| Title: | Enhancing Shelf Life Prediction of Fresh Pizza with Regression Models and Low Cost Sensors |
| Author(s): | Wunderlich P; Pauli D; Neumaier M; Wisser S; Danneel HJ; Lohweg V; Dorksen H; |
| Address: | "inIT-Institute Industrial IT, OWL University of Applied Sciences and Arts, 32657 Lemgo, Germany. Institute for Life Science Technologies (ILT.NRW), OWL University of Applied Sciences and Arts, 32657 Lemgo, Germany" |
| ISSN/ISBN: | 2304-8158 (Print) 2304-8158 (Electronic) 2304-8158 (Linking) |
| Abstract: | "The waste of food presents a challenge for achieving a sustainable world. In Germany alone, over 10 million tonnes of 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" |
| Keywords: | food waste low-cost sensors machine learning regression models spoilage prediction sustainability; |
| Notes: | "PubMed-not-MEDLINEWunderlich, Paul Pauli, Daniel Neumaier, Michael Wisser, Stephanie Danneel, Hans-Jurgen Lohweg, Volker Dorksen, Helene eng 13FH3I03IA/German Federal Ministry of Education and Research (BMBF)/ Switzerland 2023/03/30 Foods. 2023 Mar 22; 12(6):1347. doi: 10.3390/foods12061347" |
