orange: Orange Juice dataset

In plspm: Partial Least Squares Path Modeling (PLS-PM)

Orange Juice dataset

Description

This data set contains the physico-chemical, sensory and hedonic measurements of 6 orange juices.

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.

Examples

  data(orange)
  orange

plspm documentation built on June 22, 2024, 11:29 a.m.