Beef muscle discrimination based on two-trace two-dimensional correlation spectroscopy (2T2D COS) combined with snapshot visible-near infrared multispectral imaging - UMR Herbivores
Journal Articles Meat Science Year : 2024

Beef muscle discrimination based on two-trace two-dimensional correlation spectroscopy (2T2D COS) combined with snapshot visible-near infrared multispectral imaging

Abstract

The purpose of this work was to assess the potential of 2T2D COS PLS-DA (two-trace two-dimensional correlation spectroscopy and partial least squares discriminant analysis) in conjunction with Visible Near infrared multispectral imaging (MSI) as a quick, non-destructive, and precise technique for classifying three beef muscles -Longissimus thoracis, Semimembranosus, and Biceps femoris- obtained from three breeds - the Blonde d’Aquitaine, Limousine, and Aberdeen Angus. The experiment was performed on 240 muscle samples. Before performing PLSDA, spectra were extracted from MSI images and processed by SNV (Standard Normal Variate), MSC (Multivariate Scattering Correction) or AREA (area under curve equal 1) and converted in synchronous and asynchronous 2T2D COS maps. The results of the study highlighted that combining synchronous and asynchronous 2T2D COS maps before performing PLS-DA was the best strategy to discriminate between the three muscles (100% of classification accuracy and 0% of error).
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hal-04582563 , version 1 (22-05-2024)

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Abderrahmane Aït-Kaddour, Mohammed Loudiyi, Oumayma Boukria, Jasur Safarov, Shaxnoza Sultanova, et al.. Beef muscle discrimination based on two-trace two-dimensional correlation spectroscopy (2T2D COS) combined with snapshot visible-near infrared multispectral imaging. Meat Science, 2024, 214, pp.109533. ⟨10.1016/j.meatsci.2024.109533⟩. ⟨hal-04582563⟩
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