AIMC Topic: Food Analysis

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Combining deep learning and fluorescence imaging to automatically identify fecal contamination on meat carcasses.

Scientific reports Feb 14, 2022
Food safety and foodborne diseases are significant global public health concerns. Meat and poultry carcasses can be contaminated by pathogens like E. coli and salmonella, by contact with animal fecal matter and ingesta during slaughter and processing...
Feces Chickens Escherichia coli Animals Deep Learning Salmonella Abattoirs Food Safety Optical Imaging Food Contamination Food Analysis Meat
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A Cross-Sectional Reproducibility Study of a Standard Camera Sensor Using Artificial Intelligence to Assess Food Items: The FoodIntech Project.

Nutrients Jan 5, 2022
Having a system to measure food consumption is important to establish whether individual nutritional needs are being met in order to act quickly and to minimize the risk of undernutrition. Here, we tested a smartphone-based food consumption assessmen...
Humans Neural Networks, Computer Food Analysis Nutrition Assessment Image Processing, Computer-Assisted Artificial Intelligence Smartphone Reproducibility of Results Cross-Sectional Studies
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A machine learning-driven approach for prioritizing food contact chemicals of carcinogenic concern based on complementary in silico methods.

Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association Jan 1, 2022
Carcinogenicity is one of the most critical endpoints for the risk assessment of food contact chemicals (FCCs). However, the carcinogenicity of FCCs remains insufficiently investigated. To fill the data gap, the application of standard experimental m...
Machine Learning Food Contamination Computer Simulation Food Analysis Carcinogens
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An Exploratory Approach to Deriving Nutrition Information of Restaurant Food from Crowdsourced Food Images: Case of Hartford.

Nutrients Nov 18, 2021
Deep learning models can recognize the food item in an image and derive their nutrition information, including calories, macronutrients (carbohydrates, fats, and proteins), and micronutrients (vitamins and minerals). This technology has yet to be imp...
Nutritive Value Connecticut Photography Deep Learning Food Analysis Nutrients Census Tract Food Labeling Crowdsourcing Restaurants Humans
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An Innovative Machine Learning Approach to Predict the Dietary Fiber Content of Packaged Foods.

Nutrients Sep 14, 2021
Underconsumption of dietary fiber is prevalent worldwide and is associated with multiple adverse health conditions. Despite the importance of fiber, the labeling of fiber content on packaged foods and beverages is voluntary in most countries, making ...
Machine Learning Algorithms Diet Nutritional Status Energy Intake Nutrients Humans Feeding Behavior Australia Nutrition Policy Food Packaging Nutritive Value Beverages Food Analysis Automation Fast Foods Food Labeling Dietary Fiber
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Rapid Assessment of Quality Changes in French Fries during Deep-frying Based on FTIR Spectroscopy Combined with Artificial Neural Network.

Journal of oleo science Sep 8, 2021
Fourier transform infrared (FTIR) spectroscopy combined with backpropagation artificial neural network (BP-ANN) were utilized for rapid and simultaneous assessment of the lipid oxidation indices in French fries. The conventional indexes (i.e. total p...
Soybean Oil Food Quality Palm Oil Sunflower Oil Rapeseed Oil Neural Networks, Computer Solanum tuberosum Cooking Hot Temperature Spectroscopy, Fourier Transform Infrared Food Analysis Oxidation-Reduction
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Automating water quality analysis using ML and auto ML techniques.

Environmental research Jul 20, 2021
Generation of unprocessed effluents, municipal refuse, factory wastes, junking of compostable and non-compostable effluents has hugely contaminated nature-provided water bodies like rivers, lakes and ponds. Therefore, there is a necessity to look int...
Humans Food Analysis Artificial Intelligence Water Quality Machine Learning Algorithms
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Spectrofluorometric analysis combined with machine learning for geographical and varietal authentication, and prediction of phenolic compound concentrations in red wine.

Food chemistry May 18, 2021
Fluorescence spectroscopy is rapid, straightforward, selective, and sensitive, and can provide the molecular fingerprint of a sample based on the presence of various fluorophores. In conjunction with chemometrics, fluorescence techniques have been ap...
Food Analysis Australia Phenols Machine Learning Vitis Spectrometry, Fluorescence Chromatography, High Pressure Liquid Discriminant Analysis Wine
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Aflatoxin rapid detection based on hyperspectral with 1D-convolution neural network in the pixel level.

Food chemistry Apr 29, 2021
Aflatoxin is commonly exists in moldy foods, it is classified as a class one carcinogen by the World Health Organization. In this paper, we used one dimensional convolution neural network (1D-CNN) to classify whether a pixel contains aflatoxin. First...
Hyperspectral Imaging Humans Neural Networks, Computer Aflatoxins Image Processing, Computer-Assisted Food Contamination Food Analysis
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Deep Neural Networks for Image-Based Dietary Assessment.

Journal of visualized experiments : JoVE Mar 13, 2021
Due to the issues and costs associated with manual dietary assessment approaches, automated solutions are required to ease and speed up the work and increase its quality. Today, automated solutions are able to record a person's dietary intake in a mu...
Beverages Image Processing, Computer-Assisted Humans Smartphone Neural Networks, Computer Food Analysis Nutrition Assessment
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