Autores UPV
Talens Oliag Pau,
Mora Soler Leticia,
N. Morsy,
F. Barbin Douglas,
G. ElMasry,
Sun Da-Wen
Abstract
This study was carried out to investigate the ability of hyperspectral imaging technique in the NIR spectral
region of 9001700 nm for the prediction of water and protein contents in Spanish cooked hams.
Multivariate analyses using partial least-squares regression (PLSR) and partial least squares-discriminant
analysis (PLS-DA) were applied to the spectral data extracted from the images to develop statistical models
for predicting chemical attributes and classify the different qualities. Feature-related wavelengths
were identified for protein (930, 971, 1051, 1137, 1165, 1212, 1295, 1400, 1645 and 1682 nm) and water
(930, 971, 1084, 1212, 1645 and 1682 nm) and used for regression models with fewer predictors. The
PLS-DA model using optimal wavelengths (966, 1061, 1148, 1256, 1373 and 1628 nm) successfully classified
the examined hams in different quality categories. The results revealed the potentiality of NIR
hyperspectral imaging technique as an objective and non-destructive method for the authentication
and classification of cooked hams.