Please use this identifier to cite or link to this item: http://hdl.handle.net/2067/46970
Title: Pine nut species recognition using NIR spectroscopy and image analysis
Authors: Moscetti, Roberto 
Berhe, Daniel Hagos
Agrimi, Mariagrazia 
Haff, Ron P.
Liang, Peishih
Ferri, Serena
Monarca, Danilo 
Massantini, Riccardo 
Journal: JOURNAL OF FOOD ENGINEERING 
Issue Date: 2021
Abstract: 
NIR spectroscopy and physical properties derived from image analysis were evaluated as potential features for the classification of seed kernels from two pine nut species (P. pinea L. and P. sibirica Du Tour) using Partial Least Squares Discriminant Analysis (PLS-DA). Model performances were evaluated in terms of specificity, sensitivity and accuracy. Data pre-treatments were essential for achieving excellent performances (accuracy rate > 95%) in all tests. The interval PLS-DA highlighted that the most important features for (1) the NIR method were the absorption bands at 1640–1658, 1720–1738 and 1880–1998 nm, while for (2) the image analysis were kernel eccentricity, kernel major axis length, kernel lightness (L*) and kernel perimeter. The results demonstrate potential of both techniques for discriminating the two pine nut species.
URI: http://hdl.handle.net/2067/46970
ISSN: 0260-8774
DOI: 10.1016/j.jfoodeng.2020.110357
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections:A1. Articolo in rivista

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