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Southern Spain lamb types discrimination by using visible spectroscopy and basic physicochemical traits.

M Juárez1, M J Alcalde, A Horcada

  • 1Department of Agroforestry Science, Agricultural Engineering College, University of Seville, 41013 Seville, Spain.

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Summary

Visible spectroscopy can classify lamb meat types with 83% accuracy. Combining spectral data with physicochemical traits improves classification to 95%, offering a more precise method for meat analysis.

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Area of Science:

  • Food Science
  • Spectroscopy
  • Meat Science

Background:

  • Accurate classification of lamb meat (breed × production system) is crucial for quality control and traceability.
  • Visible spectroscopy offers a non-destructive method for analyzing meat composition and characteristics.

Purpose of the Study:

  • To investigate the efficacy of visible spectroscopy for classifying six types of lamb meat.
  • To compare the accuracy of classification using only spectral data versus a combination of spectral and physicochemical data.

Main Methods:

  • Visible spectroscopy (400-700nm) was employed to analyze lamb meat samples.
  • Discriminant analysis was performed using selected wavelengths (400, 410, 420, 450, 510, 610, 670nm).
  • Physicochemical traits (myoglobin, water holding capacity, pH, a*, L*, protein, ash, moisture) were measured and incorporated into a combined model.

Main Results:

  • Visible spectroscopy alone achieved a lamb meat classification accuracy of approximately 83%.
  • Incorporating basic physicochemical traits alongside spectral data enhanced classification accuracy to 95%.
  • The combined approach yielded a 12% improvement in accuracy compared to using spectral data solely.

Conclusions:

  • Visible spectroscopy is a viable tool for classifying lamb meat types.
  • Combining visible spectroscopy with physicochemical analysis significantly improves classification accuracy, providing a robust method for meat authentication.