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Espresso spent coffee grounds were chemically characterized to predict their potential, as a source of bioactive compounds, by comparison with the ones from the soluble coffee industry. Sampling included a total of 50 samples from 14 trademarks, collected in several coffee shops and prepared with distinct coffee machines. A high compositional variability was verified, particularly with regard to such water-soluble components as caffeine, total chlorogenic acids (CGA), and minerals, supported by strong positive correlations with total soluble solids retained. This is a direct consequence of the reduced extraction efficiency during espresso coffee preparation, leaving a significant pool of bioactivity retained in the extracted grounds. Besides the lipid (12.5%) and nitrogen (2.3%) contents, similar to those of industrial coffee residues, the CGA content (478.9 mg/100 g), for its antioxidant capacity, and its caffeine content (452.6 mg/100 g), due to its extensive use in the food and pharmaceutical industries, justify the selective assembly of this residue for subsequent use.
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http://dx.doi.org/10.1021/jf3018854 | DOI Listing |
Haematologica
September 2025
Department of Biochemistry and Molecular Genetics, University of Colorado Denver - Anschutz Medical Campus, Aurora, CO, USA; Omix Technologies Inc, Aurora, CO.
Caffeine is the most widely consumed psychoactive substance globally, yet its peripheral physiological effects remain incompletely understood. Leveraging comprehensive data from 13,091 blood donors in the REDS RBC-Omics study, we identify caffeine as a significant modulator of red blood cell (RBC) storage quality and transfusion outcomes. Elevated caffeine levels were reproducible across multiple donations from 643 recalled donors, selected based on their extremes in hemolytic propensity.
View Article and Find Full Text PDFMolecules
July 2025
Department of Biochemistry and Biotechnology, CCE, State University of Londrina, Londrina 86057-970, PR, Brazil.
Spent coffee grounds (SCGs) are lignocellulosic residues generated from producing espresso or soluble coffee and have no commercial value. This study aimed to develop a new single-step process for extracting bioactive compounds from SCGs based on ultrasonication in an aqueous medium and simultaneously recovering the residual solid fraction, resulting in the integral utilization of the residue. This process resulted in a liquid aqueous extract (LAE) rich in bioactive compounds (caffeine: 400.
View Article and Find Full Text PDFSaudi Dent J
August 2025
College of Dentistry, King Saud University, Riyadh, Saudi Arabia.
The aim of this study was to evaluate the effect of Saudi coffee consumption in comparison to various types of commonly used coffees (instant black, Turkish, and espresso) on the color of novel aesthetic dental ceramics. A total of 144 flat cylindrical discs with an 8-mm diameter and 0.5-mm thickness were created using CAD/CAM technology.
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July 2025
Department of Food Science and Biotechnology, Dongguk University-Seoul, 32, Dongguk-ro, Ilsandong-gu, Goyang-si, 10326, Gyeonggi-do, Republic of Korea.
Since the use of animal-based milk raises environmental concerns and health issues such as allergies and lactose intolerance, the demand for plant-based milk alternatives is growing. This study aimed to (1) develop nut-based milks-almond, cashew, hazelnut, and walnut-combined with Arabica espresso and (2) assess their physicochemical and sensory qualities. Oleic, linoleic, and linolenic acids increased significantly in roasted nut-based milk coffees (p < 0.
View Article and Find Full Text PDFPeerJ Comput Sci
April 2025
Department of Telecommunications, University of Ruse, Ruse, Bulgaria.
As one of the most widely consumed beverages worldwide, coffee is characterized by its diverse flavor profiles and complex production processes. In this study, deep learning-based image processing techniques are employed for the automatic classification of coffee bean species with high accuracy. To achieve this, images of three different coffee bean species (Starbucks Pike Place, Espresso, and Kenya) were classified using five CNN-based models: Xception, DenseNet201, InceptionV3, InceptionResNetV2, and DenseNet121.
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