This data set contains the physico-chemical, sensory and hedonic measurements of 6 orange juices.
Usage
orange
Format
A data frame with 6 observations and 112 variables. The variables refer to three latent concepts: 1) PHYCHEM=Physico-Chemical, 2) SENSORY=Sensory, and 3) HEDONIC=Hedonic.
Num
Variable
Description
Concept
1
glucose
Glucose (g/l)
physico-chemical
2
fructose
Fructose (g/l)
physico-chemical
3
saccharose
Saccharose (g/l)
physico-chemical
4
sweet.power
Sweetening power (g/l)
physico-chemical
5
ph1
pH before processing
physico-chemical
6
ph2
pH after centrifugation
physico-chemical
7
titre
Titre (meq/l)
physico-chemical
8
citric.acid
Citric acid (g/l)
physico-chemical
9
vitamin.c
Vitamin C (mg/100g)
physico-chemical
10
smell.int
Smell intensity
sensory
11
odor.typi
Odor typicity
sensory
12
pulp
Pulp
sensory
13
taste.int
Taste intensity
sensory
14
acidity
Acidity
sensory
15
bitter
Bitterness
sensory
16
sweet
Sweetness
sensory
17
judge1
Ratings of judge 1
hedonic
18
judge2
Ratings of judge 2
hedonic
...
...
...
...
112
judge96
Ratings of judge 96
hedonic
Source
Laboratoire de Mathematiques Appliques, Agrocampus, Rennes.
References
Tenenhaus, M., Pages, J., Ambroisine, L., and Guinot, C. (2005) PLS methodology to study relationships between hedonic jedgements and product characteristics. Food Quality and Preference, 16(4), pp. 315-325.
Pages, J., and Tenenhaus, M. (2001) Multiple factor analysis combined with PLS path modelling. Application to the analysis of relationships between physicochemical, sensory profiles and hedonic judgements. Chemometrics and Intelligent Laboratory Systems, 58, pp. 261-273.
Pages, J. (2004) Multiple Factor Analysis: Main Features and Application to Sensory Data. Revista Colombiana de Estadistica, 27, pp. 1-26.