Smartphone detection of minced beef adulteration

Abstract 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.

[1]  Efstathios Z. Panagou,et al.  Multispectral image analysis approach to detect adulteration of beef and pork in raw meats , 2015 .

[2]  Hui Wang,et al.  Use of smartphone videos and pattern recognition for food authentication , 2020, Sensors and Actuators B: Chemical.

[3]  Sylvio Barbon Junior,et al.  Comparison of rapid techniques for classification of ground meat , 2019, Biosystems Engineering.

[4]  Yankun Peng,et al.  Detection of adulteration with duck meat in minced lamb meat by using visible near-infrared hyperspectral imaging. , 2019, Meat science.

[5]  Yi Yang,et al.  Hyperspectral imaging for a rapid detection and visualization of duck meat adulteration in beef , 2019, Food Analytical Methods.

[6]  Qing-Song Xu,et al.  libPLS: An integrated library for partial least squares regression and linear discriminant analysis , 2018 .

[7]  Yoshio Makino,et al.  Assessment of Visible Near-Infrared Hyperspectral Imaging as a Tool for Detection of Horsemeat Adulteration in Minced Beef , 2015, Food and Bioprocess Technology.

[8]  Zhe Wang,et al.  Quantification of extra virgin olive oil adulteration using smartphone videos. , 2020, Talanta.

[9]  M. H. Stevenson,et al.  Some observations on the absorption spectra of various myoglobin derivatives found in meat. , 1996, Meat science.

[10]  Hasan Murat Velioglu,et al.  Identification of offal adulteration in beef by laser induced breakdown spectroscopy (LIBS). , 2018, Meat science.

[11]  R. Boqué,et al.  Fast detection and quantification of pork meat in other meats by reflectance FT-NIR spectroscopy and multivariate analysis. , 2020, Meat science.

[12]  A. Adedeji,et al.  Assessing different processed meats for adulterants using visible-near-infrared spectroscopy. , 2018, Meat science.

[13]  J. Premanandh.  Horse meat scandal – A wake-up call for regulatory authorities , 2013 .

[14]  Hongdong Li,et al.  Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration. , 2009, Analytica chimica acta.

[15]  G. Nychas,et al.  Rapid detection of frozen-then-thawed minced beef using multispectral imaging and Fourier transform infrared spectroscopy. , 2018, Meat science.

[16]  H. Wold.  Nonlinear Iterative Partial Least Squares (NIPALS) Modelling: Some Current Developments , 1973 .

[17]  Yoshio Makino,et al.  Hyperspectral imaging in tandem with multivariate analysis and image processing for non-invasive detection and visualization of pork adulteration in minced beef , 2015 .

[18]  O. G. Meza-Márquez,et al.  Application of mid-infrared spectroscopy with multivariate analysis and soft independent modeling of class analogies (SIMCA) for the detection of adulterants in minced beef. , 2010, Meat science.

[19]  Ronald D. Snee,et al.  Validation of Regression Models: Methods and Examples , 1977 .

[20]  Yoshio Makino,et al.  Hyperspectral imaging and multispectral imaging as the novel techniques for detecting defects in raw and processed meat products: Current state-of-the-art research advances , 2018 .

[21]  Hui Wang,et al.  Camera2Video: A Low-Cost Food Authentication System Using Smartphone Videos , 2019, SSIP 2019.

[22]  Jinling Zhao,et al.  Rapid detection of adulteration of minced beef using Vis/NIR reflectance spectroscopy with multivariate methods. , 2020, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[23]  A. Adedeji,et al.  Application of Hyperspectral Imaging and Machine Learning Methods to Detect and Quantify Adulterants in Minced Meats , 2020, Food Analytical Methods.