Package 'SensoryDataSets'

Brown Bread Sensory Evaluation

Description

This dataset, brown_bread_matrix, is a matrix containing sensory evaluation data from 570 panelists and 16 brown bread products, labelled A through P to protect the identity of the manufacturer. Each panelist rated a subset of 6 products using the 9-point Hedonic Scale.

Usage

data(brown_bread_matrix)

Format

An integer matrix with 570 observations and 16 variables:

A-P

Integer values representing panelists' ratings of 16 brown bread products using the 9-point Hedonic Scale

Details

The dataset name has been changed to 'brown_bread_matrix' to follow the descriptive naming convention adopted for the SensoryDataSets package while maintaining clarity. The suffix 'matrix' indicates that the dataset is a matrix. The original content has not been modified in any way.

Source

Data taken from the sensory package version 1.1.

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Sensory Evaluation of Grape Blends

Description

This dataset, grapeblend_tbl, is a tibble containing data from a sensory analysis conducted with 6 participants evaluating different blends of the grape cultivars Bordo and Niagara. Five sensory attributes were evaluated: color (CR), aroma (AR), flavor (SB), body (CP), and global evaluation (GB). The response values correspond to the average scores given by the evaluators.

Usage

data(grapeblend_tbl)

Format

A tibble with 25 observations and 3 variables:

Blend

Character vector identifying the grape blend

variable

Character vector indicating the sensory attribute evaluated: color (CR), aroma (AR), flavor (SB), body (CP), or global evaluation (GB)

resp

Numeric vector representing the average sensory scores given by the evaluators

Details

The dataset name has been changed from 'sensorial' to 'grapeblend_tbl' to follow the descriptive naming convention adopted for the SensoryDataSets package while maintaining clarity. The suffix 'tbl' indicates that the dataset is a tibble. The original content has not been modified in any way.

Source

Data taken from the AgroR package version 1.3.7.

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Sensory and Physico-Chemical Data of Olive Oils

Description

This dataset, olive_oil_df, is a data frame containing sensory and physico-chemical measurements for 16 olive oil samples. It includes scores on 6 attributes evaluated by a sensory panel and measurements of 5 physico-chemical quality parameters. The first five oils are Greek, the next five are Italian, and the last six are Spanish.

Usage

data(olive_oil_df)

Format

A data frame with 16 observations and 2 variables:

chemical

Numeric matrix containing measurements of 5 physico-chemical quality parameters for the 16 olive oil samples

sensory

Numeric matrix containing scores on 6 sensory attributes for the 16 olive oil samples

Details

The dataset name has been changed to 'olive_oil_df' to follow the descriptive naming convention adopted for the SensoryDataSets package while maintaining clarity. The suffix 'df' indicates that the dataset is a data frame. The original content has not been modified in any way.

Source

Data taken from the pls package version 2.9-0.

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Sensory Description of Orange Juices

Description

This dataset, orange_juice_df, is a data frame containing the sensory description of 12 orange juice samples evaluated on 8 attributes.

Usage

data(orange_juice_df)

Format

A data frame with 12 observations and 8 variables:

Color.intensity

Numeric vector representing color intensity scores

Odor.intensity

Numeric vector representing odor intensity scores

Attack.intensity

Numeric vector representing attack intensity scores

Sweet

Numeric vector representing sweetness scores

Acid

Numeric vector representing acidity scores

Bitter

Numeric vector representing bitterness scores

Pulp

Numeric vector representing pulp scores

Typicity

Numeric vector representing typicity scores

Details

The dataset name has been changed from 'orange' to 'orange_juice_df' to follow the descriptive naming convention adopted for the SensoryDataSets package while maintaining clarity. The suffix 'df' indicates that the dataset is a data frame. The original content has not been modified in any way.

Source

Data taken from the missMDA package version 1.22.

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Perfume Ideal Profile Data

Description

This dataset, perfume_ideal_df, is a data frame containing sensory and hedonic evaluations of perfumes using the Ideal Profile Method. Twelve perfumes, including two duplicated products, were evaluated once by 103 Dutch consumers, resulting in 14 product levels and 1,442 observations. Each perfume was described using 21 sensory attributes, for which both perceived and ideal intensities were recorded. Overall liking scores were also collected.

Usage

data(perfume_ideal_df)

Format

A data frame with 1,442 observations and 45 variables:

user

Factor with 103 levels identifying consumers

product

Factor with 14 levels identifying perfume products

intensity

Numeric vector representing perceived overall intensity

id_int

Numeric vector representing ideal overall intensity

freshness

Numeric vector representing perceived freshness

id_fresh

Numeric vector representing ideal freshness

jasmin

Numeric vector representing perceived jasmine intensity

id_jasm

Numeric vector representing ideal jasmine intensity

rose

Numeric vector representing perceived rose intensity

id_rose

Numeric vector representing ideal rose intensity

camomille

Numeric vector representing perceived chamomile intensity

id_camo

Numeric vector representing ideal chamomile intensity

fresh_lemon

Numeric vector representing perceived fresh lemon intensity

id_fresh_lem

Numeric vector representing ideal fresh lemon intensity

vanilla

Numeric vector representing perceived vanilla intensity

id_vanilla

Numeric vector representing ideal vanilla intensity

citrus

Numeric vector representing perceived citrus intensity

id_citrus

Numeric vector representing ideal citrus intensity

anis

Numeric vector representing perceived anise intensity

id_anis

Numeric vector representing ideal anise intensity

sweet_fruit

Numeric vector representing perceived sweet fruit intensity

id_sweet_fruit

Numeric vector representing ideal sweet fruit intensity

honey

Numeric vector representing perceived honey intensity

id_honey

Numeric vector representing ideal honey intensity

caramel

Numeric vector representing perceived caramel intensity

id_caram

Numeric vector representing ideal caramel intensity

spicy

Numeric vector representing perceived spicy intensity

id_spicy

Numeric vector representing ideal spicy intensity

woody

Numeric vector representing perceived woody intensity

id_woody

Numeric vector representing ideal woody intensity

leather

Numeric vector representing perceived leather intensity

id_leather

Numeric vector representing ideal leather intensity

nutty

Numeric vector representing perceived nutty intensity

id_nutty

Numeric vector representing ideal nutty intensity

musk

Numeric vector representing perceived musk intensity

id_musk

Numeric vector representing ideal musk intensity

animal

Numeric vector representing perceived animal note intensity

id_animal

Numeric vector representing ideal animal note intensity

earthy

Numeric vector representing perceived earthy intensity

id_earthy

Numeric vector representing ideal earthy intensity

incense

Numeric vector representing perceived incense intensity

id_incense

Numeric vector representing ideal incense intensity

green

Numeric vector representing perceived green note intensity

id_green

Numeric vector representing ideal green note intensity

liking

Integer vector representing overall liking scores

Details

The dataset name has been changed to 'perfume_ideal_df' to follow the descriptive naming convention adopted for the SensoryDataSets package while maintaining clarity. The suffix 'df' indicates that the dataset is a data frame. The original content has not been modified in any way.

Source

Data taken from the SensoMineR package version 1.28.

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Sensory Profiles Given by Seven Panels

Description

This dataset, sensopanels_df, is a data frame containing sensory profiles provided by 7 panels. Six products were evaluated using 14 sensory attributes for each panel, resulting in 98 sensory variables.

Usage

data(sensopanels_df)

Format

A data frame with 6 observations and 98 variables:

CocoaA1

Numeric vector representing CocoaA sensory scores from panel 1

MilkA1

Numeric vector representing MilkA sensory scores from panel 1

Sweetness1

Numeric vector representing sweetness scores from panel 1

Acidity1

Numeric vector representing acidity scores from panel 1

Bitterness1

Numeric vector representing bitterness scores from panel 1

CocoaF1

Numeric vector representing CocoaF sensory scores from panel 1

MilkF1

Numeric vector representing MilkF sensory scores from panel 1

Caramel1

Numeric vector representing caramel scores from panel 1

Vanilla1

Numeric vector representing vanilla scores from panel 1

Astringency1

Numeric vector representing astringency scores from panel 1

Crunchy1

Numeric vector representing crunchiness scores from panel 1

Melting1

Numeric vector representing melting scores from panel 1

Sticky1

Numeric vector representing stickiness scores from panel 1

Granular1

Numeric vector representing granularity scores from panel 1

CocoaA2

Numeric vector representing CocoaA sensory scores from panel 2

MilkA2

Numeric vector representing MilkA sensory scores from panel 2

Sweetness2

Numeric vector representing sweetness scores from panel 2

Acidity2

Numeric vector representing acidity scores from panel 2

Bitterness2

Numeric vector representing bitterness scores from panel 2

CocoaF2

Numeric vector representing CocoaF sensory scores from panel 2

MilkF2

Numeric vector representing MilkF sensory scores from panel 2

Caramel2

Numeric vector representing caramel scores from panel 2

Vanilla2

Numeric vector representing vanilla scores from panel 2

Astringency2

Numeric vector representing astringency scores from panel 2

Crunchy2

Numeric vector representing crunchiness scores from panel 2

Melting2

Numeric vector representing melting scores from panel 2

Sticky2

Numeric vector representing stickiness scores from panel 2

Granular2

Numeric vector representing granularity scores from panel 2

CocoaA3

Numeric vector representing CocoaA sensory scores from panel 3

MilkA3

Numeric vector representing MilkA sensory scores from panel 3

Sweetness3

Numeric vector representing sweetness scores from panel 3

Acidity3

Numeric vector representing acidity scores from panel 3

Bitterness3

Numeric vector representing bitterness scores from panel 3

CocoaF3

Numeric vector representing CocoaF sensory scores from panel 3

MilkF3

Numeric vector representing MilkF sensory scores from panel 3

Caramel3

Numeric vector representing caramel scores from panel 3

Vanilla3

Numeric vector representing vanilla scores from panel 3

Astringency3

Numeric vector representing astringency scores from panel 3

Crunchy3

Numeric vector representing crunchiness scores from panel 3

Melting3

Numeric vector representing melting scores from panel 3

Sticky3

Numeric vector representing stickiness scores from panel 3

Granular3

Numeric vector representing granularity scores from panel 3

CocoaA4

Numeric vector representing CocoaA sensory scores from panel 4

MilkA4

Numeric vector representing MilkA sensory scores from panel 4

Sweetness4

Numeric vector representing sweetness scores from panel 4

Acidity4

Numeric vector representing acidity scores from panel 4

Bitterness4

Numeric vector representing bitterness scores from panel 4

CocoaF4

Numeric vector representing CocoaF sensory scores from panel 4

MilkF4

Numeric vector representing MilkF sensory scores from panel 4

Caramel4

Numeric vector representing caramel scores from panel 4

Vanilla4

Numeric vector representing vanilla scores from panel 4

Astringency4

Numeric vector representing astringency scores from panel 4

Crunchy4

Numeric vector representing crunchiness scores from panel 4

Melting4

Numeric vector representing melting scores from panel 4

Sticky4

Numeric vector representing stickiness scores from panel 4

Granular4

Numeric vector representing granularity scores from panel 4

CocoaA5

Numeric vector representing CocoaA sensory scores from panel 5

MilkA5

Numeric vector representing MilkA sensory scores from panel 5

Sweetness5

Numeric vector representing sweetness scores from panel 5

Acidity5

Numeric vector representing acidity scores from panel 5

Bitterness5

Numeric vector representing bitterness scores from panel 5

CocoaF5

Numeric vector representing CocoaF sensory scores from panel 5

MilkF5

Numeric vector representing MilkF sensory scores from panel 5

Caramel5

Numeric vector representing caramel scores from panel 5

Vanilla5

Numeric vector representing vanilla scores from panel 5

Astringency5

Numeric vector representing astringency scores from panel 5

Crunchy5

Numeric vector representing crunchiness scores from panel 5

Melting5

Numeric vector representing melting scores from panel 5

Sticky5

Numeric vector representing stickiness scores from panel 5

Granular5

Numeric vector representing granularity scores from panel 5

CocoaA6

Numeric vector representing CocoaA sensory scores from panel 6

MilkA6

Numeric vector representing MilkA sensory scores from panel 6

Sweetness6

Numeric vector representing sweetness scores from panel 6

Acidity6

Numeric vector representing acidity scores from panel 6

Bitterness6

Numeric vector representing bitterness scores from panel 6

CocoaF6

Numeric vector representing CocoaF sensory scores from panel 6

MilkF6

Numeric vector representing MilkF sensory scores from panel 6

Caramel6

Numeric vector representing caramel scores from panel 6

Vanilla6

Numeric vector representing vanilla scores from panel 6

Astringency6

Numeric vector representing astringency scores from panel 6

Crunchy6

Numeric vector representing crunchiness scores from panel 6

Melting6

Numeric vector representing melting scores from panel 6

Sticky6

Numeric vector representing stickiness scores from panel 6

Granular6

Numeric vector representing granularity scores from panel 6

CocoaA7

Numeric vector representing CocoaA sensory scores from panel 7

MilkA7

Numeric vector representing MilkA sensory scores from panel 7

Sweetness7

Numeric vector representing sweetness scores from panel 7

Acidity7

Numeric vector representing acidity scores from panel 7

Bitterness7

Numeric vector representing bitterness scores from panel 7

CocoaF7

Numeric vector representing CocoaF sensory scores from panel 7

MilkF7

Numeric vector representing MilkF sensory scores from panel 7

Caramel7

Numeric vector representing caramel scores from panel 7

Vanilla7

Numeric vector representing vanilla scores from panel 7

Astringency7

Numeric vector representing astringency scores from panel 7

Crunchy7

Numeric vector representing crunchiness scores from panel 7

Melting7

Numeric vector representing melting scores from panel 7

Sticky7

Numeric vector representing stickiness scores from panel 7

Granular7

Numeric vector representing granularity scores from panel 7

Details

The dataset name has been changed to 'sensopanels_df' to follow the descriptive naming convention adopted for the SensoryDataSets package while maintaining clarity. The suffix 'df' indicates that the dataset is a data frame. The original content has not been modified in any way.

Source

Data taken from the SensoMineR package version 1.28.

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SensoryDataSets: A Collection of Sensory Evaluation and Consumer Science Datasets

Description

This package provides a curated collection of datasets for sensory evaluation, consumer research, and related statistical applications. The collection includes hedonic and liking scores, sensory profiles, descriptive evaluations, physicochemical measurements, wine quality and bitterness assessments, and data from products such as bread, olive oil, Orange juice, grape blends, wine, cocktails, chocolate, perfume, and smoothies. Additional datasets support examples involving missing values, response times, and electrophysiological measurements.

Details

SensoryDataSets: A Collection of Sensory Evaluation and Consumer Science Datasets

A Comprehensive Collection of Sensory Evaluation and Consumer Science Datasets.

Author(s)

Maintainer: Dian Rebaza Fernández[email protected]

See Also

Useful links:

• https://github.com/dianarebaza/sensorydatasets

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View Available Datasets in SensoryDataSets

Description

This function lists all datasets available in the 'SensoryDataSets' package. If the 'SensoryDataSets' package is not loaded, it stops and shows an error message. If no datasets are available, it returns a message and an empty vector.

Usage

view_datasets_SensoryDataSets()

Value

A character vector with the names of the available datasets. If no datasets are found, it returns an empty character vector.

Examples

if (requireNamespace("SensoryDataSets", quietly = TRUE)) {
  library(SensoryDataSets)
  view_datasets_SensoryDataSets()
}

---

White Bread Liking Study

Description

This dataset, white_bread_matrix, is a matrix containing liking evaluation data from 420 panelists and 12 white bread products, labelled A through L to protect the identity of the manufacturer. Each panelist rated 6 white breads using the 9-point Hedonic Scale.

Usage

data(white_bread_matrix)

Format

An integer matrix with 420 observations and 12 variables:

A-L

Integer values representing panelists' liking ratings of 12 white bread products using the 9-point Hedonic Scale

Details

The dataset name has been changed to 'white_bread_matrix' to follow the descriptive naming convention adopted for the SensoryDataSets package while maintaining clarity. The suffix 'matrix' indicates that the dataset is a matrix. The original content has not been modified in any way.

Source

Data taken from the sensory package version 1.1.

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Bitterness of Wine

Description

This dataset, wine_bitter_df, is a data frame containing sensory evaluation data on the bitterness of wine. It includes responses and ordered ratings together with information on temperature, contact condition, bottle, and judge for 72 observations.

Usage

data(wine_bitter_df)

Format

A data frame with 72 observations and 6 variables:

response

Numeric vector representing the sensory response

rating

Ordered factor with 5 levels representing wine bitterness ratings

temp

Factor with 2 levels indicating temperature conditions

contact

Factor with 2 levels indicating contact conditions

bottle

Factor with 8 levels identifying wine bottles

judge

Factor with 9 levels identifying sensory judges

Details

The dataset name has been changed to 'wine_bitter_df' to follow the descriptive naming convention adopted for the SensoryDataSets package while maintaining clarity. The suffix 'df' indicates that the dataset is a data frame. The original content has not been modified in any way.

Source

Data taken from the serp package version 0.2.5.

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Quality of Portuguese White Wine

Description

This dataset, winequality_df, is a data frame containing physicochemical measurements and quality ratings for 4,898 white wine samples from Portugal. It includes 11 physicochemical characteristics of the wines and an overall wine quality score.

Usage

data(winequality_df)

Format

A data frame with 4,898 observations and 12 variables:

fixed.acidity

Numeric vector representing fixed acidity

volatile.acidity

Numeric vector representing volatile acidity

citric.acid

Numeric vector representing citric acid content

residual.sugar

Numeric vector representing residual sugar content

chlorides

Numeric vector representing chloride content

free.sulfur.dioxide

Numeric vector representing free sulfur dioxide content

total.sulfur.dioxide

Numeric vector representing total sulfur dioxide content

density

Numeric vector representing wine density

pH

Numeric vector representing pH

sulphates

Numeric vector representing sulphate content

alcohol

Numeric vector representing alcohol content

quality

Integer vector representing wine quality ratings

Details

The dataset name has been changed to 'winequality_df' to follow the descriptive naming convention adopted for the SensoryDataSets package while maintaining clarity. The suffix 'df' indicates that the dataset is a data frame. The original content has not been modified in any way.

Source

Data taken from the sgd package version 1.1.3.

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Sensory Wine Evaluation

Description

This dataset, winetable_tb, is a contingency table containing data from a sensory evaluation of wine. The table consists of 8 rows and 5 columns representing the categories involved in the sensory evaluation.

Usage

data(winetable_tb)

Format

A contingency table with 8 rows and 5 columns containing integer frequencies from a sensory wine evaluation.

Details

The dataset name has been changed to 'winetable_tb' to follow the descriptive naming convention adopted for the SensoryDataSets package while maintaining clarity. The suffix 'tb' indicates that the dataset is a table. The original content has not been modified in any way.

Source

Data taken from the raters package version 2.1.1.

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