Wunderlich P, Pauli D, Neumaier M, Wisser S, Danneel HJ, Lohweg V, Dörksen H. Enhancing Shelf Life Prediction of Fresh Pizza with Regression Models and Low Cost Sensors.
Foods 2023;
12:foods12061347. [PMID:
36981272 PMCID:
PMC10048631 DOI:
10.3390/foods12061347]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 03/15/2023] [Accepted: 03/20/2023] [Indexed: 03/30/2023] Open
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.
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