AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Food Contamination

Showing 21 to 30 of 147 articles

Clear Filters

[Migration of Metals Contained in Laminated Films for Food Packaging].

Shokuhin eiseigaku zasshi. Journal of the Food Hygienic Society of Japan
Multilayer laminated films are widely used as food packaging materials. The substances contained in these films have the potential to migrate into food in contact, but the actual situation is unknown. In this study, we first determined the contents o...

Comparative performance of artificial neural networks and support vector Machines in detecting adulteration of apple juice concentrate using spectroscopy and time domain NMR.

Food research international (Ottawa, Ont.)
The detection of adulteration in apple juice concentrate is critical for ensuring product authenticity and consumer safety. This study evaluates the effectiveness of artificial neural networks (ANN) and support vector machines (SVM) in analyzing spec...

Impurity detection of premium green tea based on improved lightweight deep learning model.

Food research international (Ottawa, Ont.)
Tea may be mixed with impurities during picking and processing, which can lower their quality. At present, the sorting of impurities in premium green tea mainly relies on manual labor, which is inefficient. In response to the technical challenges in ...

Rapid identification of horse oil adulteration based on deep learning infrared spectroscopy detection method.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
As a natural oil, horse oil has unique biological activity ingredients and therapeutic characteristics, which has important application value and market potential in healthcare, food, skin care and other fields. However, fraud is rampant in the horse...

Rapid detection of microplastics in chicken feed based on near infrared spectroscopy and machine learning algorithm.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
The main objective of this study was to evaluate the potential of near infrared (NIR) spectroscopy and machine learning in detecting microplastics (MPs) in chicken feed. The application of machine learning techniques in building optimal classificatio...

Machine Learning for Predicting Zearalenone Contamination Levels in Pet Food.

Toxins
Zearalenone (ZEN) has been detected in both pet food ingredients and final products, causing acute toxicity and chronic health problems in pets. Therefore, the early detection of mycotoxin contamination in pet food is crucial for ensuring the safety ...

Deep Learning-Assisted Fluorescence Single-Particle Detection of Fumonisin B Powered by Entropy-Driven Catalysis and Argonaute.

Analytical chemistry
Timely and accurate detection of trace mycotoxins in agricultural products and food is significant for ensuring food safety and public health. Herein, a deep learning-assisted and entropy-driven catalysis (EDC)-Argonaute powered fluorescence single-p...

Enhancing beer authentication, quality, and control assessment using non-invasive spectroscopy through bottle and machine learning modeling.

Journal of food science
Fraud in alcoholic beverages through counterfeiting and adulteration is rising, significantly impacting companies economically. This study aimed to develop a method using near-infrared (NIR) spectroscopy (1596-2396 nm) through the bottle, along with ...

Prediction of Deoxynivalenol contamination in wheat kernels and flour based on visible near-infrared spectroscopy, feature selection and machine learning modelling.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Contamination of wheat by the mycotoxin Deoxynivalenol (DON), produced by Fusarium fungi, poses significant challenges to the quality of crop yield and food safety. Visible and near-infrared (vis-NIR) spectroscopy has emerged as a promising, non-dest...

Initializing a Public Repository for Hosting Benchmark Datasets to Facilitate Machine Learning Model Development in Food Safety.

Journal of food protection
While there is clear potential for artificial intelligence (AI) and machine learning (ML) models to help improve food safety, the development and deployment of these models in the food safety domain are by and large lacking. The absence of publicly a...