AIMC Topic: Volatile Organic Compounds

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Characterization and exploration of dynamic variation of volatile compounds in vine tea during processing by GC-IMS and HS-SPME/GC-MS combined with machine learning algorithm.

Food chemistry
It is imperative to unravel the dynamic variation of volatile components of vine tea during processing to provide guidance for tea quality evaluation. In this study, the dynamic changes of volatile compounds of vine tea during processing were charact...

Pioneering noninvasive colorectal cancer detection with an AI-enhanced breath volatilomics platform.

Theranostics
The sensitivity and specificity of current breath biomarkers are often inadequate for effective cancer screening, particularly in colorectal cancer (CRC). While a few exhaled biomarkers in CRC exhibit high specificity, they lack the requisite sensit...

IoT-based monitoring system and air quality prediction using machine learning for a healthy environment in Cameroon.

Environmental monitoring and assessment
This paper is aimed at developing an air quality monitoring system using machine learning (ML), Internet of Things (IoT), and other elements to predict the level of particulate matter and gases in the air based on the air quality index (AQI). It is a...

Coastal ozone dynamics and formation regime in Eastern China: Integrating trend decomposition and machine learning techniques.

Journal of environmental sciences (China)
Machine-learning is a robust technique for understanding pollution characteristics of surface ozone, which are at high levels in urban China. This study introduced an innovative approach combining trend decomposition with Random Forest algorithm to i...

Portable Mass Spectrometry for On-site Detection of Hazardous Volatile Organic Compounds via Robotic Extractive Sampling.

Analytical chemistry
Various hazardous volatile organic compounds (VOCs) are frequently released into environments during accidental events that cause many hazards to ecosystems and humans. Therefore, rapid, sensitive, and on-site detection of hazardous VOCs is crucial t...

Graphene and metal-organic framework hybrids for high-performance sensors for lung cancer biomarker detection supported by machine learning augmentation.

Nanoscale
Conventional diagnostic methods for lung cancer, based on breath analysis using gas chromatography and mass spectrometry, have limitations for fast screening due to their limited availability, operational complexity, and high cost. As potential repla...

Data-driven, explainable machine learning model for predicting volatile organic compounds' standard vaporization enthalpy.

Chemosphere
The accurate prediction of standard vaporization enthalpy (ΔH°) for volatile organic compounds (VOCs) is of paramount importance in environmental chemistry, industrial applications and regulatory compliance. To overcome traditional experimental metho...

Online sequential analysis of volatile and semivolatile organic compounds in water matrices by double robotic sample preparations and dual-channel mono and comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry system.

Journal of chromatography. A
The monitoring of organic compounds in aquatic matrices poses challenges due to its complexity and time-intensive nature. To address these challenges, we introduce a novel approach utilizing a dual-channel mono (D) and comprehensive two-dimensional (...

The Classification of VOCs Based on Sensor Images Using a Lightweight Neural Network for Lung Cancer Diagnosis.

Sensors (Basel, Switzerland)
The application of artificial intelligence to point-of-care testing (POCT) disease detection has become a hot research field, in which breath detection, which detects the patient's exhaled VOCs, combined with sensor arrays of convolutional neural net...

Application of a generalized hybrid machine learning model for the prediction of HS and VOCs removal in a compact trickle bed bioreactor (CTBB).

Chemosphere
This study presents a generalized hybrid model for predicting HS and VOCs removal efficiency using a machine learning model: K-NN (K - nearest neighbors) and RF (random forest). The approach adopted in this study enabled the (i) identification of odo...