Near-Infrared Spectroscopy and Machine Learning: Analysis and Classification Methods of Rice Article Swipe
Pedro Sampaio
,
Carla Brites
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.5772/intechopen.99017
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.5772/intechopen.99017
Nowadays, the conventional biochemical methods used to differentiate and characterize rice types, biochemical properties, authentication, and contamination issues are difficult to implement due to the high cost of reagents, time requirement and environmental issues. Actually, the success of agri-food technology is directly related to the quality of analysis of experimental data acquired by sensors or techniques such as the infrared-spectroscopy. To overcome these technical limitations, a rapid and non-destructive methodology for discrimination and classification of rice has been investigated. Near-infrared spectroscopy is considered as fast, clean, and non-destructive analytical tools and its spectra present significant biomolecular information that must be analysed by sophisticated methodologies. Machine learning plays an important role in the analysis of the spectral data being used several methods such as Partial Least Squares, Principal Component Analysis, Partial Least Squares-Discriminant Analysis, Support Vector Machine, Artificial Neuronal Network, among others which can successfully be applied for food classification and discrimination as well as in terms of authentication and contamination issues. The quality control of rice is extremely important at every stage of production, beginning with estimation of raw agricultural materials and monitoring their quality during storage, estimating food quality during the production process and of the final products as well as the determination of their authenticity and the detection of adulterants.
Related Topics
Concepts
Artificial intelligence
Principal component analysis
Computer science
Partial least squares regression
Pattern recognition (psychology)
Linear discriminant analysis
Quality (philosophy)
Machine learning
Least squares support vector machine
Authentication (law)
Support vector machine
Biochemical engineering
Data mining
Process engineering
Engineering
Computer security
Philosophy
Epistemology
Metadata
- Type
- book-chapter
- Language
- en
- Landing Page
- https://doi.org/10.5772/intechopen.99017
- https://www.intechopen.com/citation-pdf-url/77638
- OA Status
- hybrid
- Cited By
- 7
- References
- 127
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3192765649
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3192765649Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5772/intechopen.99017Digital Object Identifier
- Title
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Near-Infrared Spectroscopy and Machine Learning: Analysis and Classification Methods of RiceWork title
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book-chapterOpenAlex work type
- Language
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enPrimary language
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2021Year of publication
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2021-08-19Full publication date if available
- Authors
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Pedro Sampaio, Carla BritesList of authors in order
- Landing page
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https://doi.org/10.5772/intechopen.99017Publisher landing page
- PDF URL
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https://www.intechopen.com/citation-pdf-url/77638Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://www.intechopen.com/citation-pdf-url/77638Direct OA link when available
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Artificial intelligence, Principal component analysis, Computer science, Partial least squares regression, Pattern recognition (psychology), Linear discriminant analysis, Quality (philosophy), Machine learning, Least squares support vector machine, Authentication (law), Support vector machine, Biochemical engineering, Data mining, Process engineering, Engineering, Computer security, Philosophy, EpistemologyTop concepts (fields/topics) attached by OpenAlex
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7Total citation count in OpenAlex
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2025: 2, 2024: 1, 2023: 4Per-year citation counts (last 5 years)
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127Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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