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The Constrained Median: A Way to Incorporate Side Information in the Assessment of Food Samples

Marc Sader (UGent) , Raul Perez Fernandez (UGent) , Lotta Kuuliala (UGent) , Frank Devlieghere (UGent) and Bernard De Baets (UGent)
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Abstract
<jats:p>A classical problem in the field of food science concerns the consensus evaluation of food samples. Typically, several panelists are asked to provide scores describing the perceived quality of the samples, and subsequently, the overall (consensus) scores are determined. Unfortunately, gathering a large number of panelists is a challenging and very expensive way of collecting information. Interestingly, side information about the samples is often available. This paper describes a method that exploits such information with the aim of improving the assessment of the quality of multiple samples. The proposed method is illustrated by discussing an experiment on raw Atlantic salmon (Salmo salar), where the evolution of the overall score of each salmon sample is studied. The influence of incorporating knowledge of storage days, results of a clustering analysis, and information from additionally performed sensory evaluation tests is discussed. We provide guidelines for incorporating different types of information and discuss their benefits and potential risks.</jats:p>

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Please use this url to cite or link to this publication:

MLA
Sader, Marc, et al. “The Constrained Median: A Way to Incorporate Side Information in the Assessment of Food Samples.” Mathematics, 2020.
APA
Sader, M., Perez Fernandez, R., Kuuliala, L., Devlieghere, F., & De Baets, B. (2020). The Constrained Median: A Way to Incorporate Side Information in the Assessment of Food Samples. Mathematics.
Chicago author-date
Sader, Marc, Raul Perez Fernandez, Lotta Kuuliala, Frank Devlieghere, and Bernard De Baets. 2020. “The Constrained Median: A Way to Incorporate Side Information in the Assessment of Food Samples.” Mathematics.
Chicago author-date (all authors)
Sader, Marc, Raul Perez Fernandez, Lotta Kuuliala, Frank Devlieghere, and Bernard De Baets. 2020. “The Constrained Median: A Way to Incorporate Side Information in the Assessment of Food Samples.” Mathematics.
Vancouver
1.
Sader M, Perez Fernandez R, Kuuliala L, Devlieghere F, De Baets B. The Constrained Median: A Way to Incorporate Side Information in the Assessment of Food Samples. Mathematics. 2020;
IEEE
[1]
M. Sader, R. Perez Fernandez, L. Kuuliala, F. Devlieghere, and B. De Baets, “The Constrained Median: A Way to Incorporate Side Information in the Assessment of Food Samples,” Mathematics, 2020.
@article{8658473,
  abstract     = {<jats:p>A classical problem in the field of food science concerns the consensus evaluation of food samples. Typically, several panelists are asked to provide scores describing the perceived quality of the samples, and subsequently, the overall (consensus) scores are determined. Unfortunately, gathering a large number of panelists is a challenging and very expensive way of collecting information. Interestingly, side information about the samples is often available. This paper describes a method that exploits such information with the aim of improving the assessment of the quality of multiple samples. The proposed method is illustrated by discussing an experiment on raw Atlantic salmon (Salmo salar), where the evolution of the overall score of each salmon sample is studied. The influence of incorporating knowledge of storage days, results of a clustering analysis, and information from additionally performed sensory evaluation tests is discussed. We provide guidelines for incorporating different types of information and discuss their benefits and potential risks.</jats:p>},
  articleno    = {406},
  author       = {Sader, Marc and Perez Fernandez, Raul and Kuuliala, Lotta and Devlieghere, Frank and De Baets, Bernard},
  issn         = {2227-7390},
  journal      = {Mathematics},
  language     = {eng},
  title        = {The Constrained Median: A Way to Incorporate Side Information in the Assessment of Food Samples},
  url          = {http://dx.doi.org/10.3390/math8030406},
  year         = {2020},
}

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