Green coffee bean extract attenuates gentamicin induced acute nephrotoxicity in rats

Gentamicin induced acute nephrotoxicity (GIAN) is considered as one of the important causes of acute renal failure. In recent years’ great effort has been focused on the introduction of herbal medicine as a novel therapeutic agent for prevention of GIAN. Hence, the current study was designed to investigate the effect of green coffee bean extract (GCBE) on GIAN in rats. Results of the present study showed that rat groups that received oral GCBE for 7 days after induction of GIAN (by a daily intraperitoneal injection of gentamicin for 7days), reported a significant improvement in renal functions tests when compared to the GIAN model groups. Moreover, there was significant amelioration in renal oxidative stress markers (renal malondialdehyde, renal superoxide dismutase) and renal histopathological changes in the GCBE-treated groups when compared to GIAN model group. These results indicate that GCBE has a potential role in ameliorating renal damage involved in GIAN.

Effect of the Post-Harvest Processing on Protein Modification in Green Coffee Beans by Phenolic Compounds

The protein fraction, important for coffee cup quality, is modified during post-harvest treatment prior to roasting. Proteins may interact with phenolic compounds, which constitute the major metabolites of coffee, where the processing affects these interactions. This allows the hypothesis that the proteins are denatured and modified via enzymatic and/or redox activation steps. The present study was initiated to encompass changes in the protein fraction. The investigations were limited to major storage protein of green coffee beans. Fourteen Coffea arabica samples from various processing methods and countries were used. Different extraction protocols were compared to maintain the status quo of the protein modification. The extracts contained about 4–8 µg of chlorogenic acid derivatives per mg of extracted protein. High-resolution chromatography with multiple reaction monitoring was used to detect lysine modifications in the coffee protein. Marker peptides were allocated for the storage protein of the coffee beans. Among these, the modified peptides K.FFLANGPQQGGK.E and R.LGGK.T of the α-chain and R.ITTVNSQK.I and K.VFDDEVK.Q of β-chain were detected. Results showed a significant increase (p < 0.05) of modified peptides from wet processed green beans as compared to the dry ones. The present study contributes to a better understanding of the influence of the different processing methods on protein quality and its role in the scope of coffee cup quality and aroma.

Growing Inequality in the Coffee Global Value Chain: A Complex Network Assessment

Following the liberalization of the coffee sector, governance and power balance in the international coffee trade has changed, with associated impacts on livelihoods in producing countries, most of which are middle- and low-income countries. Drawing on trade data for the period 1995–2018, we examine the dynamics and evolution of the international green coffee market to shed light on the re-distribution of value in the coffee supply chain. Data analysis shows that, over the studied period, the green coffee trade has increased considerably while the number of countries with a relevant role in trade has decreased, so that large exporting countries cover a higher share of trade, to the detriment of small exporting countries. We analyzed various properties of the global coffee trade network to provide insight on the relative contribution of countries not only in terms of their export value but also in terms of other selected features. The green coffee trade has gone from being distributed in many traditionally coffee-producing countries to concentrating mainly on the major coffee producers, as well as in some non-producing countries. These changes in the structure of the international green coffee market have led to greater inequality between producing and importing countries.

Influence of 8-week daily consumption of a new product combining green coffee hydroxycinnamates and beta-glucans on polyphenol bioavailability in subjects with overweight and obesity

Nutraceuticals based on plant extracts rich in polyphenols, as well as dietary fibres, are new strategies to fight overweight/obesity and associated diseases. However, to understand the potential effects of polyphenols...

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

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.

1D and 2D NMR spectra of coffee from 27 countries

715 green coffee samples were gathered by Almacafé S.A. (Bogotá, Colombia) between 2012 and 2014 from 27 different countries and analysed at the Nuclear Magnetic Resonance (NMR) laboratory at Universidad del Valle (Cali, Colombia). Over 1000 methanolic coffee extracts were prepared and a total of 4563 spectra were acquired in a fully automatic manner using a 400 MHz NMR spectrometer (Bruker Biospin, Germany). The dataset spans the variance that could be expected for an industrial application of origin monitoring, including samples from different harvest time, collected over several years and processed by at least two distinct operators. The resulting 1D and 2D spectra can be used to develop and evaluate feature extraction methods, multivariate algorithms and automation monitoring techniques, as a dataset for teaching, or as a reference for new studies on similar samples and approach.