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| 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
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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 (R2C vs. 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.
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Received: 2021-09-13
Accepted: 2022-03-02
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Corresponding Authors:
LIU Gui-shan, FAN Nai-yun
E-mail: liugs@nxu.edu.cn；fny0606@163.com
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