Application of several alternative Projective Mapping acquisition methodologies to evaluate algae-enriched tinned mackerel
Sensory analysis primarily uses descriptive methods to characterize product attributes efficiently. Projective Mapping (PM) compares products by grouping samples based on perceived similarities, often enhanced by Ultra Flash Profiling (UFP). This study compares three data acquisition methodologies (paper-based, screen-based, and digital sensor) for PM, applied to six samples of tinned mackerel with algae. A panel of 80 consumers of the product typology were asked to realise the positioning of the samples on the plan, taking into account their similarities and differences, and then to indicate the acceptability of each product on a 9-point hedonic scale. The PM exercise was carried out by the participants in each of the methodologies (paper, screen and digital). The results indicate that the acceptability of the products tended to be high, with a higher value for the products containing seaweed compared to the control sample. A subsequent qualitative analysis showed that the sample containing Kombu seaweed had a higher correlation with odour attributes, particularly sea air. On the other hand, the incorporation of Wakame and Himanthalia seaweed brings greater prominence to flavour aspects, with a greater intensity of seaweed and sea flavour. The use of canned macroalgae is promising and has the potential to be successful with consumers.
Keywords: canned fish products, consumer evaluation, projective mapping, sensory properties
Acknowledgements: Author Bruna Barbosa acknowledges Non-Academic Doctoral grant No. 2023.03467.BDANA, funded by the Foundation for Science and Technology (FCT) and supported by Sense Test, Lda. Authors Barbosa, Rocha and Cunha acknowledge additional support from FCT through UID 05748: GreenUPorto - Centro de Investigação em Produção Agroalimentar Sustentável.
Publish date 2025-11-17
Author(s) Diego Martínez, Rui Costa Lima, Bruna Barbosa, Luís Baião, Maria João Monteiro, Ruben Jorge, Teresa A.C. Oliveira, Carlos Cabral, Luís Miguel Cunha, Célia Rocha
