AIMC Topic: Waxes

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Predicting oleogels properties using non-invasive spectroscopic techniques and machine learning.

Food research international (Ottawa, Ont.) Feb 26, 2025
Oleogelators are considered food additives that require approval from regulatory authorities. Therefore, classifying these ingredients that may have characteristics (e.g., waxiness), cost and origin (e.g., animal or vegetable) is crucial to ensure co...
Machine Learning Spectroscopy, Near-Infrared Organic Chemicals Spectrum Analysis, Raman Cellulose Principal Component Analysis Least-Squares Analysis Fatty Acids, Nonesterified Food Additives Soybean Oil Waxes Sunflower Oil
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How Machine Learning Methods Helped Find Putative Rye Wax Genes Among GBS Data.

International journal of molecular sciences Oct 12, 2020
The standard approach to genetic mapping was supplemented by machine learning (ML) to establish the location of the rye gene associated with epicuticular wax formation (glaucous phenotype). Over 180 plants of the biparental F population were genotype...
Machine Learning Phenotype Biomarkers Genotype Genetic Markers Secale Waxes Quantitative Trait Loci Genes, Plant Chromosome Mapping Genetics, Population
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Bone Wax Is Effective in Reducing Blood Loss After Total Knee Arthroplasty.

The Journal of arthroplasty Dec 24, 2016
BACKGROUND: Total knee arthroplasty (TKA) is associated with major blood loss and blood transfusion is often required. This study aimed to evaluate the efficacy of bone wax in reducing blood loss and transfusion rates after TKA.
Humans Prospective Studies Female Aged Aged, 80 and over Male Middle Aged Treatment Outcome Body Mass Index Blood Loss, Surgical Blood Transfusion Anticoagulants Postoperative Hemorrhage Hemoglobins Arthroplasty, Replacement, Knee Bone Cements Palmitates Electrocoagulation Hemostasis Tourniquets Waxes
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