scholarly journals Following Coffee Production from Cherries to Cup: Microbiological and Metabolomic Analysis of Wet Processing ofCoffea arabica

2019 ◽  
Vol 85 (6) ◽  
Author(s):  
Sophia Jiyuan Zhang ◽  
Florac De Bruyn ◽  
Vasileios Pothakos ◽  
Julio Torres ◽  
Carlos Falconi ◽  
...  
Keyword(s):  
Microbial Communities ◽  
Fine Tuning ◽  
Coffee Bean ◽  
Green Coffee ◽  
Green Coffee Beans ◽  
Coffee Beans ◽  
Wet Processing ◽  
On Farm ◽  
Postharvest Processing

ABSTRACTA cup of coffee is the final product of a complex chain of operations. Wet postharvest processing of coffee is one of these operations, which involves a fermentation that inevitably has to be performed on-farm. During wet coffee processing, the interplay between microbial activities and endogenous bean metabolism results in a specific flavor precursor profile of the green coffee beans. Yet, how specific microbial communities and the changing chemical compositions of the beans determine the flavor of a cup of coffee remains underappreciated. Through a multiphasic approach, the establishment of the microbial communities, as well as their prevalence during wet processing ofCoffea arabica, was followed at an experimental farm in Ecuador. Also, the metabolites produced by the microorganisms and those of the coffee bean metabolism were monitored to determine their influence on the green coffee bean metabolite profile over time. The results indicated that lactic acid bacteria were prevalent well before the onset of fermentation and that the fermentation duration entailed shifts in their communities. The fermentation duration also affected the compositions of the beans, so that longer-fermented coffee had more notes that are preferred by consumers. As a consequence, researchers and coffee growers should be aware that the flavor of a cup of coffee is determined before as well as during on-farm processing and that under the right conditions, longer fermentation times can be favorable, although the opposite is often believed.IMPORTANCECoffee needs to undergo a long chain of events to transform from coffee cherries to a beverage. The coffee postharvest processing is one of the key phases that convert the freshly harvested cherries into green coffee beans before roasting and brewing. Among multiple existing processing methods, the wet processing has been usually applied for Arabica coffee and produces decent quality of both green coffee beans and the cup of coffee. In the present case study, wet processing was followed by a multiphasic approach through both microbiological and metabolomic analyses. The impacts of each processing step, especially the fermentation duration, were studied in detail. Distinct changes in microbial ecosystems, processing waters, coffee beans, and sensory quality of the brews were found. Thus, through fine-tuning of the parameters in each step, the microbial diversity and endogenous bean metabolism can be altered during coffee postharvest processing and hence provide potential to improve coffee quality.

scholarly journals Exploring the Impacts of Postharvest Processing on the Microbiota and Metabolite Profiles during Green Coffee Bean Production

2016 ◽  
Vol 83 (1) ◽  
Author(s):  
Florac De Bruyn ◽  
Sophia Jiyuan Zhang ◽  
Vasileios Pothakos ◽  
Julio Torres ◽  
Charles Lambot ◽  
...  
Keyword(s):  
Lactic Acid ◽  
Green Coffee ◽  
Content Type ◽  
Green Coffee Beans ◽  
Dry Processing ◽  
Coffee Beans ◽  
Metabolite Profiles ◽  
Present Case Study ◽  
On Farm ◽  
Postharvest Processing

ABSTRACT The postharvest treatment and processing of fresh coffee cherries can impact the quality of the unroasted green coffee beans. In the present case study, freshly harvested Arabica coffee cherries were processed through two different wet and dry methods to monitor differences in the microbial community structure and in substrate and metabolite profiles. The changes were followed throughout the postharvest processing chain, from harvest to drying, by implementing up-to-date techniques, encompassing multiple-step metagenomic DNA extraction, high-throughput sequencing, and multiphasic metabolite target analysis. During wet processing, a cohort of lactic acid bacteria (i.e., Leuconostoc, Lactococcus, and Lactobacillus) was the most commonly identified microbial group, along with enterobacteria and yeasts (Pichia and Starmerella). Several of the metabolites associated with lactic acid bacterial metabolism (e.g., lactic acid, acetic acid, and mannitol) produced in the mucilage were also found in the endosperm. During dry processing, acetic acid bacteria (i.e., Acetobacter and Gluconobacter) were most abundant, along with Pichia and non-Pichia (Candida, Starmerella, and Saccharomycopsis) yeasts. Accumulation of associated metabolites (e.g., gluconic acid and sugar alcohols) took place in the drying outer layers of the coffee cherries. Consequently, both wet and dry processing methods significantly influenced the microbial community structures and hence the composition of the final green coffee beans. This systematic approach to dissecting the coffee ecosystem contributes to a deeper understanding of coffee processing and might constitute a state-of-the-art framework for the further analysis and subsequent control of this complex biotechnological process. IMPORTANCE Coffee production is a long process, starting with the harvest of coffee cherries and the on-farm drying of their beans. In a later stage, the dried green coffee beans are roasted and ground in order to brew a cup of coffee. The on-farm, postharvest processing method applied can impact the quality of the green coffee beans. In the present case study, freshly harvested Arabica coffee cherries were processed through wet and dry processing in four distinct variations. The microorganisms present and the chemical profiles of the coffee beans were analyzed throughout the postharvest processing chain. The up-to-date techniques implemented facilitated the investigation of differences related to the method applied. For instance, different microbial groups were associated with wet and dry processing methods. Additionally, metabolites associated with the respective microorganisms accumulated on the final green coffee beans.

scholarly journals Quality of coffee beans from peeled green fruits after temporary immersion in water

2020 ◽  
Vol 24 (10) ◽  
pp. 713-718
Author(s):  
Ana P. de F. Coelho ◽  
Juarez de S. e Silva ◽  
Antônio P. S. Carneiro ◽  
Evandro de C. Melo ◽  
Camilla S. da Silva ◽  
...  
Keyword(s):  
Green Coffee ◽  
Crop Season ◽  
Temporary Immersion ◽  
Green Coffee Beans ◽  
Randomized Design ◽  
Coffee Beans ◽  
Wet Processing ◽  
The Mean ◽  
Completely Randomized Design

ABSTRACT The harvest of green coffee fruits affects their quality; they should be separated from the ripe fruits during processing. The proportion of harvested green fruits can be high, requiring information and technologies to adequately manage and add value to coffee beans from this fruit category. The objective of this work was to evaluate the quality of coffee beans from green fruits separated during a wet processing and peeled after temporary immersion in water. A completely randomized design was used, consisting of six treatments (ripe peeled coffee fruits dried on suspended yards, non-peeled green coffee fruits under traditional dry management on a concrete yard, and peeled and non-peeled green coffee fruits temporarily immersed in water and dried on suspended and concrete yards) and four replications, in the 2018 crop season. Samples of coffee beans temporarily immersed in water were peeled and separated into peeled and non-peeled fractions and dried in suspended and concrete yards. The peeling yield of green coffee beans and the physical and sensorial characteristics of the processed coffee beans were evaluated. The mean peeling yield was 62% and allowed the separation of more developed green fruits, equating them to ripe peeled coffee beans regarding physical and sensorial quality.

scholarly journals Drying and sortation to improve green beans and cup quality of Wamena coffee

2021 ◽  
Vol 306 ◽  
pp. 03024
Author(s):  
Adnan ◽  
Martina Sri Lestari
Keyword(s):  
Simple Technique ◽  
Descriptive Analysis ◽  
Quality Score ◽  
Local Culture ◽  
Green Coffee ◽  
Green Beans ◽  
Green Coffee Beans ◽  
Coffee Beans ◽  
Cup Quality

Drying and sortation are the most important steps to improve green coffee beans and cup quality. However, farmers very often neglect these steps. Therefore, a simple technique and soft approach are required to encourage farmers to implement drying and sortation technology. The study aim is to assess suitable drying and sortation technology to improve green coffee beans and cup quality to local culture in Jayawijaya Regency, Papua. The study was conducted using 2 factors; a. Combination of drying floor using a tarp and without sortation (DFWTS), b. Combination of drying tables and with sortation (DTWS). Drying tables were designed as two separate parts. The first part was the permanent tables, and the second part was removable boxes in dimension 80 x 80 cm located on top of the permanent tables. Descriptive analysis was conducted based on SNI 01-2907-2008 by the Indonesian Coffee and Cocoa Research Institute. The results show DTWS produce green coffee beans in compliance with SNI 01-2907-2008 at 4a grade, compared to DFWTS is rejected. Green coffee beans quality is likely to affect cup quality. DTWS obtain cup quality score 83.0 compare to DFWTS is 81.25. In conclusion, DTWS improve green coffee beans and cup quality.

scholarly journals The Effect of Orlistat, Green Coffee Bean Extract, and Its Combinations on Lipid Profi le and Adiponectin Levels

Jurnal NERS ◽  
2017 ◽  
Vol 9 (1) ◽  
pp. 26 ◽  
Author(s):  
Joko Setyono ◽  
Dwi Adi Nugroho ◽  
Mustofa Mustofa ◽  
Saryono Saryono
Keyword(s):  
Chlorogenic Acid ◽  
Water Extract ◽  
Ethanol Extract ◽  
Control Group ◽  
Coffee Bean ◽  
Green Coffee ◽  
Green Coffee Beans ◽  
Coffee Beans ◽  
Significant Difference ◽  
Obese Rats

Introduction: Obesity prevalence is estimated increases, reached 19.1% of the population aged 15 years and over. This study aimed to determine the differences of the anti- obesity effect of orlistat, an extract of green coffee beans (Coffea canephora robusta), and its combination to the adiponectin levels and lipid profi le. Method: This research was true experimental post -test only with control group design with completely randomized design (CRD). Experimental animals (Rattus novergicus) were divided into 6 group, group 1 ( negative control ), group 2 ( positive control ), group 3 was group of obese rats fed orlistat dose of 15.9 mg/kg, group 4 was the group of obese rats were fed ethanol extract of green coffee beans dose of 400 mg/kg, group 5 was the group of obese rats were given water extract of green coffee beans dose of 400 mg/kg, and group 6 was group of obese rats were fed a combination of orlistat dose of 15.9 mg/kgand ethanol extract of green coffee beans at a dose of 400 mg/kg. Lipid profi les and adiponectin levels were measured with a spectrophotometer at 500nm absorbance. The data were analyzed by one-way ANOVA, and then post hoc Least Significant Difference (LSD) with α = 0.05. Result: Ethanol extract of green coffee is more effi cient in lowering LDL cholesterol, increasing HDL cholesterol, and lowering the total cholesterol levels on HFD diet-induced mice, but there was no difference in lowering triglycerides . The combination of ethanol extract of green coffee with orlistat showedthe increasing of adiponectin levels were highest than the other treatment groups. Discussion: The ethanol extract of green coffee readily diffuses through the digestive tract epithelium. Green coffee contains chlorogenic acid active compounds that can increase the body’s metabolism, increase fatty acid oxidation, reduce levels of triglycerides in the liver, and working to inhibit lipase and amylase pancreaticenzymes. In addition to chlorogenic acid, polyphenol content in coffee is also potentially reduce visceral fat accumulation. Preparations extract by ethanol allows the absorption process is done effi ciently and quickly.Keywords: obesity, orlistat, greencoffee, lipid profi le, adiponectin

scholarly journals Quality of wholemeal wheat bread enriched with green coffee beans

2016 ◽  
Vol 8 (2) ◽  
pp. 112-119 ◽  
Author(s):  
D. Dziki ◽  
◽  
Urszula Gawlik-Dziki ◽  
Renata Rozyło ◽  
Monika Siastała ◽  
...  
Keyword(s):  
Wheat Bread ◽  
Green Coffee ◽  
Green Coffee Beans ◽  
Coffee Beans

scholarly journals Application of colour, shape, and texture parameters for classifying the defect of Gayo Arabica green coffee bean using computer vision

2022 ◽  
Vol 951 (1) ◽  
pp. 012097
Author(s):  
A Maghfirah ◽  
I S Nasution
Keyword(s):  
Computer Vision ◽  
Intelligent System ◽  
Training Data ◽  
National Standard ◽  
Support Vector ◽  
Coffee Bean ◽  
Green Coffee ◽  
Coffee Beans ◽  
Green Coffee Bean

Abstract Coffee is the most important commodity in the trading industry. Determination of the quality of coffee is still done manually so that it cannot separate good quality coffee beans with bad quality coffee beans. This research conducted the development of a visual-based intelligent system using computer vision to be able to classify the quality of rice coffee based on the Indonesian National Standard (SNI). The models used in the study are the K-Nearest Neighbour (K-NN) method and the Support Vector Machine (SVM) method with 13 parameters used such as; area, contrast, energy, correlation, homogeneity, circularity, perimeter, and colour index R(red), G (green), B (blue), L*, a* and b*. A total of 1200 Arabica green coffee bean captured using Kinect V2 camera with training data of 1000 samples and testing data of 200 samples.

Incidence of Toxigenic and Other Molds in Green Coffee Beans

1983 ◽  
Vol 46 (11) ◽  
pp. 969-973 ◽  
Author(s):  
PHILIP B. MISLIVEC ◽  
VERNEAL R. BRUCE ◽  
REGINA GIBSON
Keyword(s):  
South America ◽  
South American ◽  
Coffee Bean ◽  
Green Coffee ◽  
African Countries ◽  
Surface Disinfection ◽  
Green Coffee Beans ◽  
Coffee Beans ◽  
Before And After ◽  
Green Coffee Bean

The mold flora of 944 green coffee bean samples from 31 coffee-producing countries was determined before and after surface disinfection with 5% NaOCl. Molds were detected on 99.1 % of 47,200 beans not surface-disinfected and in 47.9% of 47,200 disinfected beans. Although the percentage of differences in occurrence of mold before disinfection was minimal (93.4 to 100%) on a country-by-country basis, after disinfection the beans from Asiatic and African countries showed more internal invasion (80.5%) than those from Central and South America (49.4%). Aspergillus spp., which dominated the mold flora of 944 samples before and after disinfection, included the toxigenic A. ochraceus, A. flavus and A. versicolor as well as A. niger, A. tamarii, A. wentii and species of the A. glaucus group. The genus Penicillium, including the toxigenic P. cyclopium, P. citrinum and P. expansum, was detected regularly, although its occurrence was substantially lower than that of the aspergilli, especially after surface disinfection. The rare detection of Alternaria and Fusarium indicated that toxigenic species of these genera do not readily invade green coffee beans. A. flavus and A. tamarii were prevalent in Central and South American beans, whereas other aspergilli were prevalent in Asiatic and African beans. The penicillia were prevalent in Central and South American beans.

scholarly journals Quality and Defect Inspection of Green Coffee Beans Using a Computer Vision System

2019 ◽  
Vol 9 (19) ◽  
pp. 4195 ◽  
Author(s):  
García ◽  
Candelo-Becerra ◽  
Hoyos
Keyword(s):  
Image Processing ◽  
Nearest Neighbor ◽  
Vision System ◽  
Coffee Bean ◽  
Green Coffee ◽  
K Nearest Neighbor ◽  
Green Coffee Beans ◽  
Coffee Beans ◽  
Supervised Learning Algorithms ◽  
K Nearest Neighbor Algorithm

There is an increased industry demand for efficient and safe methods to select the best-quality coffee beans for a demanding market. Color, morphology, shape and size are important factors that help identify the best quality beans; however, conventional techniques based on visual and/or mechanical inspection are not sufficient to meet the requirements. Therefore, this paper presents an image processing and machine learning technique integrated with an Arduino Mega board, to evaluate those four important factors when selecting best-quality green coffee beans. For this purpose, the k-nearest neighbor algorithm is used to determine the quality of coffee beans and their corresponding defect types. The system consists of logical processes, image processing and the supervised learning algorithms that were programmed with MATLAB and then burned into the Arduino board. The results showed this method has a high effectiveness in classifying each single green coffee bean by identifying its main visual characteristics, and the system can handle several coffee beans present in a single image. Statistical analysis shows the process can identify defects and quality with high accuracy. The artificial vision method was helpful for the selection of quality coffee beans and may be useful to increase production, reduce production time and improve quality control.

scholarly journals Investigation of Physical and Sensory Properties of Ethiopian Specialty Dry Processed Green Coffee Beans

2020 ◽  
Vol 24 (1) ◽  
pp. 39-48
Author(s):  
Yishak Worku Wondimkun ◽  
Shimelis Admassu Emire ◽  
Tarekegn Berhanu Esho
Keyword(s):  
Physical Properties ◽  
Process Optimization ◽  
Sensory Properties ◽  
Coffee Bean ◽  
Green Coffee ◽  
Green Coffee Beans ◽  
Linear Dimensions ◽  
Standard Procedures ◽  
Coffee Beans ◽  
Relationship Of

AbstractEthiopia is known for its specialty Arabica coffees affected by mix-up. Physical and sensory properties of dry processed green coffee beans have been reported for the influence on the sensorial quality and coffee process optimization. The aim of this study was to investigate physical and sensory properties of sixteen varieties and to determine relationship of attributes. Physical properties of coffee beans were taken by measuring linear dimensions, densities and weight. Moreover, professional cuppers were analyzed sensory properties by using standard procedures. In this study, the longest (10.40 mm), the widest (6.82 mm) and the thickest (4.48 mm) varieties were Odicha, Feyate and Challa, respectively whereas the shortest (8.28 mm), narrowest (5.59 mm) and thinnest (3.52 mm) were 74110, Mocha and Bultum, respectively. The shape & make value of variety Bultum was “fair good” whereas variety Feyate was “very good”. Furthermore, the results of “shape & make” were significantly correlated with measured physical properties. The results indicate that most physical and sensory properties of coffee varieties have significant (P ≤ 0.05 differences. These properties were influenced by growing regions and variety difference. The outcome of this study can be used for coffee bean characterization and process optimization to improve beverage quality.

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