AIMC Topic: Taste

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Fruit wines classification enabled by combing machine learning with comprehensive volatiles profiles of GC-TOF/MS and GC-IMS.

Food research international (Ottawa, Ont.)
Fruit wines, produced through the fermentation of various fruits, are well-documented for their distinct flavor profiles. Intelligent sensory analysis, GC-TOF/MS and GC-IMS were used for the analysis of the volatile profile of eight types of fruit wi...

Machine learning-based classification and prediction of typical Chinese green tea taste profiles.

Food research international (Ottawa, Ont.)
The taste of Chinese green tea is highly diverse. In this study, a combination of unsupervised and supervised learning methods was utilized to develop a model for classifying and predicting typical Chinese green tea taste. Three clustering methods we...

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 ...

Effectively saltiness enhanced odorants screening and prediction by database establish, sensory evaluation and deep learning method.

Food chemistry
Odor-taste interaction has gained success in enhancing saltiness perception. This work aimed to provide candidate odorants for saltiness enhancement. Volatile compounds and their frequencies in salty foods were systematically analyzed. The compounds ...

Robots in the kitchen: The automation of food preparation in restaurants and the compounding effects of perceived love and disgust on consumer evaluations.

Appetite
Restaurants are swiftly embracing automation to prepare food, experimenting with innovations from robotic arms for frying foods to pizza-making robots. While these advances promise to enhance efficiency and productivity, their impact on consumer psyc...

Computational screening of umami tastants using deep learning.

Molecular diversity
Umami, a fundamental human taste modality, refers to the savory flavors in meats and broths, often associated with monosodium glutamate and protein richness. With limited knowledge of umami molecules, the food industry seeks efficient approaches for ...

Artificial intelligence as a tool for predicting the quality attributes of garlic (Allium sativum L.) slices during continuous infrared-assisted hot air drying.

Journal of food science
Effective drying methods are a highly suitable solution for ensuring stable food supply chains, reducing postharvest agricultural losses, and preventing the spoilage of perishable fruits and vegetables. Moreover, machine learning techniques are innov...

Development of analytical "aroma wheels" for Oolong tea infusions (Shuixian and Rougui) and prediction of dynamic aroma release and colour changes during "Chinese tea ceremony" with machine learning.

Food chemistry
The flavour of tea as a worldwide popular beverage has been studied extensively. This study aimed to apply established flavour analysis techniques (GC-MS, GC-O-MS and APCI-MS/MS) in innovative ways to characterise the flavour profile of oolong tea in...

Flavor Engineering: A comprehensive review of biological foundations, AI integration, industrial development, and socio-cultural dynamics.

Food research international (Ottawa, Ont.)
This state-of-the-art review comprehensively explores flavor development, spanning biological foundations, analytical methodologies, and the socio-cultural impact. It incorporates an industrial perspective and examines the role of artificial intellig...

Using machine-learning approaches to investigate the volatile-compound fingerprint of fishy off-flavour from beef with enhanced healthful fatty acids.

Meat science
Machine learning classification approaches were used to discriminate a fishy off-flavour identified in beef with health-enhanced fatty acid profiles. The random forest approach outperformed (P < 0.001; receiver operating characteristic curve: 99.8 %,...