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Hand-on Sensory Statistics – online session 4 of 4
February 25 @ 5:30 pm - February 26 @ 3:00 am
Statistical Analysis of Sensory Data collected from Sensory Panels using XLSTAT SENSORY and EyeOpenR
Sensory characterization of products has traditionally been collected from trained sensory panels, there is now an increasing use of “rapid methods” of data collection that do not require training. This course covers analysis of data from both sources. We assume only very basic statistical knowledge and there is a refresher session at the beginning to remind you of the basics.
The course uses XLSTAT SENSORY and EyeOpenR, as part of the course fee you will be given a complimentary licence to EyeOpenR software for 1 year.
- Analysis of Variance review, standard errors, multiple comparison tests
- The Mixed model for sensory data, assumptions and problem data
- Panel Performance using ANOVA, the MAM model and simple multivariate methods
- Principal Component Analysis – how it works, interpretation of plots, covariance v correlation. Visualisation of sample differences, interpretation of maps
- Canonical Variates Analysis – a technique that better displays product differences v panel variation
- Rapid methods of data collection, Free Sorting Tasks and Napping, guidance in design and approaches to analysis.
- Relating sensory to instrumental/consumer liking 1: Simple regression modelling, variable selection. Modelling curvature, limitations.
- Relating sensory to instrumental/consumer liking 2: Partial Least Squares Regression, How it works, guidance in model building.
Rapid Methods Free Sorting Tasks – Data collection and analysis using Multi Dimensional Scaling and Cluster Analysis
Relating Sensory data to instrumental or liking data
Regression modelling refresher, multiple regression, variable selection, modelling curvature. Limitations
Partial Least Squares as a technique to build models to predict one block of data from another, e.g. sensory from instrumental data or liking from sensory. Graphical explanation of the technique and comparison with PCA. Key statistics and their interpretation, Strategies for model building.