AIMC Topic: Volatile Organic Compounds

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Automatic and precise identification of volatile organic compounds from gas chromatography in prolonged atmospheric monitoring.

Journal of chromatography. A
Long-term continuous monitoring of volatile organic compounds (VOCs) is pivotal for climate change research, air quality assessment, pollution source identification, and public health early warning systems. Prolonged VOC monitoring is routinely imple...

Characterize and explore the dynamic changes in the volatility profiles of sauce-flavor baijiu during different rounds by GC-IMS, GC-MS and GC×GC-MS combined with machine learning.

Food research international (Ottawa, Ont.)
The production process of sauce-flavor baijiu (SFB) involves seven distillations, yielding base baijiu of 7 rounds (RSFB), which are then blended to form the final product. Therefore, the quality of the base baijiu is closely related to the quality o...

Using machine learning to unravel chemical and meteorological effects on ground-level ozone: Insights for ozone-climate control strategies.

Environment international
In the context of climate change, various countries/regions across East Asia have witnessed severe ground-level ozone (O) pollution, which poses potential health risks to the public. The complex relationships between O and its drivers, including the ...

Volatomics for Diagnosis and Risk Stratification of MASLD: A Proof-Of-Concept Study.

Alimentary pharmacology & therapeutics
BACKGROUND AND AIMS: Human breath contains numerous volatile organic compounds (VOCs) produced by physiological and metabolic processes or perturbed in pathological states. Electronic nose (eNose) technology has been extensively validated as a non-in...

An efficient method for chili pepper variety classification and origin tracing based on an electronic nose and deep learning.

Food chemistry
The quality of chili peppers is closely related to their variety and geographical origin. The market often substitutes high-quality chili peppers with inferior ones, and cross-contamination occurs during processing. The existing methods cannot quickl...

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...

Unveiling the systemic impact of airborne microplastics: Integrating breathomics and machine learning with dual-tissue transcriptomics.

Journal of hazardous materials
Airborne microplastics (MPs) pose significant respiratory and systemic health risks upon inhalation; however, current assessment methods remain inadequate. This study integrates breathomics and transcriptomics to establish a non-invasive approach for...

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...

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...