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| Rapid and Non-Destructive Detection of Tan Sheep Meat MetMb Contents Using Hyperspectral Imaging |
| CHENG Li-juan1, LIU Gui-shan1*, HE Jian-guo1, WAN Guo-ling1, MA Chao2, BAN Jing-jing1, MA Li-min1, YANG Guo-hua1, YUAN Rui-rui1 |
1. School of Agriculture Department of Food, Ningxia University, Yinchuan 750021, China
2. School of Physics and Electrical and Electronic Engineering, Ningxia University, Yinchuan 750021, China |
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Abstract The proportion of Metmyoglobin (MetMb) in meat directly affects the color of the meat. This paper combined the visible near-infrared spectroscopy (ViS-NIR) data of Tan sheep meat with the chemometric method to explore the feasibility of rapid non-destructive detection of MetMb content in Tan sheep by hyperspectral imaging technology and develop a quantitative function of MetMb content. The MetMb content of the sample was measured by a spectrophotometer, and the interest region of 200 sample spectral images during storage were extracted by ENVI4.8 software. The relationships between MetMb content and spectral date of samples were established to quantitatively analyze. In this study, according to the ratio of 3∶1, the whole dataset (n=200) was divided into a calibration set (n=50) for performing independent validation (external validation) of the developed calibration models using the sample set partitioning based on joint x-y distance method. Several spectral preprocessing techniques such as multiplicative scatter correction (MSC), first derivative (1st derivative) and De-trending were applied to eliminate noise. Competitive Adaptive Reweighted Sampling (CARS), Interval variable iterative space shrinkage approach (iVISSA), Interval Random Frog (IRF), Variables combination population analysis (VCPA) and Successie Projection Algorithm (SPA) were used to select and optimize variables. Partial least squares regression (PLSR), which was one classical linear calibration method, were used for developing prediction models based on full-band and feature bands. The results showed that the original spectral model was best, and its R2C=0.852, R2P=0.788, RMSEC=4.604, RMSEP=5.729. The CARS, IRF, SPA, iVISSA, VCPA, IRF+SPA and iVISSA+SPA methods were applied to select 16, 13, 48, 14, 45, 13, 10 and 11 feature wavelengths from the original spectra, accounting for 38.4%, 10.4%, 11.2%, 36%, 10.4%, 8%, 8.8% and 12.8% of the full wavelength, respectively. The IRF+SPA-PLSR model was the best among the models developed, and its R2C, R2P, RMSEC and RMSEP values were 0.808, 0.826, 5.253 and 5.149, respectively. The IRF+SPA algorithm greatly reduced calculating time and generated more accurate and more robust prediction model compared with full band. Finally, the quantitative linear relationship between spectral data and MetMb parameters was established based on the IRF+SPA algorithm, and the quantitative function was:  (MetMb)=3.249 7+1.9002λ468-4.791 2λ482+5.913 5λ512-1.856 2λ530-5.879 3λ545+2.246 3λ560+5.066 1λ580-2.320 1λ588+1.214 9λ790-1.348 8λ814. It is shows that Vis-NIR is feasible for the rapid non-destructive detection of MetMb content in Tan sheep. Simultaneously, the quantitative function developed provides a reference for the rapid determination of MetMb content in Tan sheep.
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Received: 2019-03-16
Accepted: 2019-07-29
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Corresponding Authors:
LIU Gui-shan
E-mail: liugs@nxu.edu.cn
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