Food and Bioprocess Technology

, Volume 12, Issue 2, pp 347–357 | Cite as

Rapid Identification of Genetically Modified Maize Using Laser-Induced Breakdown Spectroscopy

  • Xiaodan Liu
  • Xuping Feng
  • Fei Liu
  • Jiyu Peng
  • Yong HeEmail author
Original Paper


The safety of genetically modified (GM) food has attracted worldwide attention with a high-frequency appearance in people’s daily life. It is increasingly urgent to find a fast and efficient method to detect GM products to provide reference for safety evaluation. In the current study, we used laser-induced breakdown spectroscopy coupled with chemometrics methods to identify transgenic maize from their non-transgenic parent. One-hundred and twenty GM maize and 120 non-GM maize samples were firstly examined by laser-induced breakdown spectroscopy (LIBS) system. After obtaining the LIBS spectra, principal component analysis (PCA) was introduced to explore the separability of two kinds of samples, and 32 and 30 characteristic emission lines were selected using PCA loadings and weighted regression coefficients (BW), respectively. Classification models based on full spectra and characteristic emission lines were further built by applying partial least squares discrimination analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), and extreme learning machine (ELM). The overall results demonstrated that all models achieved an excellent identification rate, especially the PCA loading-ELM model showed the best performance in the identification of GM maize, with 100% identification accuracy in both calibration and prediction sets. It can be concluded that LIBS combined with chemometrics methods provide a promising way for identification of transgenic maize.


Laser-induced breakdown spectroscopy Chemometrics methods Identification GM maize 


Funding Information

This study was funded by Major Science and Technology Projects in Zhejiang (2015C02007), and National Key R&D Program of China (2016YFD0700304).


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Xiaodan Liu
    • 1
    • 2
  • Xuping Feng
    • 1
    • 2
  • Fei Liu
    • 1
    • 2
  • Jiyu Peng
    • 1
    • 2
  • Yong He
    • 1
    • 2
    Email author
  1. 1.College of Biosystems Engineering and Food ScienceZhejiang UniversityHangzhouChina
  2. 2.Key Laboratory of Spectroscopy SensingMinistry of AgricultureHangzhouChina

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