Fast spark discharge-laser-induced breakdown spectroscopy method for rice botanic origin determination

Food Chem. 2020 Nov 30:331:127051. doi: 10.1016/j.foodchem.2020.127051. Epub 2020 Jun 15.

Abstract

A simple, fast, and efficient spark discharge-laser-induced breakdown spectroscopy (SD-LIBS) method was developed for determining rice botanic origin using predictive modeling based on support vector machine (SVM). Seventy-two samples from four rice varieties (Guri, Irga 424, Puitá, and Taim) were analyzed by SD-LIBS. Spectral lines of C, Ca, Fe, Mg, N and Na were selected as input variables for prediction model fitting. The SVM algorithm parameters were optimized using a central composite design (CCD) to find the better classification performance. The optimum model for discriminating rice samples according to their botanical variety was obtained using C = 5.25 and γ = 0.119. This model achieved 96.4% of correct predictions in test samples and showed sensitivities and specificities per class within the range of 92-100%. The developed method is robust and eco-friendly for rice botanic identification since its prediction results are consistent and reproducible and its application does not generate chemical waste.

Keywords: Botanical origin; Rice; SD-LIBS; Support vector machine.

MeSH terms

  • Algorithms
  • Food Analysis / instrumentation
  • Food Analysis / methods*
  • Food Analysis / statistics & numerical data
  • Lasers
  • Machine Learning
  • Metals / analysis
  • Oryza / chemistry*
  • Sensitivity and Specificity
  • Spectrum Analysis / instrumentation
  • Spectrum Analysis / methods*
  • Spectrum Analysis / statistics & numerical data
  • Support Vector Machine

Substances

  • Metals