# Napping (method of data collection)

**This article was considered for deletion at Wikipedia on July 10 2014. This is a backup of Wikipedia:Napping_(method_of_data_collection). All of its AfDs can be found at Wikipedia:Special:PrefixIndex/Wikipedia:Articles_for_deletion/Napping_(method_of_data_collection), the first at Wikipedia:Wikipedia:Articles_for_deletion/Napping_(method_of_data_collection).**

oooh, orphan

The **Napping** is a method of data collection related to the perception of a set of stimuli, such as food products. It is mainly used in sensory analysis but its potential field of application goes far beyond this area. ^{[1]}

## Contents

## Context

We are interested in how a set of products is perceived. Classically, a panel of tasters is asked to evaluate each product using a score sheet with several descriptors, such as sourness, bitterness, etc. For each descriptor and each product, each taster scores, on a scale ranging for example from 0 to 10, the intensity of the descriptor as he perceives it in the product. This method is fundamental in sensory analysis. But it says nothing about the importance of the descriptors in the perception of the products studied. The Napping was conceived in this perspective.

## Description of the protocol

We present to the tasters all the products, say wines to fix ideas. We then asked each taster to position all wines on a sheet of paper (hence the origin of the word *Napping*, from the French word *nappe* which means *tablecloth*) so that the distances on the paper reflect the perceived distances.
It must be clear to the panelists that there is no right or wrong answer, that we are looking for a personal perception and that all the tasters have not necessarily the same view of the products. Indeed, for example, some may grant great importance to olfactory aspects, others to taste aspects, etc.

In practice, the data entry is done using a screen on which the taster reproduces the position of wines on the tablecloth.^{[2]}

## Data

The data set of one taster is the horizontal (X) and vertical (Y) coordinates of each product. This data set is called *nappe*. If there are <math> I </math> products, these data can be gathered into a table with <math>I</math> rows (products) and two columns (X and Y). If there are <math>J</math> tasters, the whole data set is in a table with <math>I</math> rows and <math>2J</math> columns (Figure 1).

## Statistical analysis

To analyze this type of data, the multiple factor analysis (MFA ^{[3]}) fits perfectly. In this analysis, each taster is a group of two variables. To keep the distances on tablecloths, variables must not be reduced. The MFA provides several graphs whose two main ones are the following.

- A representation of the "average" products, that is to say as close as possible to the set of the representations provided by the tasters (the "nappes"). The first dimension of this average representation is somewhat the dimension common to the greatest number of "nappes".
- A representation of tasters indicating the importance that each of them has granted to the dimensions of the average representation. One recognizes the INDSCAL model. According to this view, the MFA provides an estimate of its parameters.

## Small example

### Data

Eight Bordeaux wines were selected according to an experimental design to study the relative importance of the Appellation (Graves or Médoc), the variety (Cabernet Sauvignon or Merlot) and ageing (vats or barrels) on the overall perception of this type of wine. The wine labels start with the order number followed by three letters reminding the categories of the factors of the design (Table 1).

Label | Appellation | Variety | Ageing | |
---|---|---|---|---|

<math>1</math> | 1GCf | Graves | Cabernet | barrels |

<math>2</math> | 2MCf | Médoc | Cabernet | barrels |

<math>3</math> | 3GMf | Graves | Merlot | barrels |

<math>4</math> | 4MMf | Médoc | Merlot | barrels |

<math>5</math> | 5GCc | Graves | Cabernet | vats |

<math>6</math> | 6MCc | Médoc | Cabernet | vats |

<math>7</math> | 7GMc | Graves | Merlot | vats |

<math>7</math> | 8MMc | Médoc | Merlot | vats |

To obtain data on overall perception of these wines, we gathered a panel of three persons (= judges) who were asked to use the approach of napping. The data are in Figure 2.

The general problem of this study mainly revolves around the question: what is the relationship between the overall perception of these wines and the three factors of the design used to select wines?

### Methodology

These data were subjected to MFA in which:

- Each of the three "nappes" is an active group,
- Each of the three experimental factors is an additional group.

### Results

The MFA provided, among numerous results, the graphs of Figures 3 and 4.

Figure 3 is interpreted as usually in factorial analysis. Two wines are close each other if they are close on the nappes. A category is at the barycenter of the wines which possesses it. Figure 3 leads to the following interpretations.

- The first axis contrasts wines {1, 2, 3, 4} to the others. These wines have been aged in barrels (see their labels) which is not the case for the others. This opposition is an opposition between ageing. It is clearly visible on each of the three "nappes".
- The second axis opposes wines 3, 4, 7 and 8 to the others. These wines were made with Merlot, the other with Cabernet. There is therefore an opposition between varieties. It can be seen on the "nappes" of judges 1 and 2.

The relationship square (Figure 4) must be interpreted in reference to figure 3 left. Interpretation of judges: the coordinate of judge <math>j</math> on axis <math>s</math> is high if the dimension <math>s</math> on figure 3 left correspond to a important dimension of nappe <math>j</math>. Interpretation of qualitative variable : the coordinate of variable <math>k</math> on axis <math>s</math> is the squared correlation ratio between the variable <math>k</math> and axis <math>s</math> of the representation of wines (figure 3 left). Figure 4 shows the following results.

- The first dimension of the average configuration of wines (opposition between wines {1, 2, 3, 4} and wines {5, 6, 7, 8}) is important for the three judges (their coordinate is maximum along this axis); the most important factor in the perception of these wines is the way of ageing.
- The second dimension is related only to judges 1 and 2 (a little more strongly for judge1 which has a slightly higher coordinate than the judge 2 along the second axis) and is much less important than the first one (in fact, the direct examination of the "nappes" first shows the opposition between ageing ways). This dimension is also closely linked to the variety but is not identical with it.
- There is no connection between the appellation and the two main dimensions of variability of the images of these wines.

The representation of qualitative variables defining wines brings little here compared to Figure 3 because the variables are few and have only two categories. It is not the case in practice of napping where this graph provides a visualization all the more valuable than variables are numerous and include many categories.

## Conclusion

These data illustrate the interest of napping. Data are collected giving a great freedom of expression to the tasters. But they contain in themselves the latent variables of perception of wines by the judges. Proper statistical analysis allows to identify these latent variables (here, first the ageing and then the variety).

## Extension: the sorted napping

The napping belongs to holistic methods so called because they require judges to consider each product as a whole (unlike the usual descriptive methods that analyze each product descriptor by descriptor). Another holistic method is the free sorting in which judges are asked to gather the product into groups.

We can combine napping and free sorting by asking panelists, at the end of napping, to gather products which they consider particularly close. This procedure is called *the sorted napping*. Data from sorted napping are complex in that they contain both quantitative variables (coordinates on the "nappes") and qualitative variables (partition from free sorting). The well fitted statistical treatment is Hierarchical MFA (HMFA). A description of HMFA with an application to sorted napping data is in Pagès 2013.

## History

The napping was introduced by Jérôme Pagès in 2003 in French ^{[4]} and in 2005 in English.^{[5]}

The first study using napping was performed with Pascale Deneulin (with wines from Loire valley), then student at Agrocampus Rennes^{[6]} The sorted napping has been introduced in 2010 ^{[7]} These methods have been widely used by Lucie Perrin in her work on Loire wines.^{[8]}

The napping is still the subject of research and gives rise to theses ^{[9]} or articles. ^{[10]}^{[11]}

## Software

The R package SensoMineR contains all the necessary functions for the analysis of data from the napping, the free sorting and the sorted napping. This software also contains functions for all common problems of sensory analysis. It is based on the R package FactoMineR, which contains functions for the most useful methods of data analysis.

## Notes and references

- ↑ Applications outside of food products are developed by / SenseLab.
- ↑ This is done for example in the software FIZZ developed by Biosystèmes).
- ↑ MFA is the core of a recent book: Pagès J. (2014).
*Multiple Factor Analysis by Example Using R*. Chapman & Hall/CRC The R Series London 272 p - ↑ Pagès J. (2003). Recueil direct de distances sensorielles : application à l’évaluation de dix vins blancs du Val de Loire. Sciences des aliments. 23 679–688.
- ↑ Pagès J.(2005). Collection and analysis of perceived product inter-distances using multiple factor analysis; application to the study of ten white from the Loire Valley. Food quality and preference 16 7 642–649
- ↑ Following this work, napping is often used in the field of wine (/ Example.)
- ↑ Pagès, J.; Cadoret, M. and Lê, S. (2010). "The Sorted Napping: a new holistic approach in sensory evaluation".
*Journal of Sensory Studies***25**(5): 637–658. Template:Citation error. - ↑ For example, the "ultra flash profiling" she uses directly derives from the sorted napping : Perrin L. & Pagès J. (2009) Construction of a product space from the Ultra-flash profiling method: application to ten red wines from the Loire Valley. Journal of Sensory Studies 24 3 372-395. Other example of ultraflash profiling / Schneppe
*et al*. - ↑ For example the thesis of / C. Delholm
- ↑ Giacalone, D.; Ribeiro, L. and Frost, M. (2013). "Consumer-based product profiling: application of partial napping® for sensory characterization of specialty beers by novices and experts".
*Journal of food product marketing***19**(3): 201–218. Template:Citation error. - ↑ Dehlholma C., Brockhoff P., Meinert L., Aaslyng M. &Brediea W. (2012. Rapid descriptive sensory methods – Comparison of Free Multiple Sorting, Partial Napping, Napping, Flash Profiling and conventional profiling. ‘’Food Quality and Preference’’, 26 (2)267-277

## External links

- FactoMineR, a R package devoted to exploratory data analysis.
- SensoMineR, a R package devoted to statistical analysis of sensory data.