An intelligent neural network algorithm for uncertainty handling in sensor failure scenario of food quality assurance model

The quality of food is usually tested by sensing the product odor using e-nose technique.However, in a real-time testing environment, some of the employed sensors may fail tooperate, which imposes great uncertainty on the food quality assurance model. To handlethe uncertainty, a support vector machine (SVM) classifier algorithm is developed todeal with the failure sensor effect using a data imputation strategy. The proposed modelis evaluated experimentally by means of benchmark datasets, and validated in a real-time environment by programming an Arduino-UNO controller in the internet of things(IoT) environment.

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Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).