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Coffea

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Characterization and bioactivities of coffee husks extract encapsulated with polyvinylpyrrolidone.

Food research international (Ottawa, Ont.)
Coffee processing generates large amounts of residues of which a portion still has bioactive properties due to their richness in phenolic compounds. This study aimed to obtain a coffee husks extract (CHE) and to encapsulate it (ECHE) with polyvinylpy...

Impact of microwave-assisted extraction on roasted coffee carbohydrates, caffeine, chlorogenic acids and coloured compounds.

Food research international (Ottawa, Ont.)
Microwave-assisted extraction (MAE) allows to quickly achieve soluble compounds from solid matrices due to the promotion of temperatures higher than the solvent (atmospheric) boiling point, once a closed-vessel system is used for operating at high pr...

Sensory characterization and acceptance of coffee brews of C. arabica and C. canephora blended with steamed defective coffee.

Food research international (Ottawa, Ont.)
Steam treatment has been reported as an alternative to improve the cup quality of coffee; in this research, it was applied to C. canephora defective coffee. The aim of the study was to evaluate the sensory perception of steamed defective C. canephora...

Machine learning and statistics to qualify environments through multi-traits in Coffea arabica.

PloS one
Several factors such as genotype, environment, and post-harvest processing can affect the responses of important traits in the coffee production chain. Determining the influence of these factors is of great relevance, as they can be indicators of the...

Marker effects and heritability estimates using additive-dominance genomic architectures via artificial neural networks in Coffea canephora.

PloS one
Many methodologies are used to predict the genetic merit in animals and plants, but some of them require priori assumptions that may increase the complexity of the model. Artificial neural network (ANN) has advantage to not require priori assumptions...

Models for predicting coffee yield from chemical characteristics of soil and leaves using machine learning.

Journal of the science of food and agriculture
BACKGROUND: Coffee farming constitutes a substantial economic resource, representing a source of income for several countries due to the high consumption of coffee worldwide. Precise management of coffee crops involves collecting crop attributes (cha...

Coffee Leaf Rust Disease Detection and Implementation of an Edge Device for Pruning Infected Leaves via Deep Learning Algorithms.

Sensors (Basel, Switzerland)
Global warming and extreme climate conditions caused by unsuitable temperature and humidity lead to coffee leaf rust () diseases in coffee plantations. Coffee leaf rust is a severe problem that reduces productivity. Currently, pesticide spraying is c...

Rapid discrimination of different primary processing Arabica coffee beans using FT-IR and machine learning.

Food research international (Ottawa, Ont.)
In this study, fourier transform infrared spectroscopy (FT-IR) analysis was combined with machine learning, while various analytical techniques such as colorimetry, low-field nuclear magnetic resonance spectroscopy, scanning electron microscope, two-...

Rapid identification of coffee species and origin using affordable multi-channel spectral sensor combined with machine learning.

Food research international (Ottawa, Ont.)
The rapid identification of coffee species and origin is critical for ensuring quality control and authenticity in the coffee industry. This study explores the use of an affordable multi-channel spectral sensor, AS7265X (410-940 nm), combined with ma...

Hybrid vision GNNs based early detection and protection against pest diseases in coffee plants.

Scientific reports
Agriculture is an essential foundation that supports numerous economies, and the longevity of the coffee business is of paramount significance. Controlling and safeguarding coffee farms from harmful pests, including the Coffee Berry Borer, Mealybugs,...