AIMC Topic: Odorants

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Characterize the dynamic changes of volatile compounds during the roasting process of Wuyi rock tea (Shuixian) integrating GC-IMS and GC × GC-O-MS combined with machine learning.

Food chemistry
Understanding aroma compounds' changes during Shuixian roasting is vital for scientific guidance. This study used gas chromatography-ion mobility spectrometry (GC-IMS) and two-dimensional gas chromatography-olfactory-mass spectrometry (GC × GC-O-MS) ...

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...

Machine learning models for terroir classification and blend similarity prediction: A proof-of-concept to enhance cocoa quality evaluation.

Food chemistry
Flavour is a key quality attribute of cocoa, essential for industry standards and consumer preferences. Automated methods for assessing flavour quality support industrial laboratories in achieving high sample throughput. Targeted and untargeted HS-SP...

Prediction and metabolomics reveal aroma profiles of mead aged in glass bottle and oak barrels.

Food chemistry
Metabolomic analysis of volatile compounds in mead during aging in glass bottles and oak barrels revealed significant impacts on sensory characteristics. Using headspace solid-phase microextraction gas chromatography-mass spectrometry with three colu...

Sliding-window enhanced olfactory visual images combined with deep learning to predict TVB-N content in chilled mutton.

Meat science
A novel data enhancement method for olfactory visual images was proposed in this study, combined with deep learning to achieve the accurate prediction of total volatile basic nitrogen (TVB-N) content in chilled mutton. Specifically, the sliding-windo...

Machine learning-assisted aroma profile prediction in Jiang-flavor baijiu.

Food chemistry
The complex flavor of Jiang-flavor Baijiu (JFB) arises from the interaction of hundreds of compounds at both physicochemical and sensory levels, making accurate perception challenging. Modern machine learning techniques offer precise and scientific a...

Machine learning-enhanced flavoromics: Identifying key aroma compounds and predicting sensory quality in sauce-flavor baijiu.

Food chemistry
The quality of Sauce-flavor baijiu hinges on sensory characteristics and key aroma compounds, which traditional methods struggle to evaluate accurately and effectively. This study explores the sensory characteristics and aroma compounds of Sauce-flav...

Robust Odor Detection in Electronic Nose Using Transfer-Learning Powered Scentformer Model.

ACS sensors
Mimicking the olfactory system of humans, the use of electronic noses (E-noses) for the detection of odors in nature has become a hot research topic. This study presents a novel E-nose based on deep learning architecture called Scentformer, which add...

Discriminative Detection for Multiple Volatile Organic Compounds via Dynamic Temperature Modulation Based on Mixed Potential Gas Sensor.

ACS sensors
Gas sensors combined with artificial intelligence capable of distinguishing multiple odors hold great promise in volatile organic compounds (VOCs) discriminative detection. However, various issues such as large size, high expenses, and mutual interfe...

Intelligent Olfactory System Utilizing Ceria Nanoparticle-Integrated Laser-Induced Graphene.

ACS nano
The digitization of human senses has driven innovation across various technologies and transformed our daily lives, yet the digitization of olfaction remains a challenging frontier. Artificial olfactory systems, or electronic noses (e-noses), offer g...