Application of Hyperspectral Imaging and Machine Learning Methods to Detect and Quantify Adulterants in Minced Meats

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

The effectiveness of hyperspectral imaging (400–1000 nm) was proved as a nondestructive method to detect, classify, and quantify plant- and animal-based adulterants in minced beef and pork. Machine learning techniques were implemented to build classification and prediction models. Samples were first classified into adulterated (1 class) or pure (5 classes). The type of adulterant (6 classes) was then evaluated. Finally, the level of each adulterant was estimated using partial least squares regression. The optimal classification models based on selected wavelengths of test set yielded classification rates of 75–100% and 100% for pure and adulterated samples, respectively. Whereas, the rates were 83–100% depending on adulterant type. Prediction models for adulterant level yielded correlation coefficient, r, and ratio of performance to prediction, RPD, of 0.69(1.41) for beef adulterated with pork and textured vegetable protein (TVP), and 0.93(2.82) for beef adulterated with TVP. Improvement in results may be achieved with larger sample size.

Original languageEnglish
Pages (from-to)970-981
Number of pages12
JournalFood Analytical Methods
Volume13
Issue number4
DOIs
StatePublished - Apr 1 2020

Bibliographical note

Funding Information:
The information reported in this paper (18-05-081) is a project of the Kentucky Agricultural Experiment Station and it is published with the approval of the Director.

Publisher Copyright:
© 2020, Springer Science+Business Media, LLC, part of Springer Nature.

Keywords

  • Adulteration
  • Food fraud
  • Gluten
  • Hyperspectral imaging
  • Minced beef
  • Pork
  • Texturized vegetable protein

ASJC Scopus subject areas

  • Analytical Chemistry
  • Food Science
  • Applied Microbiology and Biotechnology
  • Safety, Risk, Reliability and Quality
  • Safety Research

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