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Coffee

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Rapid classification of coffee origin by combining mass spectrometry analysis of coffee aroma with deep learning.

Food chemistry
Mislabeling the geographical origin of coffee is a prevalent form of fraud. In this study, a rapid, nondestructive, and high-throughput method combining mass spectrometry (MS) analysis and intelligence algorithms to classify coffee origin was develop...

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...

Artificial Q-Grader: Machine Learning-Enabled Intelligent Olfactory and Gustatory Sensing System.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Portable and personalized artificial intelligence (AI)-driven sensors mimicking human olfactory and gustatory systems have immense potential for the large-scale deployment and autonomous monitoring systems of Internet of Things (IoT) devices. In this...

Enhanced phytoremediation of vanadium using coffee grounds and fast-growing plants: Integrating machine learning for predictive modeling.

Journal of environmental management
Vanadium (V) contamination posed a significant environmental challenge, while phytoremediation offered a sustainable solution. Phytoremediation performance was often limited by the slow growth cycles of traditional plants. A novel approach to enhanci...

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...

Prediction of coffee traits by artificial neural networks and laser-assisted rapid evaporative ionization mass spectrometry.

Food research international (Ottawa, Ont.)
BACKGROUND: Coffee is an important commodity in the worldwide economy and smart technologies are important for accurate quality control and consumer-oriented product development. Sensory perception is probably the most important information since it ...

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,...

Optimization process of coffee pulp wines combined with the artificial neural network and response surface methodology.

Scientific reports
Coffee pulp wine was made from coffee pulp. The level range of fermentation factors was determined by one-factor experiment. The significance factors affecting fermentation were screened by Plackett-Burman and steepest climbing experiments, which wer...