Identification of species affiliation and determination of fish eggs adulteration by vibrational spectroscopy and digital colorometry methods

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The efficiency of combining spectroscopic and chemometric methods for identification and classification of salmon, sturgeon and particle fish caviar, as well as for differentiation of natural and imitated samples, has been shown. Analysis of infrared spectra in the near and middle regions made it possible to identify the peculiarities of chemical composition and structure of the samples under study, providing reliable differentiation of natural and imitated caviar. The use of Raman spectroscopy helped to identify characteristic spectral differences related to protein-lipid composition and the presence of carotenoids, which allowed to clearly differentiate the samples. The application of principal component analysis (PCA), hierarchical cluster analysis (HCA) and formal independent modeling of class analogies (SIMCA) algorithms improved classification accuracy, providing separation of samples by fish species. Digital colorometry based on the analysis of optical characteristics in the UV and IR ranges showed to be an affordable and reliable method that can be an alternative to more expensive spectroscopic approaches.

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作者简介

V. Amelin

All-Russian State Centre for Quality and Standardization of Animal Drugs and Feeds; Alexander Grigorievich and Nikolai Grigorievich Stoletov Vladimir State University

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Email: amelinvg@mail.ru
俄罗斯联邦, Moscow; Vladimir

O. Emelyanov

Alexander Grigorievich and Nikolai Grigorievich Stoletov Vladimir State University

Email: amelinvg@mail.ru
俄罗斯联邦, Vladimir

A. Khrushchev

Alexander Grigorievich and Nikolai Grigorievich Stoletov Vladimir State University

Email: amelinvg@mail.ru
俄罗斯联邦, Vladimir

A. Tretyakov

All-Russian State Centre for Quality and Standardization of Animal Drugs and Feeds

Email: amelinvg@mail.ru
俄罗斯联邦, Moscow

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2. Fig. 1. Raman spectra of (a) natural (5–15) and (b) simulated (1–4, 16, 1I–6I) red caviar.

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3. Fig. 2. PCA and HCA plots for natural and simulated red caviar.

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4. Fig. 3. SIMCA plot for refining group boundaries between natural and simulated red caviar samples (significance level 0.05).

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5. Fig. 4. Raman spectra of (a) simulated (26–30), natural (31) black caviar and (b) caviar of cyprinid fish species (17–25).

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6. Fig. 5. PCA and HCA plots for simulated (26–30) and natural (31) black caviar samples.

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7. Fig. 6. PCA plot for caviar of different fish species.

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8. Fig. 7. SIMCA plots for refining group boundaries between samples of natural red caviar from different species (significance level 0.05).

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9. Fig. 8. Near-infrared spectra for different types of caviar. Spectrum numbers correspond to sample numbers in Table 1.

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10. Fig. 9. Mid-infrared spectra for different types of caviar. Spectrum numbers correspond to sample numbers in Table 1.

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11. Fig. 10. SIMCA plot for refining group boundaries between natural and simulated red caviar samples (significance level 0.05): (a) near-IR, (b) mid-IR regions.

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12. Fig. 11. PCA and HCA plots for simulated (26–30) and natural (31) black caviar samples: (a) near-IR, (b) mid-IR region.

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13. Fig. 12. PCA plot for caviar of different fish species: (a) near-IR, (b) mid-IR region.

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14. Fig. 13. SIMCA plot for refining group boundaries between natural and simulated red caviar samples (significance level 0.05) under (a) UV and (b) IR irradiation.

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15. Fig. 14. PCA and HCA plots for simulated (26–30) and natural (31) black caviar samples under (a) UV and (b) IR irradiation.

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16. Fig. 15. PCA plots for caviar of different fish species under (a) UV and (b) IR irradiation.

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