AIMC Topic: Tea

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Development of a partial least squares-artificial neural network (PLS-ANN) hybrid model for the prediction of consumer liking scores of ready-to-drink green tea beverages.

Food research international (Ottawa, Ont.)
In order to develop products that would be preferred by consumers, the effects of the chemical compositions of ready-to-drink green tea beverages on consumer liking were studied through regression analyses. Green tea model systems were prepared by do...

Modeling of yield and environmental impact categories in tea processing units based on artificial neural networks.

Environmental science and pollution research international
In this study, an artificial neural network (ANN) model was developed for predicting the yield and life cycle environmental impacts based on energy inputs required in processing of black tea, green tea, and oolong tea in Guilan province of Iran. A li...

Support vector machine classification trees based on fuzzy entropy of classification.

Analytica chimica acta
The support vector machine (SVM) is a powerful classifier that has recently been implemented in a classification tree (SVMTreeG). This classifier partitioned the data by finding gaps in the data space. For large and complex datasets, there may be no ...

A Comparative Analysis of Machine Learning with WorldView-2 Pan-Sharpened Imagery for Tea Crop Mapping.

Sensors (Basel, Switzerland)
Tea is an important but vulnerable economic crop in East Asia, highly impacted by climate change. This study attempts to interpret tea land use/land cover (LULC) using very high resolution WorldView-2 imagery of central Taiwan with both pixel and obj...

Investigation of six bioactive anthraquinones in slimming tea by accelerated solvent extraction and high performance capillary electrophoresis with diode-array detection.

Food chemistry
A rapid and effective method for effective separation and rapid simultaneous determination of six bioactive anthraquinones by capillary zone electrophoresis was developed. An accelerated solvent extraction procedure was used for the extraction of ant...

Towards biological plausibility of electronic noses: A spiking neural network based approach for tea odour classification.

Neural networks : the official journal of the International Neural Network Society
The paper presents a novel encoding scheme for neuronal code generation for odour recognition using an electronic nose (EN). This scheme is based on channel encoding using multiple Gaussian receptive fields superimposed over the temporal EN responses...

Deep learning-assisted self-cleaning cellulose colorimetric sensor array for monitoring black tea withering dynamics.

Food chemistry
The withering process is a critical stage in developing the aroma profile of black tea. In this study, we presented an eco-friendly cellulose film-based colorimetric sensor array (CSA) for detecting volatile organic compounds (VOCs) and assessing wit...

Near-infrared spectroscopy coupled with Gramian angular field two-dimensional convolutional neural network for white tea adulteration detection.

Journal of the science of food and agriculture
BACKGROUND: The flavor profile and product quality of white tea, heavily dependent on its place of origin, significantly influence consumers' purchasing decisions. Quantitative adulteration testing for tea origin has encountered challenges due to the...

A nanozyme colorimetric sensor combined with cloud-based machine learning algorithm-assisted WeChat mini program for intelligent identification of Chinese green tea.

Food research international (Ottawa, Ont.)
Green tea has become increasingly renowned among consumers by virtue of its exceptional flavor and high nutritional value. There is often a strong correlation between the varieties of green tea, quality and corresponding price. In this work, a simple...

Towards precision agriculture tea leaf disease detection using CNNs and image processing.

Scientific reports
In this study, we introduce a groundbreaking deep learning (DL) model designed for the precise task of classifying common diseases in tea leaves, leveraging advanced image analysis techniques. Our model is distinguished by its complex multi-layer arc...