Streamlined approaches for image classification using principal component analysis and hierarchical clustering of extrudates from coffee and sorghum blends-Reference-Cited by-同舟云学术

Streamlined approaches for image classification using principal component analysis and hierarchical clustering of extrudates from coffee and sorghum blends

Published:2023-10-11 Issue:1 Volume:21 Page:606-613
ISSN:1947-6337
Container-title:CyTA - Journal of Food
language:en
Short-container-title:CyTA - Journal of Food

Author:

Chávez Davy William Hidalgo¹,Leite Coelho Da Silva Felipe²,Pinto Renan Vicente²,Piler De Carvalho Carlos Wanderley³,Freitas-Silva Otniel⁴

Affiliation:

1. Departamento de Ciência e Tecnologia de Alimentos, Universidade Federal Rural do Rio de Janeiro, Seropédica, Rio de Janeiro, Brazil

2. Departamento de Matemática, Universidade Federal Rural do Rio de Janeiro, Seropédica, Rio de Janeiro, Brazil

3. Cereal and Food Extrusion, Embrapa Agroindústria de Alimentos, Rio de Janeiro, Brazil

4. Post Harvest, Embrapa Agroindústria de Alimentos, Guaratiba, Rio de Janeiro, Brazil

Funder

FAPERJ

Publisher

Informa UK Limited

Subject

Industrial and Manufacturing Engineering,General Chemical Engineering,General Chemistry,Food Science

Link

https://www.tandfonline.com/doi/pdf/10.1080/19476337.2023.2263513

Reference24 articles.

1. Comparing performances of data mining algorithms for classification of green coffee beans;Arboleda E. R.;International Journal of Engineering and Advanced Technology,2019

2. Image segmentation method for coal particle size distribution analysis

3. Feature based image classification by using principal component analysis;Bajwa I. S.;ICGST International Journal on Graphics, Vision and Image Processing (GVIP),2009

4. Enhanced deep learning algorithm development to detect pain intensity from facial expression images

5. Bossard, L., Guillaumin, M., & Van Gool, L. (2014). Food-101–mining discriminative components with random forests. In European conference on computer vision, Zurich, Switzerland.