Discrimination of Defective (Full Black, Full Sour and Immature) and Nondefective Coffee Beans by Their Physical Properties

Belay, Abebe; Bekele, Yirgalem; Abraha, Ataklti; Comen, Dagne; Kim, Hyung Kook; Hwang, Yoon‐Hwae

doi:10.1111/jfpe.12113

Cited by 15 publications

(14 citation statements)

References 17 publications

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“…The coffee beans tend to the spherical shape by increasing moisture, causing a slight increase in the sphericity (Equation 6), as shown in Figure (between 0.827 ± 0.009 and 0.842 ± 0.007). The operational sphericity (Mohsenin, ; Wadell, ) of the coffee beans (Equation 3) were between 0.695 ± 0.009 and 0.732 ± 0.007, which approximate to the values obtained for the coffee bean by other researchers (Afonso Júnior et al, ; Belay et al, ; Dias, ; Yuwana, Silvia, & Sidebang, ). The effect of moisture on the dimensions and on sphericity of coffee beans were best described by the power law model (Equation 13; coefficients on Table ).…”

Section: Resultssupporting

confidence: 77%

Diffusion of water and caffeine in coffee beans using the hemispherical geometry approach

Huamaní-Meléndez

Darros‐Barbosa

2018

J Food Process Engineering

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Geometrical, morphological, and shape factors are crucial for the accurate prediction of mass transfer rate of caffeine and water in the extraction of caffeine in aqueous medium from coffee beans. A new expression for the sphericity was deduced considering the hydrated coffee bean as a triaxial semilipsoid with moving boundaries applied to a nonsteady state analytical solution to describe the diffusion phenomena for the hemispherical geometry to best represent the morphology of the coffee bean during the caffeine extraction at varying temperatures from 30 to 60 °C. The sphericity increases with moisture in a power law trend. The hemisphere mathematical model presented an excellent adjustment to the data, with diffusion coefficients between 1.03 ×·10−11 and 9.00 ×·10−10 m2/s for caffeine and 0.69 ×·10−10 and 2.03 ×·10−10 m2/s for water; strongly influenced by temperature with activation energy of 59.93 and 41.24 kJ/mol, respectively, for caffeine and water. Practical applications The decaffeination of coffee beans satisfies an important segment of the coffee market. Currently, many industries use the decaffeination technology based on organic solvents; others extract caffeine in aqueous medium and on a smaller scale the supercritical fluid extraction process. The present research provides important properties and a mathematical model to better represent the mass transfer phenomena, with the associated diffusion coefficients, along with a suitable expression for the size and shape of the coffee beans changing with the process, which can be used for the proper and accurate design and control of the extraction process. The proposed model used the hemispherical geometry approach with a reduced number of variables (e.g., only one equivalent radius), unlike other approaches that need three dimensions to represent the coffee bean size and the associated shape factor, which makes it time consuming, requiring more complex calculations, and not necessarily more accurate.

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Section: Resultssupporting

confidence: 77%

Diffusion of water and caffeine in coffee beans using the hemispherical geometry approach

Huamaní-Meléndez

Darros‐Barbosa

2018

J Food Process Engineering

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“…Currently, it represents 20% of the total coffee produced in Brazil (Mazzafera, 1999; Ramalakshmi, Kubra, & Rao, 2007). In Ethiopia, the percentage of defected beans are higher than other countries (Belay et al., 2014).…”

Section: Introductionmentioning

confidence: 99%

“…These studies are shown that there is difference in the physical and chemical properties between immature, black, sour, and nondefective coffee beans. Recent study showed the physical property alone can successfully separate defective and nondefective beans (Belay et al., 2014). Other studies (Mendonça, Franca, & Oliveira, 2009) also applied sieving for separation of these beans.…”

Section: Introductionmentioning

confidence: 99%

First order derivative spectra to determine caffeine and chlorogenic acids in defective and nondefective coffee beans

Habtamu

Belay

2020

Food Science & Nutrition

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In this research, the application of the first order derivative spectra was employed to determine the levels of caffeine (CAF) and chlorogenic acids (CGA) in defective (immature, black, and sour) and nondefective coffee beans without using extraction or background correction techniques. The extreme points of first order derivate spectra of these compounds were at the wavelength of 260 and 292 nm enable to quantify the contents of CAF and CGA, respectively. The level of CAF and CGA in coffee beans determined by this method is ranged from 1.2 ± 0.12–1.46 ± 0.47% and 4.04 ± 0.44–4.43 ± 0.43%, respectively. The study results also indicated total contents of CAF and CGA levels discriminate the defective and nondefective coffee beans with higher CAF and CGA contents being observed in defective coffee beans. As the method is extremely rapid, easy, and inexpensive and also requires minimal sample preparation for the quantification of CAF and CGA contents in coffee, it could be a valuable quality control technique.

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“…In addition, the defective bean removal is a critical stage that affects the bean's value, as many experts explicitly point out that defective beans are a key factor for providing high-quality coffee. Thus, removing defective beans becomes a necessary step before brewing for significantly increasing their competition and profits [2]. The SCAA has classified the defective beans into 13 classes [3], as shown in upper portion of Figure 1.…”

Section: Introductionmentioning

confidence: 99%

“…The SCAA has classified the defective beans into 13 classes [3], as shown in upper portion of Figure 1. Most popular defective bean removal processes are achieved by manual or mechanical manners in the past decades [1,2,4,5]. Nevertheless, these solutions focus on removing few sorts of defects in the SCAA's classification.…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning-Based Defective Bean Inspection with GAN-Structured Automated Labeled Data Augmentation in Coffee Industry

et al. 2019

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In the production process from green beans to coffee bean packages, the defective bean removal (or in short, defect removal) is one of most labor-consuming stages, and many companies investigate the automation of this stage for minimizing human efforts. In this paper, we propose a deep-learning-based defective bean inspection scheme (DL-DBIS), together with a GAN (generative-adversarial network)-structured automated labeled data augmentation method (GALDAM) for enhancing the proposed scheme, so that the automation degree of bean removal with robotic arms can be further improved for coffee industries. The proposed scheme is aimed at providing an effective model to a deep-learning-based object detection module for accurately identifying defects among dense beans. The proposed GALDAM can be used to greatly reduce labor costs, since the data labeling is the most labor-intensive work in this sort of solutions. Our proposed scheme brings two main impacts to intelligent agriculture. First, our proposed scheme is can be easily adopted by industries as human effort in labeling coffee beans are minimized. The users can easily customize their own defective bean model without spending a great amount of time on labeling small and dense objects. Second, our scheme can inspect all classes of defective beans categorized by the SCAA (Specialty Coffee Association of America) at the same time and can be easily extended if more classes of defective beans are added. These two advantages increase the degree of automation in the coffee industry. The prototype of the proposed scheme was developed for studying integrated tests. Testing results of a case study reveal that the proposed scheme can efficiently and effectively generate models for identifying defective beans with accuracy and precision values up to 80 % .

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Discrimination of Defective (Full Black, Full Sour and Immature) and Nondefective Coffee Beans by Their Physical Properties

Cited by 15 publications

References 17 publications

Diffusion of water and caffeine in coffee beans using the hemispherical geometry approach

Diffusion of water and caffeine in coffee beans using the hemispherical geometry approach

First order derivative spectra to determine caffeine and chlorogenic acids in defective and nondefective coffee beans

Deep-Learning-Based Defective Bean Inspection with GAN-Structured Automated Labeled Data Augmentation in Coffee Industry

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