Literature DB >> 24802557

Applying parallel factor analysis and Tucker-3 methods on sensory and instrumental data to establish preference maps: case study on sweet corn varieties.

Attila Gere1, Viktor Losó, Annamária Györey, Sándor Kovács, László Huzsvai, András Nábrádi, Zoltán Kókai, László Sipos.   

Abstract

BACKGROUND: Traditional internal and external preference mapping methods are based on principal component analysis (PCA). However, parallel factor analysis (PARAFAC) and Tucker-3 methods could be a better choice. To evaluate the methods, preference maps of sweet corn varieties will be introduced.
RESULTS: A preference map of eight sweet corn varieties was established using PARAFAC and Tucker-3 methods. Instrumental data were also integrated into the maps. The triplot created by the PARAFAC model explains better how odour is separated from texture or appearance, and how some varieties are separated from others.
CONCLUSION: Internal and external preference maps were created using parallel factor analysis (PARAFAC) and Tucker-3 models employing both sensory (trained panel and consumers) and instrumental parameters simultaneously. Triplots of the applied three-way models have a competitive advantage compared to the traditional biplots of the PCA-based external preference maps. The solution of PARAFAC and Tucker-3 is very similar regarding the interpretation of the first and third factors. The main difference is due to the second factor as it differentiated the attributes better. Consumers who prefer 'super sweet' varieties (they place great emphasis especially on taste) are much younger and have significantly higher incomes, and buy sweet corn products rarely (once a month). Consumers who consume sweet corn products mainly because of their texture and appearance are significantly older and include a higher ratio of men.
© 2014 Society of Chemical Industry.

Entities:  

Keywords:  Tucker-3; parallel factor analysis; preference mapping algorithm; sensory analysis; sweet corn

Mesh:

Year:  2014        PMID: 24802557     DOI: 10.1002/jsfa.6673

Source DB:  PubMed          Journal:  J Sci Food Agric        ISSN: 0022-5142            Impact factor:   3.638


  1 in total

1.  Classification of Bee Pollen and Prediction of Sensory and Colorimetric Attributes-A Sensometric Fusion Approach by e-Nose, e-Tongue and NIR.

Authors:  László Sipos; Rita Végh; Zsanett Bodor; John-Lewis Zinia Zaukuu; Géza Hitka; György Bázár; Zoltan Kovacs
Journal:  Sensors (Basel)       Date:  2020-11-26       Impact factor: 3.576

  1 in total

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