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Proceedings Paper

Hyperspectral imaging technique for determination of pork freshness attributes
Author(s): Yongyu Li; Leilei Zhang; Yankun Peng; Xiuying Tang; Kuanglin Chao; Sagar Dhakal
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Paper Abstract

Freshness of pork is an important quality attribute, which can vary greatly in storage and logistics. The specific objectives of this research were to develop a hyperspectral imaging system to predict pork freshness based on quality attributes such as total volatile basic-nitrogen (TVB-N), pH value and color parameters (L*,a*,b*). Pork samples were packed in seal plastic bags and then stored at 4°C. Every 12 hours. Hyperspectral scattering images were collected from the pork surface at the range of 400 nm to 1100 nm. Two different methods were performed to extract scattering feature spectra from the hyperspectral scattering images. First, the spectral scattering profiles at individual wavelengths were fitted accurately by a three-parameter Lorentzian distribution (LD) function; second, reflectance spectra were extracted from the scattering images. Partial Least Square Regression (PLSR) method was used to establish prediction models to predict pork freshness. The results showed that the PLSR models based on reflectance spectra was better than combinations of LD "parameter spectra" in prediction of TVB-N with a correlation coefficient (r) = 0.90, a standard error of prediction (SEP) = 7.80 mg/100g. Moreover, a prediction model for pork freshness was established by using a combination of TVB-N, pH and color parameters. It could give a good prediction results with r = 0.91 for pork freshness. The research demonstrated that hyperspectral scattering technique is a valid tool for real-time and nondestructive detection of pork freshness.

Paper Details

Date Published: 2 June 2011
PDF: 9 pages
Proc. SPIE 8027, Sensing for Agriculture and Food Quality and Safety III, 80270H (2 June 2011); doi: 10.1117/12.883652
Show Author Affiliations
Yongyu Li, China Agricultural Univ. (China)
Leilei Zhang, China Agricultural Univ. (China)
Yankun Peng, China Agricultural Univ. (China)
Xiuying Tang, China Agricultural Univ. (China)
Kuanglin Chao, U.S.D.A Agricultural Research Service (United States)
Sagar Dhakal, China Agricultural Univ. (China)


Published in SPIE Proceedings Vol. 8027:
Sensing for Agriculture and Food Quality and Safety III
Moon S. Kim; Shu-I Tu; Kaunglin Chao, Editor(s)

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