Artigo de periodico

Discrimination of maize transgenic and non-transgenic varieties by laser induced spectroscopy (LIBS) and machine learning algorithms (2024)

• Authors:
  • Ribeiro, Matheus Cicero
  • Marangoni, Bruno S.
  • Cabral, Jader de Souza
  • Nicolodelli, Gustavo
  • Senesi, Giorgio S
  • Caires, Anderson R. L.
  • Gonçalves, Daniel A
  • Menegatti, Carlos Renato
  • Milori, Debora Marcondes Bastos Pereira
  • Cena, Cícero
• DOI: 10.1016/j.microc.2024.110898
• Subjects: APRENDIZADO COMPUTACIONAL; QUIMIOMETRIA
• Keywords: Transgenic maize; Non-transgenic maize; LIBS; Chemometric analysis; Machine learning algorithms
• Agências de fomento:
  • Financiamento CAPES
  • Financiamento PROCAD/CAPES
  • Financiamento FAPESP
  • Financiamento CNPq
  • Financiamento FAPEMIG
  • Financiamento SISFOTON
• Language: Inglês
• Abstract: Background: In the last decades, the production and consumption of genetically modified agricultural products have increased markedly due to the worldwide population growth and rising demand for food and feed. Consequently, genetically modified crops have been extensively produced and consumed, which required identifying and discriminating transgenic and non-transgenic products. Results: Laser-induced breakdown spectroscopy (LIBS) combined with chemometric methods was applied to identify and discriminate two varieties of conventional (not-genetically modified, NGM) maize from four vari eties of transgenic maize (genetically modified, GM). The LIBS spectra acquired under reduced pressure (100 Torr) conditions over two ranges, i.e., 175–330 nm and 275–770 nm, were subjected to Standard Normal Variation (SNV) and multivariate methods such as Principal Component Analysis (PCA) to reduce data matrices dimensionality and spectral noise. The supervised machine learning algorithms k-nearest neighbor (k-NN) and support vector machine (SVM) have been applied to discriminate among NGM and GM maize reserving 25 % of data for external validation. The training data were employed for hyperparameter optimization of classifiers using the Leave-One-Out Cross-Validation (LOOCV) method. Considering all six maize varieties simultaneously, the highest training accuracy achieved was 90.56 %, with an external validation accuracy of 88.33 %. In an alternative approach based on pairwise combinations of one GM variety against one NGM variety, the best outcome achieved was 100 % LOOCV and external validation accuracy. Conclusions: These results showed that LIBS supported by appropriate chemometric methods represents an alternative screening technique for identifying and discriminating transgenic from non-transgenic maize.
• Imprenta:
  • Publisher: Elsevier
  • Publisher place: Valencia-ESP
  • Date published: 2024
• Source:
  • Título: Microchemical journal
  • ISSN: 0026-265X
  • Volume/Número/Paginação/Ano: v. 203, art. n.110898, p. 1-11, 2024

Informações sobre o DOI: 10.1016/j.microc.2024.110898 (Fonte: oaDOI API)
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• ABNT

  RIBEIRO, Matheus Cicero et al. Discrimination of maize transgenic and non-transgenic varieties by laser induced spectroscopy (LIBS) and machine learning algorithms. Microchemical journal, v. 203, n. art. 110898, p. 1-11, 2024Tradução . . Disponível em: https://doi.org/10.1016/j.microc.2024.110898. Acesso em: 25 fev. 2026.

• APA

  Ribeiro, M. C., Marangoni, B. S., Cabral, J. de S., Nicolodelli, G., Senesi, G. S., Caires, A. R. L., et al. (2024). Discrimination of maize transgenic and non-transgenic varieties by laser induced spectroscopy (LIBS) and machine learning algorithms. Microchemical journal, 203( art. 110898), 1-11. doi:10.1016/j.microc.2024.110898

• NLM

  Ribeiro MC, Marangoni BS, Cabral J de S, Nicolodelli G, Senesi GS, Caires ARL, Gonçalves DA, Menegatti CR, Milori DMBP, Cena C. Discrimination of maize transgenic and non-transgenic varieties by laser induced spectroscopy (LIBS) and machine learning algorithms [Internet]. Microchemical journal. 2024 ; 203( art. 110898): 1-11.[citado 2026 fev. 25 ] Available from: https://doi.org/10.1016/j.microc.2024.110898

• Vancouver

  Ribeiro MC, Marangoni BS, Cabral J de S, Nicolodelli G, Senesi GS, Caires ARL, Gonçalves DA, Menegatti CR, Milori DMBP, Cena C. Discrimination of maize transgenic and non-transgenic varieties by laser induced spectroscopy (LIBS) and machine learning algorithms [Internet]. Microchemical journal. 2024 ; 203( art. 110898): 1-11.[citado 2026 fev. 25 ] Available from: https://doi.org/10.1016/j.microc.2024.110898