4.7 Article

Chestnut Cultivar Identification through the Data Fusion of Sensory Quality and FT-NIR Spectral Data

Journal

FOODS
Volume 10, Issue 11, Pages -

Publisher

MDPI
DOI: 10.3390/foods10112575

Keywords

food quality assessment; non-destructive analysis; near-infrared spectrum; visible spectrum; sensory panel; Italy

Funding

  1. Departments of excellence 2018' program (i.e., 'Dipartimenti di eccellenza') of the Italian Ministry of Education, University and Research

Ask authors/readers for more resources

The world production of chestnuts has significantly grown in recent decades, as consumer attitudes towards healthy foods drive interest in chestnuts. This study evaluated Castanea spp. fruits from Italy and used data fusion strategies to enhance the identification efficiency of chestnut products. The research highlighted the potential of different chestnut cultivars for various food products and laid the foundations for a superior data fusion approach for chestnut identification.
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 glace. 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.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available