• Spectroscopy and Spectral Analysis
  • Vol. 42, Issue 11, 3631 (2022)
You-rui SUN1、*, Mei GUO1、*, Gui-shan LIU1、1; *;, Nai-yun FAN1、1; *;, Hao-nan ZHANG2、2;, Yue LI1、1;, Fang-ning PU2、2;, Shi-hu YANG1、1;, and Hao WANG2、2;
Author Affiliations
  • 11. College of Food and Wine, Ningxia University, Yinchuan 750021, China
  • 22. School of Physics and Electronic-Electrical Engineering, Ningxia University, Yinchuan 750021, China
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    DOI: 10.3964/j.issn.1000-0593(2022)11-3631-06 Cite this Article
    You-rui SUN, Mei GUO, Gui-shan LIU, Nai-yun FAN, Hao-nan ZHANG, Yue LI, Fang-ning PU, Shi-hu YANG, Hao WANG. Fusion of Visible Near-Infrared (VNIR) Hyperspectral Imaging and Texture Feature for Prediction of Total Phenolics Content in Tan Mutton[J]. Spectroscopy and Spectral Analysis, 2022, 42(11): 3631 Copy Citation Text show less

    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 RC2 and RP2 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 (RC2vs. RP2=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 (RC2=0.850 0, RP2=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.
    You-rui SUN, Mei GUO, Gui-shan LIU, Nai-yun FAN, Hao-nan ZHANG, Yue LI, Fang-ning PU, Shi-hu YANG, Hao WANG. Fusion of Visible Near-Infrared (VNIR) Hyperspectral Imaging and Texture Feature for Prediction of Total Phenolics Content in Tan Mutton[J]. Spectroscopy and Spectral Analysis, 2022, 42(11): 3631
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