期刊
TRENDS IN FOOD SCIENCE & TECHNOLOGY
卷 111, 期 -, 页码 333-345出版社
ELSEVIER SCIENCE LONDON
DOI: 10.1016/j.tifs.2021.03.011
关键词
Seafood; Fish sensory analysis; QIM Concept; Freshness attributes
资金
- FCT-Fundacao para a Ciencia e a Tecnologia through the CQM Base Fund [UIDP/00674/2020]
- ARDITI-Agencia Regional para o Desenvolvimento da Investigacao Tecnologica e Inovacao [M1420-01-0145-FEDER-000005]
- ARDITI under the M1420 Project [09-5369-FSE-000001]
- Ilhapeixe S.A. under the M1420 Project [09-5369-FSE-000001]
- Fundação para a Ciência e a Tecnologia [UIDP/00674/2020] Funding Source: FCT
The Quality Index Method (QIM) is a widely used approach for fish sensory grading, providing information on fish freshness status based on structured scaling. However, its reliability in predicting shelf-life is undermined by inherent assumptions, and improvements are needed to enhance its predictive power and acceptability.
Background: The Quality Index Method (QIM) is a widely used approach for fish sensory grading, based on a structured scaling for freshness measurements, providing information concerning the fish freshness status, as a prediction of the remaining shelf-life for specific species or products. However, its tendency to be used in an oversimplified way and other common misapplications could lead to discredit of a methodology with great potential. Scope and approach: Review the principles of QIM methodology, discussing its concept, applications, and understand their limits, as a useful strategy to propose improvements, reinforce its predictive power and consequent acceptability. Key findings and conclusions: QIM methodology is based on a compromise between the number of fish samples necessary and the number of attributes, with sensory relevance in fish spoilage, that allows verifying if quality requirements are fulfilled. However, the assumptions inherent to the method, undermine the reliability of the shelf-life predictions. Determination of the variability associated with assessors, product, and correct structure of datasets for statistical analysis, will improve the predictive power of the method. However, it could lead to an increase in the method complexity that would drive it away from the industry?s needs for fast and easily implemented methods.
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