Streamlined approaches for image classification using principal component analysis and hierarchical clustering of extrudates from coffee and sorghum blends
Davy William Hidalgo Chávez, Felipe Leite Coelho Da Silva, Renan Vicente Pinto, Carlos Wanderley Piler De Carvalho, Otniel Freitas-Silva
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引用次数: 0
Abstract
This 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.
期刊介绍:
CyTA – Journal of Food is an Open Access journal that publishes original peer-reviewed research papers dealing with a wide range of subjects which are essential to the food scientist and technologist. Topics include: chemical analysis of food; additives and toxins in food; sensory, nutritional and physiological aspects of food; food microbiology and biotechnology; changes during the processing and storage of foods; effect of the use of agrochemicals in foods; quality control in food; and food engineering and technology.