• Acta Optica Sinica
  • Vol. 42, Issue 1, 0130002 (2022)
Jian Hu1, Yaoze Feng1、2、3、*, Yijian Wang1, Jie Huang1, Guifeng Jia1、2, and Ming Zhu1、2
Author Affiliations
  • 1College of Engineering, Huazhong Agricultural University, Wuhan, Hubei 430070, China
  • 2Key Laboratory of Agricultural Equipment in Mid-Lower Yangtze River, Ministry of Agriculture and Rural Affairs, Wuhan, Hubei 430070, China
  • 3Interdisciplinary Sciences Research Institute, Huazhong Agricultural University, Wuhan, Hubei 430070, China
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    DOI: 10.3788/AOS202242.0130002 Cite this Article Set citation alerts
    Jian Hu, Yaoze Feng, Yijian Wang, Jie Huang, Guifeng Jia, Ming Zhu. Detection of Umami Substances and Umami Intensity Based on Near-Infrared Spectroscopy[J]. Acta Optica Sinica, 2022, 42(1): 0130002 Copy Citation Text show less
    Flowchart of CMIC algorithm
    Fig. 1. Flowchart of CMIC algorithm
    Original spectrogram of sample
    Fig. 2. Original spectrogram of sample
    Performance analysis results of MSG concentration extracted by CMIC algorithm. (a) Variation trend under different window lengths; (b) variation trend of combined bands; (c) variation trend of number of resevered sub-intervals; (d) feature variables extracted by CMIC algorithm
    Fig. 3. Performance analysis results of MSG concentration extracted by CMIC algorithm. (a) Variation trend under different window lengths; (b) variation trend of combined bands; (c) variation trend of number of resevered sub-intervals; (d) feature variables extracted by CMIC algorithm
    Performance analysis results of IMP concentration extracted by CMIC algorithm. (a) Scatter diagram for verification of IMP concentration detection in mixed solution; (b) feature variables extracted by CMIC algorithm
    Fig. 4. Performance analysis results of IMP concentration extracted by CMIC algorithm. (a) Scatter diagram for verification of IMP concentration detection in mixed solution; (b) feature variables extracted by CMIC algorithm
    Performance analysis results of extracting EUC values by CMIC algorithm. (a) Scatter diagram for verification of EUC value in mixed solution; (b) feature variables extracted by CMIC algorithm
    Fig. 5. Performance analysis results of extracting EUC values by CMIC algorithm. (a) Scatter diagram for verification of EUC value in mixed solution; (b) feature variables extracted by CMIC algorithm
    AlgorithmNumber of variablesPCRc2Rp2RMSEc /(g·dL-1)RMSEp /(g·dL-1)
    Original3112200.73960.61130.01730.0204
    iPLS18370.86080.80500.01270.0145
    UVE1767200.68850.52710.01900.0225
    CARS73140.91800.87930.00970.0114
    CMIC414140.90960.88860.01020.0109
    Table 1. Comparison of different MSG concentration detection models
    AlgorithmNumber of variablesPCRc2Rp2RMSEc /(g·dL-1)RMSEp /(g·dL-1)
    Original3112160.78340.72720.10870.1302
    iPLS19570.90060.87880.07370.0868
    UVE1368190.93980.90870.05730.0753
    CARS85200.94460.91030.05500.0747
    CMIC602130.92390.91820.06440.0713
    Table 2. Comparison of different IMP concentration detection models
    AlgorithmNumber of variablesPCRc2Rp2RMSEc /(g·dL-1)RMSEp /(g·dL-1)
    Original3112120.75340.75963.49553.6533
    iPLS283100.83820.78402.83193.4625
    UVE1476140.79480.79303.18893.3899
    CARS73140.85850.80222.64763.3137
    CMIC417140.83930.80972.82213.2506
    Table 3. Comparison of different EUC value detection models
    Jian Hu, Yaoze Feng, Yijian Wang, Jie Huang, Guifeng Jia, Ming Zhu. Detection of Umami Substances and Umami Intensity Based on Near-Infrared Spectroscopy[J]. Acta Optica Sinica, 2022, 42(1): 0130002
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