Application of machine learning to the monitoring and prediction of food safety: A review.

Machine learning (ML) has proven to be a useful technology for data analysis and modeling in a wide variety of domains, including food science and engineering. The use of ML models for the monitoring and prediction of food safety is growing in recent years. Currently, several studies have reviewed ML applications on foodborne disease and deep learning applications on food.

The role of access to finance from different finance providers in production risks of horticulture in Indonesia.

This paper aims to investigate the association between perceived production risk of horticultural farms and access to finance from different finance providers. The collection of data was conducted among 434 farmers in Indonesia particularly farmers produced mango, mangosteen, chili and red onion. Production risk was measured by the coefficient of variation, skewness, kurtosis and the 25th percentile.

Measuring the impact of COVID-19 on stock prices and profits in the food supply chain.

The coronavirus disease 2019 pandemic has shocked financial and commodity markets around the world. We are analyzing stock prices and information from financial reports to examine the impact of the pandemic on stock price volatility and profits of companies in the food supply chain. We use a data set of 71 major listed companies in the food value chain from stock indices in the US, Japan, and Europe.

Optimization of Sampling for Monitoring Chemicals in the Food Supply Chain Using a Risk-Based Approach: The Case of Aflatoxins and Dioxins in the Dutch Dairy Chain.

Food safety monitoring faces the challenge of tackling multiple chemicals along the various stages of the food supply chain. Our study developed a methodology for optimizing sampling for monitoring multiple chemicals along the dairy supply chain. We used a mixed integer nonlinear programming approach to maximize the performance of the sampling in terms of reducing the risk of the potential disability adjusted life years (DALYs) in the population.

Factors underlying Dutch farmers' intentions to adapt their agronomic management to reduce Fusarium species infection in wheat.

Infection of wheat by Fusarium species can lead to Fusarium Head Blight (FHB) and mycotoxin contamination, thereby reducing food quality and food safety, and leading to economic losses. Agronomic management through the implementation of various pre-harvest measures can reduce the probability of Fusarium spp. infection in the wheat field.