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Mathematical and statistical methods in food science and technology /
Mathematical and Statistical Approaches in Food Science and Technology offers an accessible guide to applying statistical and mathematical technologies in the food science field whilst also addressing the theoretical foundations. Using clear examples and case-studies by way of practical illustration...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Chichester, West Sussex, UK ; Hoboken, NJ :
Wiley Blackwell,
2013.
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| Colección: | IFT Press series.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Series Page; Title Page; Copyright; Titles in the IFT Press series; About the Editors; List of Contributors; Acknowledgments; Section 1; Chapter 1: The Use and Importance of Design of Experiments (DOE) in Process Modelling in Food Science and Technology; Introduction; Overview of Experimental Designs; Response Surface Methodology: A Tool for Analysing and Optimizing Products and Processes; Process Optimization; Statistical Packages; Final Remarks and Perspectives; References; Chapter 2: The Use of Correlation, Association and Regression to Analyse Processes and Products; Introduction.
- Process AnalysisMultivariate Methods; Outlier Detection; Model Accuracy and Validation; Overfitting and Underfitting; Routine Analyses and Applications; Summary; References; Chapter 3: Case study: Optimization of Enzyme-Aided Extraction of Polyphenols from Unripe Apples by Response Surface Methodology; Introduction; Experiments; Results and Discussion; Conclusion; References; Chapter 4: Case Study: Statistical Analysis of Eurycomanone Yield Using a Full Factorial Design; Introduction; Materials and Methods; Results and Discussion; Conclusions; References; Section 2.
- Chapter 5: Applications of Principal Component Analysis (PCA) in Food Science and TechnologyIntroduction; Goal; Definition; Effective Computation; Some Properties; Representation of the Individuals: A Geometrical Interpretation; Dimensionality Reduction; Covariance or Correlation Matrix?; Determining the Number of Components; Some Patterns in R or in S and their Interpretation; Relationship with the Orthogonal Regression; Multiple Regression on Principal Components; References; Chapter 6: Multiple Factor Analysis: Presentation of the Method Using Sensory Data; Introduction; Data.
- Weighting to Balance Groups of VariablesSuperimposed Representation of the Wines Analysed by Each Panel; Supplementary Categorical Variables; Representing the Dimensions of Separate Analyses; Representing Groups of Variables; Constructing Confidence Ellipses Around the Wines; Conclusions and Implications for Other Applications; Appendix 6.A Software and Technical Point; References; Chapter 7: Cluster Analysis: Application in Food Science and Technology; Introduction; Hierarchical Cluster Analysis; K-Means Clustering; Fuzzy Clustering Algorithms; Conclusions; References.
- Chapter 8: Principal Component Regression (PCR) and Partial Least Squares Regression (PLSR)Introduction; Theory; The PLSR Residual Issue in Process Monitoring; PLSR Score Loading Correspondence; Model Reduction Methods; Established PCR and PLSR Practices; Some Emerging Methods in Food Science; References; Chapter 9: Multiway Methods in Food Science; Introduction; Methods and Concepts; Sources for Multiway Data; Future Perspectives; References; Chapter 10: Multidimensional Scaling (MDS); Introduction
- What is MDS?; Application of MDS in Food Science; Quality of Results; MDS Procedure.
- Example
- A Sorting Task of Wine Glass Shapes.