| Autor: |
Paul Wunderlich, Daniel Pauli, Michael Neumaier, Stephanie Wisser, Hans-Jürgen Danneel, Volker Lohweg, Helene Dörksen |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Foods, Vol 12, Iss 6, p 1347 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2304-8158 |
| DOI: |
10.3390/foods12061347 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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