Fusion of Visible Near-Infrared (VNIR) Hyperspectral Imaging and Texture Feature for Prediction of Total Phenolics Content in
Tan Mutton
SUN You-rui1, GUO Mei1, LIU Gui-shan1*, FAN Nai-yun1*, ZHANG Hao-nan2, LI Yue1, PU Fang-ning2, YANG Shi-hu1, WANG Hao2
1. College of Food and Wine, Ningxia University, Yinchuan 750021, China
2. School of Physics and Electronic-Electrical Engineering, Ningxia University, Yinchuan 750021, China
Abstract：The visible near-infrared (Vis-NIR) hyperspectral imaging technology was used to rapidly detect Tan mutton’s total phenol concentration (TPC) content. The prediction mode and visualization of TPC content in Tan mutton were built and realized based on spectral information in combination with texture features. Firstly, the calibration set and prediction set were divided by 3∶1, and then multiplicative scatter correction (MSC), Baseline, De-trending, savitzky-golay (S-G), and Standard normal variate transformation (SNV), and Normalize were used for model optimization. Secondly, feature bands were obtained by competitive adaptive reweighted sampling (CARS), bootstrapping soft shrinkage (BOSS), interval variable iterative space shrinkage approach (iVISSA) and variable combination population analysis coupled with iteratively retained informative variables (VCPA-IRIV), respectively. Then textural feature variables for the first principal component image were extracted by gray-level co-occurrence matrix (GLCM), respectively. Finally, partial least squares regression (PLSR) and least-squares support vector machines (LSSVM) models were built and optimized to predict TPC content. The results showed that: (1) The PLSR model yielded promising results after De-trending-SNV preprocessing, and R2P and R2C were 0.793 2 and 0.874 9; (2) The 23, 35, 57 and 43 characteristic bands based on the original spectral were extracted by CARS，BOSS，iVISSA and VCPA-IRIV methods, respectively, accounting for 18.4%, 28%, 45.6% and 16.8% of the total bands; (3) The simplified BOSS-LSSVM model yielded good results in assessing TPC content (R2Cvs.R2P=0.851 3 vs. 0.745 9, RMSEC vs. RMSEP=0.116 8 vs. 0.155 0); (4) Compared with predictive models based on characteristic wavelengths, the simply model BOSS-ASM-ENT-CON-LSSVM despited good results (R2C=0.850 0, R2P=0.770 9, RMSEC=0.116 0, RMSEP=0.144 7); (5) The simplified BOSS-PLSR model was displayed on the sample image in the form of pseudo-color to realize the visualization expression of TPC content.
Key words：Tan mutton; Hyperspectral imaging; Total phenol concentration; Fusion of spectra and texture feature; Visualization
孙有瑞，郭 美，刘贵珊，樊奈昀，张浩楠，李 月，蒲芳宁，杨世虎，王 昊. 高光谱技术融合纹理信息的羊肉总酚浓度快速检测[J]. 光谱学与光谱分析, 2022, 42(11): 3631-3636.
SUN You-rui, GUO Mei, LIU Gui-shan, FAN Nai-yun, ZHANG Hao-nan, LI Yue, PU Fang-ning, YANG Shi-hu, WANG Hao. Fusion of Visible Near-Infrared (VNIR) Hyperspectral Imaging and Texture Feature for Prediction of Total Phenolics Content in
Tan Mutton. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3631-3636.
[1] YUAN Yu-dong, YOU Li-qin, LUO Rui-ming, et al(苑昱东，尤丽琴，罗瑞明，等). Food Science(食品科学), 2019, 40(18): 195.
[2] Zhao Zhifang, Yu Hanyue, Zhang Siyu, et al. Optik, 2020, 212: 164737.
[3] REN Ying-chun, LIU Gui-shan, ZHANG Jing-jing, et al(任迎春，刘贵珊，张晶晶，等) . Chinese Journal of Luminescence(发光学报), 2019, 40(3): 396.
[4] Baek Insuck, Lee Hoonsoo, Cho Byoung-kwan, et al. Food Control, 2021, 124: 107854.
[5] YU Yang, ZHANG Yu, TIAN Hai-qing, et al(于 洋，张 珏，田海清，等). Journal of Agricultural Science and Technology(中国农业科技导报)，2021，23(12)：101.
[6] YANG Xiao-yu, DING Jia-xing, FANG Meng-meng, et al(杨晓玉，丁佳兴，房盟盟，等). Food and Machinery(食品与机械), 2017, 33(11): 131.
[7] Bonah E, Huang X Y, Aheto J H, et al. Infrared Physics & Technology, 2020, 107: 103327.
[8] Cheng Weiwei, Klavs Martin Sørensend, Søren Balling Engelsen, et al. Journal of Food Engineering, 2019, 263: 311.
[9] Peter C Wootton-Beard, Aisling Moran, Lisa Ryan. Food Research International, 2011, 44(1): 217.
[10] Chen C, Han L, Yu Q L, et al. Canadian Journal of Animal Science, 2015, 95(2): 1.
[11] Deng B C, Yun Y H, Cao D S, et al. Analytica Chimica Acta, 2016, 908: 63.
[12] LU Bing, SUN Jun, YANG Ning, et al(芦 兵，孙 俊，杨 宁，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2019, 39(8): 2515.
[13] Pan L Q, Zhu Q B, Lu R F, et al. Food Chemistry, 2015, 167: 264.
[14] Zheng X C, Li Y Y, Wei W S, et al. Meat Science, 2019, 149: 55.
[15] Douglas B, Gamal E, Sun D W, et al. Meat Science, 2012, 90: 259.
