AIMC Topic: Taste

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Non-invasive setup for grape maturation classification using deep learning.

Journal of the science of food and agriculture
BACKGROUND: The San Francisco Valley region from Brazil is known worldwide for its fruit production and exportation, especially grapes and wines. The grapes have high quality not only due to the excellent morphological characteristics, but also to th...

Accurate Prediction of Sensory Attributes of Cheese Using Near-Infrared Spectroscopy Based on Artificial Neural Network.

Sensors (Basel, Switzerland)
The acceptance of a food product by the consumer depends, as the most important factor, on its sensory properties. Therefore, it is clear that the food industry needs to know the perceptions of sensory attributes to know the acceptability of a produc...

Novel scaffold of natural compound eliciting sweet taste revealed by machine learning.

Food chemistry
Sugar replacement is still an active issue in the food industry. The use of structure-taste relationships remains one of the most rational strategy to expand the chemical space associated to sweet taste. A new machine learning model has been setup ba...

Aroma perceptual interactions of benzaldehyde, furfural, and vanillin and their effects on the descriptor intensities of Huangjiu.

Food research international (Ottawa, Ont.)
Aldehydes are important in the aroma of Huangjiu and contribute the almond and sweet aromas to Huangjiu. The perceptual interactions of 3 important aldehyde compounds were investigated using S-curves. Three volatiles, benzaldehyde, furfural, and vani...

A predictive model for the evaluation of flavor attributes of raw and cooked beef based on sensor array analyses.

Food research international (Ottawa, Ont.)
There are currently no standardized objective measures to evaluate beef flavor attributes, especially the comparison between raw beef and cooked beef. Beef flavor attribute is one of the most significant parameters for consumers. This study described...

Detection of bitterness and astringency of green tea with different taste by electronic nose and tongue.

PloS one
An electronic nose was used to evaluate the bitterness and astringency of green tea, and the possible application of the sensor was assessed for the evaluation of different tasting green tea samples. Three different grades of green tea were measured ...

Predictive modeling for odor character of a chemical using machine learning combined with natural language processing.

PloS one
Recent studies on machine learning technology have reported successful performances in some visual and auditory recognition tasks, while little has been reported in the field of olfaction. In this paper we report computational methods to predict the ...

Assessment of Beer Quality Based on a Robotic Pourer, Computer Vision, and Machine Learning Algorithms Using Commercial Beers.

Journal of food science
UNLABELLED: Sensory attributes of beer are directly linked to perceived foam-related parameters and beer color. The aim of this study was to develop an objective predictive model using machine learning modeling to assess the intensity levels of senso...

Pharmacokinetic and safety profile of tofacitinib in children with polyarticular course juvenile idiopathic arthritis: results of a phase 1, open-label, multicenter study.

Pediatric rheumatology online journal
BACKGROUND: Juvenile idiopathic arthritis (JIA) is the most common pediatric rheumatic disease and a leading cause of childhood disability. The objective of this study was to characterize the PK, safety, and taste acceptability of tofacitinib in pati...

Development and characterization of an α-l-rhamnosidase mutant with improved thermostability and a higher efficiency for debittering orange juice.

Food chemistry
The glycoside hydrolase, α-l-rhamnosidase, could remove the bitter taste of naringin from citrus juices. However, most α-l-rhamnosidases are easily deactivated at high temperatures, limiting the practice in debittering citrus juices. The V529A mutant...