Multispectral image analysis approach to detect adulteration of beef and pork in raw meats

By Athina Ropodi, Dimitrios E Pavlidis, Fady R. Mohareb, Efstathios Z Panagou, George - John Nychas. 2015

138 citations recorded by the source corpus.

Abstract The aim of this study was to investigate the potential of multispectral imaging supported by multivariate data analysis for the detection of minced beef fraudulently substituted with pork and vice versa. Multispectral images in 18 different wavelengths of 220 meat samples in total from four independent experiments (55 samples per experiment) were acquired for this work. The appropriate amount of beef and pork-minced meat was mixed in order to achieve nine different proportions of adulteration and two categories of pure pork and beef. After an image processing step, data from the first three experiments were used for partial least squares-discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) so as to discriminate among all adulteration classes, as well as among adulterated, pure beef and pure pork samples. Results showed very good discrimination between pure and adulterated samples, for PLS-DA and LDA, yielding 98.48% overall correct classification. Additionally, 98.48% and 96.97% of the samples were classified within a ± 10% category of adulteration for LDA and PLS-DA respectively. Lastly, the models were further validated using the data of the fourth experiment for independent testing, where all pure and adulterated samples were classified correctly in the case of PLS-DA, while LDA was proved to be less accurate.

Subfields: Analytical Chemistry, Animal Science and Zoology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/3fd3fe01986b22a00d16cf242e0469813d5e2e97

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In Analytical Chemistry

1. Efstathios Z Panagou
2. Athina Ropodi
3. George - John Nychas
4. A. Adedeji
5. Wei Wang
6. Ahmed M. Rady

In Animal Science and Zoology

1. Efstathios Z Panagou
2. George - John Nychas
3. A. Adedeji
4. Ahmed M. Rady

In Molecular Biology

1. Efstathios Z Panagou
2. Athina Ropodi
3. George - John Nychas
4. A. Adedeji
5. Ahmed M. Rady
6. Yezhou Yang

Citations in the corpus

• Hyperspectral imaging for a rapid detection and visualization of duck meat adulteration in beef (2019, background): “Compared with previous studies, other single adulterant including chicken (Kamruzzaman et al. 2016), pork (Kamruzzaman et al. 2015b; Ropodi et al. 2015), and horsemeat (Kamruzzaman et al. 2015a; Ropodi et al. 2017) adulterated in beef were also successfully detected using HSI.”
• Multispectral imaging (MSI): A promising method for the detection of minced beef adulteration with horsemeat (2017, methodology): “Ropodi et al. (2015) investigated the case of minced beef adulteration with pork using MSI, whereas HSI was used for the detection of minced lamb adulterated with pork (Kamruzzaman, Sun, ElMasry, & Allen, 2013).”
• Application of Hyperspectral Imaging and Machine Learning Methods to Detect and Quantify Adulterants in Minced Meats (2020, methodology): “Finally, Ropodi et al. (2015) obtained 0% precision for prediction models of beef adulterating pork and vice versa.”