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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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DOIResolve DOI: https://doi.org/10.3390/analytica6030029
AuthorSearch for: 1; Search for: 1; Search for: 2ORCID identifier: https://orcid.org/0000-0002-1130-1509; Search for: 3ORCID identifier: https://orcid.org/0009-0009-1779-2116; Search for: 3ORCID identifier: https://orcid.org/0000-0001-8432-3066; Search for: 2ORCID identifier: https://orcid.org/0000-0002-5829-7113; Search for: 2ORCID identifier: https://orcid.org/0000-0002-4557-8207; Search for: 1ORCID identifier: https://orcid.org/0000-0003-2365-9150
Affiliation
  1. Institut National de la Recherche Scientifique, Centre Énergie Matériaux Télécommunications
  2. National Research Council Canada. Clean Energy Innovation
  3. National Research Council Canada. Aquatic and Crop Resource Development
FunderSearch for: National Research Council Canada
FormatText, Article
Subjectbarley forage; protein prediction; essential nutrients; laser-induced breakdown spectroscopy (LIBS); chemometric; extreme learning machine (ELM); partial least squares (PLS); multivariate analysis; feed quality
Abstract
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 published
PublisherMDPI AG
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  • Analytica 6, no. 3, 29 (28 August 2025): 1–21.
LanguageEnglish
Peer reviewedYes
Identifieranalytica6030029
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Record identifierf483a96a-25c6-40d4-8ade-aaa4b5850285
Record created2026-05-05
Record modified2026-06-25

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