Abstract
The existing researches focused on using the commercial full wavelength spectrometer to determine the quality parameters of meat by detecting the meat emulsion, which were difficult to achieve online and non-destructive detection of the moisture content of intact meat. Moreover, the accuracy of pieces moisture detection is low, and people did not consider differences in the organizational structure of the pork meat itself. In this paper, we have developed a portable data acquisition system based on discrete wavelengths of spectral, and used it to detect the moisture content of fresh intact pork meat within a certain depth range. Based on the steady-state spatially resolved spectroscopy and considering the muscle fiber structure and direction of intact pork meat, we have designed a device with a symmetrical structure, which has a wavelength of 1300nm, 1450nm, 1550nm and 970nm LED light source for detecting the moisture content of the samples obtained from theLongissimus, within a certain depth range, and verified the stability and linearity of the system. The results show that the coefficient of determination is 0.49, and the detection range is 73.19%~77.654%. This study shows that scattering properties of meat is one of the main factors affecting the stability of detection.
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Liang, X., Lv, P., Zhang, X.: Research and development of the standards for moisture content of pork and beef meat (08), 38–43 (2001) (in Chinese) ISSN 1008-5467
Zhu, D., Wu, X., Liu, H., Xu, Y., Li, J.: Effect of fresh meat. Science and Technology of Food Industry (16), 363–366 (2013) (in Chinese)
Prieto, N., Roehe, R., Lavín, P., et al.: Application of near infrared reflectance spectroscopy to predict meat and meat products quality: A review. Meat Science 83(2), 175–186 (2009)
Barbin, D.F., ElMasry, G., Sun, D., et al.: Non-destructive determination of chemical composition in intact and minced pork using near-infrared hyperspectral imaging. Food Chemistry 138(2), 1162–1171 (2013)
Sun, T., Xu, H., Ying, Y.: Progress in Application of Near Infrared Spectroscopy to Nondestructive On-line Detection of Products/Food Quality. Spectroscopy and Spectral Analysis (01), 122–126 (2009) (in Chinese)
Xu, X., Cheng, F., Ying, Y.: Application and Recent Development of Research on Near Infrared Spectroscopy for Meat Quality Evaluation. Spectroscopy and Spectral Analysis (07), 1876–1880 (2009) (in Chinese)
Wen, X., Wang, Z., Huang, L.: Measurement of myoglobin in pork meat by using steady spatially-resolved spectroscopy. Transactions of the CSAE 26(supp. 2), 375–379 (2010) (in Chinese)
Zhang, G., Wen, X., Wang, Z., Zhao, D., Huang, L.: Measurement of Pork Tenderness by Using Steady Spatially-Resolved Spectroscopy. Spectroscopy and Spectral Analysis (10), 2793–2796 (2010) (in Chinese)
Ji, R., Hung, L., Liu, L., Wang, Z.: Method for Measuring Water Content in Fresh Meat Using Diffusion Reflectance Near Infrared Spectroscopy and Experiment. Spectroscopy and Spectral Analysis (08), 1767–1771 (2008)
Lai, J., Li, Z., Wang, Q., He, A.: System Model of Light Transporting in Biological Tissues and its Application. Acta Photonica Sinica (07), 1312–1317 (2007) (in Chinese)
Barbin, D., Elmasry, G., Sun, D.-W., et al.: Near-infrared hyperspectral imaging for grading and classification of pork. Meat Science 90(1), 259–268 (2012)
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Fan, LF., Wang, JX., Zhao, PF., Li, H., Wang, ZY., Huang, L. (2015). Research on Detection Moisture of Intact Meat Based on Discrete LED Wavelengths. In: Li, D., Chen, Y. (eds) Computer and Computing Technologies in Agriculture VIII. CCTA 2014. IFIP Advances in Information and Communication Technology, vol 452. Springer, Cham. https://doi.org/10.1007/978-3-319-19620-6_46
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DOI: https://doi.org/10.1007/978-3-319-19620-6_46
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