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不同产地苹果糖度可见近红外光谱在线检测

刘燕德 徐海 孙旭东 姜小刚 饶宇 徐佳 王军政

刘燕德, 徐海, 孙旭东, 姜小刚, 饶宇, 徐佳, 王军政. 不同产地苹果糖度可见近红外光谱在线检测[J]. 中国光学(中英文), 2020, 13(3): 482-491. doi: 10.3788/CO.2019-0128
引用本文: 刘燕德, 徐海, 孙旭东, 姜小刚, 饶宇, 徐佳, 王军政. 不同产地苹果糖度可见近红外光谱在线检测[J]. 中国光学(中英文), 2020, 13(3): 482-491. doi: 10.3788/CO.2019-0128
LIU Yan-de, XU Hai, SUN Xu-dong, JIANG Xiao-gang, RAO Yu, XU Jia, WANG Jun-zheng. On-line detection of soluble solids content of apples from different origins by visible and near-infrared spectroscopy[J]. Chinese Optics, 2020, 13(3): 482-491. doi: 10.3788/CO.2019-0128
Citation: LIU Yan-de, XU Hai, SUN Xu-dong, JIANG Xiao-gang, RAO Yu, XU Jia, WANG Jun-zheng. On-line detection of soluble solids content of apples from different origins by visible and near-infrared spectroscopy[J]. Chinese Optics, 2020, 13(3): 482-491. doi: 10.3788/CO.2019-0128

不同产地苹果糖度可见近红外光谱在线检测

doi: 10.3788/CO.2019-0128
基金项目: 国家自然科学基金(No.31760344);江西省创新能力建设项目(No.S2016-90)
详细信息
    作者简介:

    刘燕德(1967—),女,江西泰和人,博士,教授,博士生导师,1990年、2001年于江西农业大学分别获得学士、硕士学位,2006年于浙江大学获得博士学位,主要从事光电检测技术与装备方面的研究。E-mail:jxliuyd@163.com

    通讯作者:

    刘燕德(1967−),女,江西泰和人,博士,教授,博士生导师,1990年、2001年于江西农业大学分别获得学士、硕士学位,2006年于浙江大学获得博士学位,主要从事光电检测技术与装备方面的研究。E-mail:jxliuyd@163.com

  • 中图分类号: O657.33

On-line detection of soluble solids content of apples from different origins by visible and near-infrared spectroscopy

Funds: Supported by National Natural Science Foundation of China(No.31760344); Jiangxi Provincial Project for Innovation Capacity Construction(No.S2016-90)
More Information
  • 摘要: 为了实现不同产地苹果糖度的快速在线无损检测,减少产地差异对近红外光谱检测模型的影响,建立了不同产地苹果糖度的在线检测通用模型。首先,采用水果动态在线检测设备采集了包括栖霞、洛川与会宁3个产地的红富士苹果的漫透射光谱。其次,采用偏最小二乘算法(PLS),结合无信息变量消除(UVE)方法,筛选出58个特征变量,建立了苹果糖度的UVE-PLS通用模型,该模型对个体产地预测集及总预测集的均方根误差分别为0.50~0.74°Brix与0.63°Brix,较原始个体模型分别提高了23.2%~44.4%与35.7%。最后,提出了一个新的外部验证样本集对模型性能进行评价,其残留预测偏差为2.33,预测值在±1.0°Brix和±1.5°Brix误差范围内的占比分别为85%与100%。实验结果表明:建立多个产地苹果糖度的在线检测通用模型,能够提高其他产地样本糖度的预测稳健性,并且采用合适的波长筛选方法能够简化模型。开发不同产地水果内部品质通用模型在波长有限的光谱设备中具有良好的应用潜力。

     

  • 图 1  漫透射检测机构

    Figure 1.  Mechanism of diffuse transmission detection

    图 2  光谱采集触发装置

    Figure 2.  Trigger device of spectral acquisition

    图 3  样品原始光谱

    Figure 3.  Samples′ original spectra

    图 4  经过MSC的苹果光谱的前3个主成分得分图

    Figure 4.  The first three PC score plot of apple spectra after MSC processing

    图 5  UVE选择变量结果

    Figure 5.  Results of variables selected by UVE

    图 6  UVE-PLS通用模型糖度测量值与预测值的散点图

    Figure 6.  Scatter plots of measured values versus predicted values for SSC by using UVE-PLS universal model

    图 7  糖度测量值与预测值的散点图

    Figure 7.  Scatter plot of measured value versus predicted value of SSC for a new external sample set

    表  1  样本集糖度含量统计

    Table  1.   Statistical values of the SSC(°Brix)for sample sets

    产地校正集预测集
    数量范围平均值标准差数量范围平均值标准差
    11298.8~16.612.931.43439.3~15.512.471.24
    21358.5~16.4131.28418.9~15.112.931.23
    312710.1~18.214.971.214011.7~17.615.031.17
    3918.5~18.213.621.611248.9~17.613.451.64
    下载: 导出CSV

    表  2  单个产地的PLS建模结果

    Table  2.   Results of PLS modeling for single origin

    产地LVs校正集预测集RPD
    $R_{\rm{C}}^2$RMSEC(°Brix)$R_{\rm{P}}^2 $RMSEP(°Brix)
    190.920.400.900.413.02
    2100.890.420.850.472.62
    3110.860.460.800.512.29
    下载: 导出CSV

    表  3  不同产地红富士苹果的预测结果

    Table  3.   Prediction results of Fuji apples from different origins

    产地预测集
    123
    $R_{\rm{P}}^2 $RMSEP(°Brix)$R_{\rm{P}}^2 $RMSEP(°Brix)$R_{\rm{P}}^2 $RMSEP(°Brix)$R_{\rm{P}}^2 $RMSEP(°Brix)
    1//0.540.820.731.240.421.30
    20.540.90//0.671.340.670.98
    30.731.440.721.25//0.681.27
    下载: 导出CSV

    表  4  苹果糖度通用模型预测结果

    Table  4.   Results of SSC of apples predicted by universal modeling

    模型变量数LVs$R_{\rm{C}}^2 $RMSEC(°Brix)$R_{\rm{P}}^2 $RMSEP(°Brix)RPD
    Ori-PLS400120.840.640.850.632.60
    UVE-PLS5880.820.680.850.632.60
    下载: 导出CSV

    表  5  UVE-PLS糖度通用模型的实际性能

    Table  5.   The practical performance of UVE-PLS universal model for SSC

    RMSEP/(°Brix)RPD
    栖霞洛川会宁
    0.670.510.720.642.33
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-06-21
  • 修回日期:  2019-08-20
  • 刊出日期:  2020-06-01

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