The DOI matches its Crossref record; 81 of the 81 references it cites were found in a registry or an index.
Quick and dirty but still pretty good: a review of new descriptive methods in food science
Abstract
Summary: For food scientists and industrials, descriptive profiling is an essential tool that involves the evaluation of both the qualitative and quantitative sensory characteristics of a product by a panel. Recently, in response to industrial demands to develop faster and more cost‐effective methods of descriptive analysis, several methods have been offered as alternatives to conventional profiling. These methods can be classified in three families: (i) verbal‐based methods (flash profile and check‐all‐that‐apply), (ii) similarity‐based methods (free sorting task and projective mapping aka Napping®) and (iii) reference‐based methods (polarised sensory positioning and pivot profile). We successively present these three classes of methods in terms of origin, principles, statistical analysis, applications to food products, variations of the methods and the Pros and Cons.
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Citations
Cites
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Sensory Evaluation by Quantitative Descriptive Analysis
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Multiple factor analysis (AFMULT package)
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Cited by
Kumar, R., Hegde, A. S., Sharma, K., Parmar, P., & Srivatsan, V. (2022) · Food Research International · doi:10.1016/j.foodres.2022.111338 · Cited by 209
Metadata
- Authors
- Valentin, D., Chollet, S., Lelièvre, M., & Abdi, H.
- Venue
- International Journal of Food Science & Technology
- Publisher
- Oxford University Press (OUP)
- Published
- 2012-05-22
- Volume
- 47
- Issue
- 8
- Pages
- 1563-1578
- ISSN
- 0950-5423 (ISSN-L)
- 1365-2621
- OpenAlex
- W2098571322
- Cited by
- 391
- References
- 81
- FWCI
- 31.10
- Type
- journal article
- Language
- en
- Open access
- closed
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- Valentin, D., Chollet, S., Lelièvre, M., & Abdi, H. (2012). Quick and dirty but still pretty good: a review of new descriptive methods in food science. International Journal of Food Science & Technology. https://doi.org/10.1111/j.1365-2621.2012.03022.x
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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