Multivariate Analysis in Accelerated Shelf-Life Assessment—An Overview Article Swipe
Laíres Lima
,
Ana I. Pereira
,
Manuela Pintado
,
Márcio Carocho
,
Lillian Barros
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/asec2023-15520
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/asec2023-15520
To meet the market demand for high-quality products, researchers and manufacturers have invested in the development of accurate methods for estimating shelf-life. Tests that consider the simultaneous effects of different parameters on food degradation are useful tools in shelf-life studies, as these parameters can directly influence quality and safety. With this in mind, the objective of this review is to gather pertinent information from recent studies (2006–2022) pertaining to multivariate analysis applied in accelerated shelf-life tests in order to facilitate a comprehensive understanding. The review focuses on multivariate techniques commonly employed in accelerated shelf-life modeling, namely, principal components analysis, partial least squares regression, orthogonal projections to latent structures discriminant analysis, and hierarchical cluster analysis. Through an extensive literature review, the collected data represent the evolution of these methods, taking into account current trends, advances in food shelf-life techniques, and future perspectives. It was observed that the recent literature provides limited information on the determination of shelf-life under multiple accelerated factors. However, the studies analyzed showed that multivariate analysis can be a useful tool in the interpretation of quality characteristics and can accurately predict the shelf-life of foods compared to univariate kinetic procedures. The multivariate statistical methods addressed in this work are presented as a promising method for foods tested, being applied together with different chemometric techniques. This comprehensive review contributes to the body of knowledge surrounding accelerated shelf-life testing, offering valuable insights for researchers, manufacturers, and stakeholders in the food industry.
Related Topics
Concepts
Multivariate statistics
Univariate
Multivariate analysis
Shelf life
Computer science
Partial least squares regression
Off the shelf
Linear discriminant analysis
Principal component analysis
Quality (philosophy)
Data mining
Machine learning
Artificial intelligence
Engineering
Software engineering
Epistemology
Philosophy
Mechanical engineering
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/asec2023-15520
- https://www.mdpi.com/2673-4591/56/1/251/pdf?version=1704780972
- OA Status
- gold
- References
- 37
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390768571
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4390768571Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/asec2023-15520Digital Object Identifier
- Title
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Multivariate Analysis in Accelerated Shelf-Life Assessment—An OverviewWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-10-31Full publication date if available
- Authors
-
Laíres Lima, Ana I. Pereira, Manuela Pintado, Márcio Carocho, Lillian BarrosList of authors in order
- Landing page
-
https://doi.org/10.3390/asec2023-15520Publisher landing page
- PDF URL
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https://www.mdpi.com/2673-4591/56/1/251/pdf?version=1704780972Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2673-4591/56/1/251/pdf?version=1704780972Direct OA link when available
- Concepts
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Multivariate statistics, Univariate, Multivariate analysis, Shelf life, Computer science, Partial least squares regression, Off the shelf, Linear discriminant analysis, Principal component analysis, Quality (philosophy), Data mining, Machine learning, Artificial intelligence, Engineering, Software engineering, Epistemology, Philosophy, Mechanical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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37Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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