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Laser & Optoelectronics Progress, Volume. 60, Issue 17, 1730003(2023)

Comparative Analysis of Characteristic Wavelength Screening of Apple Soluble Solids Based on Near-Infrared Spectroscopy

Jinfu Zhang1,2,3, Bin Tang1,2,3, Jianxu Wang1,2,3、*, Yanfei Chuan1,2,3, Zourong Long1,2,3, Qing Chen1,2,3, Junfeng Miao1,2,3, Linfeng Cai1,2,3, Mingfu Zhao1,2,3, and Mi Zhou2
Author Affiliations
  • 1Chongqing Key Laboratory of Optical Fiber Sensing and Photoelectric Detection, Chongqing 400054, China
  • 2Intelligent Optical Fiber Perception Technology, Chongqing University Engineering Research Center, Chongqing 400054, China
  • 3School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing 400054, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (12)
    Equations (5)
    References (16)
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    Figures & Tables(12)
    Schematic diagram of the spectrum acquisition platform

    Fig. 1. Schematic diagram of the spectrum acquisition platform

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    Original spectra of 120 samples

    Fig. 2. Original spectra of 120 samples

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    Comparison of original and preprocessed spectra

    Fig. 3. Comparison of original and preprocessed spectra

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    Flow chart of PLS modeling based on mutual information

    Fig. 4. Flow chart of PLS modeling based on mutual information

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    Relationship between number of PLS principal components and training sets R2 and MSE

    Fig. 5. Relationship between number of PLS principal components and training sets R2 and MSE

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    Full spectrum PLS analysis results

    Fig. 6. Full spectrum PLS analysis results

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    Screening results of key variables based on CARS. (a) Reserved variables per run; (b) RMSECV of PLS model based on cars; (c) change of variable regression coefficient during each operation, and the asterisk indicates the lowest RMSECV

    Fig. 7. Screening results of key variables based on CARS. (a) Reserved variables per run; (b) RMSECV of PLS model based on cars; (c) change of variable regression coefficient during each operation, and the asterisk indicates the lowest RMSECV

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    Analysis of CARS-PLS results

    Fig. 8. Analysis of CARS-PLS results

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    Analysis of MI-PLS results

    Fig. 9. Analysis of MI-PLS results

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    Selected variables

    Fig. 10. Selected variables

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    • Table 1. Statistics of SSC of 120 samples in the data sets

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      Table 1. Statistics of SSC of 120 samples in the data sets

      Data setNumber of samplesMin /%Max /%Mean /%Standard deviation /%
      Calibration set968.7814.6211.200.92
      Prediction set248.9213.5311.180.92
      Overall1208.7814.6211.190.91

    • Table 2. SSC prediction results of apple by PLS models using the variables obtained by different variable selection algorithms

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      Table 2. SSC prediction results of apple by PLS models using the variables obtained by different variable selection algorithms

      Modeling methodNumber of wavelengthsLVsR2RMSECRMSEP
      Full band -PLS303160.85110.94131.1915
      CARS-PLS12100.87460.86400.9757
      MI-PLS56150.92180.68220.8235
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    Jinfu Zhang, Bin Tang, Jianxu Wang, Yanfei Chuan, Zourong Long, Qing Chen, Junfeng Miao, Linfeng Cai, Mingfu Zhao, Mi Zhou. Comparative Analysis of Characteristic Wavelength Screening of Apple Soluble Solids Based on Near-Infrared Spectroscopy[J]. Laser & Optoelectronics Progress, 2023, 60(17): 1730003

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    Paper Information

    Category: Spectroscopy

    Received: Jun. 30, 2022

    Accepted: Sep. 5, 2022

    Published Online: Sep. 13, 2023

    The Author Email: Jianxu Wang (wangjianxu@cqut.edu.cn)

    DOI:10.3788/LOP221956

    Topics

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