Nondestructive testing is critical in the quality detection of products. Among several techniques, time-domain nuclear magnetic resonance (TD-NMR) is gaining attention in the field of food quality control, thanks to its ability to detect liquid and solid materials, which are strictly related to the quality of some kinds of products. In this article, a novel method for the inline quality evaluation of the inshell hazelnuts, based on TD-NMR analysis, is disclosed. Different studies have been carried out on the quality control of hazelnuts or, more in general, shell fruit. They usually focus on laboratory application and the analysis of a single physical property. Conversely, the proposed method focuses on the signal processing with the aim of reducing the execution time making the procedure suitable for an inline application. Moreover, the main hidden defects are analyzed together to identify the defective nuts from the good ones in a two-class classification procedure.

Quality Assessment of the Inshell Hazelnuts Based on TD-NMR Analysis

Di Caro D.;Liguori C.;Pietrosanto A.;Sommella P.
2020-01-01

Abstract

Nondestructive testing is critical in the quality detection of products. Among several techniques, time-domain nuclear magnetic resonance (TD-NMR) is gaining attention in the field of food quality control, thanks to its ability to detect liquid and solid materials, which are strictly related to the quality of some kinds of products. In this article, a novel method for the inline quality evaluation of the inshell hazelnuts, based on TD-NMR analysis, is disclosed. Different studies have been carried out on the quality control of hazelnuts or, more in general, shell fruit. They usually focus on laboratory application and the analysis of a single physical property. Conversely, the proposed method focuses on the signal processing with the aim of reducing the execution time making the procedure suitable for an inline application. Moreover, the main hidden defects are analyzed together to identify the defective nuts from the good ones in a two-class classification procedure.
2020
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4746582
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