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

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 animals. Traditional lab analysis methods for mycotoxin detection can be time-consuming and may not always be suitable for large-scale screenings. However, in recent years, machine learning (ML) methods have gained popularity for use in the detection of mycotoxins and in the food safety industry in general due to their accurate and timely predictions. We provide a systematic review on some of the recent ML applications for detecting/predicting the presence of mycotoxin on a variety of food ingredients, highlighting their advantages, challenges, and potential for future advancements. We address the need for reproducibility and transparency in ML research through open access to data and code. An observation from our findings is the frequent lack of detailed reporting on hyperparameters in many studies and a lack of open source code, which raises concerns about the reproducibility and optimisation of the ML models used. The findings reveal that while the majority of studies predominantly utilised neural networks for mycotoxin detection, there was a notable diversity in the types of neural network architectures employed, with convolutional neural networks being the most popular.

Creating an intramuscular injection pad for the SimMan 3G.

This short report outlines the rationale, design and method of production for a thigh-mounted intramuscular (IM) pad in the thigh of a SimMan 3G manikin. The aim of this project was to create an IM injection site in the manikin's thigh to allow simulation participants to practise administering IM injections in a safe, supported environment. After creating a prototype from a plastic bottle, a module was designed to use with the SimMan 3G. A mould of SimMan's leg was created using plaster of Paris, and then a relief was added to this mould to create the shape required to hold the sponge. Once the mould was completed, glass reinforced plastic (GRP) was applied to create the final module. Using an electric rotary tool, a hole was cut in the SimMan's thigh to enable the module to be fitted. The final product was waterproof, lightweight and strong. It sits discretely beneath the SimMan 3G's leg skin enabling students to practise high-fidelity IM injections on the manikin's leg without faculty intervention. This module is a cost-effective solution for allowing participants to practise IM injections on a manikin during healthcare simulation.