Lesson 6 - Sensory Analysis
This lesson deals with the sensory analysis of beer. It details how to set up the elements of a sensory panel, such as panel selection, training, and the design of the sensory facilities, and then covers how to run the panel and collect the results. It discusses the various types of sensory test and their benefits before going on to cover the use of statistics to convert sensory data into information about the sensory quality of beer. Finally, it looks at the presentation of results from sensory tests and lists key flavour compounds in beer.
Two-out-of-Five test
In the Two-out-of-Five test the assessors are presented with five samples in a randomised order to avoid presentation bias. They are then instructed to find the two samples that are different to the other three.
The two-out-of-five test is more statistically efficient than the triangle and paired comparison tests. However, with a large number of samples to assess, sensory fatigue is a significant concern for assessors undertaking it. So much so, that the two-out-of-five test is used as a training exercise to check the panellist's ability to adequately cleanse their palate between samples and accurately remember the flavour of several of the samples at the same time. The two-out-of-five test is also very effective for evaluating the appearance of the beer, as sensory fatigue is not applicable to visual assessment.
The table below compares the various difference tests:
Table: Difference tests compared
|
Test Name |
Purpose |
Strengths |
Weaknesses |
Panel Size |
|
Triangle |
QC, recipe change, new product development, panel selection, monitoring |
Standard test, simple, good statistical efficiency |
Prone to position bias (middle is different) |
18-30 |
|
Tetrad |
QC, recipe change, new product development |
Statistically very strong |
Sensory fatigue |
10 |
|
Paired comparison |
Consumer research |
Simple and easy to set up |
Statistically weak |
300 for general public, 20 for trained assessors |
|
Duo-Trio |
QC |
Good for intensely flavoured beers |
Statistically weaker than Triangle test |
26 |
|
A – not-A |
QC, and for identifying recipe change |
Effective when ‘A’ is main brand |
Fatigue significant later in test |
30 |
|
Two-out-of-five |
Only for use with delicately flavoured beers |
Statistically more efficient than Triangle test |
Fatigue particularly significant |
15 |
In this section we have seen that difference tests can be used to identify changes in our beers, to test and screen our flavour panellists, and to understand the preferences of our customers. The next section covers how we use sensory methods to describe the flavour of beer.