AIMC Topic: Phenols
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Unveiling the role of artificial intelligence in tetracycline antibiotics removal using UV/sulfite/phenol advanced reduction process.
Journal of environmental management
Sep 14, 2024
UV/sulfite-based advanced reduction processes (ARP) have attracted increasing attention due to their high capability for removing a wide range of pollutants. Therefore, developing UV/sulfite ARP systems with assisted Artificial Intelligence (AI) mode...
Artificial intelligence-based data extraction for next generation risk assessment: Is fine-tuning of a large language model worth the effort?
Toxicology
Aug 23, 2024
To underpin scientific evaluations of chemical risks, agencies such as the European Food Safety Authority (EFSA) heavily rely on the outcome of systematic reviews, which currently require extensive manual effort. One specific challenge constitutes th...
Machine learning approach for high-throughput phenolic antioxidant screening in black Rice germplasm collection based on surface FTIR.
Food chemistry
Aug 2, 2024
Pigmented rice contains beneficial phenolic antioxidants but analysing them across germplasm collections is laborious and time-consuming. Here we utilised rapid surface Fourier transform infrared (FTIR) spectroscopy and machine learning algorithms (M...
Effects of environmental phenols on eGFR: machine learning modeling methods applied to cross-sectional studies.
Frontiers in public health
Aug 1, 2024
PURPOSE: Limited investigation is available on the correlation between environmental phenols' exposure and estimated glomerular filtration rate (eGFR). Our target is established a robust and explainable machine learning (ML) model that associates env...
Prediction of retention data of phenolic compounds by quantitative structure retention relationship models under reverse-phase liquid chromatography.
Journal of chromatography. A
Jul 9, 2024
Quantitative Structure-Retention Relationship models were developed to identify phenolic compounds using a typical LC- system, with both UV and MS detection. A new chromatographic method was developed for the separation of fifty-two standard phenolic...
Prediction of micropollutant degradation kinetic constant by ultrasonic using machine learning.
Chemosphere
Jun 24, 2024
A prediction model based on XGBoost is proposed for ultrasonic degradation of micropollutants' kinetic constants. After parameter optimization through iteration, the model achieves Evaluation metrics with R and SMAPE reaching 0.99 and 2.06%, respecti...
Effects on quality characteristics of ultrasound-treated gilaburu juice using RSM and ANFIS modeling with machine learning algorithm.
Ultrasonics sonochemistry
May 22, 2024
Gilaburu (Viburnum opulus L.) is a red-colored fruit with a sour taste that grows in Anatolia. It is rich in various antioxidant and bioactive compounds. In this study, bioactive compounds and ultrasound parameters of ultrasound-treated gilaburu wate...
Hepatic toxicity prediction of bisphenol analogs by machine learning strategy.
The Science of the total environment
May 21, 2024
Toxicological studies have demonstrated the hepatic toxicity of several bisphenol analogs (BPs), a prevalent type of endocrine disruptor. The development of Adverse Outcome Pathway (AOP) has substantially contributed to the rapid risk assessment for ...
Machine learning assists prediction of genes responsible for plant specialized metabolite biosynthesis by integrating multi-omics data.
BMC genomics
Apr 29, 2024
BACKGROUND: Plant specialized (or secondary) metabolites (PSM), also known as phytochemicals, natural products, or plant constituents, play essential roles in interactions between plants and environment. Although many research efforts have focused on...
An artificial intelligence-based model for predicting reproductive toxicity of bisphenol analogues mixtures to the rotifer Brachionus calyciflorus.
The Science of the total environment
Apr 16, 2024
The joint toxicity effects of mixtures, particularly reproductive toxicity, one of the main causes of aquatic ecosystem degradation, are often overlooked as it is impractical to test all mixtures. This study developed and evaluated the following mode...
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