• Laser & Optoelectronics Progress
  • Vol. 57, Issue 1, 013002 (2020)
Yande Liu*, Yu Zhang, Hai Xu, Xiaogang Jiang, and Junzheng Wang
Author Affiliations
  • National and Local Joint Engineering Research Center of Fruit Intelligent Photoelectric Detection Technology and Equipment, School of Mechatronics & Vehicle Engineering, East China Jiaotong University, Nanchang, Jiangxi 330013, China
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    DOI: 10.3788/LOP57.013002 Cite this Article Set citation alerts
    Yande Liu, Yu Zhang, Hai Xu, Xiaogang Jiang, Junzheng Wang. Detection of Sugar Content of Pomegranates from Different Producing Areas Based on Near-Infrared Spectroscopy[J]. Laser & Optoelectronics Progress, 2020, 57(1): 013002 Copy Citation Text show less
    Dynamic detection device for near-infrared diffuse transmission. (a) Schematic of light path; (b) arrangement of light source
    Fig. 1. Dynamic detection device for near-infrared diffuse transmission. (a) Schematic of light path; (b) arrangement of light source
    Typical spectra of two types of pomegranates
    Fig. 2. Typical spectra of two types of pomegranates
    Spectra and appearances of samples with rough and normal surfaces. (a) Appearances; (b) spectra
    Fig. 3. Spectra and appearances of samples with rough and normal surfaces. (a) Appearances; (b) spectra
    Score scattered plot of principal component analysis
    Fig. 4. Score scattered plot of principal component analysis
    PLS-DA models. (a) PLS-DA model for calibration set; (b) PLS-DA model for prediction set
    Fig. 5. PLS-DA models. (a) PLS-DA model for calibration set; (b) PLS-DA model for prediction set
    PLS-DA models of sugar content of pomegranates from two different producing areas. (a) Sichuan pomegranate; (b) Yunnan pomegranate
    Fig. 6. PLS-DA models of sugar content of pomegranates from two different producing areas. (a) Sichuan pomegranate; (b) Yunnan pomegranate
    Pomegranate speciesNumber (N)RD /mmLD /mmMass /gRS /BrixMean RSSD
    Sichuan6063-8763-80198-33412.7-16.314.320.711
    Yunnan4079-9667-94246.2-443.812.9-15.714.220.570
    Test874-8268-79228-306.512.2-1613.760.638
    Table 1. Related parameters of pomegranate
    Data setNRpRMSEPRcRMSECMisjudgment rate /%
    Calibration set75--0.851.041.6
    Prediction set250.821.16--3
    Table 2. Reconstructed results of PLS-DA model
    PretreatmentmethodOriginRpRMSECRcRMSEP
    HybridmodelingSichuanand Yunnan0.490.660.460.61
    OriginalspectraSichuan0.820.370.890.33
    Yunnan0.800.340.850.29
    S-Gsmoothing +3*Sichuan0.740.440.680.52
    Yunnan0.800.340.800.33
    S-Gsmoothing+7*Sichuan0.740.440.670.53
    Yunnan0.770.350.710.39
    NormalizationSichuan0.640.500.670.53
    Yunnan0.690.410.780.34
    MSCSichuan0.630.500.630.56
    Yunnan0.710.410.770.35
    BaselineSichuan0.820.370.900.31
    Yunnan0.810.330.870.27
    Baseline+S-GSichuan0.740.440.670.53
    smoothing+3*Yunnan0.780.340.820.31
    Table 3. Results of models optimized by different pretreatment methods
    Yande Liu, Yu Zhang, Hai Xu, Xiaogang Jiang, Junzheng Wang. Detection of Sugar Content of Pomegranates from Different Producing Areas Based on Near-Infrared Spectroscopy[J]. Laser & Optoelectronics Progress, 2020, 57(1): 013002
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