Streamlined approaches for image classification using principal component analysis and hierarchical clustering of extrudates from coffee and sorghum blends
ABSTRACTThis article describes simple methods to group images including principal component analysis (PCA) and hierarchical clustering of principal components (HCPC). Images of expanded and low expanded extrudates were processed using two optimization alternatives: a) image size reduction (from 2126...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2023-12-01
|
| Series: | CyTA - Journal of Food |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/19476337.2023.2263513 |
| _version_ | 1797392459706138624 |
|---|---|
| author | Davy William Hidalgo Chávez Felipe Leite Coelho Da Silva Renan Vicente Pinto Carlos Wanderley Piler De Carvalho Otniel Freitas-Silva |
| author_facet | Davy William Hidalgo Chávez Felipe Leite Coelho Da Silva Renan Vicente Pinto Carlos Wanderley Piler De Carvalho Otniel Freitas-Silva |
| author_sort | Davy William Hidalgo Chávez |
| collection | DOAJ |
| description | ABSTRACTThis article describes simple methods to group images including principal component analysis (PCA) and hierarchical clustering of principal components (HCPC). Images of expanded and low expanded extrudates were processed using two optimization alternatives: a) image size reduction (from 2126 to 25 pixels); and b) grayscale conversion before size reduction. After applying PCA and HCPC, all tests yielded consistently similar results with the same PCA distribution and identical HCPC groups. Furthermore, expanded and low expanded extrudates formed groups with their respective peers. The RAM allocated to images and the time required to process them was reduced from 1727 Mb to less than 5 Mb and from ~ 2000s to just 0.1s, respectively. These results demonstrate the feasibility of using these two simple multivariate statistical techniques for image classification. |
| first_indexed | 2024-03-08T23:47:32Z |
| format | Article |
| id | doaj.art-52aaf43a5e22405e8678ed74d28f7a50 |
| institution | Directory Open Access Journal |
| issn | 1947-6337 1947-6345 |
| language | English |
| last_indexed | 2024-03-08T23:47:32Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | CyTA - Journal of Food |
| spelling | doaj.art-52aaf43a5e22405e8678ed74d28f7a502023-12-13T14:31:47ZengTaylor & Francis GroupCyTA - Journal of Food1947-63371947-63452023-12-0121160661310.1080/19476337.2023.2263513Streamlined approaches for image classification using principal component analysis and hierarchical clustering of extrudates from coffee and sorghum blendsDavy William Hidalgo Chávez0Felipe Leite Coelho Da Silva1Renan Vicente Pinto2Carlos Wanderley Piler De Carvalho3Otniel Freitas-Silva4Departamento de Ciência e Tecnologia de Alimentos, Universidade Federal Rural do Rio de Janeiro, Seropédica, Rio de Janeiro, BrazilDepartamento de Matemática, Universidade Federal Rural do Rio de Janeiro, Seropédica, Rio de Janeiro, BrazilDepartamento de Matemática, Universidade Federal Rural do Rio de Janeiro, Seropédica, Rio de Janeiro, BrazilCereal and Food Extrusion, Embrapa Agroindústria de Alimentos, Rio de Janeiro, BrazilPost Harvest, Embrapa Agroindústria de Alimentos, Guaratiba, Rio de Janeiro, BrazilABSTRACTThis article describes simple methods to group images including principal component analysis (PCA) and hierarchical clustering of principal components (HCPC). Images of expanded and low expanded extrudates were processed using two optimization alternatives: a) image size reduction (from 2126 to 25 pixels); and b) grayscale conversion before size reduction. After applying PCA and HCPC, all tests yielded consistently similar results with the same PCA distribution and identical HCPC groups. Furthermore, expanded and low expanded extrudates formed groups with their respective peers. The RAM allocated to images and the time required to process them was reduced from 1727 Mb to less than 5 Mb and from ~ 2000s to just 0.1s, respectively. These results demonstrate the feasibility of using these two simple multivariate statistical techniques for image classification.https://www.tandfonline.com/doi/10.1080/19476337.2023.2263513Image classificationimage analysisprincipal component analysishierarchical clustering of principal components |
| spellingShingle | Davy William Hidalgo Chávez Felipe Leite Coelho Da Silva Renan Vicente Pinto Carlos Wanderley Piler De Carvalho Otniel Freitas-Silva Streamlined approaches for image classification using principal component analysis and hierarchical clustering of extrudates from coffee and sorghum blends CyTA - Journal of Food Image classification image analysis principal component analysis hierarchical clustering of principal components |
| title | Streamlined approaches for image classification using principal component analysis and hierarchical clustering of extrudates from coffee and sorghum blends |
| title_full | Streamlined approaches for image classification using principal component analysis and hierarchical clustering of extrudates from coffee and sorghum blends |
| title_fullStr | Streamlined approaches for image classification using principal component analysis and hierarchical clustering of extrudates from coffee and sorghum blends |
| title_full_unstemmed | Streamlined approaches for image classification using principal component analysis and hierarchical clustering of extrudates from coffee and sorghum blends |
| title_short | Streamlined approaches for image classification using principal component analysis and hierarchical clustering of extrudates from coffee and sorghum blends |
| title_sort | streamlined approaches for image classification using principal component analysis and hierarchical clustering of extrudates from coffee and sorghum blends |
| topic | Image classification image analysis principal component analysis hierarchical clustering of principal components |
| url | https://www.tandfonline.com/doi/10.1080/19476337.2023.2263513 |
| work_keys_str_mv | AT davywilliamhidalgochavez streamlinedapproachesforimageclassificationusingprincipalcomponentanalysisandhierarchicalclusteringofextrudatesfromcoffeeandsorghumblends AT felipeleitecoelhodasilva streamlinedapproachesforimageclassificationusingprincipalcomponentanalysisandhierarchicalclusteringofextrudatesfromcoffeeandsorghumblends AT renanvicentepinto streamlinedapproachesforimageclassificationusingprincipalcomponentanalysisandhierarchicalclusteringofextrudatesfromcoffeeandsorghumblends AT carloswanderleypilerdecarvalho streamlinedapproachesforimageclassificationusingprincipalcomponentanalysisandhierarchicalclusteringofextrudatesfromcoffeeandsorghumblends AT otnielfreitassilva streamlinedapproachesforimageclassificationusingprincipalcomponentanalysisandhierarchicalclusteringofextrudatesfromcoffeeandsorghumblends |