AIMC Topic: Food Contamination

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A Support Vector Machine-Assisted Metabolomics Approach for Non-Targeted Screening of Multi-Class Pesticides and Veterinary Drugs in Maize.

Molecules (Basel, Switzerland)
The contamination risks of plant-derived foods due to the co-existence of pesticides and veterinary drugs (P&VDs) have not been fully understood. With an increasing number of unexpected P&VDs illegally added to foods, it is essential to develop a non...

The development of honey recognition models with broad applicability based on the association of isotope and elemental content with ANNs.

Food chemistry
Honey adulteration represents a worldwide problem, driven by the illicit economic gain that producers, traders, or merchants pursue. Among the falsification methods that can unfairly influence the price is the incorrect declaration of the botanical o...

Ensuring food safety by artificial intelligence-enhanced nanosensor arrays.

Advances in food and nutrition research
Current analytical methods utilized for food safety inspection requires improvement in terms of their cost-efficiency, speed of detection, and ease of use. Sensor array technology has emerged as a food safety assessment method that applies multiple c...

Development of automatic tuning for combined preprocessing and hyperparameters of machine learning and its application to NIR spectral data of coconut milk adulteration.

Food chemistry
This study proposed a novel approach to automatically select the preprocessing methods and hyperparameters of machine learning (ML) algorithms based on their best performance in cross-validation for near-infrared (NIR) spectroscopy data. The proposed...

Rapid identification and quantitative analysis of malachite green in fish via SERS and 1D convolutional neural network.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Rapid and quantitative detection of malachite green (MG) in aquaculture products is very important for safety assurance in food supply. Here, we develop a point-of-care testing (POCT) platform that combines a flexible and transparent surface-enhanced...

Machine learning powered CN-coordinated cobalt nanoparticles embedded cellulosic nanofibers to assess meat quality via clenbuterol monitoring.

Biosensors & bioelectronics
The World Anti-Doping Agency (WADA) has prohibited the use of clenbuterol (CLN) because it induces anabolic muscle growth while potentially causing adverse effects such as palpitations, anxiety, and muscle tremors. Thus, it is vital to assess meat qu...

Machine Learning Applied to the Detection of Mycotoxin in Food: A Systematic Review.

Toxins
Mycotoxins, toxic secondary metabolites produced by certain fungi, pose significant threats to global food safety and public health. These compounds can contaminate a variety of crops, leading to economic losses and health risks to both humans and an...

Near-field microwave sensing technology enhanced with machine learning for the non-destructive evaluation of packaged food and beverage products.

Scientific reports
In the food industry, the increasing use of automatic processes in the production line is contributing to the higher probability of finding contaminants inside food packages. Detecting these contaminants before sending the products to market has beco...

Tracing the origin and authenticity of Spanish PDO honey using metagenomics and machine learning.

International journal of food microbiology
The Protected Designation of Origin (PDO) indication for foods intends to guarantee the conditions of production and the geographical origin of regional products within the European Union. Honey products are widely consumed due to their health-promot...

Deep learning assisted logic gates for real-time identification of natural tetracycline antibiotics.

Food chemistry
The overuse and misuse of tetracycline (TCs) antibiotics, including tetracycline (TTC), oxytetracycline (OTC), doxycycline (DC), and chlortetracycline (CTC), pose a serious threat to human health. However, current rapid sensing platforms for tetracyc...