Assessment of Visible Near-Infrared Hyperspectral Imaging as a Tool for Detection of Horsemeat Adulteration in Minced Beef

By M. Kamruzzaman, Yoshio Makino, Seiichi Oshita, Shu Liu. Food and Bioprocess Technology, 2015

139 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/31897047522b2132fabbc84f2f5da4b6d0fd5d2a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. A. Adedeji
2. Wei Wang
3. Ahmed M. Rady

In Animal Science and Zoology

1. A. Adedeji
2. Ahmed M. Rady

In Molecular Biology

1. A. Adedeji
2. Ahmed M. Rady
3. Yezhou Yang

Citations in the corpus

• Application of Hyperspectral Imaging and Machine Learning Methods to Detect and Quantify Adulterants in Minced Meats (2020, methodology): “They determined that the following wavelengths, 515, 595, 650, and 880 nm, are important for detecting horsemeat in minced beef (Kamruzzaman et al. 2015).”
• Hyperspectral imaging for a rapid detection and visualization of duck meat adulteration in beef (2019, background): “…adulteration level, such as pork in lamb (Kamruzzaman et al. 2013b), horsemeat in beef (Kamruzzaman et al. 2015a; Ropodi et al. 2017), pork in beef (Kamruzzaman et al. 2015b; Ropodi et al. 2015), chicken in beef (Kamruzzaman et al. 2016), and gelatin in prawn (Wu et al. 2013), to the best of our…”