AIMC Topic: Cosmetics
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The Microorganism Detection System (SDM) for microbiological control of cosmetic products.
Annals of agricultural and environmental medicine : AAEM
Dec 16, 2021
The Microorganism Detection System (SDM) is a new solution using artificial intelligence, unique on the international scale, to correctly identify and count microorganisms, with particular emphasis on specificlisted microorganisms (Document of Standa...
Methodology for preparing a cosmetic sample for the development of Microorganism Detection System (SDM) software and artificial intelligence learning to recognize specific microbial species.
Annals of agricultural and environmental medicine : AAEM
Dec 16, 2021
INTRODUCTION AND OBJECTIVE: The article presents the methodology of preparing a cosmetic sample for analysi, and the creation of a dataset for teaching artificial intelligence to recognize specific species of microorganisms in cosmetic samples in ter...
Quantitative analysis of fragrance allergens in various matrixes of cosmetics by liquid-liquid extraction and GC-MS.
Journal of food and drug analysis
Dec 15, 2021
Fragrances are the most common chemicals in cosmetics to which people expose every day. However, the unwanted allergic reactions such as contact dermatitis caused by direct contact with fragrances may happen. In Directive 2003/15/EC of the EU, cosmet...
Plastic additives and personal care products in south China house dust and exposure in child-mother pairs.
Environmental pollution (Barking, Essex : 1987)
Dec 18, 2020
Indoor environment constitutes an important source of industrial additive chemicals to human exposure. We hypothesized that the influence of residential environment on human exposure varies among different types of additive chemicals and differs betw...
Prediction of treatment effect perception in cosmetics using machine learning.
Journal of biopharmaceutical statistics
Jul 25, 2020
Perception of treatment effect (TE) in cosmetics is multifaceted and influenced by multiple parameters that need to be considered simultaneously when evaluating TE. Here we provide a global approach to predicting TE perception using Random Forest (RF...
A New Machine-Learning Tool for Fast Estimation of Liquid Viscosity. Application to Cosmetic Oils.
Journal of chemical information and modeling
Apr 13, 2020
The viscosities of pure liquids are estimated at 25 °C, from their molecular structures, using three modeling approaches: group contributions, COSMO-RS σ-moment-based neural networks, and graph machines. The last two are machine-learning methods, whe...
Skin Doctor: Machine Learning Models for Skin Sensitization Prediction that Provide Estimates and Indicators of Prediction Reliability.
International journal of molecular sciences
Sep 28, 2019
The ability to predict the skin sensitization potential of small organic molecules is of high importance to the development and safe application of cosmetics, drugs and pesticides. One of the most widely accepted methods for predicting this hazard is...
A mode-of-action ontology model for safety evaluation of chemicals: Outcome of a series of workshops on repeated dose toxicity.
Toxicology in vitro : an international journal published in association with BIBRA
Apr 4, 2019
Repeated dose toxicity evaluation aims at assessing the occurrence of adverse effects following chronic or repeated exposure to chemicals. Non-animal approaches have gained importance in the last decades because of ethical considerations as well as d...
Use of a convolutional neural network for the classification of microbeads in urban wastewater.
Chemosphere
Oct 18, 2018
Scientists are on the lookout for a practical model that can serve as a standard for sorting out, identifying, and characterizing microplastics which are common occurrences in water sources and wastewaters. The microbeads (MBs) used in cosmetics and ...
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