AIMC Topic: Minerals
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Micronutrient supplementation influences the composition and diet-originating function of the gut microbiome in healthy adults.
Clinical nutrition (Edinburgh, Scotland)
Jun 25, 2025
BACKGROUND & AIMS: Studies in-vitro and in animals propose that vitamins and minerals can alter the human gut microbiome. Human trials replicating these findings are scarce or used micronutrient supplementation in supraphysiological doses. We explore...
Transfer learning from inorganic materials to ivory detection.
Scientific reports
May 3, 2025
This paper describes the automatic identification of ivory using Raman spectroscopy data and deep neural network (DNN) models pre-trained on open-source data from inorganic minerals. The proposed approach uses transfer learning (TL) from foundation m...
Sustainable separation of molybdenum from mixed mineral acids generated as semiconductor industry waste streams using tributyl phosphate (TBP) by effects of hybrid machine learning models.
Journal of environmental management
Oct 16, 2024
This study explores the separation and optimization of molybdenum (Mo) from mixed mineral acids derived from semiconductor industry waste streams with tributyl phosphate (TBP) by implementing machine learning (ML) models. Considerable experimental te...
Preventive machine learning models incorporating health checkup data and hair mineral analysis for low bone mass identification.
Scientific reports
Aug 13, 2024
Machine learning (ML) models have been increasingly employed to predict osteoporosis. However, the incorporation of hair minerals into ML models remains unexplored. This study aimed to develop ML models for predicting low bone mass (LBM) using health...
Infrared spectroscopy coupled with machine learning algorithms for predicting the detailed milk mineral profile in dairy cattle.
Food chemistry
Aug 8, 2024
Milk minerals are not only essential components for human health, but they can be informative for milk quality and cow's health. Herein, we investigated the feasibility of Fourier Transformed mid Infrared (FTIR) spectroscopy for the prediction of a d...
Mathematical optimization of multilinear and artificial neural network regressions for mineral composition of different tea types infusions.
Scientific reports
Aug 7, 2024
The objective of this study was to investigate the change in mineral composition depending on tea variety, tea concentration, and steeping time. Four different tea varieties, black Ceylon (BC), black Turkish (BT), green Ceylon (GC), and green Turkish...
An eco-friendly approach for analysing sugars, minerals, and colour in brown sugar using digital image processing and machine learning.
Food research international (Ottawa, Ont.)
Jun 28, 2024
Brown sugar is a natural sweetener obtained by thermal processing, with interesting nutritional characteristics. However, it has significant sensory variability, which directly affects product quality and consumer choice. Therefore, developing rapid ...
Relationships between minerals' intake and blood homocysteine levels based on three machine learning methods: a large cross-sectional study.
Nutrition & diabetes
Jun 1, 2024
BACKGROUND: Blood homocysteine (Hcy) level has become a sensitive indicator in predicting the development of cardiovascular disease. Studies have shown an association between individual mineral intake and blood Hcy levels. The effect of mixed mineral...
Prediction of Cr(VI) and As(V) adsorption on goethite using hybrid surface complexation-machine learning model.
Water research
Apr 6, 2024
This study aimed to develop surface complexation modeling-machine learning (SCM-ML) hybrid model for chromate and arsenate adsorption on goethite. The feasibility of two SCM-ML hybrid modeling approaches was investigated. Firstly, we attempted to uti...
Identification and prediction of association patterns between nutrient intake and anemia using machine learning techniques: results from a cross-sectional study with university female students from Palestine.
European journal of nutrition
Mar 21, 2024
PURPOSE: This study utilized data mining and machine learning (ML) techniques to identify new patterns and classifications of the associations between nutrient intake and anemia among university students.
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