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Characteristic Variables Optimization for TVB-N in Pork Based on Two-Dimensional Correlation Spectroscopy |
WANG Wen-xiu, PENG Yan-kun*, FANG Xiao-qian, BU Xiao-pu |
National Research and Development Center for Agro-processing Equipment, College of Engineering, China Agricultural University, Beijing 100083, China |
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Abstract In order to investigate the feasibility of two-dimensional (2D) visible/near-infrared (Vis/NIR) spectroscopy method to optimize the characteristic variables of total volatile basic nitrogen (TVB-N) in pork, storage time was employed as external disturbance and 2D correlation spectral characteristics of pork samples with different freshness degrees were studied in this paper. First, Vis/NIR reflectance spectra in the spectral region of 400~1 000 nm of 56 pork samples stored for 1~14 days were collected. Partial least squares regression (PLSR) model was established to relate full-band spectra after pre-processed with standard normalized variate (SNV) and TVB-N values with determination coefficient in the prediction set (R2p) of 0.792 1 and standard error in the prediction set (SEP) of 3.658 2 mg·(100 g)-1. Then ten samples which had a certain concentration gradient were selected for 2D correlation spectrum analysis (with storage time of 0, 36, 72, 108, 144, 180, 216, 252, 288 and 324 h) according to the reference values of TVB-N determined by the standard methods. To eliminate the influence of noise and environmental temperature, the original spectra were pre-treated with first derivative and seven bands were selected for 2D correlation spectrum analysis according to the spectral differences between different samples. The wavelength ranges were 400~420, 450~465, 500~550, 555~580, 586~717, 726~787 and 860~960 nm, respectively. By analyzing synchronization spectrum and autocorrelation spectrum of each band, 23 variables were selected as the sensitive wavelengths to TVB-N. Then simplified PLSR model was built based on the selected feature variables. Compared with the model based on full band spectral data, the model performance was improved with R2p increased to 0.865 8 and the SEP dropped to 3.246 0 mg·(100 g)-1. The results showed that it was feasible to optimize the characteristic variables of TVB-N based on 2D correlation spectrum and this method was capable of selecting feature variables which were related to target attribute. The study also provided a new method to select the characteristic wavelengths from NIR spectra.
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Received: 2017-08-11
Accepted: 2017-12-16
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Corresponding Authors:
PENG Yan-kun
E-mail: ypeng@cau.edu.cn
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