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Rapid spectroscopic analysis for food and feed quality control: prediction of protein and nutrient content in barley forage using LIBS and chemometrics

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DOITrouver le DOI : https://doi.org/10.3390/analytica6030029
AuteurRechercher : 1; Rechercher : 1; Rechercher : 2Identifiant ORCID : https://orcid.org/0000-0002-1130-1509; Rechercher : 3Identifiant ORCID : https://orcid.org/0009-0009-1779-2116; Rechercher : 3Identifiant ORCID : https://orcid.org/0000-0001-8432-3066; Rechercher : 2Identifiant ORCID : https://orcid.org/0000-0002-5829-7113; Rechercher : 2Identifiant ORCID : https://orcid.org/0000-0002-4557-8207; Rechercher : 1Identifiant ORCID : https://orcid.org/0000-0003-2365-9150
Affiliation
  1. Institut National de la Recherche Scientifique, Centre Énergie Matériaux Télécommunications
  2. Conseil national de recherches Canada. Innovations dans les énergies propres
  3. Conseil national de recherches Canada. Développement des cultures et des ressources aquatiques
Bailleur de fondsRechercher : National Research Council Canada
FormatTexte, Article
Sujetbarley forage; protein prediction; essential nutrients; laser-induced breakdown spectroscopy (LIBS); chemometric; extreme learning machine (ELM); partial least squares (PLS); multivariate analysis; feed quality
Résumé
Rapid and accurate assessment of nutritional quality, particularly crude protein content and essential nutrient concentrations, remains a major challenge in the food and feed industries. In this study, laser-induced breakdown spectroscopy (LIBS) was combined with advanced chemometric modeling to predict the levels of crude protein and key macro- and micronutrients (Ca, Mg, K, Na, Fe, Mn, P, Zn) in 61 barley forage samples composed of whole aerial plant parts ground prior to analysis. LIBS offers a compelling alternative to traditional analytical methods by enabling real-time analysis with minimal sample preparation. To minimize interference from atmospheric nitrogen, nitrogen spectral lines were excluded from the protein calibration model in favor of spectral lines from elements biochemically associated with proteins. We compared the performance of Partial Least Squares (PLSR) regression and Extreme Learning Machine (ELM) using fivefold cross-validation. ELM outperformed PLS in terms of prediction, achieving a coefficient of determination (R2) close to 1 and a ratio of performance to deviation (RPD) exceeding 2.5 for proteins and several nutrients. These results underscore the potential of LIBS-ELM integration as a robust, non-destructive, and in situ tool for rapid forage quality assessment, particularly in complex and heterogeneous plant matrices.
Date de publication
Maison d’éditionMDPI AG
Licence
Dans
  • Analytica 6, nº 3, 29 (28 août 2025) : 1–21.
Langueanglais
Publications évaluées par des pairsOui
Identificateuranalytica6030029
Exporter la noticeExporter en format RIS
Signaler une correctionSignaler une correction (s'ouvre dans un nouvel onglet)
Identificateur de l’enregistrementf483a96a-25c6-40d4-8ade-aaa4b5850285
Enregistrement créé2026-05-05
Enregistrement modifié2026-06-25

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