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Hazelnut Quality Sorting Using High Dynamic Range Short-Wave Infrared Hyperspectral Imaging

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Abstract

A rapid, robust, unbiased, and inexpensive discriminant method capable of classifying hazelnut (Corylus avellana, L.) in compliance with the statements set out by the Commission Regulation (EC) No. 1284/2002 is economically important to the fresh and processed industries. Thus, in this study, the feasibility of high dynamic range (HDR) hyperspectral imaging for hazelnut kernel sorting (cv. Tonda Gentile Romana) of four quality classes (Class Extra, Class I, Class II, and Waste) has been investigated. Two different exposure times (5 and 8 ms) were selected for experiments, and the respective spectra were combined to obtain a HDR over the full spectral range. The illumination setup was optimized to improve the intensity and uniformity of the light along the field of view of the camera. PLS-DA was used to classify the kernels based on their spectra and the spectral pretreatment was optimized through an iterative routine. The performance of each PLS-DA model was defined based on its accuracy, sensitivity, and selectivity rates. All of the selected models provided a very good (>90 %) or good (>80 %) sensitivity and selectivity for the predefined classes. Misclassified kernels were primarily assigned to the low-quality classes (i.e., Class II and Waste). Moreover, the spatial domain was used to evaluate the feasibility of distinguishing hazelnut classes on the basis of their size and shape. It was found that hazelnut dimensions can be used to improve the accuracy of the classification of the kernels. Thanks to this combination of both spectral and spatial information spectral imaging could be used for quality sorting of hazelnuts.

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Acknowledgments

This research has been financially supported by Mipaaf through the project “Miglioramento della filiera corilicola laziale - Mi.F.CO.L.” represented by the “AOP Nocciola Italia Soc. Cons. s.r.l” and “CeFAS - Azienda speciale CCIAA Viterbo”. The authors gratefully acknowledge I.W.T.-Flanders for the financial support through the Chameleon project (IWT 100021).

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Authors and Affiliations

  1. Department of Science and Technology for Agriculture, Forest, Nature and Energy, Tuscia University, Via S. Camillo de Lellis snc, 01100, Viterbo, Italy

    Roberto Moscetti, Massimo Cecchini & Monarca Danilo

  2. KU Leuven Department of Biosystems, MeBioS, Kasteelpark Arenberg 30, 3001, Leuven, Belgium

    Wouter Saeys, Janos C. Keresztes & Mohammad Goodarzi

  3. Department for Innovation in Biological, Agro-Food and Forest System, Tuscia University, Via S. Camillo de Lellis snc, 01100, Viterbo, Italy

    Riccardo Massantini

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  1. Roberto Moscetti
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  2. Wouter Saeys
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  3. Janos C. Keresztes
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  4. Mohammad Goodarzi
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  5. Massimo Cecchini
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  6. Monarca Danilo
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  7. Riccardo Massantini
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Correspondence to Riccardo Massantini.

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Moscetti, R., Saeys, W., Keresztes, J.C. et al. Hazelnut Quality Sorting Using High Dynamic Range Short-Wave Infrared Hyperspectral Imaging. Food Bioprocess Technol 8, 1593–1604 (2015). https://doi.org/10.1007/s11947-015-1503-2

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  • DOI: https://doi.org/10.1007/s11947-015-1503-2

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