Assessing different processed meats for adulterants using visible-near-infrared spectroscopy

Ahmed Rady, Akinbode Adedeji

Research output: Contribution to journalArticlepeer-review

77 Scopus citations


The main objective of this study was to investigate the use of spectroscopic systems in the range of 400–1000 nm (visible/near-infrared or Vis-NIR) and 900–1700 nm (NIR) to assess and estimate plant and animal proteins as potential adulterants in minced beef and pork. Multiple machine learning techniques were used for classification, adulterant prediction, and wavelength selection. Samples were first evaluated for the presence or absence of adulterants (6 classes), and secondly for adulterant type (6 classes) and level. Selected wavelengths models generally resulted in better classification and prediction outputs than full wavelengths. The first stage classification rates were 96% and 100% for pure/unadulterated and adulterated samples, respectively. Whereas, the second stage had classification rates of 69–100%. The optimal models for predicting adulterant levels yielded correlation coefficient, r of 0.78–0.86 and ratio of performance to deviation, RPD, of 1.19–1.98. The results from this study illustrate potential application of spectroscopic technology to rapidly and accurately detect adulterants in minced beef and pork.

Original languageEnglish
Pages (from-to)59-67
Number of pages9
JournalMeat Science
StatePublished - Feb 2018

Bibliographical note

Funding Information:
The information reported in this paper (#:17-05-034) is a project of the Kentucky Agricultural Experiment Station and it is published with the approval of the Director. This work is supported by the National Institute of Food and Agriculture , U.S. Department of Agriculture , Hatch- Multistate project #: 1007893 .

Publisher Copyright:
© 2017 Elsevier Ltd


  • Adulteration
  • Chicken
  • Gluten
  • Minced beef
  • Pork
  • Texturized vegetable protein
  • Vis-NIR spectroscopy

ASJC Scopus subject areas

  • Food Science


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