Title: Hyperspectral reflectance imaging technique for visualization of moisture distribution in cooked chicken breast Authors
|Kandpal, Lalit -|
|Lee, Hoonsoo -|
|Cho, Byoung-Kwan -|
Submitted to: Sensors
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: September 1, 2013
Publication Date: September 30, 2013
Citation: Kandpal, L., Lee, H., Kim, M.S., Cho, B. 2013. Hyperspectral reflectance imaging technique for visualization of moisture distribution in cooked chicken breast. Sensors. 13(10):13289-300. Interpretive Summary: Spectral imaging has proven to be an efficient tool for measuring the quality properties of meat. The hyperspectral imaging (HSI) technique was investigated for the determination of moisture content in cooked chicken breast meat. Upon applying spectral imaging analysis methods, the resultant image allowed visualization of the dehydration and water distribution within different chicken breast meat regions. The demonstrated results presented in this study will benefit poultry industry, as well as food technologists.
Technical Abstract: Spectroscopy has proven to be an efficient tool for measuring the properties of meat. In this article, the hyperspectral imaging (HSI) technique is investigated for the determination of moisture content in cooked chicken breast over the VIS/NIR (400–1000 nm) spectral ranges. Moisture measurements were performed using an oven drying method. A partial least squares regression (PLS-R) model was developed to extract a relationship between HSI spectra and moisture content. The PLS-R model possessed maximum R^2 p of 0.90 and an SEP of 0.74% in the full wavelength range. For the NIR range, the PLS-R model yielded an R^2 p of 0.94 and an SEP of 0.71%. The most absorption peaks around 760 and 970 nm, which are related to the third and second overtones of the O-H stretching modes, representing the water contents in the samples. Finally PLS images were constructed to visualize the dehydration and water distribution within different sample regions. The high correlation coefficient and low prediction error from the PLS-R analysis validates that the HSI technique as an effective tool for visualizing the chemical quality of meat.