Ph.D.-course

 

 

The Analysis of Sensory and Consumer Data

 

at

Technical University of Denmark,

Lyngby, Denmark

 

September 9 – September 13, 2013

 

 

Organized by:

 

DTU Compute

Technical University of Denmark

 

 

 

 

Course Objectives

To improve the ability of analysing human perception data. Some of the newest statistical methodologies will be covered using the open source software R, among others the packages sensR, ordinal and lmerTest will be used together with the PanelCheck software.

 

Learning Objectives
A student who has met the objectives of the course will be able to:

  1. Work with R and Panelcheck
  2. Plan and analyze simple discrimination and similarity experiments using sensR
  3. Analyze replicated discrimination data
  4. Perform and understand simple Thurstonian modeling
  5. Use mixed models for sensory profile data and consumer preference data by PanelCheck and R-packages.
  6. Analyze sensory profile data with the new scale correction method (using R)
  7. Use PanelCheck for simple analysis as well as multivariate analysis (including Tucker-1)
  8. Analyze A-not-A and Same-different data
  9. Analyze ordinal human perception data using the R-package ordinal

Language

All lectures will be given in English.

 

Organizers

Per Bruun Brockhoff, B324, R220, (+45) 2044 1711,

perbb@dtu.dk

Rune H.B. Christensen, B324, R220, rhbc@dtu.dk


 

Programme, overview
Monday: Simple discimination using R (package sensR)
Tuesday: Multivariate Analysis using PanelCheck
Wednesday: Mixed models using PanelCheck AND R. (newly developed R-routines)
Thursday: Advanced discrimination using R (packages sensR and ordinal)
Friday: More on replicated (discrimination/similarity) data analysis.
Brief student presentations.

In addition an introduction to R is given and discussions/supervision of participants individual data projects are included.  


We begin Monday morning at 9am and finish Friday at 3pm.

Programme, in more detail

Day 1, Monday: Simple discimination using R (package sensR)
We present the R-package sensR as a tool for the planning and analysis of sensory discrimination and similarity experiments. The sensR package includes easily accessible tools for handling the five basic sensory test protocols: duo-trio, triangle, 2-AFC, 3-AFC, tetrad test. For all of these sensR provides:

  1. Hypothesis tests
  2. Confidence intervals (Standard and improved  - likelihood based)
  3. Power and sample size calculations
  4. Simulation
  5. Thurstonian analysis
  6. Plotting features
  7. Exercises based on given or own data.

Day 2, Tuesday: Multivariate Analysis using PanelCheck

  1. Installation and PanelCheck GUI (Graphical User Interface)
  2. Structure of QDA data
  3. Data import
  4. Performance indices (new in PanelCheck)
  5. Plots based on one-way ANOVA / one-factor fixed model
  6. Plots based on Principal Component Analysis (PCA)
  7. Simple plots and methods
  8. PanelCheck workflow
  9. Export of plots in presentations (PowerPoint)Excercises
    1. Analysis of a given or own data set (groups of 2 to 3 students).
    2. Preparing a presentation with results and conclusions from PanelCheck analysis
    3. Present results and conclusions to other students (5 to 10 minutes)

Day 3, Wednesday: Mixed models using PanelCheck AND R. (Package lmerTest and other R-routines)

  1.   From oneway and twoway ANOVA to 3-way mixed model ANOVA (PanelCheck)
  2. Correcting for scale effects in sensory profile data (specialized R-routines)
  3. Using the newly developed R-package lmerTest for the (automated) analysis of more complex structured sensory and consumer data  such as:
    1. Unbalanced sensory profile data (e.g. missings)
    2. Incomplete consumer preference data
    3. 2- (or higher)way product structure in sensory
    4. 2- (or higher)way product structure in consumer (Conjoint)-         
    5. Extending Conjoint to include Consumer background/design factors/covariates
    6. Complex blocking, product replication, product batch structures in as well sensory as consumer
    7. A mixed model approach for performing external preference mapping
    8. Extending mixed model external preference mapping to include product and consumer background/design factors/covariates (segments)
  4. Exercises based on given or own data.

-          Day 4, Thursday: Advanced discrimination using R (packages sensR and ordinal)

  1. Similarity tests for 2-AFC, 3-AFC, Duo-Trio, Triangle and Tetrad data
  2. Power and sample size estimation for similarity tests
  3. Advanced test protocols: A-not A, Same-Different, 2-AC
  4. Ordinal based discrimination protocols: A-not A with sureness and Degree-of-difference test.
  5. ROC curve estimation and AUC
  6. Exercises based on given or own data

Day 5, Friday: More on replicated (discrimination/similarity) data analysis.  


  1. Beta-Binomial (standard and corrected) analysis for replicated data
  2. Replicated Thurstonian Model for discrimination analysis
  3. Simulation of replicated difference tests
  4. Replicated categorical ratings/ordinal data more generally
  5. Exercises based on given or own data.
  6. Brief student presentations.

 

 

Teachers:

Professor Per Bruun Brockhoff

Post doc Rune H.B. Christensen
PhD Student Alexandra Kuznetsova

DTU Compute, Technical University of Denmark

Research Scientist, PhD Oliver Tomic, Nofima Mat, Ås, Norway

  

Participants

The course is designed for Ph.D. students within Statistics/Data analysis with interest in human perception  data and
Ph.D. students within non-statistical areas such as sensory science, food science, marketing etc. with interest in data
analysis and statistics.

 

Study Material, by Teaching Days:

(It would be a good idea to orientate yourself in the given material, although parts
of it indeed most likely is a bit technical for the typical participant of this course - then focus on the example parts.)

Day 1:
Chapter 7 and 11 of: T. Næs, P.B. Brockhoff and O. Tomic, (2010). Statistics for Sensory and Consumer Science, John Wiley & Sons.

Brockhoff, P.B. and Christensen, R.H.B. (2010). Thurstonian models for sensory discrimination tests as generalized linear models. FQP, 21(3), 330-338.


Day 2:
Chapter 3, 4, 5 and 14 of: T. Næs, P.B. Brockhoff and O. Tomic, (2010). Statistics for Sensory and Consumer Science, John Wiley & Sons.

O. Tomic, C. Forde, C. Delahunty, T. Næs, Performance indices in descriptive sensory analysis - a complimentary screening tool for assessor and panel performance, Food Quality and Preference 28 (2013), 122-133


Day 3:
Chapter 5, 8, 12 and 13 of: T. Næs, P.B. Brockhoff and O. Tomic, (2010). Statistics for Sensory and Consumer Science, John Wiley & Sons.

Kuznetsova, A., Christensen, R.H.B., Bavay C. and Brockhoff, P.B. (2013). Automated Mixed ANOVA
Modelling of sensory and consumer data. To be submitted to: FQP.

Brockhoff, P. B., Schlich, P., & Skovgaard, I. M. (2012). Accounting for scaling differences in sensory profile
data: improved mixed model analysis of variance. Submitted to: Food Quality and Preference.

Day 4:
Chapter 7 of: T. Næs, P.B. Brockhoff and O. Tomic, (2010). Statistics for Sensory and Consumer Science, John Wiley & Sons.

Brockhoff, P.B. and Christensen, R.H.B. (2010). Thurstonian models for sensory discrimination tests as generalized linear models. FQP, 21(3), 330-338.

Christensen, R.H.B. Cleaver, G. and Brockhoff, P.B. (2011). Statistical and Thurstonian models for the A-not A protocol with and without sureness, FQP 22(6), 542-54.

Christensen, R. H. B., H.-S. Lee and P. B. Brockhoff (2012). Estimation of the Thurstonian model for the 2-
AC protocol. Food Quality and Preference. 24(1), 119-128.

Day 5:
Chapter 7 of: T. Næs, P.B. Brockhoff and O. Tomic, (2010). Statistics for Sensory and Consumer Science, John Wiley & Sons.

Christensen, R. H. B. and P. B. Brockhoff (2013). Analysis of replicated categorical ratings data from sensory experiments. To appear in Journ. of SFdS.

Christensen, R. H. B., H.-S. Lee and P. B. Brockhoff (2012). Estimation of the Thurstonian model for the 2-
AC protocol. Food Quality and Preference. 24(1), 119-128.


Evaluation and Diplomas

To pass the full 2.5 ECTS course, active participation in all activities is required INCLUDING the submission and
approval of a report subsequent to the course period. Grades: Pass/Fail. ECTS points: 2.5

 

Registration

Email Camilla Lund Poulsen, DTU Compute, Technical University of Denmark, Building 324, DK-2800 Kgs.Lyngby, Denmark.  
E-mail: capo@dtu.dk

 

Registration fee

For PhD (and potential master)  students: No fee. For others: 400 Euro for the whole course and 100 Euro for single
day participation. (A discount for Sensometrics Society members will be given)

Housing

Accommodation in hostels/hotels is to  be arranged (and covered) by the participants themselves.

See the Visit Copenhagen website at http://www.visitcopenhagen.dk/.

 

Updated pre-course information 


VENUE and course start:
Monday 9. September, Room S12, Building 101, DTU Lyngby


All the teaching will take place in this room, which is a part of
the DTU meeting center in the main university building - called
building 101:

http://www.dtu.dk/english/About/Practical-information/Directions/DTU-Lyngby-Campus


We begin teaching activities at 9am, but invite you to register and
enjoy some breakfast between 8am and 9am.


R software
As is clear that apart from Tuesday (using PanelCheck Software) we will
use R in the course. We will introduce R as a start Monday, but we
recommend that you (try to) install the newest version of R AND Rstudio
on your laptop (so please bring one - we do NOT supply computers for
participants and they ARE indeed necessary) before you come -
please see the following short intro (with relevant links) to get started:

http://02402.imm.dtu.dk/enote/afsnit/NUID145/

If you want to watch a few very short videos on how to get started with
R then you can go to this Youtube Playlist:

http://www.youtube.com/playlist?list=PLOU2XLYxmsIK9qQfztXeybpHvru-TrqAP
(A number of short videos illustrating the use of R - shown within a Mac version,
but apart from the appearance of the sub-windows exactly the same works for RStudio
in any operating system)


Individual Project Part of SummerSchool:
To complete ("pass") the 2.5ECTS course 02930 you must at the end of (or shortly after)
the course submit a brief report or presentation of some data analysis using methods/tools from the course on your own data. 


So please prepare to bring some data for the course. On the Friday afternoon we have scheduled 90 minutes of participants (brief) presentations of this. This individual (OR group based, if relevant) project activity is planned to take
place in parallel with the organized program, in the following way:


  1. Monday/Tuesday: All participants will have a one-to-one meeting with one of teachers of the course: Per Bruun Brockhoff, Rune H.B. Christensen, Alexandra Kuznetsova or Christine Linander. In this meeting the participant will present the data (and background) to the "expert" with the aim to identify a suitable mini-project for the course.
  2. Wednesday/Thursday: Participants work a little on their mini-project in parallel with other activity (under supervision by course teachers)
  3. Friday: 5-8 minutes presentations in plenum by participants.
  4. After course: submit brief report or presentation

Basic Statistics Brush-up??
Finally, if you think that you need to brush up a little on parts of your basic stats before coming,
you may want to have a look at some of the around 100 online modulized basic stats videos by Per Bruun Brockhoff:
http://02402.imm.dtu.dk/podcast/english-lecture-recordings-e12/

Or find the same video-collection in iTunes-U by searching for "DTU Statistics"