Nutrient Content Prediction and Geographical Origin Identification of Bananas by Combining Hyperspectral Imaging with Chemometrics Article Swipe
Huamei Xiao
,
Chunlin Li
,
Mingyue Wang
,
Zhibo Huan
,
Hanyi Mei
,
Jing Nie
,
Karyne M. Rogers
,
Zhen Wu
,
Yuwei Yuan
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/foods13223631
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/foods13223631
The nutritional quality of bananas and their geographical origin authenticity are very important for trade. There is an urgent need for rapid, non-destructive testing to improve the origin and quality assurance for importers, distributors, and consumers. In this study, 99 banana samples from a range of producing countries were collected. Hyperspectral data were combined with chemometric methods to construct quantitative and qualitative models for bananas, predicting soluble solids content (SSC), potassium content (K), and country of origin. A second derivative analysis combined with competitive adaptive weighted sampling (CARS) and random frog jumping (RF) was selected as the best pre-treatment method for the prediction of SSC and K content, respectively. Partial least squares (PLS) models achieved R2p values of 0.8012 and 0.8606 for SSC and K content, respectively. Chinese domestic and imported bananas were classified with a prediction accuracy of 95.83% using partial least squares-discriminant analysis (PLS-DA) and an RF method that screened the spectral variables after a second pretreatment. These results showed that hyperspectral imaging technology could be effectively used to non-destructively predict the nutrient contents of bananas and identify their geographical origin. In the future, this technology can be applied to determine the nutritional quality composition and geographical origin of bananas from other countries.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/foods13223631
- OA Status
- gold
- Cited By
- 2
- References
- 48
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404376765
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4404376765Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/foods13223631Digital Object Identifier
- Title
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Nutrient Content Prediction and Geographical Origin Identification of Bananas by Combining Hyperspectral Imaging with ChemometricsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-11-14Full publication date if available
- Authors
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Huamei Xiao, Chunlin Li, Mingyue Wang, Zhibo Huan, Hanyi Mei, Jing Nie, Karyne M. Rogers, Zhen Wu, Yuwei YuanList of authors in order
- Landing page
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https://doi.org/10.3390/foods13223631Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3390/foods13223631Direct OA link when available
- Concepts
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Hyperspectral imaging, Chemometrics, Identification (biology), Content (measure theory), Nutrient, Pattern recognition (psychology), Computer science, Environmental science, Biological system, Artificial intelligence, Mathematics, Biology, Botany, Machine learning, Ecology, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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