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Proceedings Paper

Rapid non-destructive assessment of pork edible quality by using VIS/NIR spectroscopic technique
Author(s): Leilei Zhang; Yankun Peng; Sagar Dhakal; Yulin Song; Juan Zhao; Songwei Zhao
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Paper Abstract

The objectives of this research were to develop a rapid non-destructive method to evaluate the edible quality of chilled pork. A total of 42 samples were packed in seal plastic bags and stored at 4°C for 1 to 21 days. Reflectance spectra were collected from visible/near-infrared spectroscopy system in the range of 400nm to 1100nm. Microbiological, physicochemical and organoleptic characteristics such as the total viable counts (TVC), total volatile basic-nitrogen (TVB-N), pH value and color parameters L* were determined to appraise pork edible quality. Savitzky-Golay (SG) based on five and eleven smoothing points, Multiple Scattering Correlation (MSC) and first derivative pre-processing methods were employed to eliminate the spectra noise. The support vector machines (SVM) and partial least square regression (PLSR) were applied to establish prediction models using the de-noised spectra. A linear correlation was developed between the VIS/NIR spectroscopy and parameters such as TVC, TVB-N, pH and color parameter L* indexes, which could gain prediction results with Rv of 0.931, 0.844, 0.805 and 0.852, respectively. The results demonstrated that VIS/NIR spectroscopy technique combined with SVM possesses a powerful assessment capability. It can provide a potential tool for detecting pork edible quality rapidly and non-destructively.

Paper Details

Date Published: 29 May 2013
PDF: 8 pages
Proc. SPIE 8721, Sensing for Agriculture and Food Quality and Safety V, 872106 (29 May 2013); doi: 10.1117/12.2015893
Show Author Affiliations
Leilei Zhang, China Agricultural Univ. (China)
Yankun Peng, China Agricultural Univ. (China)
Sagar Dhakal, China Agricultural Univ. (China)
Yulin Song, China Agricultural Univ. (China)
Juan Zhao, China Agricultural Univ. (China)
Songwei Zhao, China Agricultural Univ. (China)


Published in SPIE Proceedings Vol. 8721:
Sensing for Agriculture and Food Quality and Safety V
Moon S. Kim; Shu-I Tu; Kuanglin Chao, Editor(s)

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