AIMC Topic: Food

Clear Filters Showing 61 to 70 of 83 articles

Machine Learning Methods as a Tool for Predicting Risk of Illness Applying Next-Generation Sequencing Data.

Risk analysis : an official publication of the Society for Risk Analysis
Next-generation sequencing (NGS) data present an untapped potential to improve microbial risk assessment (MRA) through increased specificity and redefinition of the hazard. Most of the MRA models do not account for differences in survivability and vi...

Combining deep residual neural network features with supervised machine learning algorithms to classify diverse food image datasets.

Computers in biology and medicine
Obesity is increasing worldwide and can cause many chronic conditions such as type-2 diabetes, heart disease, sleep apnea, and some cancers. Monitoring dietary intake through food logging is a key method to maintain a healthy lifestyle to prevent and...

Detection of delta-9-tetrahydrocannabinol (THC) in oral fluid, blood and urine following oral consumption of low-content THC hemp oil.

Forensic science international
Hemp-derivative (Cannabis sativa L.) food products containing trace levels of Δ-9-tetrahydrocannabinol (THC) are proposed for consumption in Australia and New Zealand; however, it is unclear whether use of these products will negatively affect existi...

Exploring the Impact of Food on the Gut Ecosystem Based on the Combination of Machine Learning and Network Visualization.

Nutrients
Prebiotics and probiotics strongly impact the gut ecosystem by changing the composition and/or metabolism of the microbiota to improve the health of the host. However, the composition of the microbiota constantly changes due to the intake of daily di...

NutriNet: A Deep Learning Food and Drink Image Recognition System for Dietary Assessment.

Nutrients
Automatic food image recognition systems are alleviating the process of food-intake estimation and dietary assessment. However, due to the nature of food images, their recognition is a particularly challenging task, which is why traditional approache...

Artificial neural network based modeling to evaluate methane yield from biogas in a laboratory-scale anaerobic bioreactor.

Bioresource technology
The performance of a laboratory-scale anaerobic bioreactor was investigated in the present study to determine methane (CH4) content in biogas yield from digestion of organic fraction of municipal solid waste (OFMSW). OFMSW consists of food waste, veg...

Artificial neural network-based exploration of gene-nutrient interactions in folate and xenobiotic metabolic pathways that modulate susceptibility to breast cancer.

Gene
In the current study, an artificial neural network (ANN)-based breast cancer prediction model was developed from the data of folate and xenobiotic pathway genetic polymorphisms along with the nutritional and demographic variables to investigate how m...

FoodWiki: Ontology-Driven Mobile Safe Food Consumption System.

TheScientificWorldJournal
An ontology-driven safe food consumption mobile system is considered. Over 3,000 compounds are being added to processed food, with numerous effects on the food: to add color, stabilize, texturize, preserve, sweeten, thicken, add flavor, soften, emuls...

DietCam: Multiview Food Recognition Using a Multikernel SVM.

IEEE journal of biomedical and health informatics
Food recognition is a key component in evaluation of everyday food intakes, and its challenge is due to intraclass variation. In this paper, we present an automatic food classification method, DietCam, which specifically addresses the variation of fo...

Review of Advanced Technologies and Circular Pathways for Food Waste Valorization.

Journal of agricultural and food chemistry
Food waste (FW) valorization provides a sustainable solution to global waste challenges by enhancing resource efficiency and enabling a circular bioeconomy. This review highlights the need to shift from linear disposal to circular strategies and asse...