@misc{13015,
  abstract     = {{<jats:p>food are discarded annually, with a worldwide total exceeding 1.3 billion tonnes. A significant contributor to this issue are consumers throwing away still edible food due to the expiration of its best-before date. Best-before dates currently include large safety margins, but more precise and cost effective prediction techniques are required. To address this challenge, research was conducted on low-cost sensors and machine learning techniques were developed to predict the spoilage of fresh pizza. The findings indicate that combining a gas sensor, such as volatile organic compounds or carbon dioxide, with a random forest or extreme gradient boosting regressor can accurately predict the day of spoilage. This provides a more accurate and cost-efficient alternative to current best-before date determination methods, reducing food waste, saving resources, and improving food safety by reducing the risk of consumers consuming spoiled food.}},
  author       = {{Wunderlich, Paul and Pauli, Daniel and Neumaier, Michael and Wisser, Stephanie and Danneel, Hans-Jürgen and Lohweg, Volker and Dörksen, Helene}},
  booktitle    = {{Foods}},
  issn         = {{2304-8158}},
  keywords     = {{Plant Science, Health Professions (miscellaneous), Health (social science), Microbiology, Food Science}},
  number       = {{6}},
  publisher    = {{MDPI }},
  title        = {{{Enhancing Shelf Life Prediction of Fresh Pizza with Regression Models and Low Cost Sensors}}},
  doi          = {{10.3390/foods12061347}},
  volume       = {{12}},
  year         = {{2023}},
}

@inproceedings{6840,
  author       = {{Pauli, Daniel and Wisser, Stephanie}},
  location     = {{Online}},
  title        = {{{Datenanalyse und autonome Prognostik zur Verbesserung der  Transparenz und Sicherheit von Lebensmitteln}}},
  year         = {{2021}},
}