6.2 Dividing The Data and Processing

The 19 attributes in the original data are difficult to be split into just 2 group as it includes basic tastes, compound flavors, overall assessment, textures and other variations of the basic tastes. Based on the widely accepted theory of basic tastes in Psychological research post 2000, I have decided to split my dataset into 2 groups of basic tastes (salty, acidic, umami, bitter and umami) and others. Below, you can find the 2 data sets after splitting. Note that Fatty is a new addition to the basiv tastes with a lot of research showing the existence of taste receptors for edible lipids.

Reference: https://pubs.acs.org/doi/full/10.1021/acs.jafc.9b03542

#split into 2 data set for PLSC

#the first set of basic tastes including: Salty, Umami, Acidic, Bitter, and Fatty
basic.taste <- sausage.processed[, c(3:6,13)]

#show off the data table
a    <- kable(basic.taste)                            #taking a peak at our data
scroll_box(a, width = "100%", 
           height = "200px"
           )
Salty Umami Acidic Bitter Fatty
3.0 4.0 1.0 0.0 2.1
2.5 4.0 2.0 0.0 4.0
3.5 2.0 2.0 0.0 4.0
4.0 5.0 2.0 0.0 5.1
2.5 2.0 1.5 0.0 4.0
5.5 4.5 2.5 0.0 5.1
3.0 4.0 1.0 0.0 2.1
5.0 1.0 2.0 0.0 2.1
3.0 2.0 2.0 3.0 2.0
3.0 3.0 2.0 5.0 3.0
2.0 2.5 1.0 2.0 4.0
3.0 2.0 1.0 4.0 4.0
3.0 4.0 2.5 3.5 2.0
3.0 2.0 4.0 3.0 5.0
5.0 5.0 2.0 2.0 3.0
2.0 2.0 4.0 4.0 5.0
3.0 3.0 2.0 3.0 3.0
3.0 2.0 3.0 1.5 2.0
3.0 2.0 2.0 0.0 3.0
4.0 3.0 1.0 0.5 3.0
3.0 2.0 4.0 1.0 2.0
5.0 3.0 4.0 1.5 3.0
4.0 3.0 2.0 0.0 3.0
3.0 0.0 4.0 2.0 5.0
2.0 4.0 2.0 0.0 3.0
3.5 3.0 1.0 0.0 3.0
4.0 3.0 1.0 0.0 2.0
2.0 3.0 1.5 0.0 4.0
3.0 4.0 1.0 0.0 3.0
5.0 2.0 2.5 0.0 3.0
2.0 6.0 3.0 0.0 6.0
2.0 4.0 3.0 0.0 3.0
2.0 4.0 0.0 0.0 4.0
3.5 3.0 0.0 0.0 4.0
3.0 1.5 0.0 0.0 3.0
2.0 5.0 0.0 0.0 4.0
3.0 4.0 0.0 0.0 2.5
5.0 3.0 0.0 0.0 4.5
2.0 1.0 0.0 0.0 3.0
3.0 3.0 0.0 0.0 5.0
3.0 4.0 1.0 0.0 3.0
4.0 3.5 2.0 0.0 3.5
2.0 3.0 1.5 0.0 3.0
2.0 5.0 1.0 0.0 3.0
2.5 2.0 2.5 0.0 4.0
4.0 3.5 2.0 0.0 3.5
3.0 4.0 2.0 0.0 2.0
5.0 3.5 1.0 0.0 5.0
2.0 4.0 1.0 0.0 3.0
3.0 2.5 0.5 0.0 3.0
3.0 2.0 1.0 0.0 3.0
2.0 3.0 0.5 0.0 4.0
2.5 2.5 1.5 0.0 4.0
6.0 4.0 2.0 0.0 4.5
2.0 3.0 1.0 0.0 1.0
4.0 2.0 1.0 0.0 4.0
3.0 4.0 1.0 0.0 2.0
3.5 4.0 2.0 0.0 3.0
4.0 3.0 1.0 0.0 2.0
2.0 3.0 1.5 0.0 2.0
4.0 4.0 2.0 0.0 2.0
3.0 4.0 2.5 0.0 3.0
5.0 4.0 3.0 0.0 3.5
2.0 2.0 1.0 0.0 3.0
# the second set includes all others (compound flavors, flavors synonymous with the basic, textures)
others      <- sausage.processed[, -c(3:6,13)]
b    <- kable(others)                            #taking a peak at our data
scroll_box(b, width = "100%", 
           height = "200px"
           )
Aroma impact Flavor impact Alliaceous Animalic savory Bloody Floury Dark meat Eggy HVP Juicy savory Rubbery Smokey Spicy White meat
4.0 6.0 2.5 2.0 5.0 1.5 3.0 1.0 2.0 4.0 4.0 3.0 3.0 5.0
5.0 8.0 3.0 1.0 2.0 3.0 2.2 3.0 2.0 4.0 4.0 4.5 2.0 5.0
3.0 6.0 2.5 3.0 0.0 3.0 2.5 0.5 2.0 2.0 3.0 1.0 3.0 5.0
5.0 5.5 4.0 2.0 3.0 2.0 5.2 2.0 2.0 6.0 5.0 4.0 3.0 3.0
6.0 6.0 2.0 1.0 1.5 3.0 4.0 0.0 4.5 3.0 5.2 2.0 2.0 5.0
6.0 7.0 3.5 5.0 2.0 4.0 4.0 2.0 2.0 2.5 3.0 3.5 7.0 6.0
4.0 6.0 4.0 3.0 2.0 4.5 4.0 2.0 6.0 3.5 3.0 4.0 3.0 5.0
3.0 9.0 3.0 4.0 3.0 1.5 3.5 3.0 3.0 4.0 2.2 2.0 3.0 6.0
3.0 5.0 2.0 2.0 3.9 2.0 2.0 3.0 2.0 1.0 4.0 4.0 3.0 5.0
4.5 8.0 2.0 1.5 0.9 4.0 2.0 1.0 2.5 3.0 3.5 2.0 2.0 5.0
5.0 6.0 3.0 2.0 2.0 3.0 2.0 2.0 3.0 2.0 2.4 3.0 3.0 4.0
5.0 6.0 2.0 1.0 0.9 3.5 2.0 0.0 2.0 5.0 3.0 0.0 3.0 3.0
6.0 6.0 4.0 2.0 1.5 4.0 4.0 1.0 4.0 3.0 4.0 2.0 2.5 6.0
4.0 5.0 3.0 4.5 3.0 3.0 2.5 3.5 2.0 3.5 2.4 2.0 3.5 5.0
3.0 6.0 3.5 1.0 3.9 4.0 2.0 4.0 3.0 2.0 4.0 2.0 3.0 5.0
3.0 5.0 2.5 3.0 2.0 2.0 6.0 2.0 0.0 1.0 5.4 3.5 5.0 3.0
4.0 5.3 3.0 2.0 2.0 5.0 3.0 1.0 3.0 2.0 0.0 2.0 3.0 2.0
5.0 5.5 2.0 4.0 1.5 4.5 3.0 1.5 1.5 2.0 0.0 3.0 2.0 1.2
4.0 8.3 2.0 4.0 1.0 4.0 5.0 3.0 3.0 4.0 0.0 3.0 2.0 3.0
6.0 5.0 3.0 4.0 0.0 4.0 3.0 0.0 2.0 0.5 0.0 2.0 3.0 1.2
6.0 6.0 2.5 2.5 2.0 5.0 4.0 1.5 2.0 3.0 0.0 1.5 1.0 4.2
7.5 8.0 3.0 4.5 0.0 7.0 4.0 0.0 4.0 5.0 0.0 4.5 2.0 4.2
5.0 7.0 3.0 2.0 2.0 6.0 4.0 2.0 3.0 3.0 0.0 4.0 3.0 1.2
4.0 7.0 4.0 2.0 5.0 6.0 4.0 1.0 3.0 3.0 0.0 2.0 5.0 3.5
3.0 4.9 3.0 2.0 2.0 3.0 3.0 1.0 0.0 3.0 3.0 2.0 3.0 3.0
5.0 5.0 3.0 3.5 2.5 2.0 0.0 3.0 0.0 2.5 2.0 3.5 3.5 4.5
5.0 7.9 3.0 1.3 1.0 2.0 2.0 1.0 0.0 1.0 4.0 4.0 2.0 3.0
3.0 7.9 3.0 4.3 1.0 4.0 0.0 3.0 0.0 5.0 3.0 2.5 3.0 5.0
5.0 6.0 3.0 1.3 0.0 4.0 0.0 0.0 0.0 2.0 4.0 2.0 2.0 4.0
7.0 7.0 2.0 1.5 1.0 2.0 3.0 2.0 0.0 3.5 3.0 3.0 2.0 5.0
6.0 7.9 4.0 4.3 2.0 4.0 0.0 2.5 0.0 5.0 6.0 4.0 3.5 7.0
4.0 5.0 4.0 3.0 2.0 3.0 3.0 0.0 0.0 3.0 5.0 4.0 1.0 3.0
4.0 7.0 2.0 2.0 1.5 0.0 5.0 3.0 3.5 3.0 2.0 4.5 0.0 4.8
5.0 4.0 4.0 1.0 3.0 0.0 2.0 2.0 4.0 3.0 4.0 4.0 0.0 2.0
4.0 8.0 2.0 4.0 1.5 0.0 4.0 2.0 0.0 5.0 2.0 3.0 0.0 3.0
3.0 8.0 3.5 1.0 0.0 0.0 4.0 1.0 1.0 3.0 2.0 1.0 0.0 1.8
3.0 5.0 2.0 2.0 2.5 0.0 4.0 3.0 2.0 3.0 2.0 3.0 0.0 4.8
5.0 5.0 3.0 3.0 2.0 0.0 6.0 1.0 2.0 3.0 2.0 3.5 0.0 4.0
6.0 6.0 2.0 4.5 1.0 0.0 5.5 0.5 2.0 4.0 3.0 2.0 0.0 4.0
4.0 6.0 3.0 0.0 3.0 0.0 4.0 1.0 3.0 4.0 5.0 3.0 0.0 3.0
4.0 5.0 4.0 2.0 2.0 0.0 5.0 2.0 0.0 3.0 0.0 3.0 0.0 0.0
4.0 6.0 3.0 2.0 2.0 0.0 5.0 3.0 0.0 3.0 0.0 3.5 0.0 3.0
5.0 8.0 3.0 2.5 2.0 0.0 5.0 3.5 0.0 5.0 0.0 3.0 0.0 2.0
4.0 6.0 3.0 4.0 1.0 0.0 3.0 0.0 0.0 4.0 0.0 3.0 0.0 2.0
7.0 8.0 2.0 1.5 3.0 0.0 6.0 0.0 0.0 1.5 0.0 3.0 0.0 3.0
7.0 6.0 2.5 4.0 2.0 0.0 5.0 0.0 0.0 4.0 0.0 4.0 0.0 3.0
4.5 7.0 4.0 2.0 3.0 0.0 6.0 3.0 0.0 4.0 0.0 4.0 0.0 1.0
5.0 6.0 2.0 0.0 4.0 0.0 6.0 0.0 0.0 3.0 0.0 4.0 0.0 1.0
4.0 3.0 3.0 3.0 2.0 2.0 4.0 2.0 3.0 3.0 4.0 3.0 3.0 3.0
4.0 5.0 2.0 0.7 2.5 4.0 5.0 3.5 4.0 2.0 3.5 3.0 3.0 2.5
3.0 8.0 3.5 3.7 2.0 3.0 5.0 4.0 4.0 5.0 3.0 2.0 3.5 3.5
6.3 6.0 5.5 3.7 0.0 3.5 4.0 0.5 3.0 3.0 4.0 3.0 3.0 2.0
6.0 5.0 2.0 1.5 3.0 4.0 5.0 0.0 0.0 4.0 3.5 2.0 3.0 4.0
6.3 7.0 3.5 3.0 1.5 4.0 3.5 1.0 5.0 4.0 5.5 3.0 1.5 6.0
4.0 6.0 4.0 3.0 1.0 5.0 5.0 2.0 4.0 5.0 6.0 4.0 3.0 2.0
3.0 6.0 3.0 0.7 4.0 5.0 5.0 0.0 3.0 2.0 6.0 2.0 3.0 3.0
5.0 5.0 3.0 2.0 3.0 3.0 0.0 2.0 3.0 3.0 2.0 3.0 3.0 5.0
4.5 6.0 2.5 2.0 2.0 3.0 0.0 2.0 2.5 3.0 3.5 3.0 3.5 5.0
5.5 8.0 3.0 0.5 1.0 2.0 0.0 2.0 1.0 2.0 3.0 4.0 1.0 3.0
5.0 9.0 3.0 3.5 0.0 3.0 0.0 3.0 2.0 4.0 2.0 2.0 4.0 5.0
7.0 6.0 4.0 2.0 0.0 3.0 0.0 0.0 1.0 3.0 4.0 3.0 2.0 4.0
6.0 7.0 3.0 2.0 1.0 2.0 0.0 0.0 1.5 3.5 4.0 2.5 2.0 3.0
7.0 7.0 3.5 4.0 0.0 6.0 0.0 2.0 3.0 4.0 4.0 2.5 2.0 7.0
7.0 6.0 4.0 3.0 3.0 4.0 0.0 1.0 0.0 3.0 3.0 3.5 2.0 4.0