Coverart for item
The Resource We have the technology : how biohackers, foodies, physicians, and scientists are transforming human perception, one sense at a time, Kara Platoni

We have the technology : how biohackers, foodies, physicians, and scientists are transforming human perception, one sense at a time, Kara Platoni

Label
We have the technology : how biohackers, foodies, physicians, and scientists are transforming human perception, one sense at a time
Title
We have the technology
Title remainder
how biohackers, foodies, physicians, and scientists are transforming human perception, one sense at a time
Statement of responsibility
Kara Platoni
Creator
Author
Subject
Language
eng
Summary
  • Sensory science is increasingly proving that we don't perceive reality, we construct it through perception
  • "This book will describe the most basic and used statistical methods for analysis of data from trained sensory panels and consumer panels with a focus on applications of the methods. It will start with a chapter discussing the differences and similarities between data from trained sensory and consumer tests"--
Assigning source
Provided by publisher
Cataloging source
YDXCP
http://library.link/vocab/creatorName
Platoni, Kara
Dewey number
612.8
Illustrations
photographs
Index
index present
LC call number
QP441
LC item number
.P53 2015
Literary form
non fiction
Nature of contents
bibliography
http://library.link/vocab/subjectName
  • Technology
  • Perception
Label
We have the technology : how biohackers, foodies, physicians, and scientists are transforming human perception, one sense at a time, Kara Platoni
Instantiates
Publication
Copyright
Bibliography note
Includes bibliographical references (pages 257-265) and index
Carrier category
volume
Carrier category code
  • nc
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • Pt. one. The five senses -- pt. two. Metasensory perception -- pt. three. Hacking perception
  • Machine generated contents note: Contents -- Preface -- Acknowledgements -- Chapter 1. Introduction -- Chapter 2. Important data collection techniques for sensory and consumer studies -- 2.1. Sensory panel methodologies -- 2.2 Consumer tests -- Chapter 3. Quality control of sensory profile data -- 3.1. General introduction -- 3.2. Visual inspection of raw data -- 3.3 Mixed model ANOVA for assessing the importance of the sensory attributes. -- 3.4 Overall assessment of assessor differences using all variables simultaneously -- 3.5 Methods for detecting differences in use of the scale -- 3.6. Comparing the assessors' ability to detect differences between the products. -- 3.7. Relations between individual assessor ratings and the panel average -- 3.8. Individual line plots for detailed inspection of assessors -- 3.9. Miscellaneous methods -- Chapter 4. Correction methods and other remedies for improving sensory profile data. -- 4.1. Introduction -- 4.2. Correcting for different use of the scale. -- 4.3. Computing improved panel averages -- 4.4 Pre-processing of data for three-way analysis -- Chapter 5. Detecting and studying sensory differences and similarities between products. -- 5.1 Introduction -- 5.2 Analysing sensory profile data -- univariate case -- 5.3 Analysing sensory profile data -- multivariate case -- Chapter 6. Relating sensory data to other measurements. -- 6.2 Estimating relations between consensus profiles and external data -- 6.3 Estimating relations between individual sensory profiles and external data -- Chapter 7. Discrimination and similarity testing -- 7.1 Introduction -- 7.2 Analysis of data from basic sensory discrimination tests -- 7.3 Examples of basic discrimination testing -- 7.4. Power calculations in discrimination tests. -- 7.5 Thurstonian modelling -- what is it really? -- 7. 6 Similarity versus difference testing -- 7.7 Replications -- what to do? -- 7.8 Designed experiments, extended analysis and other test protocols -- Chapter 8. Investigating important factors influencing food acceptance and choice (conjoint analysis). -- 8.1 Introduction. -- 8.2. Preliminary analysis of consumer data sets (raw data overview). -- 8.3 Experimental designs for rating based consumer studies -- 8.4 Analysis of categorical effect variables -- 8.5. Incorporating additional information about consumers -- 8.6 Modelling of factors as continuous variables -- 8.7. Reliability/validity testing for rating based methods. -- 8.8. Rank based methodology -- 8.9. Choice based conjoint analysis -- 8.10 Market share simulation -- Chapter 9. Preference mapping for understanding relations between sensory product attributes and consumer acceptance -- 9.1 Introduction -- 9.2 External and internal preference mapping -- 9.3. Examples of linear preference mapping. -- 9.4 Ideal point preference mapping. -- 9.5. Selecting samples for preference mapping -- 9.6. Incorporating additional consumer attributes -- 9.7 Combining preference mapping with additional information about the samples -- Chapter 10. Segmentation of consumer data. -- 10.1 Introduction -- 10.2 Segmentation of rating data -- 10.3. Relating segments to consumer attributes. Chapter 11. Basic Statistics -- Chapter 11 Basic Statistics -- 11.1 Basic concepts and principles. -- 11.2 Histogram, frequency and probability11.3. Some basic properties of a distribution (mean, variance and standard deviation) -- 11.4. Hypothesis testing and confidence intervals for the mean -- 11.5 Statistical process control -- 11.6 Relationships between two or more variables -- 11.7. Simple linear regression. -- 11.8 Binomial distribution and tests -- 11.9 Contingency tables and homogeneity testing -- Chapter 12. Design of experiments for sensory and consumer data -- 12. 1. Introduction. -- 12.2. Important concepts and distinctions. -- 12.3. Full factorial designs -- 12.4. Fractional factorial designs -- screening designs -- 12.5. Randomised blocks and incomplete block designs -- 12.6 Split-plot and nested designs -- 12.7 Power of experiments -- Chapter 13. ANOVA for sensory and consumer data -- 13.1 Introduction -- 13.2 One-way ANOVA -- 13.3 Single replicate two-way ANOVA -- 13.4 Two-way ANOVA with randomized replications Chapter 13.5 Multi-way ANOVA -- 13.6. ANOVA for fractional factorial designs. -- 13.7 Fixed and random effects in ANOVA: Mixed models. -- 13.8 Nested and split-plot models. Chapter 13.9 Post hoc testing -- Chapter 14. Principal Component Analysis -- 14.1 Interpretation of complex data sets by PCA 14.2 Data structures for the PCA -- 4.3 PCA -- Description of the method -- 14.4. Projections and linear combinations. -- 14.5. The scores and loadings plots -- 14.6. Correlation loadings plot. -- 14.7 Standardisation -- 14.8 Calculations and missing values -- 14.9. Validation -- 14.10 Outlier diagnostics -- 14.11 Tucker-1 -- 14.12 The relation between PCA and factor analysis (FA) -- Chapter 15. Multiple regression, principal components regression and partial least squares regression. -- 15.1 Introduction. -- 15.2. Multivariate linear regression -- 15.3. The relation between ANOVA and regression analysis -- 15.4 Linear regression used for estimating polynomial models -- 15.5 Combining continuous and categorical variables. -- 15.6. Variable selection for multiple linear regression -- 15.7. Principal components regression (PCR) -- 15.8. Partial Least Squares (PLS) regression -- 15.9. Model validation -- prediction performance -- 15.10. Model diagnostics and outlier detection -- 15.11 Discriminant analysis -- 15.12 Generalised linear models, logistic regression and multinomial regression -- Chapter 16. Cluster analysis -- unsupervised classification -- 16.1 Introduction -- 16.2 Hierarchical clustering -- 16.3. Partitioning methods. -- 16.4. Cluster analysis for matrices. -- 17. Miscellaneous methodologies -- 17.1. Three-way analysis of sensory data -- 17.2. Relating three-way data to two-way data -- 17.3. Path modelling -- 17.4. MDS-multidimensional scaling Chapter 17.5 Analysing rank data -- 17.6. The L-PLS method -- 17.7. Missing value estimation -- Nomenclature, symbols and abbreviations -- Index
Control code
1629125
Dimensions
24 cm
Extent
xxi, 274 pages
Isbn
9780465089970
Media category
unmediated
Media MARC source
rdamedia
Media type code
  • n
Other physical details
photograph
System control number
  • (Sirsi) 1629125
  • (OCoLC)905686157
Label
We have the technology : how biohackers, foodies, physicians, and scientists are transforming human perception, one sense at a time, Kara Platoni
Publication
Copyright
Bibliography note
Includes bibliographical references (pages 257-265) and index
Carrier category
volume
Carrier category code
  • nc
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • Pt. one. The five senses -- pt. two. Metasensory perception -- pt. three. Hacking perception
  • Machine generated contents note: Contents -- Preface -- Acknowledgements -- Chapter 1. Introduction -- Chapter 2. Important data collection techniques for sensory and consumer studies -- 2.1. Sensory panel methodologies -- 2.2 Consumer tests -- Chapter 3. Quality control of sensory profile data -- 3.1. General introduction -- 3.2. Visual inspection of raw data -- 3.3 Mixed model ANOVA for assessing the importance of the sensory attributes. -- 3.4 Overall assessment of assessor differences using all variables simultaneously -- 3.5 Methods for detecting differences in use of the scale -- 3.6. Comparing the assessors' ability to detect differences between the products. -- 3.7. Relations between individual assessor ratings and the panel average -- 3.8. Individual line plots for detailed inspection of assessors -- 3.9. Miscellaneous methods -- Chapter 4. Correction methods and other remedies for improving sensory profile data. -- 4.1. Introduction -- 4.2. Correcting for different use of the scale. -- 4.3. Computing improved panel averages -- 4.4 Pre-processing of data for three-way analysis -- Chapter 5. Detecting and studying sensory differences and similarities between products. -- 5.1 Introduction -- 5.2 Analysing sensory profile data -- univariate case -- 5.3 Analysing sensory profile data -- multivariate case -- Chapter 6. Relating sensory data to other measurements. -- 6.2 Estimating relations between consensus profiles and external data -- 6.3 Estimating relations between individual sensory profiles and external data -- Chapter 7. Discrimination and similarity testing -- 7.1 Introduction -- 7.2 Analysis of data from basic sensory discrimination tests -- 7.3 Examples of basic discrimination testing -- 7.4. Power calculations in discrimination tests. -- 7.5 Thurstonian modelling -- what is it really? -- 7. 6 Similarity versus difference testing -- 7.7 Replications -- what to do? -- 7.8 Designed experiments, extended analysis and other test protocols -- Chapter 8. Investigating important factors influencing food acceptance and choice (conjoint analysis). -- 8.1 Introduction. -- 8.2. Preliminary analysis of consumer data sets (raw data overview). -- 8.3 Experimental designs for rating based consumer studies -- 8.4 Analysis of categorical effect variables -- 8.5. Incorporating additional information about consumers -- 8.6 Modelling of factors as continuous variables -- 8.7. Reliability/validity testing for rating based methods. -- 8.8. Rank based methodology -- 8.9. Choice based conjoint analysis -- 8.10 Market share simulation -- Chapter 9. Preference mapping for understanding relations between sensory product attributes and consumer acceptance -- 9.1 Introduction -- 9.2 External and internal preference mapping -- 9.3. Examples of linear preference mapping. -- 9.4 Ideal point preference mapping. -- 9.5. Selecting samples for preference mapping -- 9.6. Incorporating additional consumer attributes -- 9.7 Combining preference mapping with additional information about the samples -- Chapter 10. Segmentation of consumer data. -- 10.1 Introduction -- 10.2 Segmentation of rating data -- 10.3. Relating segments to consumer attributes. Chapter 11. Basic Statistics -- Chapter 11 Basic Statistics -- 11.1 Basic concepts and principles. -- 11.2 Histogram, frequency and probability11.3. Some basic properties of a distribution (mean, variance and standard deviation) -- 11.4. Hypothesis testing and confidence intervals for the mean -- 11.5 Statistical process control -- 11.6 Relationships between two or more variables -- 11.7. Simple linear regression. -- 11.8 Binomial distribution and tests -- 11.9 Contingency tables and homogeneity testing -- Chapter 12. Design of experiments for sensory and consumer data -- 12. 1. Introduction. -- 12.2. Important concepts and distinctions. -- 12.3. Full factorial designs -- 12.4. Fractional factorial designs -- screening designs -- 12.5. Randomised blocks and incomplete block designs -- 12.6 Split-plot and nested designs -- 12.7 Power of experiments -- Chapter 13. ANOVA for sensory and consumer data -- 13.1 Introduction -- 13.2 One-way ANOVA -- 13.3 Single replicate two-way ANOVA -- 13.4 Two-way ANOVA with randomized replications Chapter 13.5 Multi-way ANOVA -- 13.6. ANOVA for fractional factorial designs. -- 13.7 Fixed and random effects in ANOVA: Mixed models. -- 13.8 Nested and split-plot models. Chapter 13.9 Post hoc testing -- Chapter 14. Principal Component Analysis -- 14.1 Interpretation of complex data sets by PCA 14.2 Data structures for the PCA -- 4.3 PCA -- Description of the method -- 14.4. Projections and linear combinations. -- 14.5. The scores and loadings plots -- 14.6. Correlation loadings plot. -- 14.7 Standardisation -- 14.8 Calculations and missing values -- 14.9. Validation -- 14.10 Outlier diagnostics -- 14.11 Tucker-1 -- 14.12 The relation between PCA and factor analysis (FA) -- Chapter 15. Multiple regression, principal components regression and partial least squares regression. -- 15.1 Introduction. -- 15.2. Multivariate linear regression -- 15.3. The relation between ANOVA and regression analysis -- 15.4 Linear regression used for estimating polynomial models -- 15.5 Combining continuous and categorical variables. -- 15.6. Variable selection for multiple linear regression -- 15.7. Principal components regression (PCR) -- 15.8. Partial Least Squares (PLS) regression -- 15.9. Model validation -- prediction performance -- 15.10. Model diagnostics and outlier detection -- 15.11 Discriminant analysis -- 15.12 Generalised linear models, logistic regression and multinomial regression -- Chapter 16. Cluster analysis -- unsupervised classification -- 16.1 Introduction -- 16.2 Hierarchical clustering -- 16.3. Partitioning methods. -- 16.4. Cluster analysis for matrices. -- 17. Miscellaneous methodologies -- 17.1. Three-way analysis of sensory data -- 17.2. Relating three-way data to two-way data -- 17.3. Path modelling -- 17.4. MDS-multidimensional scaling Chapter 17.5 Analysing rank data -- 17.6. The L-PLS method -- 17.7. Missing value estimation -- Nomenclature, symbols and abbreviations -- Index
Control code
1629125
Dimensions
24 cm
Extent
xxi, 274 pages
Isbn
9780465089970
Media category
unmediated
Media MARC source
rdamedia
Media type code
  • n
Other physical details
photograph
System control number
  • (Sirsi) 1629125
  • (OCoLC)905686157

Library Locations

    • Haverhill Public LibraryBorrow it
      99 Main Street, Haverhill, MA, 01830, US
      42.7772345 -71.0767683
Processing Feedback ...