Visible/near infrared spectroscopy in muscle beef analysis: How device choice and sampling affect prediction accuracy
Year: 2026
Type: article
Abstract: This study evaluated the performance of two portable spectral devices (visible: Vis; near-infrared: NIR) and one benchtop combined Vis/NIR instrument for predicting beef biochemical traits related to meat quality. A total of 497 muscle samples were collected from 55 fattening farms across five European countries. Reference values were obtained exclusively from pooled samples. For the benchtop device, two sampling strategies were compared: 1) direct scanning of pooled samples ( n = 99) used for reference analyses, and 2) scanning individual samples followed by spectral averaging to match pooled references. Partial least squares regression models were developed using several preprocessing methods. For heme iron, predictive performance was also evaluated by separately analyzing the visible region of the benchtop device. Results showed that the benchtop Vis/NIR instrument clearly outperformed the portable devices, providing higher prediction accuracies for all traits across the full spectral range. Compared with portable instruments, the benchtop device achieved superior performance for proximate composition (R 2 = 0.51–0.85 vs 0.10–0.22), major fatty acids (R 2 = 0.50–0.68 vs 0.01–0.31), and total and heme iron (R 2 = 0.79–0.93 vs 0.46–0.91). Moreover, scanning individual samples and averaging spectra yielded better prediction models than direct scanning of pooled samples, with good accuracies for moisture, protein, fat content, major saturated and monounsaturated fatty acids, and iron. Conversely, poor prediction (R 2 < 0.40) was observed for polyunsaturated fatty acids, TBARS, carnosine, anserine, zinc, selenium and B-group vitamins. Overall, Vis/NIR spectroscopy showed strong potential for beef quality assessment, although further optimization is required for portable devices.
Source: Meat Science
Institutions: University of Milan, Université Clermont Auvergne, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, VetAgro Sup, Unité Mixte de Recherche sur les Herbivores, Qualité des Produits Animaux, Ghent University, University of Bologna, Universidad de Extremadura, Universitat Autònoma de Barcelona, University of Padua
Language: English
Subfield: Analytical Chemistry
Field: Chemistry
Domain: Physical Sciences
SDG: Zero hunger
Open Access status: hybrid
Estimated APC paid: $4,740
Funders: Università degli Studi di Milano, European Commission, Ministerio de Ciencia e Innovación, Région Auvergne-Rhône-Alpes, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, Horizon 2020 Framework Programme, HORIZON EUROPE Framework Programme, Research Executive Agency
Awards: G1636598885, INnovative Tools for Assessment and Authentication of chicken meat, beef and dairy products’ QualiTies, G2971835756, G3709597884, A holistic frameWork with Anticounterfeit and inTelligence-based technologieS that will assist food chain stakehOlders in rapidly identifying and preveNting the spread of fraudulent practices., G530657041
OpenAlex ID: W7214003729
