Mohammed Kamruzzaman, Yoshio Makino, Seiichi Oshita
The main objective of this study was to evaluate the potential of visible near-infrared (VNIR) hyperspectral imaging (400–1000 nm) and machine learning to detect adulteration in fresh minced beef with chicken. Minced beef samples were adulterated with minced chicken in the range 0–50% (w/w) at appro...
Mohammed Kamruzzaman, Yoshio Makino, Seiichi Oshita
The requirement of real-time monitoring of food products has encouraged the development of non-destructive measurement systems. Hyperspectral imaging is a rapid, reagentless, non-destructive analytical technique that integrates traditional spectroscopic and imaging techniques into one system to atta...
Mohammed Kamruzzaman, Yoshio Makino, Seiichi Oshita, Shu Liu
Mohammed Kamruzzaman, Yoshio Makino, Seiichi Oshita
A hyperspectral imaging system in the spectral range of 400-1000 nm was investigated to develop a multispectral real-time imaging system allowing the meat industry to determine moisture content in red meat including beef, lamb, and pork. Multivariate calibration models were developed using partial l...
Mohammed Kamruzzaman, Yoshio Makino, Seiichi Oshita
A hyperspectral imaging system in the spectral range of 400-1000 nm was tested to develop an online monitoring system for red meat (beef, lamb, and pork) color in the meat industry. Instead of selecting different sets of important wavelengths for beef, lamb, and pork, a set of feature wavelengths we...
Mohammed Kamruzzaman, Yoshio Makino, Seiichi Oshita
A hyperspectral imaging system was investigated for determination of feature wavelengths to be used in a design of a multispectral system for real-time monitoring of water holding capacity (WHC) in red meat. Hyperspectral images of different red meat samples were acquired in the spectral range of 40...
Mohammed Kamruzzaman, Yoshio Makino, Seiichi Oshita
Pork adulteration in minced beef was detected for the first time using a hyperspectral imaging (HIS) technique.