Affiliation:
1. Universidade Federal Rural do Rio de Janeiro, Brazil
Abstract
ABSTRACT The quality and price of coffee drinks can be affected by contamination with impurities during roasting and grinding. Methods that enable quality control of marketed products are important to meet the standards required by consumers and the industry. The purpose of this study was to estimate the percentage of impurities contained in coffee using textural and colorimetric descriptors obtained from digital images. Arabica coffee beans (Coffea arabica L.) at 100% purity were subjected to roasting and grinding processes, and the initially pure ground coffee was gradually contaminated with impurities. Digital images were collected from coffee samples with 0, 10, 30, 50, and 70% impurities. From the images, textural descriptors of the histograms (mean, standard deviation, entropy, uniformity, and third moment) and colorimetric descriptors (RGB color space and HSI color space) were obtained. The principal component regression (PCR) method was applied to the data group of textural and colorimetric descriptors for the development of linear models to estimate coffee impurities. The selected models for the textural descriptors data group and the colorimetric descriptors data group were composed of two and three principal components, respectively. The model from the colorimetric descriptors showed a greater capacity to estimate the percentage of impurities in coffee when compared to the model from the textural descriptors.
Subject
Agronomy and Crop Science,Environmental Engineering
Reference25 articles.
1. Texture image analysis and texture classification methods - A review;Armi L.;International Online Journal of Image Processing and Pattern Recognition,2019
2. Programa Permanente de Controle da Pureza do Café: Regulamento,2020
3. Tomato quality based on colorimetric characteristics of digital images;Bello T. B.;Revista Brasileira de Engenharia Agrícola e Ambiental,2020
4. A didactic proposal in teaching exploratory data analysis with MDF pictures;Böck F. C.;Revista Virtual de Química,2015
5. Multivariate approach in the evaluation of the production and composition of buffalo milk in Nordestine Semiarid;Cavalcante P. O. S.;Revista Caatinga,2019
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