FIRST DERIVATIVE PREDICTION OF RAW BROILER SHEAR FORCE USING VISIBLE SHORT WAVE NEAR INFRARED SPECTROSCOPY
DOI:
https://doi.org/10.11113/jt.v78.9414Keywords:
Broilers, near infrared spectroscopy, texture analyser, PCRAbstract
A non-destructive,fast, reliable and low cost technique which is Near-Infrared Spectroscopy (NIRS) is required to replace conventional destructive texture analyser in shear force measurement. The combination of visible and shortwave near infrared (VIS-SWNIR) spectrometer and principal component regression (PCR) to assess the quality attribute of raw broiler meat texture (shear force value (kg)) was investigated. Wavelength region of visible and shortwave 662-1005 nm was selected for prediction after pre-processing. Absorbance spectra was pre-processed using the optimal Savitzky-Golay smoothing mode with 1st order derivative, 2nd degree polynomial and 31 filter points to remove the baseline shift effect. Potential outliers were identified through externally studentised residual approach. The PCR model were trained with 90 samples in calibration and validated with 44 samples in prediction datasets. From the PCR analysis, correlation coefficient of calibration (RC), the root mean square calibration (RMSEC), correlation coefficient of prediction (RP) and the root mean square prediction (RMSEP) of visible and shortwave (662-1005 nm) with 4 principal components were 0.4645,0.0898, 0.4231 and 0.0945. The predicted results can be improved by applying the 2nd order derivative and the non-linear model.
References
Ghazali, R., Abdul Rahim, H., Shikh Maidin, M. and Sahlan, S. 2013. Low Cost Visible/Near-Infrared Reflectance Spectroscopy for Raw Poultry Meat Texture Prediction. World Academy of Science, Engineering and Technology. 77: 486-491.
Ghazali, R., Abdul Rahim, H., Shikh Maidin, M. and Sahlan, S. 2013. Prediction of Texture of Raw Poultry Meat by Visible and Near-Infrared. Journal of Technology. 64: 59-62.
Liao, Y.-T., Fan, Y.-X. and Cheng, F. 2010. On-line Prediction Of Fresh Pork Quality Using Visible/Near-Infrared Reflectance Spectroscopy. Meat Science. 86: 901-907.
Liu, Y., Lyon, B. G., Windham, W. R., Realini, C. E., Pringle, T. D. D. and Duckett, S. 2003. Prediction Of Color, Texture, And Sensory Characteristics Of Beef Steaks By Visible And Near Infrared Reflectance Spectroscopy. A Feasibility Study. Meat Science. 65: 1107-1115.
Department of Islamic Development Malaysia. 2009. Malaysian Protocol for the Halal Meat and Poultry Productions.
Cavitt, L. C., Youm, G. W., Meullenet, J. F., Owens, C. M., and Xiong, R. 2004. Prediction of Poultry Meat Tenderness Using Razor Blade Shear, Allo–Kramer Shear, and Sarcomere Length. Journal of Food Science. 69: 11-15.
Lambe, N. R., Navajas, E. A., Bünger, L., Fisher, A. V., Roehe, R., and Simm, G. 2009. Prediction Of Lamb Carcass Composition And Meat Quality Using Combinations Of Post-Mortem Measurements. Meat Science. 81: 711-719.
Salwani, M. S., Sazili, A. Q., Zulkifli, I., Nizam, Z., and Zul Edham, W. 2012. Effects of Head-Only Electrical Stunning on the Physico-Chemical Characteristics and Desmin Degradation of Broiler Breast Musles of Different Time Postmoterm. Journal of Animal and Veterinary Advance. 11: 2409-2416.
Ocean Optics, I. 2012. Non-Invasive Reflection Measurements of the Skin, Assessing Sampling Depth by Using Skin Surrogates. Photonics Online. 727-733.
Chia, K. S., Abdul Rahim, H., and Abdul Rahim, R. 2012. Neural Network And Principal Component Regression In Non-Destructive Soluble Solids Content Assessment: A Comparison. Journal of Zhejiang University-Science B. 13: 145-151.
Press, W. H., Flannery, B. P., Teukolsky, S. A., and Vetterling, W. T. 1993. Savitzky-Golay Smoothing Filters. Numerical Recipes in FORTRAN 77: The Art of Science Computing. 2nd Ed. Cambridge: University Press.
Rinnan, A., Berg, F.v.d., and Engelsen, S. B. 2009. Review Of The Most Common Pre-Processing Techniques For Near-Infrared Spectra. Trends in Analytical Chemistry. 28: 1201-1222.
Chen, H., Pan, T., Chen, J., and Lu, Q. 2011. Waveband Selection For NIR Spectroscopy Analysis Of Soil Organic Matter Based On SG Smoothing And MWPLS Methods. Chemometrics and Intelligent Laboratory Systems. 107: 139-146.
Chia, K. S., Abdul Rahim, H. and Abdul Rahim, R. 2013. Evaluation Of Common Pre-Processing Approaches For Visible (VIS) And Shortwave Near Infrared (SWNIR) Spectroscopy In Soluble Solids Content (SSC) Assessment. Biosystems Engineering. 115: 82-88.
Cozzolino, D. and Murray, I. 2004. Identification Of Animal Meat Muscles By Visible And Near Infrared Reflectance Spectroscopy. LWT-Food Science and Technology. 37: 447-452.
Riovanto, R., De Marchi, M., Cassandro, M. and Penasa, M. 2012. Use Of Near Infrared Transmittance Spectroscopy To Predict Fatty Acid Composition Of Chicken Meat. Food Chemistry. 134: 2459-64.
Mamani-Linares, L. W., Gallo, C. and Alomar, D. 2012. Identification Of Cattle, Llama And Horse Meat By Near Infrared Reflectance Or Transflectance Spectroscopy. Meat Science. 90: 378-385.
Cozzolino, D., De Mattos, D. and Vaz Martins, D. 2002. Visible/near Infrared Reflectance Spectroscopy For Predicting Composition And Tracing System Of Production Of Beef Muscle. Animal Science. 74: 477-484.
Grau, R., Sánchez, A. J., Girón, J., Iborra, E., Fuentes, A. and Barat, J. M. 2011. Nondestructive Assessment Of Freshness In Packaged Sliced Chicken Breasts Using SW-NIR Spectroscopy. Food Research International. 44: 331-337.
Morsy, N. and Sun, D.-W. 2013. Robust linear and non-linear models of NIR spectroscopy for detection and quantification of adulterants in fresh and frozen-thawed minced beef. Meat Science. 93: 292–302.
Andrés, S., Murray, I., Navajas, E. A., Fisher, A. V., Lambe, N. R., and Bünger, L. 2007. Prediction Of Sensory Characteristics Of Lamb Meat Samples By Near Infrared Reflectance Spectroscopy. Meat Science. 76: 509-516.
Guy, F., Prache, S., Thomas, A., Bauchart, D. and Andueza, D. 2011. Prediction Of Lamb Meat Fatty Acid Composition Using Near-Infrared Reflectance Spectroscopy (NIRS). Food Chemistry. 127: 1280-1286.
Chia, K.S., Abdul Rahim, H. and Abdul Rahim, R. 2012. Prediction Of Soluble Solids Content Of Pineapple Via Non-Invasive Low Cost Visible And Shortwave Near Infrared Spectroscopy And Artificial Neural Network. Biosystems Engineering. 1.
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