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Chemometric monitoring : product quality assessment, process fault detection, and applications

Author: Madhusree Kundu; Palash Kumar Kundu; Seshu K Damarla
Publisher: Boca Raton : CRC Press, 2018.
Edition/Format:   eBook : Document : EnglishView all editions and formats
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Kundu, Madhusree.
Chemometric monitoring.
Boca Raton : CRC Press, 2018
(DLC) 2017015762
(OCoLC)994974064
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Madhusree Kundu; Palash Kumar Kundu; Seshu K Damarla
ISBN: 9781315155135 1315155133
OCLC Number: 1005978987
Description: 1 online resource
Contents: Machine generated contents note: ch. 1 Data generation, collection, analysis, and preprocessing --
1.1. Data: Different data types and presentation of data --
1.2. Data generation: Design of experiments --
1.2.1. Factorial design and illustration --
1.2.1.1. The effect of Zn loading --
1.2.1.2. Two-factor interaction effects --
1.2.1.3. Three-factor interaction effects --
1.3. Computer-based data acquisition --
1.3.1. Sensor/transducer --
1.3.2. Analog-to-digital (A/D) converter --
1.3.3. Digital-to-analog (D/A) converter --
1.4. Basic statistical measures and regression --
1.4.1. Mean, median, mode --
1.4.2. Variance and standard deviation --
1.4.3. Covariance and correlation coefficient --
1.4.4. Frequency --
1.4.5. Distribution --
1.4.6. Uncertainty --
1.4.7. Confidence interval --
1.4.8. Hypothesis Testing --
1.4.9. Correlation --
1.4.10. Regression --
1.4.11. Chi-squared test --
1.5. Stochastic and stationary processes --
1.6. Data preprocessing --
1.6.1. Outlier detection --
1.6.2. Data reconciliation --
1.6.3. Data smoothing and filtering --
1.6.3.1. Smoothing signal --
1.6.3.2. Filtering signal --
1.6.4. Transform and transformation --
References --
ch. 2 Chemometric techniques: Theoretical postulations --
2.1. Chemometrics --
2.2. Principal component analysis (PCA) --
2.2.1. PCA decomposition of data --
2.2.2. Principle of nearest neighborhood --
2.2.3. Hotelling T2 and Q statistics --
2.3. Similarity --
2.3.1. PCA similarity --
2.3.2. Distance-based similarity --
2.3.3. Combined similarity factor --
2.3.4. Dissimilarity and Karhunen-Loeve (KL) expansion --
2.3.5. Moving window-based pattern matching using similarity/dissimilarity factors --
2.4. Clustering --
2.4.1. Hierarchical clustering --
2.4.2. Nonhierarchical clustering --
2.4.3. Modified K-means clustering using similarity factors --
2.5. Partial least squares (PLS) --
2.5.1. Linear PLS --
2.5.2. Dynamic PLS --
2.6. Cross-correlation coefficient --
2.7. Sammon's nonlinear mapping --
2.8. Moving window-based PCA --
2.8.1. Mathematical postulates of recursive PCA --
2.9. Discriminant function/hyperplane --
2.9.1. Linear discriminant analysis (LDA) --
2.9.2. Support vector machine (SVM) --
2.9.2.1. Determination of decision function in SVM --
2.9.2.2. Determination of optimal separating hyperplane in SVM --
2.10. Multiclass decision function --
2.10.1. One against the rest approach --
2.10.2. One against one approach --
2.10.3. Decision directed acyclic graph (DDAG)-based approach --
2.10.3.1. DDAG algorithm --
References --
ch. 3 Classification among various process operating conditions --
3.1. Yeast fermentation bioreactor process --
3.1.1. Modeling and dynamic simulation of yeast fermentation bioreactor --
3.1.1.1. The process description --
3.1.1.2. Mathematical model --
3.1.1.3. Analysis of dynamic behavior of yeast fermentation bioreactor --
3.1.2. Generation of process historical database for yeast fermentation process --
3.1.3. Application of modified K-means clustering algorithm on historical database for yeast fermentation process --
3.2. Commercial double-effect evaporator --
3.2.1. Modeling and dynamic simulation of double effect evaporator --
3.2.1.1. The process description --
3.2.1.2. Mathematical model of double-effect evaporator --
3.2.1.3. Analysis of dynamic behavior of double-effect evaporator model --
3.2.2. Generation of historical database for double-effect evaporation process --
3.2.3. Application of modified K-means clustering algorithm on double-effect evaporation process database --
3.3. Continous crystallization process --
3.3.1. Modeling and dynamic simulation of continuous crystallization process --
3.3.1.1. The process description --
3.3.1.2. Mathematical model of continuous crystallization process --
3.3.2. Generation of historical database for continuous crystallization process --
3.3.3. Application of modified K-means clustering algorithm in continuous crystallizer --
References --
ch. 4 Detection of abnormal operating conditions in processes using moving window-based pattern matching --
4.1. Detection of abnormal operating conditions in a fluid catalytic cracking unit --
4.1.1. Introduction to the fluid catalytic cracking (FCC) process --
4.1.2. FCC process description --
4.1.3. Generation of historical database for FCC process --
4.1.4. Application of moving window-based pattern-matching algorithm on historical database of FCC process --
4.2. Detection of abnormal operating conditions in continuous stirred tank heater --
4.2.1. Continuous stirred tank heater --
4.2.2. Generation of historical database for CSTH --
4.2.3. Application of combined similarity factor and dissimilarity factor-based pattern-matching algorithm on a CSTH historical database --
4.3. Detection of abnormal operating conditions in simulated industrial gas-phase polyethylene reactor --
4.3.1. Simulated industrial gas-phase polyethylene reactor --
4.3.2. Generation of historical database for simulated industrial polyethylene reactor --
4.3.3. Application of combined similarity factor and dissimilarity factor-based pattern-matching algorithm on polyethylene historical database --
References --
ch. 5 Design of an automated tea grader --
5.1. Electronic tongue: A biomimetic device --
5.2. Experimentation --
5.2.1. E-tongue-based instrumentation and principles --
5.2.2. E-tongue signature generation using various commercial brands of tea --
5.3. Tea data preprocessing --
5.4. Dissimilarity-based tea grader --
5.4.1. Authentication/classification algorithm --
5.4.2. Performance evaluation of the designed dissimilarity-based tea classifier --
5.5. Design of RPCA-based tea classifier --
5.5.1. Authentication/classification algorithm of unknown tea brands --
5.5.2. Performance evaluation of the designed RPCA-based classifier --
5.6. Design of FDA-based classifier for commercial tea brands --
5.6.1. FDA-based authentication/classification algorithm using DDAG --
5.6.1.1. Performance evaluation of the designed FDA-based classifier --
5.7. Comparative classification performance --
5.8. Prototype development principles of commercial tea grader --
5.8.1. Potentiostat --
5.8.1.1. Electrometer --
5.8.1.2. I/E converter --
5.8.1.3. Control amplifier --
5.8.1.4. Signal circuit --
5.8.2. DAS --
5.8.3. Proposed tea/beverage grading device --
5.9. Summary of deliverables --
References --
ch. 6 Water quality monitoring: Design of an automated classification and authentication tool --
6.1. Experimentation for e-tongue signature generation due to various mineral water brands --
6.2. Design of PLS-based mineral water authenticator/classifier --
6.2.1. Authentication/classification algorithm --
6.2.2. Performance evaluation of PLS-based authentication and classification --
6.3. Cross-correlation-based PCA and feature enhancement in classification --
6.3.1. Feature extraction --
6.3.2. Performance evaluation of a PCA and an enhanced PCA classifier --
6.4. Design of cross-correlation-based enhanced Sammon's classifier for mineral water authentication/classification --
6.4.1. Performance evaluation of the designed classifier --
6.5. Design of water classifier based on a support vector machine --
6.5.1. Mineral water sample classification using an SVM --
6.6. Relative performance evaluation of the designed classifiers --
6.7. Extended water monitor framework --
References --
ch. 7 Miscellaneous application of chemometrics --
7.1. Electrocardiogram as patient care monitor --
7.1.1. Heart and cardiovascular system --
7.1.2. Events in electrocardiogram (ECG) and ECG morphology --
7.1.3. ECG lead system --
7.1.4. ECG recording --
7.1.5. Clinical signature encased in ECG morphology --
7.1.6. Abnormality in ECG morphology --
7.1.7. Computerized ECG signal analysis --
7.1.8. Electrocardiogram and nonstationarity --
7.2. Morphological modeling of ECG --
7.2.1. Preprocessing of ECG --
7.2.2. Beat segmentation for extracting zones --
7.2.3. Modeling and reconstruction of ECG waves --
7.2.3.1. Fourier model --
7.2.3.2. Gaussian model --
7.2.4. Reconstruction of single-beat ECG waveform --
7.2.5. Performance evaluation of ECG synthesizer --
7.3. Automated ECG pattern classifier --
7.3.1. Preprocessing of the ECG --
7.3.2. ECG enhancement through beat-aligned PCA --
7.3.3. Extraction of QRS and T wave from inferior myocardial infarction data --
7.3.4. Dissimilarity-based classification/authentication of ECG --
7.4. Arsenic quantification in contaminated water using chemometrics --
7.4.1. E-tongue-based experimentation --
7.4.2. Arsenic quantifier design --
References.
Responsibility: Madhusree Kundu, Palash Kumar Kundu, Seshu K. Damarla.

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