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.
王文秀，彭彦昆，房晓倩，卜晓朴. 二维相关光谱的猪肉TVB-N特征变量优选研究[J]. 光谱学与光谱分析, 2018, 38(07): 2094-2100.
WANG Wen-xiu, PENG Yan-kun, FANG Xiao-qian, BU Xiao-pu. Characteristic Variables Optimization for TVB-N in Pork Based on Two-Dimensional Correlation Spectroscopy. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(07): 2094-2100.
[1] ZHANG Kai-hua, ZANG Ming-wu, WANG Shou-wei, et al(张凯华，臧明伍，王守伟，等). Meat Research(肉类研究), 2016, 30(1): 30.
[2] Peng Y K, Dhakal S. Transaction of the ASABE, 2015, 58(5)：1371.
[3] Reis M M, Rosenvold K. Encyclopedia of Meat Sciences, 2014, 3：70.
[4] Balage J M, da Luz E Silva S, Gomide C A, et al. Meat Science, 2015, 108：37.
[5] WANG Wen-xiu, PENG Yan-kun, SUN Hong-wei, et al(王文秀，彭彦昆，孙宏伟，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2016, 32(23): 290.
[6] YANG Ren-jie, YANG Yan-rong, LIU Hai-xue, et al(杨仁杰，杨延荣，刘海学，等). Spectroscopy and Spectral Analysis (光谱学与光谱分析), 2015, 35(8)：2124.
[7] YANG Ren-jie, LIU Rong, XU Ke-xin(杨仁杰，刘 蓉，徐可欣). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2012, 28(6): 259.
[8] CHEN Bin, ZHANG Yu-ying, LU Dao-li, et al(陈 斌，张玉莹，陆道礼，等). Journal of The Chinese Cereals and Oils Association(中国粮油学报), 2016, 31(6): 153.
[9] Qu L, Chen J B, Zhou Q, et al. Journal of Molecular Structure, 2016, 1124: 216.
[10] Liu Y, Chen Y R. Meat Science, 2000, 57(3): 299.
[11] Liu Y, Barton F E, Lyon B G, et al. Journal of Agricultural and Food Chemistry, 2004, 52(3): 505.
[12] Chao K, Chen Y R, Thayer D W, et al. Applied Engineering in Agriculture, 2002, 18(6): 745.
[13] Li H H, Chen Q S, Zhao J W, et al. LWT-Food Science and Technology, 2015, 63(1)：268.
[14] Xie A G, Sun D W, Xu Z Y, et al. Talanta, 2015, 139: 208.
[15] Park Y, Jin S, Noda I, et al. Journal of Molecular Structure, 2018, 1168: 1.
