JPGG8X5C

3067

7 avril 2021 09:29

7 avril 2021 09:29

Sliced dry-cured ham arranged in ready-to-eat packages is a convenient and widely consumed commodity characterised by heterogeneity in composition not only among different industrial batches but also through their horizontal and vertical profiles, making precise nutrition labelling of the packages a difficult task. Hyperspectral imaging techniques can serve as a steadfast solution not only to predict the overall composition of the major constituents of dry-cured ham but also to visualise their distributions. The main aim of this study was to define the optimal protocol for pretreating hyperspectral images and selecting representative hyperspectral data for developing accurate predictive models in excessively heterogeneous samples, using sliced dry-cured ham as a case study. Hyperspectral images (400–1000 nm) were acquired for heterogeneous sliced dry-cured ham and homogeneous unsliced dry-cured muscles. Partial least squares (PLS) regression models to predict fat, water, salt and protein contents were developed and tested in an independent dataset. The PLS predictive models developed from the whole surface of sliced dry-cured ham were the most accurate ones for predicting fat, water, salt and protein contents with a determination coefficient in prediction (Rp2) of 0.89, 0.85, 83 and 0.63 and standard error in prediction (SEP) of 1.43, 1.21, 0.51 and 1.57%, respectively. The chemical images resulting from the models gave advantages of hyperspectral imaging technique over traditional chemical methods to visualise the spatial distribution of different constituents within the packaged ham slices.

None

• AB - Transversal
• FREDO qualité des produits
• FREDO technologie et innovation
• FREDO transformation et valorisation
• GEO Arabie Saoudite
• GEO Egypte
• GEO Espagne
• viande

• chemical imaging
• dry-cured ham
• hyperspectral imaging
• multivariate analysis
• PLS
• ROI

Biosystems Engineering

201

67-82

January 1, 2021

1 janvier 2021

10.1016/j.biosystemseng.2020.11.008 Le DOI est une URL unique de référencement d'une publication. Il est donc plus fiable et permanent qu'une URL classique

1537-5110 L’ISSN est un code de 8 chiffres servant à identifier les journaux, revues, magazines, périodiques de toute nature et sur tous supports, papier comme électronique.