Feature extraction method of hyperspectral scattering images for prediction of total viable count in pork meat

Tao Feifei, Peng Yankun, Li Yongyu

Abstract


This study aimed to investigate the capabilities of hyperspectral scattering imaging in tandem with Gaussian function, Exponential function and Lorentzian function for rapid and nondestructive determination of total viable count (TVC) in pork meat. Two batches of fresh pork meat was purchased from a local market and stored at 10°C for 1-9 d. Totally 60 samples were used, and several samples were taken out randomly for hyperspectral scattering imaging and conventional microbiological tests on each day of the experiments. The functions of Gaussian, Exponential and Lorentzian were employed to model the hyperspectral scattering profiles of pork meat, and good fitting results were obtained by all three functions between 455 nm and 1 000 nm. The Lorentzian function performed best for fitting the hyperspectral scattering profiles of pork meat compared with other functions. Both principal component regression (PCR) and partial least squares regression (PLSR) methods were performed to establish the prediction models. Among all the developed models, the models developed using parameters CE (scattering width parameter of Exponential function) and CL (scattering width parameter of Lorentzian function) by PLSR method gave superior results for predicting pork meat TVC, with RV and RMSEV of 0.92, 0.59 log CFU/g, and 0.91, 0.61 log CFU/g, respectively. In addition, based on the improved hyperspectral scattering system, parameter c which represented the scattering widths in all three functions gave more accurate prediction results, regardless of the modeling methods (PCR or PLSR). The obtained results demonstrated that hyperspectral scattering imaging combined with the presented data analysis algorithm can be a powerful tool for evaluating the microbial safety of meat in the future.
Keywords: hyperspectral scattering imaging, pork meat, total viable count, Lorentzian function, Gaussian function, Exponential function
DOI: 10.3965/j.ijabe.20150804.1559

Citation: Tao F F, Peng Y K, Li Y Y. Feature extraction method of hyperspectral scattering images for prediction of total viable count in pork meat. Int J Agric & Biol Eng, 2015; 8(4): 95－105.

Keywords


hyperspectral scattering imaging, pork meat, total viable count, Lorentzian function, Gaussian function, Exponential function

Full Text:

PDF

References


Sofos J N. Challenges to meat safety in the 21st century. Meat Science, 2008; 78(1-2): 3−13.

Nychas G-J E, Skandamis P N, Tassou C C, Koutsoumanis K P. Meat spoilage during distribution. Meat Science, 2008; 78(1-2): 77−89.

China National Standard. GB2707-2005 Hygienic standard for fresh (frozen) meat of livestock. Standards Press of China, 2005.

Commission E. Commission regulation (EC) no 2073/2005 of November 2005 on microbiological criteria for foodstuffs (text with EEA relevance). Official Journal of the European Union, 2005; L338/1 − L338/26.

Borch E, Kant-Muermans M L, Blixt Y. Bacterial spoilage of meat and cured meat products. International Journal of Food Microbiology, 1996; 33(1): 103−120.

Dainty R H. Chemical/biochemical detection of spoilage. International Journal of Food Microbiology, 1996; 33(1): 19−33.

Ellis D I, Goodacre R. Rapid and quantitative detection of the microbial spoilage of muscle foods: Current status and future trends. Trends in Food Science and Technology, 2001; 12(11): 414−424.

Ellis D I, Broadhurst D, Kell D B, Rowland J J, Goodacre R. Rapid and quantitative detection of the microbial spoilage of meat by Fourier transform infrared spectroscopy and machine learning. Applied and Environmental Microbiology, 2002; 68(6): 2822−2828.

Barbri N E, Llobet E, Bari N E, Correig X, Bouchikhi B. Electronic nose based on metal oxide semiconductor sensors as an alternative technique for the spoilage classification of red meat. Sensors, 2008; 8: 142−156.

Shackelford S D, Wheeler T L, Koohmaraie M. Tenderness classification of beef: II, design and analysis of a system to measure beef longissimus shear force under commercial processing conditions. Journal of Animal Science, 1999; 77(6): 1474−1481.

Vote D J, Belk K E, Tatum J D, Scanga J A, Smith G C. Online prediction of beef tenderness using a computer vision system equipped with a BeefCam module. Journal of Animal Science, 2003; 81(2): 457−465.

Barbin D F, ElMasry G, Sun D-W, Allen P, Morsy N. Non-destructive assessment of microbial contamination in porcine meat using NIR hyperspectral imaging. Innovative Food Science and Emerging Technologies, 2013; 17: 180−191.

ElMasry G, Iqbal A, Sun D-W, Allen P, Ward P. Quality classification of cooked, sliced turkey hams using NIR hyperspectral imaging system. Journal of Food Engineering, 2011; 103(3): 333−344.

Feng Y Z, Sun D-W. Determination of total viable count (TVC) in chicken breast fillets by near-infrared hyperspectral imaging and spectroscopic transforms. Talanta, 2013; 105: 244−249.

Park B, Lawrence K C, Windham W R, Buhr R J. Hyperspectral imaging for detecting fecal and ingesta contaminants on poultry carcasses. Transactions of the ASABE, 2002; 45(6): 2017−2026.

Park B, Lawrence K C, Windham W R, Smith D. Performance of hyperspectral imaging system for poultry surface fecal contaminant detection. Journal of Food Engineering, 2006; 75: 340−348.

Peng Y K, Lu R. Analysis of spatially resolved hyperspectral scattering images for assessing apple fruit firmness and soluble solids content. Postharvest Biology and Technology, 2008; 48(1): 52−62.

Peng Y K, Zhang J, Wang W, Li Y Y, Wu J H, Huang H, et al. Potential prediction of the microbial spoilage of beef using spatially resolved hyperspectral scattering profiles. Journal of Food Engineering, 2011; 102(2): 163−169.

Qiao J, Ngadi M O, Wang N, Gariepy C, Prasher S O. Pork quality and marbling level assessment using a hyperspectral imaging system. Journal of Food Engineering, 2007; 83(1): 10−16.

Talens P, Mora L, Morsy N, Barbin D F, ElMasry G, Sun D W. Prediction of water and protein contents and quality classification of Spanish cooked ham using NIR hyperspectral imaging. Journal of Food Engineering, 2013; 11(3)7: 272−280.

Tao F F, Wang W, Li Y Y, Peng Y K, Wu J H, Shan J J, et al. A rapid nondestructive measurement method for assessing the total plate count on chilled pork surface. Spectroscopy and Spectral Analysis, 2010; 30(12): 3405−3409.

Tao F F, Peng Y K, Li Y Y, Chao K, Dhakal S. Simultaneous determination of tenderness and Escherichia coli contamination of pork using hyperspectral scattering technique. Meat Science, 2012; 90(3): 851−857.

Tao F F, Tang X, Peng Y K, Dhakal S. Classification of pork quality characteristics by hyperspectral scattering technique. In: Symposium Conducted at ASABE Annual International Meeting, Dallas, Texas, USA, 2012.

Tao F F, Peng Y K. A method for nondestructive prediction of pork meat quality and safety attributes by hyperspectral imaging technique. Journal of Food Engineering, 2014; 126: 98−106.

Tao F F, Peng Y K. A nondestructive method for prediction of total viable count in pork meat by hyperspectral scattering imaging. Food and Bioprocess Technology, 2015; 8(1): 17−30.

Wu J H, Peng Y K, Li Y Y, Wang W, Chen J, Dhakal S. Prediction of beef quality attributes using VIS/NIR hyperspectral scattering imaging technique. Journal of Food Engineering, 2012; 109(2): 267−273.

Zhang L L, Peng Y K, Tao F F, Zhao S W, Song Y L. Rapid non-destructive detection of total volatile basic nitrogen in pork using hyperspectral technique. Journal of Food Safety and Quality, 2012; 3(6): 575−579.

Guo Z, Huang W, Chen L, Peng Y K, Wang X. Shortwave infrared hyperspectral imaging for detection of pH value in Fuji apple. Int J Agric & Biol Eng, 2014; 7(2): 130−137.

Peng Y, Lu R. Modeling multispectral scattering profiles for prediction of apple fruit firmness. Transactions of the ASAE, 2005; 48(1): 235−242.

Peng Y K, Lu R. Prediction of apple fruit firmness and soluble solids content using characteristics of multispectral scattering images. Journal of Food Engineering, 2007; 82(2): 142−152.

Zhu Q, Huang M, Zhao X, Lu R. Analysis of hyperspectral scattering profiles using generalized Gaussian distribution for prediction of apple firmness and soluble solids content. In: Symposium Conducted at ASABE Annual International Meeting, Louisville, Kentucky, USA, 2011.

Beebe K R, Kowalski B R, Wold H. An introduction to multivariate calibration and analysis. Analytical Chemistry, 1987; 59: 1007A−1017A.

Goodacre R, Timmins E M, Burton R, Kaderbhai R, Woodward A M, Kell D B. Rapid identification of urinary tract infection bacteria using hyperspectral whole-organism fingerprinting and artificial neural networks. Microbiology, 1998; 144: 1157−1170.

Haaland D M, Thomas E V. Partial least-squares methods for spectral analyses, Relation to other quantitative calibration methods and the extraction of qualitative information. Analytical Chemistry, 1988; 60: 1193−1202.

Haaland D M, Thomas E V. Comparison of multivariate calibration methods for quantitative spectral analysis. Analytical Chemistry, 1990; 62(10): 1091−1099.

Martens H, Naes T. Multivariate Calibration, 1989; second ed., John Wiley & Sons, Ltd., Chichester, United Kingdom.

Park B, Abbott J A, Lee K J, Choi C H, Choi K H. Near-infrared diffuse reflectance for quantitative and qualitative measurement of soluble solids and firmness of delicious and gala apples. Transactions of the ASAE, 2003; 46(2): 1721−1731.

Bowen W J. The absorption spectra and extinction coefficients of myoglobin. Journal of Biological Chemistry, 1949; 179(1): 235−245.




Copyright (c)



2023-2026 Copyright IJABE Editing and Publishing Office