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.
- Minced beef
- Computer vision
- Partial least squares regression