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Title Process analytical technology for the food industry / Colm P. O'Donnell, Colette Fagan, P.J. Cullen, editors
Published New York : Springer, 2014

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Description 1 online resource (viii, 301 pages) : illustrations (some color)
Series Food Engineering Series, 1571-0297
Food engineering series.
Contents Contributors; Chapter 1; Benefits and Challenges of Adopting PAT for the Food Industry ; 1.1 Introduction; 1.1.1 Evolution of PAT; 1.1.2 Learning From Other Process Industries; 1.1.3 PAT Drivers in the Food Industry; 1.1.4 Technology Advances; 1.1.5 Challenges; References; Chapter 2; Multivariate Data Analysis (Chemometrics); 2.1 Introduction; 2.1.1 Definition of Chemometrics; 2.1.2 PAT and Chemometrics; 2.2 Design of Experiments; 2.2.1 Problem Formulation; 2.2.2 Screening Designs; 2.2.2.1 Full Factorial Designs (2k); 2.2.2.2 Fractional Factorial Designs (2k−p)
2.2.2.3 Other Screening Designs2.2.3 Optimisation Designs: Response Surface Methodology; 2.2.3.1 Central Composite Designs; 2.2.3.2 Other Optimisation Designs; 2.2.3.3 Mixture Designs; 2.3 Exploratory Analysis; 2.3.1 Data Preprocessing; 2.3.1.1 Classical Preprocessing Methods; 2.3.1.2 Signal Correction Methods; 2.3.1.3 Dimensionality Reduction Methods; 2.3.2 Principal Component Analysis; 2.3.2.1 Introduction-Objective of PCA; 2.3.2.2 Geometrical Interpretation; 2.3.2.3 Mathematical Computation; 2.3.2.4 Interpretation of PCA; 2.3.3 Outlier Detection and Handling
2.3.3.1 Outlier Detection in Exploratory Analysis2.3.3.2 Outlier Detection in Predictive Analysis; 2.3.3.3 Robust Statistics; 2.4 Quantitative Predictive Modelling; 2.4.1 Introduction; 2.4.2 Linear Modelling; 2.4.2.1 Linear Regression Principle; 2.4.2.2 Multiple Linear Regression (MLR); 2.4.2.3 Principal Component Regression (PCR); 2.4.2.4 PLS Regression; 2.4.2.5 Model Optimisation and Validation; 2.4.2.6 Science-Based Calibration; 2.4.3 Non-Linear Modelling; 2.4.3.1 Non-Linear PLS; 2.4.3.2 Local Modelling; 2.4.3.3 Least-Squares Support Vector Machines; 2.4.3.4 Artificial Neural Networks
2.4.4 Robustness Issue and Calibration Transfer2.4.4.1 Models Using a Standardisation Set; 2.4.4.2 Models Using a Small Experimental Design; 2.4.4.3 Models When Only a Few Reference Control Points are Available; 2.5 Classification; 2.5.1 Clustering Techniques; 2.5.1.1 Introduction; 2.5.1.2 Hierarchical Clustering Analysis; 2.5.1.3 Non-hierarchical Clustering Methods; 2.5.2 Supervised Discrimination; 2.5.2.1 Introduction; 2.5.2.2 Linear Supervised Discrimination; 2.5.2.3 Non-linear Supervised Discrimination; 2.5.2.4 A Particular Case: k-Nearest Neighbours (k-NN)
2.6 Multivariate Process Monitoring2.6.1 Multivariate Statistical Process Control; 2.6.1.1 Introduction; 2.6.1.2 Process Analysis; 2.6.1.3 Process Monitoring and Fault Diagnosis; 2.6.1.4 Process Control; 2.6.2 Multivariate Curve Resolution; 2.7 Multi-block and Multi-way Analyses; 2.7.1 Multi-block Analysis; 2.7.1.1 Definition of Multi-block Data Sets; 2.7.1.2 Exploratory Multi-block Analyses; 2.7.1.3 Predictive Multi-block Analyses; 2.7.2 Multi-way Analysis; 2.7.2.1 Definition of Trilinear Data Sets; 2.7.2.2 Exploratory Multi-way Analyses; 2.7.2.3 Predictive Multi-way Analyses; 2.8 Conclusion
Summary 2.2.2.3 Other Screening Designs2.2.3 Optimisation Designs: Response Surface Methodology; 2.2.3.1 Central Composite Designs; 2.2.3.2 Other Optimisation Designs; 2.2.3.3 Mixture Designs; 2.3 Exploratory Analysis; 2.3.1 Data Preprocessing; 2.3.1.1 Classical Preprocessing Methods; 2.3.1.2 Signal Correction Methods; 2.3.1.3 Dimensionality Reduction Methods; 2.3.2 Principal Component Analysis; 2.3.2.1 Introduction-Objective of PCA; 2.3.2.2 Geometrical Interpretation; 2.3.2.3 Mathematical Computation; 2.3.2.4 Interpretation of PCA; 2.3.3 Outlier Detection and Handling
2.3.3.1 Outlier Detection in Exploratory Analysis2.3.3.2 Outlier Detection in Predictive Analysis; 2.3.3.3 Robust Statistics; 2.4 Quantitative Predictive Modelling; 2.4.1 Introduction; 2.4.2 Linear Modelling; 2.4.2.1 Linear Regression Principle; 2.4.2.2 Multiple Linear Regression (MLR); 2.4.2.3 Principal Component Regression (PCR); 2.4.2.4 PLS Regression; 2.4.2.5 Model Optimisation and Validation; 2.4.2.6 Science-Based Calibration; 2.4.3 Non-Linear Modelling; 2.4.3.1 Non-Linear PLS; 2.4.3.2 Local Modelling; 2.4.3.3 Least-Squares Support Vector Machines; 2.4.3.4 Artificial Neural Networks
2.4.4 Robustness Issue and Calibration Transfer2.4.4.1 Models Using a Standardisation Set; 2.4.4.2 Models Using a Small Experimental Design; 2.4.4.3 Models When Only a Few Reference Control Points are Available; 2.5 Classification; 2.5.1 Clustering Techniques; 2.5.1.1 Introduction; 2.5.1.2 Hierarchical Clustering Analysis; 2.5.1.3 Non-hierarchical Clustering Methods; 2.5.2 Supervised Discrimination; 2.5.2.1 Introduction; 2.5.2.2 Linear Supervised Discrimination; 2.5.2.3 Non-linear Supervised Discrimination; 2.5.2.4 A Particular Case: k-Nearest Neighbours (k-NN)
2.6 Multivariate Process Monitoring2.6.1 Multivariate Statistical Process Control; 2.6.1.1 Introduction; 2.6.1.2 Process Analysis; 2.6.1.3 Process Monitoring and Fault Diagnosis; 2.6.1.4 Process Control; 2.6.2 Multivariate Curve Resolution; 2.7 Multi-block and Multi-way Analyses; 2.7.1 Multi-block Analysis; 2.7.1.1 Definition of Multi-block Data Sets; 2.7.1.2 Exploratory Multi-block Analyses; 2.7.1.3 Predictive Multi-block Analyses; 2.7.2 Multi-way Analysis; 2.7.2.1 Definition of Trilinear Data Sets; 2.7.2.2 Exploratory Multi-way Analyses; 2.7.2.3 Predictive Multi-way Analyses; 2.8 Conclusion
Annex: Figures of Merit
The Process Analytical Technology (PAT) initiative aims to move from a paradigm of 'testing quality in' to 'building quality in by design'. It can be defined as the optimal application of process analytical technologies, feedback process control strategies, information management tools, and/or product-process optimization strategies. Recently, there have been significant advances in process sensors and in model-based monitoring and control methodologies, leading to enormous opportunities for improved performance of food manufacturing processes and for the quality of food products with the adop
Analysis chemie
chemistry
voedselwetenschappen
food sciences
spectrometrie
spectrometry
microscopie
microscopy
Chemistry (General)
Chemie (algemeen)
Notes Includes index
Online resource; title from PDF title page (SpringerLink, viewed December 17, 2014)
Subject Food industry and trade -- Quality control.
Process control.
Chemical process control.
Food adulteration and inspection -- Equipment and supplies
TECHNOLOGY & ENGINEERING -- Food Science.
Chemical process control.
Food industry and trade -- Quality control.
Process control.
Form Electronic book
Author O'Donnell, C. P. (Colm P.), editor.
Fagan, Colette, editor.
Cullen, P. J. (Patrick J.), editor.
ISBN 9781493903115
149390311X
1493903101
9781493903108