Spectroscopy has proven to be an efficient tool for measuring the properties of meat. In this article, hyperspectral imaging (HSI) techniques are used to determine the moisture content in cooked chicken breast over the VIS/NIR (400–1,000 nm) spectral range. Moisture measurements were performed using an oven drying method. A partial least squares regression (PLSR) model was developed to extract a relationship between the HSI spectra and the moisture content. In the full wavelength range, the PLSR model possessed a maximum ?of 0.90 and an SEP of 0.74%. For the NIR range, the PLSR model yielded an ?of 0.94 and an SEP of 0.71%. The majority of the absorption peaks occurred around 760 and 970 nm, representing the water content in the samples. Finally, PLSR images were constructed to visualize the dehydration and water distribution within different sample regions. The high correlation coefficient and low prediction error from the PLSR analysis validates that HSI is an effective tool for visualizing the chemical properties of meat.
References
[1]
Cross, H.R.; Durland, P.R.; Sideman, S.C. Sensory Qualities of Meat. In Muscle as Food; Betchel, P.J., Ed.; Academic Press: New York, NY, USA, 1986; pp. 279–320.
[2]
Palka, K.; Daun, H. Changes in texture, cooking loss, and myoflbrillar structure of bovine M semitendinosus during heating. Meat Sci. 1999, 51, 237–243.
[3]
Kamruzzaman, M.; ElMasry, G.; Sun, D.-W.; Allen, P. Non destructive prediction and visualization of chemical composition in lamb meat using NIR hyperspectral imaging and multivariate regression. Innov. Food Sci. Emerg. Technol. 2012, 16, 218–226.
[4]
Barlocco, N.; Vadell, A.; Ballesteros, F.; Galietta, G.; Cozzolino, D. Predicting intramuscular fat, moisture and Warner-Bratzler shear force in pork muscle using near infrared reflectance spectroscopy. Anim. Sci. 2006, 82, 111–116.
[5]
Br?ndum, J.; Munck, L.; Henckel, P.; Karlsson, A.; Tornberg, E.; Engelsen, S.B. Prediction of water-holding capacity and composition of porcine meat by comparative spectroscopy. Meat Sci. 2000, 55, 177–185.
[6]
Kamdaswamy, J.; Bajwa, S.G.; Apple, J.K. Chemometric Modeling of Fat, Cholesterol and Caloric Content of Fresh and Cooked Ground Beef with NIR Reflectance Spectroscopy. Proceedings of the Sensors for Industry Conference, Houston, TX, USA, 8–10 February 2005; pp. 52–58.
[7]
Wu, D.; Wang, S.; Wang, N.; Nie, P.; He, Y.; Sun, D.W.; Yao, J. Application of time series hyperspectral imaging (TS-HIS) for determining water distribution within beef and spectral kinetic analysis during dehydration. Food Bioprocess. Technol. 2012, 1–16.
[8]
Qiao, J.; Wang, N.; Ngadi, M.O.; Gunenc, A.; Monroy, M.; Gariépy, C.; Prasher, S.O. Prediction of drip-loss, pH, and color for pork using a hyperspectral imaging technique. Meat Sci. 2007, 76, 1–8.
[9]
Ahn, C.K.; Mo, C.Y.; Kang, J.S.; Cho, B.K. Nondestructive classification of viable and non-viable radish (raphanus sativus l) seeds using hyperspectral reflectance imaging. J. Biosys. Eng. 2012, 37, 411–419.
[10]
Varmuza, K.; Filzmoser, P. Introduction to Multivariate Statistical Analysis in Chemometrics; CRC Press: Boca Raton, FL, USA, 2009.
[11]
Wu, D.; Shi, H.; Wang, S.; He, Y.; Bao, Y.; Liu, K. Rapid prediction of moisture content of dehydrated prawns using online hyperspectral imaging system. Anal. Chim. Acta. 2012, 726, 57–66.