Estimation of percentage of impurities in coffee using a computer vision system

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.摘要：咖啡饮品的品质与价格，可在烘焙与研磨过程中因杂质污染受到影响。能够对上市产品实施质量管控的方法，对于满足消费者与行业的标准要求至关重要。本研究旨在通过从数字图像（digital images）中提取的纹理描述符（textural descriptors）与比色描述符（colorimetric descriptors），估算咖啡中的杂质占比。将100%纯度的阿拉比卡咖啡豆（Coffea arabica L.）进行烘焙与研磨工序，并向初始纯净的研磨咖啡中逐步掺入杂质。针对杂质占比分别为0%、10%、30%、50%与70%的咖啡样品采集数字图像。从这些图像中，提取得到直方图（histograms）的纹理描述符，包含均值（mean）、标准差（standard deviation）、熵（entropy）、均匀性（uniformity）与三阶矩（third moment），以及比色描述符，涵盖RGB色彩空间（RGB color space）与HSI色彩空间（HSI color space）。本研究将主成分回归（Principal Component Regression, PCR）方法应用于纹理描述符与比色描述符的数据集，以构建用于估算咖啡杂质占比的线性模型。针对纹理描述符数据集与比色描述符数据集所筛选出的模型，分别由2个与3个主成分构成。相较于纹理描述符模型，基于比色描述符构建的模型对咖啡杂质占比的估算能力更强。