDING EXTRA TERMS 199* 6.2.5 GRAPHS 199* 6.3 MULTIVARIATE
CALIBRATION AND THE SPECTROSCOPY OF MIXTURES 202* 6.4 MULTIPLE LINEAR
REGRESSION 206* 6.5 PRINCIPAL COMPONENTS REGRESSION 208* 6.6 PARTIAL
LEAST SQUARES 211* 6.7 HOW GOOD IS THE CALIBRATION AND WHAT IS THE MOST
APPROPRIATE MODEL? 214* 6.7.1 AUTOPREDICTION 214* 6.7.2 CROSS-VALIDATION
215* 6.7.3 TEST SETS 215* 6.7.4 BOOTSTRAP 217* 6.8 MULTIWAY CALIBRATION
217* 6.8.1 UNFOLDING 217* 6.8.2 TRILINEAR PLSI 218* 6.8.3 N-PLSM 219*
REFERENCES 220* 7 C O U P L E D C H R O M A T O G R A P H Y
221* 7.1 INTRODUCTION 221* 7.2 PREPARING THE DATA 222* 7.2.1
PREPROCESSING 222* 7.2.2 VARIABLE SELECTION 224* 7.3 CHEMICAL
COMPOSITION OF SEQUENTIAL DATA 228* 7.4 UNIVARIATE PURITY CURVES 230*
7.5 SIMILARITY BASED METHODS 234* 7.5.1 SIMILARITY 234* 7.5.2
CORRELATION COEFFICIENTS 234* 7.5.3 DISTANCE MEASURES 235* 7.5.4 OPA AND
SIMPLISMA 236* 7.6 EVOLVING AND WINDOW FACTOR ANALYSIS 236* IX CONTENTS
7.6.1 EXPANDING WINDOWS 237* 7.6.2 FIXED SIZED WINDOWS 238* 7.6.3
VARIATIONS 239* 7.7 DERIVATIVE BASED METHODS 239* 7.8 DECONVOLUTION OF
EVOLUTIONARY SIGNALS 241* 7.9 NONITERATIVE METHODS TUER RESOLUTION 242*
7.9.1 SELECTIVITY: FINDING PURE VARIABLES 242* 7.9.2 MULTIPLE LINEAR
REGRESSION 243* 7.9.3 PRINCIPAL COMPONENTS REGRESSION 244* 7.9.4 PARTIAL
SELECTIVITY 244* 7.10 ITERATIVE METHODS FOR RESOLUTION 246* 8
EQUILIBRIA, REACTIONS AND PROCESS ANALYTICS 249* 8.1 THE STUDY OF
EQUILIBRIA USING SPECTROSCOPY 249* 8.2 SPECTROSCOPIC MONITARING OF
REACTIONS 252* 8.2.1 MID INFRARED SPECTROSCOPY 253* 8.2.2 NEAR INFRARED
SPECTROSCOPY 254* 8.2.3 UV!VISIBLE SPECTROSCOPY 255* 8.2.4 RAMAN
SPECTROSCOPY 256* 8.2.5 SUMMARY OF MAIN DATA ANALYSIS TECHNIQUES 256*
8.3 KINETICS AND MULTIVARIATE MODELS FOR THE QUANTITATIVE STUDY OF
REACTIONS 257* 8.4 DEVELOPMENTS IN THE ANALYSIS OF REACTIONS USING
ON-LINE SPECTROSCOPY 261* 8.4.1 CONSTRAINTS AND COMBINING INFORMATION
261* 8.4.2 DATA MERGING 262* 8.4.3 THREE-WAY ANALYSIS 262* 8.5 THE
PROCESS ANALYTICAL TECHNOLOGY INITIATIVE 263* 8.5.1 MULTIVARIATE TOOLS
FOR DESIGN, DATA ACQUISITION AND ANALYSIS 263* 8.5.2 PROCESS ANALYSERS
264* 8.5.3 PROCESS CONTROL TOOLS 264* 8.5.4 CONTINUOUS IMPROVEMENT AND
KNOWLEDGE MANAGEMENT TOOLS 264* REFERENCES 265* 9 IMPROVING YIELDS AND
PROCESSES USING EXPERIMENTAL DESIGNS 267* 9.1 INTRODUCTION 267* 9.2 USE
OF STATISTICAL DESIGNS FAR IMPROVING THE PERFORMANCE OF SYNTHETIC*
REACTIONS 269* 9.3 SCREENING FAR FACTARS THAT INFLUENCE THE PERFORMANCE
OF AREACTION 271* 9.4 OPTIMIZING THE PROCESS VARIABLES 275* 9.5 HANDLING
MIXTURE VARIABLES USING SIMPLEX DESIGNS 278* 9.5.1 SIMPLEX CENTROID AND
LATTICE DESIGNS 278* 9.5.2 CONSTRAINTS 280* 9.6 MORE ABOUT MIXTURE
VARIABLES 283* 9.6.1 RATIOS 283* 9.6.2 MINAR CONSTITUENTS 285* 9.6.3
COMBINING MIXTURE AND PROCESS VARIABLES 285* 9.6.4 MODELS 285* X
CONTENTS 10 BIOLOGICAL AND MEDICAL APPLICATIONS OF CHEMOMETRICS* 287
10.1* INTRODUCTION 287 10.1.1 GENOMICS, PROTEOMICS AND METABOLOMICS* 287
10.1.2 DISEASE DIAGNOSIS* 288 10.1.3 CHEMICAL TAXONOMY* 288 10.2
TAXONOMY* 289 10.3 DISCRIMINATION* 291 10.3.1 DISCRIMINANT FUNCTION* 291
10.3.2 COMBINING PARAMETERS* 293 10.3.3 SEVERAL CLASSES* 293 10.3.4
LIMITATIONS* 296 10.4 MAHALANOBIS DISTANCE* 297 10.5 BAYESIAN METHODS
AND CONTINGENCY TABLES* 300 10.6 SUPPORT VECTOR MACHINES* 303 10.7
DISCRIMINANT PARTIAL LEAST SQUARES* 306 10.8 MICRO-ORGANISMS* 308 10.8.1
MID INFRARED SPECTROSCOPY* 309 10.8.2 GROWTH CURVES* 31 1 10.8.3 FURTHER
MEASUREMENTS* 311 10.8.4 PYROLYSIS MASS SPECTROMETRY* 312 10.9 MEDICAL
DIAGNOSIS USING SPECTROSCOPY* 313 10.10 METABOLOMICS USING COUPLED
CHROMATOGRAPHY AND NUCLEAR MAGNETIC RESONANCE 314 10.10.1 COUPLED
CHROMATOGRAPHY* 314 10.10.2 NUCLEAR MAGNETIC RESONANCE 317 REFERENCES 3
17 11 BIOIOGICAI MACROMOIECULES 319 1I . I INTRODUCTION 319 11.2
SEQUENCE ALIGNMENT AND SCORING MATCHES* 320 11.3 SEQUENCE SIMILARITY*
322 11.4 TREE DIAGRAMS* 324 11.4.1 DIAGRAMMATIC REPRESENTATIONS* 324
11.4.2 DENDROGRAMS* 325 11.4.3 EVOLUTIONARY THEORY AND CLADISTICS* 325
11.4.4 PHYLOGRAMS* 326 11.5 PHYLOGENETIC TREES* 327 REFERENCES 329 12
MULTIVARIATE IMAGE ANALYSIS* 331 12.1 INTRODUCTION* 331 12.2 SCALING
IMAGES* 333 12.2.1 SCALING SPECTRAL VARIABLES* 334 12.2.2 SCALING
SPATIAL VARIABLES* 334 12.2.3 MULTIWAY IMAGE PREPROCESSING* 334 12.3
FILTERING AND SMOOTHING THE IMAGE* 335 12.4 PRINCIPAL COMPONENTS FOR THE
ENHANCEMENT OF IMAGES* 337 XI CONTENTS 12.5* REGRESSION OF IMAGES 340*
12.6* ALTEMATING LEAST SQUARES AS EMPLOYED IN IMAGE ANALYSIS 345* 12.7*
MULTIWAY METHODS IN IMAGE ANALYSIS 347* REFERENCES 349* 13 FOOD* 351*
13.1* INTRODUCTION 351* 13.1.1 ADULTERATION* 351* 13.1.2 INGREDIENTS*
352* 13.1.3 SENSORY STUDIES* 352* 13.1.4 PRODUCT QUALITY* 352* 13.1.5
IMAGE ANALYSIS* 352* 13.2* HOW TO DETERMINE THE ORIGIN 01 A FOOD
PRODUCT USING CHROMATOGRAPHY 353* 13.3* NEAR INFRARED SPECTROSCOPY 354*
13.3.1 CAJIBRATION* 354* 13.3.2 CLASSIFICATION* 355* 13.3.3 EXPLORATORY
METHODS* 355* 13.4* OTHER INFORMATION 356* 13.4.1 SPECTROSCOPIES* 356*
13.4.2 CHEMICAL COMPOSITION* 356* 13.4.3 MASS SPECTROMETRY AND
PYROLYSIS* 357* 13.5* SENSORY ANALYSIS: LINKING COMPOSITION TO
PROPERTIES 357* 13.5.1 SENSORY PANELS* 357* 13.5.2 PRINCIPAL COMPONENTS
ANALYSIS* 359* 13.5.3 ADVANTAGES* 359* 13.6* VARIMAX ROTATION 359* 13.7*
CALIBRATING SENSORY DESCRIPTORS TO COMPOSITION 365* REFERENCES 368*
INDEX* 369*
|
| adam_txt |
C O N T E N T S PREFACE? XIII? I N T R O D U C T I O N * 1 *
1.1 D E V E L O P M E N T O F C H E M O M E T R I C S * 1 *
1.1.1 E A R L Y D E V E L O P M E N T S * I * 1.1.2 1 9 8 0 S
A N D THE B O R D E R L I N E S B E T W E E N O T H E R D
I S C I P L I N E S * 1 * 1.1.3 1 9 9 0 S A N D P R O B L E
M S O F I N T E R M E D I A T E C O M P L E X I T Y * 2 *
1 . 1 . 4 C U R R E N T D E V E L O P M E N T S IN C O M P L E
X P R O B L E M S O L V I N G * 2 * 1.2* A P P L I C A T I O
N A R E A S 3 * 1.3* H O W TO USE T H I S B O O K 4 *
1.4* L I T E R A T U R E A N D O T H E R S O U R C E S O
F I N F O R M A T I O N 5 * R E F E R E N C E S 7 * 2
E X P E R I M E N T A L D E S I G N * 9 * 2.1 W H Y D E S I
G N E X P E R I M E N T S IN C H E M I S T R Y ? * 9 * 2 . 2
D E G R E E S O F F R E E D O M A N D S O U R C E S O
F E R R O R * 12* 2 . 3 A N A L Y S I S O F V A R I A N C
E A N D I N T E R P R E T A T I O N O F E R R O R S * 16*
2 . 4 M A T R I C E S , V E C T O R S A N D THE P S E U D O
I N V E R S E * 2 0 * 2.5 D E S I G N M A T R I C E S * 22*
2 . 6 F A C T O R I A L D E S I G N S * 25* 2.6.1 E X T E N D I
N G T H E N U M B E R O F F A C T O R S * 28* 2 . 6 . 2
E X T E N D I N G THE N U M B E R O F L E V E L S * 28* 2
. 7 AN E X A M P L E O F A F A C T O R I A L D E S I G N
* 2 9 * 2.8 F R A C T I O N A L F A C T O R I A L D E S I G
N S * 32* 2 . 9 P L A C K E T T - B U R M A N A N D T A G U
C H I D E S I G N S * 35* 2 . 1 0 T H E A P P L I C A T I O
N O F A P L A C K E T T - B U R M A N D E S I G N TO T H
E S C R E E N I N G * O F F A C T O R S I N F L U E N C I N
G A C H E M I C A L R E A C T I O N 37* 2. I I C E N T R
A L C O M P O S I T E D E S I G N S * 39* 2 . 1 2 M I X T U
R E D E S I G N S * 44* 2 . 1 2 . 1 S I M P L E X C E N T R
O I D D E S I G N S * 45* 2 . 1 2 . 2 S I M P L E X L A T T
I C E D E S I G N S * 47* 2 . 1 2 . 3 C O N S T R A I N E D
M I X T U R E D E S I G N S * 47* V I CONTENTS 2 . 1 3 A *
F O U R C O M P O N E N T M I X T U R E D E S I G N U S E
D T O S T U D Y B L E N D I N G * O F O L I V E O I L
S 4 9 * 2 . 1 4 S I M P L E X O P T I M I Z A T I O N *
51* 2 . 1 5 * L E V E R A G E A N D C O N F I D E N C E IN M
O D E L S 5 3 * 2 . 1 6 D E S I G N S F O R M U L T I V A
R I A T E C A L I B R A T I O N * 58* R E F E R E N C E S 6 2 *
3 S T A T I S T I C A L C O N C E P T S * 63* 3.1 S T A T I
S T I C S F O R C H E M I S T S * 6 3 * 3 . 2 E R R O R S
* 6 4 * 3.2.1* S A M P L I N G E R R O R S 65* 3 . 2 . 2 *
S A M P I E P R E P A R A T I O N E R R O R S 6 6 * 3 . 2
. 3 I N S T R U M E N T A L N O I S E * 67* 3 . 2 . 4 S O U
R C E S O F E R R O R * 67* 3 . 3 D E S C R I B I N G D A
T A * 67* 3 . 3 . 1 * D E S C R I P T I V E S T A T I S T I C S
68* 3 . 3 . 2 G R A P H I C A L P R E S E N T A T I O N *
69* 3 . 3 . 3 C O V A R I A N C E A N D C O R R E L A T I O N
C O E F F I C I E N T * 7 2 * 3 . 4 T H E N O R M A L
D I S T R I B U T I O N * 73* 3 . 4 . 1 * E R R O R D I S T R I
B U T I O N S 73* 3 . 4 . 2 * N O R M A L D I S T R I B U T I O
N F U N C T I O N S A N D T A B L E S 7 4 * 3 . 4 . 3
A P P L I C A T I O N S * 75* 3 . 5 I S A D I S T R I B U T
I O N N O R M A L ? * 76* 3.5.1* C U M U L A T I V E F R E Q U
E N C Y 76* 3 . 5 . 2 * K O L M O G O R O V - S M I R N O V T E
S T 78* 3 . 5 . 3 C O N S E Q U E N C E S * 7 9 * 3 . 6 H
Y P O T H E S I S T E S T S * 80* 3 . 7 C O M P A R I S O N
O F M E A N S : T H E T - T E S T * 81* 3.8 F - T E S T F
O R C O M P A R I S O N O F V A R I A N C E S * 85* 3 . 9
C O N F I D E N C E I N L I N E A R R E G R E S S I O N *
89* 3 . 9 . 1 * L I N E A R C A L I B R A T I O N 9 0 * 3 .
9 . 2 E X A M P L E * 9 0 * 3 . 9 . 3 C O N F I D E N C E
O F P R E D I C T I O N O F P A R A M E T E R S * 9 2 * 3
. 1 0 M O R E A B O U T C O N F I D E N C E * 93* 3 . 1 0 .
1 C O N F I D E N C E IN T H E M E A N * 93* 3 . 1 0 . 2
C O N F I D E N C E IN T H E S T A N D A R D D E V I A T I O N
* 95* 3.11 C O N S E Q U E N C E S O F O U T L I E R S A N D
H O W TO D E A L W I T H T H E M * 96* 3 . 1 2 D E T E
C T I O N O F O U TL I E R S * 100* 3 . 1 2 . 1 N O R M A
L D I S T R I B U T I O N S * 100* 3 . 1 2 . 2 L I N E A R R
E G R E S S I O N * 101* 3 . 1 2 . 3 M U L T I V A R I A T E C
A L I B R A T I O N * 103* 3 . 1 3 S H E W H A R T C H A R T S
* 104* 3. 14 M O R E A B O U T C O N T R O L C H A R T S *
106* 3 . 1 4 . 1 C U S U M C H A R T * 106* 3 . 1 4 . 2 R
A N G E C H A R T * 108* 3 . 1 4 . 3 M U L T I V A R I A T E
S T A T I S T I C A L P R O C E S S C O N T R O ! 108* R E F E
R E N C E S 109* V I I CONTENTS 4 SEQUENTIAL METHODS* 111* 4.1* S
E Q U E N T I A L D A T A 111* 4 . 2 C O R R E L O G R A M S *
112* 4 . 2 . 1 A U T O - C O R R E L O G R A M S * 113* 4 . 2 .
2 C R O S S - C O R R E L O G R A M S * 115* 4 . 2 . 3 M U L T
I V A R I A T E C O R R E L O G R A M S * 115* 4 . 3 L I N E A
R S M O O T H I N G F U N C T I O N S A N D F I L T E R S *
1 1 6 * 4 . 4 F O U R I E R T R A N S F O R M S * 1 2 0 *
4 . 5 M A X I M U M E N T R O P Y A N D B A Y E S I A N
M E T H O D S * 124* 4.5.1 B A Y E S ' T H E O R E M * 124*
4 . 5 . 2 M A X I M U M E N T R O P Y * 125* 4 . 5 . 3 M A X
I M U M E N T R O P Y A N D M O D E L L I N G * 1 2 6 * 4
. 6 F O U R I E R F I L T E R S * 128* 4 . 7 P E A K S H A P
E S IN C H R O M A T O G R A P H Y A N D S P E C T R O S C O P
Y * 134* 4.7.1 P R I N C I P A L F E A T U R E S * 135* 4 . 7 .
2 G A U S S I A N S * 1 3 6 * 4 . 7 . 3 L O R E N T Z I A N
S * 1 3 6 * 4 . 7 . 4 A S Y M M E T R I E P E A K S H A P
E S * 137* 4 . 7 . 5 U S E O F P E A K S H A P E I N F
O R M A T I O N * 138* 4 . 8 D E R I V A T I V E S IN S P E C T
R O S C O P Y A N D C H R O M A T O G R A P H Y * 138* 4 . 9
W A V E L E T S * 1 4 2 * R E F E R E N C E S 143* 5 PATTERN
RECOGNITION* 145* 5.1* I N T R O D U C T I O N 145* 5.1.1 E X P L O R
A T O R Y D A T A A N A L Y S I S * 145* 5 . 1 . 2 U N S U P
E R V I S E D P A T T E R N R E C O G N I T I O N * 1 4 6 *
5 . 1 . 3 S U P E R V I S E D P A T T E R N R E C O G N I T I O
N * 1 4 6 * 5 . 2 P R I N C I P A L C O M P O N E N T S A
N A L Y S I S * 147* 5.2.1 B A S I C I D E A S * 147* 5 . 2 . 2
M E T H O D * 1 5 0 * 5 . 3 G R A P H I C A L R E P R E S
E N T A T I O N O F S C O R E S A N D L O A D I N G S *
154* 5.3.1 C A S E S T U D Y I * 1 5 4 * 5 . 3 . 2 C A S
E S T U D Y 2 * 154* 5 . 3 . 3 S C O R E S P L O T S *
156* 5 . 3 . 4 L O A D I N G S P L O T S * 157* 5 . 3 . 5 E
X T E N S I O N S * 159* 5 . 4 C O M P A R I N G M U L T I V A
R I A T E P A T T E R N S * 159* 5 . 5 P R E P R O C E S S I N
G * 1 6 0 * 5 . 6 U N S U P E R V I S E D P A T T E R N R
E C O G N I T I O N : C L U S T E R A N A L Y S I S * 167* 5 .
7 S U P E R V I S E D P A T T E R N R E C O G N I T I O N *
171* 5.7.1 M O D E L L I N G THE T R A I N I N G S E T * 171* 5
. 7 . 2 T E S T S E T S , C R O S S - V A L I D A T I O N A
N D THE B O O T S T R A P * 172* 5 . 7 . 3 A P P L Y I N G T
H E M O D E L * 174* 5 . 8 S T A T I S T I C A L C L A S S I
F I C A T I O N T E C H N I Q U E S * 174* 5.8.1 U N I V A R I A T
E C L A S S I F I C A T I O N * 175* 5 . 8 . 2 B I V A R I A T
E A N D M U L T I V A R I A T E D I S C R I M I N A N T M O
D E L S * 175* V I I I CONTENTS 5.8.3 SIMCA 178* 5.8.4 STATISTICAL
OUTPUT 182* 5.9 K NEAREST NEIGHBOUR METHOD 182* 5.10 HOW MANY COMPONENTS
CHARACTERIZE A DATASET? 185* 5.11 MULTIWAY PATTERN RECOGNITION 187*
5.11.1 TUCKER3 MODELS 188* 5.11.2 PARAFAC 189* 5.11.3 UNFOLDING 190*
REFERENCES 190* 6 C A L I B R A T I O N 193* 6.1 INTRODUCTION 193*
6.2 UNIVARIATE CALIBRATION 195* 6.2.1 CLASSICAL CALIBRATION 195* 6.2.2
LNVERSE CALIBRATION 196* 6.2.3 CALIBRATION EQUATIONS 198* 6.2.4
INCLUDING EXTRA TERMS 199* 6.2.5 GRAPHS 199* 6.3 MULTIVARIATE
CALIBRATION AND THE SPECTROSCOPY OF MIXTURES 202* 6.4 MULTIPLE LINEAR
REGRESSION 206* 6.5 PRINCIPAL COMPONENTS REGRESSION 208* 6.6 PARTIAL
LEAST SQUARES 211* 6.7 HOW GOOD IS THE CALIBRATION AND WHAT IS THE MOST
APPROPRIATE MODEL? 214* 6.7.1 AUTOPREDICTION 214* 6.7.2 CROSS-VALIDATION
215* 6.7.3 TEST SETS 215* 6.7.4 BOOTSTRAP 217* 6.8 MULTIWAY CALIBRATION
217* 6.8.1 UNFOLDING 217* 6.8.2 TRILINEAR PLSI 218* 6.8.3 N-PLSM 219*
REFERENCES 220* 7 C O U P L E D C H R O M A T O G R A P H Y
221* 7.1 INTRODUCTION 221* 7.2 PREPARING THE DATA 222* 7.2.1
PREPROCESSING 222* 7.2.2 VARIABLE SELECTION 224* 7.3 CHEMICAL
COMPOSITION OF SEQUENTIAL DATA 228* 7.4 UNIVARIATE PURITY CURVES 230*
7.5 SIMILARITY BASED METHODS 234* 7.5.1 SIMILARITY 234* 7.5.2
CORRELATION COEFFICIENTS 234* 7.5.3 DISTANCE MEASURES 235* 7.5.4 OPA AND
SIMPLISMA 236* 7.6 EVOLVING AND WINDOW FACTOR ANALYSIS 236* IX CONTENTS
7.6.1 EXPANDING WINDOWS 237* 7.6.2 FIXED SIZED WINDOWS 238* 7.6.3
VARIATIONS 239* 7.7 DERIVATIVE BASED METHODS 239* 7.8 DECONVOLUTION OF
EVOLUTIONARY SIGNALS 241* 7.9 NONITERATIVE METHODS TUER RESOLUTION 242*
7.9.1 SELECTIVITY: FINDING PURE VARIABLES 242* 7.9.2 MULTIPLE LINEAR
REGRESSION 243* 7.9.3 PRINCIPAL COMPONENTS REGRESSION 244* 7.9.4 PARTIAL
SELECTIVITY 244* 7.10 ITERATIVE METHODS FOR RESOLUTION 246* 8
EQUILIBRIA, REACTIONS AND PROCESS ANALYTICS 249* 8.1 THE STUDY OF
EQUILIBRIA USING SPECTROSCOPY 249* 8.2 SPECTROSCOPIC MONITARING OF
REACTIONS 252* 8.2.1 MID INFRARED SPECTROSCOPY 253* 8.2.2 NEAR INFRARED
SPECTROSCOPY 254* 8.2.3 UV!VISIBLE SPECTROSCOPY 255* 8.2.4 RAMAN
SPECTROSCOPY 256* 8.2.5 SUMMARY OF MAIN DATA ANALYSIS TECHNIQUES 256*
8.3 KINETICS AND MULTIVARIATE MODELS FOR THE QUANTITATIVE STUDY OF
REACTIONS 257* 8.4 DEVELOPMENTS IN THE ANALYSIS OF REACTIONS USING
ON-LINE SPECTROSCOPY 261* 8.4.1 CONSTRAINTS AND COMBINING INFORMATION
261* 8.4.2 DATA MERGING 262* 8.4.3 THREE-WAY ANALYSIS 262* 8.5 THE
PROCESS ANALYTICAL TECHNOLOGY INITIATIVE 263* 8.5.1 MULTIVARIATE TOOLS
FOR DESIGN, DATA ACQUISITION AND ANALYSIS 263* 8.5.2 PROCESS ANALYSERS
264* 8.5.3 PROCESS CONTROL TOOLS 264* 8.5.4 CONTINUOUS IMPROVEMENT AND
KNOWLEDGE MANAGEMENT TOOLS 264* REFERENCES 265* 9 IMPROVING YIELDS AND
PROCESSES USING EXPERIMENTAL DESIGNS 267* 9.1 INTRODUCTION 267* 9.2 USE
OF STATISTICAL DESIGNS FAR IMPROVING THE PERFORMANCE OF SYNTHETIC*
REACTIONS 269* 9.3 SCREENING FAR FACTARS THAT INFLUENCE THE PERFORMANCE
OF AREACTION 271* 9.4 OPTIMIZING THE PROCESS VARIABLES 275* 9.5 HANDLING
MIXTURE VARIABLES USING SIMPLEX DESIGNS 278* 9.5.1 SIMPLEX CENTROID AND
LATTICE DESIGNS 278* 9.5.2 CONSTRAINTS 280* 9.6 MORE ABOUT MIXTURE
VARIABLES 283* 9.6.1 RATIOS 283* 9.6.2 MINAR CONSTITUENTS 285* 9.6.3
COMBINING MIXTURE AND PROCESS VARIABLES 285* 9.6.4 MODELS 285* X
CONTENTS 10 BIOLOGICAL AND MEDICAL APPLICATIONS OF CHEMOMETRICS* 287
10.1* INTRODUCTION 287 10.1.1 GENOMICS, PROTEOMICS AND METABOLOMICS* 287
10.1.2 DISEASE DIAGNOSIS* 288 10.1.3 CHEMICAL TAXONOMY* 288 10.2
TAXONOMY* 289 10.3 DISCRIMINATION* 291 10.3.1 DISCRIMINANT FUNCTION* 291
10.3.2 COMBINING PARAMETERS* 293 10.3.3 SEVERAL CLASSES* 293 10.3.4
LIMITATIONS* 296 10.4 MAHALANOBIS DISTANCE* 297 10.5 BAYESIAN METHODS
AND CONTINGENCY TABLES* 300 10.6 SUPPORT VECTOR MACHINES* 303 10.7
DISCRIMINANT PARTIAL LEAST SQUARES* 306 10.8 MICRO-ORGANISMS* 308 10.8.1
MID INFRARED SPECTROSCOPY* 309 10.8.2 GROWTH CURVES* 31 1 10.8.3 FURTHER
MEASUREMENTS* 311 10.8.4 PYROLYSIS MASS SPECTROMETRY* 312 10.9 MEDICAL
DIAGNOSIS USING SPECTROSCOPY* 313 10.10 METABOLOMICS USING COUPLED
CHROMATOGRAPHY AND NUCLEAR MAGNETIC RESONANCE 314 10.10.1 COUPLED
CHROMATOGRAPHY* 314 10.10.2 NUCLEAR MAGNETIC RESONANCE 317 REFERENCES 3
17 11 BIOIOGICAI MACROMOIECULES 319 1I . I INTRODUCTION 319 11.2
SEQUENCE ALIGNMENT AND SCORING MATCHES* 320 11.3 SEQUENCE SIMILARITY*
322 11.4 TREE DIAGRAMS* 324 11.4.1 DIAGRAMMATIC REPRESENTATIONS* 324
11.4.2 DENDROGRAMS* 325 11.4.3 EVOLUTIONARY THEORY AND CLADISTICS* 325
11.4.4 PHYLOGRAMS* 326 11.5 PHYLOGENETIC TREES* 327 REFERENCES 329 12
MULTIVARIATE IMAGE ANALYSIS* 331 12.1 INTRODUCTION* 331 12.2 SCALING
IMAGES* 333 12.2.1 SCALING SPECTRAL VARIABLES* 334 12.2.2 SCALING
SPATIAL VARIABLES* 334 12.2.3 MULTIWAY IMAGE PREPROCESSING* 334 12.3
FILTERING AND SMOOTHING THE IMAGE* 335 12.4 PRINCIPAL COMPONENTS FOR THE
ENHANCEMENT OF IMAGES* 337 XI CONTENTS 12.5* REGRESSION OF IMAGES 340*
12.6* ALTEMATING LEAST SQUARES AS EMPLOYED IN IMAGE ANALYSIS 345* 12.7*
MULTIWAY METHODS IN IMAGE ANALYSIS 347* REFERENCES 349* 13 FOOD* 351*
13.1* INTRODUCTION 351* 13.1.1 ADULTERATION* 351* 13.1.2 INGREDIENTS*
352* 13.1.3 SENSORY STUDIES* 352* 13.1.4 PRODUCT QUALITY* 352* 13.1.5
IMAGE ANALYSIS* 352* 13.2* HOW TO DETERMINE THE ORIGIN 01' A FOOD
PRODUCT USING CHROMATOGRAPHY 353* 13.3* NEAR INFRARED SPECTROSCOPY 354*
13.3.1 CAJIBRATION* 354* 13.3.2 CLASSIFICATION* 355* 13.3.3 EXPLORATORY
METHODS* 355* 13.4* OTHER INFORMATION 356* 13.4.1 SPECTROSCOPIES* 356*
13.4.2 CHEMICAL COMPOSITION* 356* 13.4.3 MASS SPECTROMETRY AND
PYROLYSIS* 357* 13.5* SENSORY ANALYSIS: LINKING COMPOSITION TO
PROPERTIES 357* 13.5.1 SENSORY PANELS* 357* 13.5.2 PRINCIPAL COMPONENTS
ANALYSIS* 359* 13.5.3 ADVANTAGES* 359* 13.6* VARIMAX ROTATION 359* 13.7*
CALIBRATING SENSORY DESCRIPTORS TO COMPOSITION 365* REFERENCES 368*
INDEX* 369* |
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| author | Brereton, Richard G. |
| author_facet | Brereton, Richard G. |
| author_role | aut |
| author_sort | Brereton, Richard G. |
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| building | Verbundindex |
| bvnumber | BV023066101 |
| callnumber-first | Q - Science |
| callnumber-label | QD75 |
| callnumber-raw | QD75.4.S8 |
| callnumber-search | QD75.4.S8 |
| callnumber-sort | QD 275.4 S8 |
| callnumber-subject | QD - Chemistry |
| classification_rvk | VC 6050 |
| ctrlnum | (OCoLC)85828378 (DE-599)BVBBV023066101 |
| dewey-full | 543.01/5195 543.015195 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 543 - Analytical chemistry |
| dewey-raw | 543.01/5195 543.015195 |
| dewey-search | 543.01/5195 543.015195 |
| dewey-sort | 3543.01 45195 |
| dewey-tens | 540 - Chemistry and allied sciences |
| discipline | Chemie / Pharmazie |
| discipline_str_mv | Chemie / Pharmazie |
| format | Book |
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| id | DE-604.BV023066101 |
| illustrated | Illustrated |
| index_date | 2024-07-02T19:30:56Z |
| indexdate | 2024-07-09T21:10:12Z |
| institution | BVB |
| isbn | 0470016868 9780470016862 |
| language | English |
| lccn | 2006030737 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016269302 |
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| owner_facet | DE-703 DE-634 |
| physical | XV, 379 S. graph. Darst. |
| publishDate | 2007 |
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| publisher | John Wiley & Sons |
| record_format | marc |
| spelling | Brereton, Richard G. Verfasser aut Applied chemometrics for scientists Richard G. Brereton Hoboken, NJ John Wiley & Sons c2007 XV, 379 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index Chemometrics Chemometrie (DE-588)4299578-4 gnd rswk-swf Chemometrie (DE-588)4299578-4 s DE-604 http://www.loc.gov/catdir/toc/ecip071/2006030737.html Table of contents only http://www.loc.gov/catdir/enhancements/fy0740/2006030737-b.html Contributor biographical information http://www.loc.gov/catdir/enhancements/fy0740/2006030737-d.html Publisher description OEBV Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016269302&sequence=000007&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Brereton, Richard G. Applied chemometrics for scientists Chemometrics Chemometrie (DE-588)4299578-4 gnd |
| subject_GND | (DE-588)4299578-4 |
| title | Applied chemometrics for scientists |
| title_auth | Applied chemometrics for scientists |
| title_exact_search | Applied chemometrics for scientists |
| title_exact_search_txtP | Applied chemometrics for scientists |
| title_full | Applied chemometrics for scientists Richard G. Brereton |
| title_fullStr | Applied chemometrics for scientists Richard G. Brereton |
| title_full_unstemmed | Applied chemometrics for scientists Richard G. Brereton |
| title_short | Applied chemometrics for scientists |
| title_sort | applied chemometrics for scientists |
| topic | Chemometrics Chemometrie (DE-588)4299578-4 gnd |
| topic_facet | Chemometrics Chemometrie |
| url | http://www.loc.gov/catdir/toc/ecip071/2006030737.html http://www.loc.gov/catdir/enhancements/fy0740/2006030737-b.html http://www.loc.gov/catdir/enhancements/fy0740/2006030737-d.html http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016269302&sequence=000007&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT breretonrichardg appliedchemometricsforscientists |