Non-destructive determination of deoxynivalenol levels in barley using near-infrared spectroscopy

Roger Ruan, Yebo Li, Xiangyan Lin, Paul Chen

Research output: Contribution to journalArticlepeer-review

40 Scopus citations

Abstract

The objective of this research was to develop a neural network based method for determination of Deoxynivalenol (DON) levels in barley using near-infrared (NIR) spectroscopy. The NIR spectra of 188 barley samples with DON level between 0.3 to 50.8 ppm were collected using the FOSS NIR Systems 6500 Near Infrared Spectrometer. The DON levels were measured with GC/mass spectrometry (GC/MS). With the NIR spectra as input variable and GC/mass measured DON as output variable, neural networks were developed and trained to predict DON levels. The prediction accuracy of the models was tested using randomly selected production sets, which had not been seen by the models. The effects of wavelength interval and ranges on the prediction accuracy of models were also examined. The results demonstrate that the combination of neural networks and NIR spectra can be conveniently used to determine DON levels in barley.

Original languageEnglish (US)
Pages (from-to)549-553
Number of pages5
JournalApplied Engineering in Agriculture
Volume18
Issue number5
StatePublished - Sep 1 2002

Keywords

  • Barley
  • Deoxynivalenol (DON)
  • GC/mass spectroscopy
  • Near-infrared spectroscopy
  • Neural network

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