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  • Poster presentation
  • Open Access

Meat quality prediction using Raman spectroscopy and chemometrics

  • 1Email author,
  • 2,
  • 2 and
  • 1
Journal of Cheminformatics20146 (Suppl 1) :P21

https://doi.org/10.1186/1758-2946-6-S1-P21

  • Published:

Keywords

  • Lactate
  • Raman Spectrum
  • Raman Spectroscopy
  • Quality Parameter
  • Weighted Regression

The feasibility of using Raman spectroscopy as a fast and non-invasive method to monitor the quality parameters in pork meat has been investigated. For this application an online prediction methodology has not been established yet. Based on raw Raman spectra of 10 pork semimembranosus muscles a range of data pre-processing and multivariate calibration methodology have been used to develop online predictive models for the meat quality parameters: the lactate and pH. The linear and nonlinear algorithms studied were comparatively analysed for speed, robustness and accuracy. Identifying the best “efficiency” evaluation procedure represented the final milestone of the present study. Thus with a cross-validated r2 value for both pH and lactate of 0.97, a RMSECV of 4.5 mmol/l for the lactate prediction and 0.06 units for the pH prediction, locally weighted regression provided the most accurate and robust model. This prove the feasibility of using Raman spectroscopy for online meat quality control applications.

Authors’ Affiliations

(1)
Department of Process Analytics and Cereal Technology, Institute of Food Science and Biotechnology, University of Hohenheim, Stuttgart, 70599, Germany
(2)
Research Centre of Food Quality, University Bayreuth, Kulmbach, 95326, Germany

Copyright

© Nache et al; licensee Chemistry Central Ltd. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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