Statistical methods for food science : introductory procedures for the food practitioner /
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| Format: | Book |
| Language: | English |
| Published: |
Ames, Iowa :
Wiley-Blackwell,
2009.
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| Subjects: |
Table of Contents:
- 9.1 Quality and nature of instrumental data
- 9.1.1 Quality of instrumental data
- 9.2 Sampling and replication
- 9.3 Experimental design issues
- 9.4 Statistical analysis of instrumental data
- 9.4.1 Summary methods
- Example 9.4-1 Analysis of instrumental data in a processing experiment
- 9.5 Chemical analysis applications
- 9.5.1 Accuracy and bias in chemical analysis
- Example 9.5-1 Bias calculation and display for chemical analysis data
- 9.5.2 Calibration studies
- 9.5.3 Precision studies
- Example 9.5- 2 Precision calculations for chemical analysis data
- 9.5.4 Uncertainty
- 9.6 Analysis of relationships
- References
- Food Product Formulation
- Introduction
- 10.1 Design application in food product development
- 10.2 Single ingredient effects
- Example 10.2-1 Display of single ingredient effect in formulation
- 10.3 Two or more ingredients
- Example 10.3-1 two ingredient effect in formulation
- 10.4 Screening of many ingredients
- Example 10.4-1 Screening experiment in formulation
- 10.5 Formulation by constraints
- Example 10.5-1 Linear programming in formulation
- References
- Statistical Quality Control
- Introduction.
- 11.1 Types of statistical quality control
- 11.1.1 Types of end determination measure in SQC
- 11.2 Sampling procedures
- 11.3 Control charts
- 11.3.1 The x-bar control chart
- Example 11.3-1 "x-bar" control chart construction and monitoring
- 11.3.2 Sampling for control charts
- 11.3.3 Compliance issues
- 11.3.4 Other variable control charts
- 11.3.5 Attribute charts
- 11.4 Acceptance sampling
- Example 11.4-1 Construction of an operating characteristic curve for acceptance sampling
- References
- Multivariate Applications
- Introduction
- 12.1 Multivariate methods and their characteristics
- 12.2 Multivariate modes
- 12.2.1 Multiple regression
- 12.2.2 MANOVA
- 12.2.3 Principle Component Analysis
- Example 12.2-1 Principal component analysis on descriptive analysis data
- 12.2.4 Cluster analysis
- 12.2.5 Correspondence analysis
- 12.2.6 Conjoint analysis
- 12.2.7 Discriminant analysis
- 12.2.8 Partial least squares regression
- 12.2.9 Preference mapping
- Example 12.2-2 Internal preference mapping on hedonic data
- 12.2.10 Procrustes analysis
- 12.3 Relationship of consumer preference with sensory measures.