AIMC Topic: Volatile Organic Compounds

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Identification of a panel of volatile organic compounds in urine for early detection of for bladder cancer.

Scientific reports
The absence of specific markers makes early detection of bladder cancer (BC) challenging. Recent studies have reported the reliable diagnostic significance of volatile organic compounds (VOCs) in several cancers. This study aimed to investigate wheth...

Honeybee colony soundscapes: Decoding distance-based cues and environmental stressors.

Ecotoxicology and environmental safety
Honey bees play a crucial role in agricultural productivity and ecological stability, yet their interactions with environmental stressors, particularly volatile organic compounds (VOCs) and pollutants, pose significant challenges to their cognitive f...

Design of Multi-Cancer VOCs Profiling Platform via a Deep Learning-Assisted Sensing Library Screening Strategy.

Analytical chemistry
The efficiency of sensor arrays in parallel discrimination of multianalytes is fundamentally influenced by the quantity and performance of the sensor elements. The advent of combinational design has notably accelerated the generation of chemical libr...

Machine Learning-Enhanced Prediction for Soil-to-Air VOC Emission and Environmental Impact Pertaining Contaminated Fractured Aquifers.

Environmental science & technology
How to scientifically and efficiently quantify the impact and hazards of volatile organic compounds (VOCs) pollution and volatilization from complex groundwater systems on surface air environments is a critical environmental issue. This paper employe...

Food Freshness Prediction Platform Utilizing Deep Learning-Based Multimodal Sensor Fusion of Volatile Organic Compounds and Moisture Distribution.

ACS sensors
Various sensing methods have been developed for food spoilage research, but in practical applications, the accuracy of these methods is frequently constrained by the limitation of single-source data and challenges in cross-validating multimodal data....

Rapid, non-invasive breath analysis for enhancing detection of silicosis using mass spectrometry and interpretable machine learning.

Journal of breath research
Occupational lung diseases, such as silicosis, are a significant global health concern, especially with increasing exposure to engineered stone dust. Early detection of silicosis is helpful for preventing disease progression, but existing diagnostic ...

Nanomaterial Innovations and Machine Learning in Gas Sensing Technologies for Real-Time Health Diagnostics.

ACS sensors
Breath sensors represent a frontier in noninvasive diagnostics, leveraging the detection of volatile organic compounds (VOCs) in exhaled breath for real-time health monitoring. This review highlights recent advancements in breath-sensing technologies...

Machine Learning Predicts Non-Preferred and Preferred Vertebrate Hosts of Tsetse Flies (Glossina spp.) Based on Skin Volatile Emission Profiles.

Journal of chemical ecology
Tsetse fly vectors of African trypanosomosis preferentially feed on certain vertebrates largely determined by olfactory cues they emit. Previously, we established that three skin-derived ketones including 6-methyl-5-hepten-2-one, acetophenone and ger...

Endogenous storage proteins influence Rice flavor: Insights from protein-flavor correlations and predictive modeling.

Food chemistry
This study investigated the correlation between endogenous storage proteins and aromatic compounds in rice, and their collective influence on rice eating quality. Six rice samples, varying in four endogenous storage proteins through gene editing gene...

Considerations regarding the selection, sampling, extraction, analysis, and modelling of biomarkers in exhaled breath for early lung cancer screening.

Journal of pharmaceutical and biomedical analysis
Lung cancer (LC) is the deadliest cancer due to the lack of efficient screening methods that detect the disease early. This review, covering the years 2011 - 2025, summarizes state-of-the-art LC screening through analysis of volatile organic compound...