An original methodology for the analysis and interpretation of word-count based methods: Multiple factor analysis for contingency tables complemented by consensual words

Kostov, Belchin; Bécue-Bertaut, Mónica; Husson, François

doi:10.1016/j.foodqual.2013.06.009

Cited by 19 publications

(25 citation statements)

References 27 publications

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“…This analysis showed that two factors accounted for 24% of the variability of the data. Similar explained variance results were found in sensory analysis with nontrained and trained panellists (Veramendi et al, 2013;Kostov et al, 2014). This low proportion is expected considering the complexity in the odour sample profile, besides when CA is used to analyse a sorting task, it could underestimate the proportion of explained variance (Veramendi et al, 2013).…”

Section: Aroma Evaluationsupporting

confidence: 77%

“…A CA was performed to determine the relationships among the different honey production regions and the attributes of the samples (Fig. Similar explained variance results were found in sensory analysis with nontrained and trained panellists (Veramendi et al, 2013;Kostov et al, 2014). This analysis showed that two factors accounted for 24% of the variability of the data.…”

Section: Aroma Evaluationmentioning

confidence: 54%

“…CA is a generalised PCA recommended for the analysis of qualitative data (Valentin et al, 2012) that offers a visualisation of the similarities between products, between descriptors and the associations between products and descriptors (Kostov et al, 2014). CA is a generalised PCA recommended for the analysis of qualitative data (Valentin et al, 2012) that offers a visualisation of the similarities between products, between descriptors and the associations between products and descriptors (Kostov et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

“…To obtain a suitable description of the odours that best described the honey samples, CA was performed. CA is a generalised PCA recommended for the analysis of qualitative data (Valentin et al, 2012) that offers a visualisation of the similarities between products, between descriptors and the associations between products and descriptors (Kostov et al, 2014). A CCA was carried out to find correlations between the aroma of honey samples and the volatile compounds.…”

Section: Discussionmentioning

confidence: 99%

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Correlations of sensory parameters with physicochemical characteristics of Argentinean honeys by multivariate statistical techniques

Patrignani

Ciappini

Tananaki

et al. 2017

Int J of Food Sci Tech

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Summary Honey acceptability is mainly determined by its colour, crystallisation degree and aroma. In the present work, the sensory characteristics and physicochemical parameters of Argentinean honeys from different ecoregions were analysed. Moisture content, Pfund colour, diastase activity, hydroxymethylfurfural content, electrical conductivity, sugar profile and volatile compounds were analytically determined in honey samples, while sensory characteristics (crystal size, fluency score, sweetness, persistence, granularity, crystallisation, colour intensity and aroma) were evaluated by a trained panel. Significant correlations were found between honey crystallisation degree and hydroxymethylfurfural content and diastase activity (P ≤ 0.05). It could be confirmed that honey crystallisation interferes with the visual perception of colour. Floral, fresh fruit, ripe fruit, balsamic and wood aromas could be successfully linked to honey volatile profile (P ≤ 0.05). These results demonstrate that the parameters that could best guarantee the consumers’ preference can be successfully associated with the chemical composition of honey by multivariate statistical analysis.

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Section: Aroma Evaluationsupporting

confidence: 77%

Section: Aroma Evaluationmentioning

confidence: 54%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Correlations of sensory parameters with physicochemical characteristics of Argentinean honeys by multivariate statistical techniques

Patrignani

Ciappini

Tananaki

et al. 2017

Int J of Food Sci Tech

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“…In each session, consensual terms were identified using the methodology proposed by Kostov et al . (). Random subsets were obtained using a bootstrap methodology.…”

Section: Methodsmentioning

confidence: 97%

Reliability of Polarized Projective Mapping with Consumers

Saldamando

Antúnez

Torres-Moreno

et al. 2015

Journal of Sensory Studies

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Polarized projective mapping (PPM) combines the main features of projective mapping and polarized sensory positioning. The main advantage of this methodology is that it allows comparing results obtained in different sessions. The aim of this study was to evaluate the reliability of PPM, focusing on individual and global repeatability, global reproducibility and data aggregation from different sessions. A consumer study with two sample sets of powdered drinks was carried out. Consumers were randomly divided into four groups: two of the groups evaluated the same sample set in two different sessions, while the other two evaluated the samples in three sessions (in one of the sessions, they evaluated the whole sample set, whereas in the other two sessions they evaluated the sample set split into two sets). Results showed that PPM can be regarded as a repeatable and reproducible methodology. Preliminary evidence of the validity of aggregating data from different sessions was gathered. PRACTICAL APPLICATIONSProjective mapping is a holistic methodology for sensory characterization with both trained assessors and consumers. It requires all samples to be simultaneously evaluated in the same session, which restricts the number of products that can be evaluated and makes it difficult to compare results from samples evaluated at different moments in time. In this context, PPM relies on the use of fixed references to enable data aggregation. Results from this work provided evidence of the reliability of the methodology for the evaluation of simple products with moderate differences among them.

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Natural Language Processing

Hamilton¹,

Lahne²

2023

Rapid Sensory Profiling Techniques

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An original methodology for the analysis and interpretation of word-count based methods: Multiple factor analysis for contingency tables complemented by consensual words

Cited by 19 publications

References 27 publications

Correlations of sensory parameters with physicochemical characteristics of Argentinean honeys by multivariate statistical techniques

Correlations of sensory parameters with physicochemical characteristics of Argentinean honeys by multivariate statistical techniques

Reliability of Polarized Projective Mapping with Consumers

Natural Language Processing

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