Geometric and statistical techniques for projective mapping of chocolate chip cookies with a large number of consumers
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2020
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| _version_ | 1866916460124700672 |
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| author | Orden, David Fernández-Fernández, Encarnación Tejedor-Romero, Marino Martínez-Moraian, Alejandra |
| author_facet | Orden, David Fernández-Fernández, Encarnación Tejedor-Romero, Marino Martínez-Moraian, Alejandra |
| contents | The so-called rapid sensory methods have proved to be useful for the sensory study of foods by different types of panels, from trained assessors to unexperienced consumers. Data from these methods have been traditionally analyzed using statistical techniques, with some recent works proposing the use of geometric techniques and graph theory. The present work aims to deepen this line of research introducing a new method, mixing tools from statistics and graph theory, for the analysis of data from Projective Mapping. In addition, a large number of n=349 unexperienced consumers is considered for the first time in Projective Mapping, evaluating nine commercial chocolate chips cookies which include a blind duplicate of a multinational best-selling brand and seven private labels. The data obtained are processed using the standard statistical technique Multiple Factor Analysis (MFA), the recently appeared geometric method SensoGraph using Gabriel clustering, and the novel variant introduced here which is based on the pairwise distances between samples. All methods provide the same groups of samples, with the blind duplicates appearing close together. Finally, the stability of the results is studied using bootstrapping and the RV and Mantel coefficients. The results suggest that, even for unexperienced consumers, highly stable results can be achieved for MFA and SensoGraph when considering a large enough number of assessors, around 200 for the consensus map of MFA or the global similarity matrix of SensoGraph. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2008_10431 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Geometric and statistical techniques for projective mapping of chocolate chip cookies with a large number of consumers Orden, David Fernández-Fernández, Encarnación Tejedor-Romero, Marino Martínez-Moraian, Alejandra Applications Computational Geometry The so-called rapid sensory methods have proved to be useful for the sensory study of foods by different types of panels, from trained assessors to unexperienced consumers. Data from these methods have been traditionally analyzed using statistical techniques, with some recent works proposing the use of geometric techniques and graph theory. The present work aims to deepen this line of research introducing a new method, mixing tools from statistics and graph theory, for the analysis of data from Projective Mapping. In addition, a large number of n=349 unexperienced consumers is considered for the first time in Projective Mapping, evaluating nine commercial chocolate chips cookies which include a blind duplicate of a multinational best-selling brand and seven private labels. The data obtained are processed using the standard statistical technique Multiple Factor Analysis (MFA), the recently appeared geometric method SensoGraph using Gabriel clustering, and the novel variant introduced here which is based on the pairwise distances between samples. All methods provide the same groups of samples, with the blind duplicates appearing close together. Finally, the stability of the results is studied using bootstrapping and the RV and Mantel coefficients. The results suggest that, even for unexperienced consumers, highly stable results can be achieved for MFA and SensoGraph when considering a large enough number of assessors, around 200 for the consensus map of MFA or the global similarity matrix of SensoGraph. |
| title | Geometric and statistical techniques for projective mapping of chocolate chip cookies with a large number of consumers |
| topic | Applications Computational Geometry |
| url | https://arxiv.org/abs/2008.10431 |