Please use this identifier to cite or link to this item: http://hdl.handle.net/2067/45930
Title: Chestnut cultivar identification through the data fusion of sensory quality and ft-nir spectral data
Authors: Corona, Piermaria 
Frangipane, Maria Teresa 
Moscetti, Roberto 
Feudo, Gabriella Lo
Castellotti, Tatiana
Massantini, Riccardo 
Journal: FOODS 
Issue Date: 2021
Abstract: 
The world production of chestnuts has significantly grown in recent decades. Consumer attitudes, increasingly turned towards healthy foods, show a greater interest in chestnuts due to their health benefits. Consequently, it is important to develop reliable methods for the selection of high-quality products, both from a qualitative and sensory point of view. In this study, Castanea spp. fruits from Italy, namely Sweet chestnut cultivar and the Marrone cultivar, were evaluated by an official panel, and the responses for sensory attributes were used to verify the correlation to the near-infrared spectra. Data fusion strategies have been applied to take advantage of the synergistic effect of the information obtained from NIR and sensory analysis. Large nuts, easy pellicle removal, chestnut aroma, and aromatic intensity render Marrone cv fruits suitable for both the fresh market and candying, i.e., marron glacé. Whereas, sweet chestnut samples, due to their characteristics, have the potential to be used for secondary food products, such as jam, mash chestnut, and flour. The research lays the foundations for a superior data fusion approach for chestnut identification in terms of classification sensitivity and specificity, in which sensory and spectral approaches compensate each other’s drawbacks, synergistically contributing to an excellent result.
URI: http://hdl.handle.net/2067/45930
ISSN: 2304-8158
DOI: 10.3390/foods10112575
Rights: CC0 1.0 Universal
Appears in Collections:A1. Articolo in rivista

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