Discriminant Analysis in R

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.

This article will discuss different types of methods and discriminant analysis in r.

1. Triangle test

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

How to clean the datasets in R? » janitor Data Cleansing » 

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 panelists (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 the following function can calculate.

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

2. Duo-Trio Test

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

Repeated Measures of ANOVA in R Complete Tutorial » 

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 of 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 below.

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

Deep Neural Network in R » Keras & Tensor Flow 

3. The Tetrad Test

The tetrad method is a different 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 similarities.

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)

4. Two out of five test

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

Exploratory Data Analysis (EDA) » Overview »

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)

5. 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)

Filtering Data in R 10 Tips -tidyverse package » 

6. 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)

datatable editor-DT package in R » Shiny, R Markdown & R »

You may also like...

6 Responses

  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?

  2. website says:

    Thanks for your marvelous posting! I definitely enjoyed reading it, you can be
    a great author. I will remember to bookmark your blog and may
    come back in the foreseeable future. I want to
    encourage you to continue your great writing, have a nice evening!

  3. If you are going for finest contents like myself, just visit this
    site everyday as it provides quality contents, thanks

  4. GMX Mail says:

    My spouse and I stumbled over here coming from a different page and thought I
    should check things out. I like what I see so now i’m following you.
    Look forward to looking at your web page repeatedly.

  5. It’s appropriate time to make a few plans for the long run and it’s
    time to be happy. I’ve learn this submit and if I may I want to
    counsel you few interesting things or advice.
    Maybe you could write next articles relating to this article.

    I desire to learn more things about it!

Leave a Reply

Your email address will not be published. Required fields are marked *

17 − two =