Hyperspectral imaging for a rapid detection and visualization of duck meat adulteration in beef
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Hyperspectral imaging (HSI) was investigated to detect and visualize beef adulteration with duck meat. Minced beef was adulterated with duck meat in range of 0–100% (w/w) at 10% increments. Visible and near-infrared (VNIR) hyperspectral images were acquired, and extracted spectra were analyzed with preprocessing methods. Partial least squares regression (PLSR) and principal component regression (PCR) modeling methods were individually utilized, and the PLSR model based on full raw spectra performed best. To make the preferred model practical, optimal wavelengths were individually selected using two-dimensional correlation spectroscopy (2D-COS) and PC loadings. The model based on competent wavelengths selected from PC loadings resulted in good performance of Rp2 = 0.96, RMSEP = 6.58%, and RPD = 4.86 with limit of detection (LOD) of 7.59%. Finally, spatially distributed visualization was achieved using the simplified model, and adulteration levels were readily discernible. Known distributed samples were also successfully visualized, and the validity of visualization was thus proved. Results demonstrated the potential of the HSI to detect minced beef adulteration with duck meat.
KeywordsHyperspectral imaging Near-infrared Beef adulteration Duck meat Two-dimensional correlation spectroscopy Visualization
This work was supported by the National Natural Science Foundation of China (Grant NO. 31772062).
Compliance with Ethical Standards
Conflict of Interest
Hongzhe Jiang declares that he has no conflict of interest. Wei Wang declares that he has no conflict of interest. Hong Zhuang declares that he has no conflict of interest. Seung-Chul Yoon declares that he has no conflict of interest. Yi Yang declares that he has no conflict of interest. Xin Zhao declares that she has no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Chou CC, Lin SP, Lee KM, Hsu CT, Vickroy TW, Zen JM (2007) Fast differentiation of meats from fifteen animal species by liquid chromatography with electrochemical detection using copper nanoparticle plated electrodes. J Chromatogr B Analyt Technol Biomed Life Sci 846:230–239CrossRefPubMedGoogle Scholar
- Mahesh S, Jayas DS, Paliwal J, White NDG (2015) Comparison of partial least squares regression (PLSR) and principal components regression (PCR) methods for protein and hardness predictions using the near-infrared (NIR) hyperspectral images of bulk samples of Canadian wheat. Food Bioprocess Technol 8:31–40CrossRefGoogle Scholar
- Tang Z, Zhou Y, Zhou Y, Zheng P (2015) Brief analysis on harm and sensory identification of five kinds of questionable meat. Meat Ind 416:47–54 (in Chinese)Google Scholar
- Zhong K, Han F, Yao K, Ren X, Chen S, Luo X, Guo L (2012) Current situation, problems, challenges and counter measures of food safety risk communication in China. Chin J Food Hyg 24:578–586 (in Chinese)Google Scholar