Smartphone detection of minced beef adulteration

Weiran Song, Yong-Huan Yun, Hui Wang, Zongyu Hou, Zhe Wang

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)
19 Downloads (Pure)


This paper presents a study on detecting minced beef adulteration based on smartphone videos recorded under a sequence of varying colours. Minced beef samples were mixed with minced pork in the range of 10‒100% (w/w) at 10% increments. Light with varying colours was generated on smartphone screen and used to illuminate the sample surface. Short videos were recorded by front camera and converted into spectrum-like data by image processing. Data samples were collected under different conditions in terms of type of smartphone, recording, distance and lighting condition, resulting in seven sets of data. A partial least squares regression model was used to predict the level of adulteration, yielding determination coefficients of 0.73‒0.98 and the root-mean-square errors of 0.04‒0.16 for prediction. Furthermore, smartphone videos were used to present distribution maps of adulteration levels. The results indicate the potential of the simple and low-cost approach in detecting adulteration of minced meat.
Original languageEnglish
Article number106088
JournalMicrochemical Journal
Early online date17 Feb 2021
Publication statusPublished (in print/issue) - 31 May 2021


  • Minced beef
  • Adulteration
  • Smartphone
  • Computer vision
  • Chemometrics
  • Partial least squares regression


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