AIMC Topic: Volatile Organic Compounds

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

Chemosphere May 6, 2024
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...
Machine Learning Volatile Organic Compounds Thermodynamics Volatilization Algorithms Models, Chemical
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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 May 4, 2024
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 (...
Robotics Volatile Organic Compounds Limit of Detection Gas Chromatography-Mass Spectrometry Water Pollutants, Chemical Liquid-Liquid Extraction
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The Classification of VOCs Based on Sensor Images Using a Lightweight Neural Network for Lung Cancer Diagnosis.

Sensors (Basel, Switzerland) Apr 28, 2024
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...
Volatile Organic Compounds Breath Tests Neural Networks, Computer Algorithms Ethanol Acetone Lung Neoplasms Humans
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Application of a generalized hybrid machine learning model for the prediction of HS and VOCs removal in a compact trickle bed bioreactor (CTBB).

Chemosphere Apr 27, 2024
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...
Machine Learning Waste Disposal, Fluid Odorants Volatile Organic Compounds Hydrogen Sulfide Air Pollutants Bioreactors
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VOC data-driven evaluation of vehicle cabin odor: from ANN to CNN-BiLSTM.

Environmental science and pollution research international Apr 26, 2024
Emissions of volatile organic compounds (VOCs) in vehicles represent a significant problem, causing unpleasant odors. To mitigate VOCs and odors in vehicles, it is critical to choose interior parts with low odor and VOC emissions. However, prevailing...
Neural Networks, Computer Gas Chromatography-Mass Spectrometry Support Vector Machine Odorants Volatile Organic Compounds Vehicle Emissions
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Rapid and non-destructive microbial quality prediction of fresh pork stored under modified atmospheres by using selected-ion flow-tube mass spectrometry and machine learning.

Meat science Mar 31, 2024
Volatile organic compounds (VOCs) indicative of pork microbial spoilage can be quantified rapidly at trace levels using selected-ion flow-tube mass spectrometry (SIFT-MS). Packaging atmosphere is one of the factors influencing VOC production patterns...
Machine Learning Animals Neural Networks, Computer Oxygen Volatile Organic Compounds Pork Meat Food Storage Mass Spectrometry Food Microbiology Swine Food Packaging
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Multichannel Hierarchical Analysis of Time-Resolved Hyperspectral Data for Advanced Colorimetric E-Nose.

ACS sensors Mar 28, 2024
The colorimetric sensor-based electronic nose has been demonstrated to discriminate specific gaseous molecules for various applications, including health or environmental monitoring. However, conventional colorimetric sensor systems rely on RGB senso...
Electronic Nose Colorimetry Volatile Organic Compounds Neural Networks, Computer
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Rapid classification of coffee origin by combining mass spectrometry analysis of coffee aroma with deep learning.

Food chemistry Feb 22, 2024
Mislabeling the geographical origin of coffee is a prevalent form of fraud. In this study, a rapid, nondestructive, and high-throughput method combining mass spectrometry (MS) analysis and intelligence algorithms to classify coffee origin was develop...
Volatile Organic Compounds Coffee Mass Spectrometry Odorants Algorithms Deep Learning
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Artificial Intelligence Sensing: Effective Flavor Blueprinting of Tea Infusions for a Quality Control Perspective.

Molecules (Basel, Switzerland) Jan 23, 2024
Tea infusions are the most consumed beverages in the world after water; their pleasant yet peculiar flavor profile drives consumer choice and acceptance and becomes a fundamental benchmark for the industry. Any qualification method capable of objecti...
Artificial Intelligence China Humans Quality Control Tea Volatile Organic Compounds Odorants Smell
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Characterization of lamb shashliks with different roasting methods by intelligent sensory technologies and GC-MS to simulate human muti-sensation: Based on multimodal deep learning.

Food chemistry Dec 24, 2023
To simulate the functions of olfaction, gustation, vision, and oral touch, intelligent sensory technologies have been developed. Headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC/MS) with electronic noses (E-noses...
Sheep Electronic Nose Deep Learning Humans Smell Animals Volatile Organic Compounds Gas Chromatography-Mass Spectrometry Solid Phase Microextraction
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