Discriminant Analysis in R

Discrimination tests are more important in sensory analysis. The main idea behind sensory discrimination analysis is to identify any significant difference or not. 

 

Here are the details of different types of discrimination methods and p value calculations based on different protocols/methods. 

 

In this article will discuss about different types of methods and discriminant analysis in r.

 

Triangle test

Triangle test is a discrimination test designed primarily to determine whether a perceptible sensory difference exists or not between two products.

 

During a triangle test, a panelist is presented with one different and two alike samples. 

 

If possible, all three samples should be presented to the panelist at once (ideal case), and the panelist should be instructed to taste/smell/touch the samples from left to right.

 

The six possible order combinations should be randomized across panelist (all possible combinations or minimum 2 replications recommended). 

 

For samples A and B, the six possible order combinations are: AAB, ABA, BAA, BBA, BAB, and ABB. 

The panelist is instructed to identify the odd sample and record his answer. 

 

Triangle discriminant analysis in r, based on following function can calculate.

discrim(10, 15, method = “triangle”, statistic = “score”,conf.level = 0.90)

Duo-Trio Test

Duo-Trio Test is used for finding sensory difference between two samples exit or not. This is alternate to triangle test.

 

Present to each subject an identified reference sample, followed by two coded samples, one of which matches the reference sample. 

 

Ask subjects to indicate which coded sample matches the reference. Count the number of correct replies.

 

As a general rule, the minimum number of subjects is 16, but for less than 28, the beta-error is high and recommended number of subjects is above 32, the discrimination is much improved if 32, 40, or a larger number subjects.

 

Count the number of correct responses and the total number of responses. Do not count “no difference” responses; subjects must guess if in doubt. 

 

R function mentioned as below.

discrim(10, 15, method = “duotrio”, statistic = “score”,conf.level = 0.90)

 

The Tetrad Test


The tetrad method is a difference test involving four samples where the assessor is presented with blind coded samples with two samples of one product and two samples of another product. 

 

The assessors must then group the products into two groups according to their similarity.

 

A considerable advantage of the tetrad test is that far fewer assessors are required compared to the triangle and duo-trio methods. 

 

If the samples are really expensive or unavailability of subjects tetrad test can be used for analysis.

 

Tetrad R function mentioned as below

discrim(10, 15, method = “tetrad”, statistic = “score”,conf.level = 0.90)

Two out of five test

In this case five samples are presented to the assessors. These samples are separated in two groups, the first one having three similar samples and the second one having two similar samples. 

 

The assessors have to identify the group of two similar samples.

 

The function for Two out of five mentioned as below

discrim(10, 15, method = “twofive”, statistic = “score”,conf.level = 0.90)

2-AFC test

2 AFC test case 2 products are presented to each assessor. The assessor has to tell which product has the highest intensity on a particular characteristic. More ideal in food sensory analysis.

 

R calculation function mentioned as below

discrim(10, 15, method = “twoAFC”, statistic = “score”,conf.level = 0.90)

3-AFC test

3-AFC test methods, 3 samples are presented to each assessor. Two are similar and the third one is different. 

 

The assessor has to tell which sample has the highest intensity on a particular characteristic.

 

R function mentioned as below

discrim(10, 15, method = “threAFC”, statistic = “score”,conf.level = 0.90) 

How to perform t test in R

 

What is Null Hypothesis

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1 Response

  1. Hairstyles says:

    I’ve been absent for some time, but now I remember why I used to love this blog. Thanks , I will try and check back more frequently. How frequently you update your site?

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