| 光谱学与光谱分析 |
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| Simultaneous Detection of Multiple Quality Parameters of Pork Based on Fused Dual Band Spectral |
| WANG Wen-xiu1, PENG Yan-kun1*, XU Tian-feng1, LIU Yuan-yuan1，2 |
1. National Research and Development Center for Agro-processing Equipment, College of Engineering, China Agricultural University, Beijing 100083, China
2. College of Mechanic and Electrical Engineering, Tarim University, Alar 843300, China |
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Abstract For dual band visible/near infrared spectroscopy system (350~1 100 and 1 000~2 500 nm), there exsits a band overlap and for the same sample the reflectivity data were unlike due to the performance difference between instruments. A band connection and data fusion method was proposed in this paper to make better use of the dual-band data. A dual-band visible/near-infrared spectroscopy system was built in the study to collect 60 pork samples’ reflectance spectra. The reflectance spectra of samples were performed with pretreatment methods of Savitzky-Golay (S-G) and standard normal variable transform to eliminate the spectral noise. Then partial least squares regression (PLSR) prediction models of pork quality attributes (color, pH and cooking loss) based on single-band spectrum and dual-band spectrum were established, respectively. For the cross of two band overlap, the data were connected and integrated using the method put forward in this paper and then PLSR models were established based on the integrated data. The PLSR model yielded prediction result with correlation coefficient of validation (Rp) of 0.948 8, 0.920 0, 0.950 5, 0.930 1 and 0.903 5 for L*, a*, b*, pH value and cooking loss, respectively. To simplify the model, uninformative variables elimination (UVE) was employed to select characteristic variables. The experimental results show that the proposed method was able to achieve a better fusion of the two band spectral data, and it was good for the establishment of a more simplified and better prediction model.
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Received: 2015-12-23
Accepted: 2016-04-03
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Corresponding Authors:
PENG Yan-kun
E-mail: ypeng@cau.edu.cn
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