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.
Highlights
Compliance with the quality standards of a product is paramount for its acceptance by consumers
This study aimed to evaluate the ability to estimate impurities in ground coffee using textural and colorimetric descriptors obtained from digital images
The descriptors of the group of colorimetric descriptors (GCD) presented low coefficients of variation, and the greatest variations were found for the descriptors associated with the RGB color space
Summary
Compliance with the quality standards of a product is paramount for its acceptance by consumers. During harvest and postharvest stages, coffee is subjected to various processes such as selection of beans, roasting, and grinding. In these last stages, impurities and defects related to coffee quality can go unnoticed through visual evaluation (Santos et al, 2016). Purity determination is generally carried out using conventional methods, in a laboratory, involving the use of gas chromatography–mass spectrometry, highperformance liquid chromatography, electron microscopy, and visual analysis on microscopic slides. These methods are costly, destructive, and require analysis time
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