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Spectroscopy and Spectral Analysis, Volume. 41, Issue 12, 3688(2021)

Rapid Determination of αs1-Casein and κ-Casein in Milk Based on Fourier Transform Infrared Spectroscopy

Shi-jie XIAO1、*, Qiao-hua WANG1、1; 2; *;, Yi-kai FAN3、3;, Rui LIU3、3;, Jian RUAN3、3;, Wan WEN4、4;, Ji-qi LI4、4;, Huai-feng SHAO4、4;, Wei-hua LIU5、5;, and Shu-jun ZHANG3、3; *;
Author Affiliations
  • 11. College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
  • 33. Key Laboratory of Animal Breeding and Reproduction of Minstry of Education, Huazhong Agricultural University, Wuhan 430070, China
  • 44. Ningxia Hui Autonomous Region Animal Husbandary Workstation, Yinchuan 750002, China
  • 55. Ningxia Hui Autonomous Region Veterinary Medicine and Feed Suqervision Institute, Yinchuan 750011, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (11)
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    References (6)
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    Figures & Tables(11)
    Original spectra of cow’s milk

    Fig. 1. Original spectra of cow’s milk

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    Average spectra of cow’s milk

    Fig. 2. Average spectra of cow’s milk

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    Mean value and variance distributions of (a)αs1 and (b) κ-casein

    Fig. 3. Mean value and variance distributions of (a)αs1 and (b) κ-casein

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    Variable selection of CARS

    Fig. 4. Variable selection of CARS

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    Screening characteristic wavelengths by UVE

    Fig. 5. Screening characteristic wavelengths by UVE

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    (a) RMSE; (b) Selected optimal wavelength number index

    Fig. 6. (a) RMSE; (b) Selected optimal wavelength number index

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    Optimal model for αs1-casein based on CARS

    Fig. 7. Optimal model for αs1-casein based on CARS

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    Optimal model for κ-casein based on UVE

    Fig. 8. Optimal model for κ-casein based on UVE

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    • Table 1. Data statistics of partitioning sample sets by SPXY algorithm

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      Table 1. Data statistics of partitioning sample sets by SPXY algorithm

      指标αs1-酪蛋白/(g·L-1)κ-酪蛋白/(g·L-1)
      样本集训练集测试集训练集测试集
      最小值2.783 42.695 31.156 11.158 4
      最大值13.782 416.208 76.475 45.992 9
      平均值7.672 48.047 03.171 22.881 9
      标准差2.407 72.928 51.127 51.125 0

    • Table 2. SVR prediction model of αs1-casein based on 3 characteristic variable selection methods

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      Table 2. SVR prediction model of αs1-casein based on 3 characteristic variable selection methods

      波长选
      择算法      		预处理
      方式      		变量
      个数      		训练集测试集
      RcRMSECRpRMSEP
      CARS原始150.814 71.400 60.756 61.933 9
      SNV380.837 41.343 70.820 91.771 1
      MSC240.825 11.402 80.839 71.825 2
      一阶导数240.882 71.136 30.899 81.372 6
      二阶导数320.844 51.324 30.800 91.808 5
      normaliz180.801 91.447 90.794 31.825 6
      一阶差分110.833 61.341 00.817 81.746 2
      二阶差分150.877 71.150 80.767 91.883 3
      UVE原始690.825 71.370 10.838 81.644 1
      SNV1290.832 71.365 50.816 91.808 8
      MSC1370.812 41.420 00.800 61.887 5
      一阶导数1080.880 31.165 30.851 11.594 7
      二阶导数1000.852 61.310 50.845 21.701 5
      normaliz1010.840 71.327 90.819 11.746 1
      一阶差分950.866 41.246 60.835 31.711 8
      二阶差分1660.941 20.840 30.821 41.711 8
      SPA原始230.825 61.388 20.839 21.676 9
      SNV440.923 70.929 10.724 42.003 4
      MSC140.694 31.741 70.794 41.849 2
      一阶导数210.827 01.381 70.821 51.767 8
      二阶导数210.943 60.808 40.837 11.603 8
      normaliz400.777 61.532 10.743 11.974 3
      一阶差分90.888 21.129 30.827 01.694 1
      二阶差分60.798 81.465 30.831 01.707 0

    • Table 3. SVR prediction model of κ-casein based on different characteristic variable selection methods

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      Table 3. SVR prediction model of κ-casein based on different characteristic variable selection methods

      波长选
      择算法      		预处理
      方式      		变量
      个数      		训练集测试集
      RcRMSECRpRMSEP
      CARS原始330.883 60.529 50.792 50.685 4
      SNV150.824 50.649 40.793 60.692 4
      MSC200.845 50.605 60.826 90.651 3
      一阶导数150.872 40.552 80.837 20.614 1
      二阶导数150.885 20.524 40.761 40.726 0
      normaliz200.852 20.595 80.823 90.657 0
      一阶差分110.858 20.580 80.850 70.606 6
      二阶差分160.864 30.566 10.873 90.576 0
      UVE原始1450.842 70.610 10.828 90.637 0
      SNV1070.893 90.507 80.848 70.596 6
      MSC1140.881 00.538 80.848 60.605 4
      一阶导数570.860 30.591 00.851 40.618 9
      二阶导数670.881 40.541 50.857 90.591 3
      normaliz1200.860 80.576 20.843 60.612 8
      一阶差分550.880 80.534 50.890 30.535 4
      二阶差分1070.914 70.473 50.887 70.558 1
      SPA原始230.850 40.593 40.848 90.608 9
      SNV170.886 20.526 30.834 10.622 4
      MSC150.861 70.576 80.820 30.638 3
      一阶导数140.853 70.587 70.885 10.540 8
      二阶导数70.807 60.664 00.850 80.618 1
      normaliz90.825 90.639 10.859 10.608 1
      一阶差分90.892 10.513 00.808 20.668 1
      二阶差分70.807 60.664 00.850 80.618 1
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    Shi-jie XIAO, Qiao-hua WANG, Yi-kai FAN, Rui LIU, Jian RUAN, Wan WEN, Ji-qi LI, Huai-feng SHAO, Wei-hua LIU, Shu-jun ZHANG. Rapid Determination of αs1-Casein and κ-Casein in Milk Based on Fourier Transform Infrared Spectroscopy[J]. Spectroscopy and Spectral Analysis, 2021, 41(12): 3688

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    Paper Information

    Category: Research Articles

    Received: Nov. 23, 2020

    Accepted: --

    Published Online: Dec. 17, 2021

    The Author Email: Shi-jie XIAO (1175760869@qq.com)

    DOI:10.3964/j.issn.1000-0593(2021)12-3688-07

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology