Food Processing


Restaurant Reviews as Foodborne Illness Indicators

@machinelearnbot

Using 106 restaurants as a sample size, these results show that restaurants with higher restaurant grades (administered by the NYC DOH) tend to have higher restaurant ratings. Using the same sample size of 106 restaurants, these results show that restaurants with a no words related to foodborne illnesses in their comments tend to have higher average restaurant ratings as compared to restaurants that have 1 or more words related to foodborne illnesses in their comments. Using 106 restaurants as a sample size, these results show that restaurants with higher restaurant grades (administered by the NYC DOH) tend to have higher restaurant ratings. Using the same sample size of 106 restaurants, these results show that restaurants with a no words related to foodborne illnesses in their comments tend to have higher average restaurant ratings as compared to restaurants that have 1 or more words related to foodborne illnesses in their comments.


The Roslin Institute (University of Edinburgh) - News

#artificialintelligence

Machine learning can predict strains of bacteria likely to cause food poisoning outbreaks, research has found. The study – which focused on harmful strains of E. coli bacteria – could help public health officials to target interventions and reduce risk to human health. The team trained the software on DNA sequences from strains isolated from cattle herds and human infections in the UK and the US. The study highlights the potential of machine learning approaches for identifying these strains early and prevent outbreaks of this infectious disease.