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Abstract <p>The effectiveness of combining spectroscopic and chemometric methods for identifying and classifying salmon, sturgeon, and ordinary fish caviar, as well as for differentiating natural and imitation samples, was demonstrated. An analysis of near- and mid-region IR spectra made it possible to identify specific features of the chemical composition and structure of the studied samples and ensured the reliable separation of natural and imitation caviar. The use of Raman spectroscopy contributed to the determination of characteristic spectral differences associated with the protein and lipid composition and the presence of carotenoids, by which the samples could be clearly differentiated. The use of principal component analysis (PCA), hierarchical cluster analysis (HCA), and soft independent modeling of class analogy (SIMCA) algorithms increased the classification accuracy and ensured the separation of samples by the fish species. Digital colorimetry based on an analysis of optical characteristics in the UV and IR regions has proven its effectiveness as an accessible and a reliable method providing an alternative to more expensive spectroscopic approaches.</p>

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Species Identification and Revelation of Facts of Fish Caviar Adulteration by Vibrational Spectroscopy and Digital Coloriometry

  • 期刊论文

  • 发表日期：

    2025年7月25日

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  • V. G. Amelin,
  • O. E. Emel’yanov,
  • A. Yu. Khrushchev,
  • A. V. Tret’yakov

摘要

Abstract

The effectiveness of combining spectroscopic and chemometric methods for identifying and classifying salmon, sturgeon, and ordinary fish caviar, as well as for differentiating natural and imitation samples, was demonstrated. An analysis of near- and mid-region IR spectra made it possible to identify specific features of the chemical composition and structure of the studied samples and ensured the reliable separation of natural and imitation caviar. The use of Raman spectroscopy contributed to the determination of characteristic spectral differences associated with the protein and lipid composition and the presence of carotenoids, by which the samples could be clearly differentiated. The use of principal component analysis (PCA), hierarchical cluster analysis (HCA), and soft independent modeling of class analogy (SIMCA) algorithms increased the classification accuracy and ensured the separation of samples by the fish species. Digital colorimetry based on an analysis of optical characteristics in the UV and IR regions has proven its effectiveness as an accessible and a reliable method providing an alternative to more expensive spectroscopic approaches.