Development of predictive models for total phenolics and free p-coumaric acid contents in barley grain by near-infrared spectroscopy

Food Chem. 2017 Jul 15:227:342-348. doi: 10.1016/j.foodchem.2017.01.063. Epub 2017 Jan 16.

Abstract

Barley grains are rich in phenolic compounds, which are associated with reduced risk of chronic diseases. Development of barley cultivars with high phenolic acid content has become one of the main objectives in breeding programs. A rapid and accurate method for measuring phenolic compounds would be helpful for crop breeding. We developed predictive models for both total phenolics (TPC) and p-coumaric acid (PA), based on near-infrared spectroscopy (NIRS) analysis. Regressions of partial least squares (PLS) and least squares support vector machine (LS-SVM) were compared for improving the models, and Monte Carlo-Uninformative Variable Elimination (MC-UVE) was applied to select informative wavelengths. The optimal calibration models generated high coefficients of correlation (rpre) and ratio performance deviation (RPD) for TPC and PA. These results indicated the models are suitable for rapid determination of phenolic compounds in barley grains.

Keywords: Barley (Hordeum vulgare L.); Ferulic acid (PubChem CID 445858); Formic acid (PubChem CID 284); Gallic acid (PubChem CID 370); Least squares support vector machine (LS-SVM); Near infrared spectroscopy (NIRS); Partial least squares (PLS); Phenolic compound; p-Coumaric acid (PubChem CID 637542).

Publication types

  • Evaluation Study

MeSH terms

  • Calibration
  • Coumaric Acids / analysis*
  • Hordeum / chemistry*
  • Least-Squares Analysis
  • Models, Theoretical
  • Phenols / analysis*
  • Propionates
  • Seeds / chemistry*
  • Spectroscopy, Near-Infrared / methods*
  • Support Vector Machine

Substances

  • Coumaric Acids
  • Phenols
  • Propionates
  • p-coumaric acid