Identification of authenticity, quality and origin of saffron using hyperspectral imaging and multivariate spectral analysis

Abstract This paper is conducted to identify the authenticity, quality, and origin of saffron using hyperspectral imaging and multivariate spectral analysis. Reflectance spectra were extracted from hyperspectral images of saffron. Successive projections algorithm, genetic algorithm, uninformative variable elimination, and competitive adaptive reweighted sampling were used to select characteristic wavelengths. Back propagation neural network model was established based on the selected wavelengths. Results showed that the model combining competitive adaptive reweighted sampling with back propagation neural network achieved the best performance. Its prediction accuracy of the one-adulterated, three-domestic and two-imported saffron was 100, 95, 94, 100, 83, and 96%, respectively.

• Publication year

  2020

• Venue

  Spectroscopy Letters

• Publication date

  2020-02-07

• Fields of study

  Chemistry, Environmental Science

• Identifiers
  DOI 10.1080/00387010.2019.1693403
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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