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中国精品科技期刊2020

基于太赫兹时域光谱的大米品种识别研究

王倩 葛宏义 蒋玉英 张元 秦一菲

王倩,葛宏义,蒋玉英,等. 基于太赫兹时域光谱的大米品种识别研究[J]. 食品工业科技,2022,43(23):19−25. doi:  10.13386/j.issn1002-0306.2021120292
引用本文: 王倩,葛宏义,蒋玉英,等. 基于太赫兹时域光谱的大米品种识别研究[J]. 食品工业科技,2022,43(23):19−25. doi:  10.13386/j.issn1002-0306.2021120292
WANG Qian, GE Hongyi, JIANG Yuying, et al. Identification of Rice Varieties Based on Terahertz Time Domain Spectroscopy[J]. Science and Technology of Food Industry, 2022, 43(23): 19−25. (in Chinese with English abstract). doi:  10.13386/j.issn1002-0306.2021120292
Citation: WANG Qian, GE Hongyi, JIANG Yuying, et al. Identification of Rice Varieties Based on Terahertz Time Domain Spectroscopy[J]. Science and Technology of Food Industry, 2022, 43(23): 19−25. (in Chinese with English abstract). doi:  10.13386/j.issn1002-0306.2021120292

基于太赫兹时域光谱的大米品种识别研究

doi: 10.13386/j.issn1002-0306.2021120292
基金项目: 国家自然科学基金(61975053)。
详细信息
    作者简介:

    王倩(1993−),女,硕士研究生,研究方向:太赫兹波检测,E-mail:wangqian20210411@163.com

    通讯作者:

    葛宏义(1983−),男,博士,副教授,研究方向:THz光谱与成像,E-mail:gehongyi2004@163.com

  • 中图分类号: TS211.7

Identification of Rice Varieties Based on Terahertz Time Domain Spectroscopy

  • 摘要: 为实现大米品种的准确鉴别,提出一种基于太赫兹时域光谱(Terahertz Time-Domain Spectroscopy, THz-TDS)技术的大米品种识别方法。利用标准差(Standard Deviation, SD)和区间偏最小二乘(Interval Partial Least Square, iPLS)选取0.53~1.21 THz波段的吸收光谱信息作为分类模型的输入数据,再采用决策树模型(Decision Tree, DT)对四种大米吸收光谱进行分类识别,并在模型训练过程中结合网格搜索算法寻找模型最优参数。为增加实验对比度,分别使用逻辑回归模型和支持向量机模型进行对比实验,其模型分类准确率分别为80.75%和88.75%。实验结果表明,太赫兹时域光谱技术结合SD、iPLS和DT方法可以实现大米品种的准确识别,准确率可达95%,为农产品品种识别提供了一种新的鉴别方法。
  • 图  1  实验样品

    Figure  1.  Experimental samples

    图  2  太赫兹时域光谱系统透射式(a)和反射式(b)

    Figure  2.  Terahertz time domain spectroscopy system transmission type (a) and reflection type (b)

    图  3  时域光谱图(a)和频域光谱图(b)

    Figure  3.  Time domain spectroscopy (a) and frequency domain spectroscopy (b)

    图  4  折射光谱(a)和吸收光谱(b)

    Figure  4.  Refraction spectrum (a) and absorption spectrum (b)

    图  5  吸收数据标准化预处理

    Figure  5.  Standardized preprocessing of absorption data

    图  6  四种样品预处理后不同波段标准差

    Figure  6.  Standard deviation of different bands after pretreatment of four samples

    图  7  最优区间均方根误差

    Figure  7.  Optimal interval root mean square error

    表  1  不同样品光谱角度

    Table  1.   Different sample spectral angles

    红米与珍珠糯米 红米与黑米 红米与富硒大米
    原始光谱角度 0.077 0.093 0.060
    标准化预处理后 2.513 2.652 1.182
    下载: 导出CSV

    表  2  四种样品预处理后不同波段标准差

    Table  2.   Standard deviation of different bands after pretreatment of four samples

    太赫兹波
    段(THz)
    河南红
    米样品
    珍珠糯
    米样品
    黑米样品
    富硒大
    米样品
    标准差
    平均值
    0~0.53 0.65 0.53 0.36 0.12 0.42
    0.53~1.21 0.06 0.07 0.08 0.06 0.07
    1.21~1.74 0.40 0.19 0.45 0.18 0.31
    1.74~2.31 0.29 0.19 0.25 0.20 0.23
    2.31~2.71 0.28 0.28 0.27 0.25 0.27
    2.71~3.40 0.25 0.30 0.30 0.23 0.27
    3.40~4.00 0.24 0.25 0.28 0.20 0.24
    0~4.00 0.59 0.38 0.45 0.21 0.41
    下载: 导出CSV

    表  3  iPLS不同分割区间下最优区间列表

    Table  3.   List of optimal intervals under different segmentation intervals of interval partial least square

    区间个数最佳区间最优因子均方根误差对应太赫兹波段(THz)
    2230.840~1.98
    3190.500~1.32
    4180.622.03~3.00
    5180.601.61~2.40
    64150.570.64~1.32
    72210.550.55~1.12
    8250.520.50~0.99
    10250.570.81~1.19
    15390.540.53~0.81
    22430.600.92~1.08
    下载: 导出CSV

    表  4  决策树分类准确率

    Table  4.   Classification accuracy of decision tree

    模型测试集准确率(%)
    河南红米珍珠糯米黑米富硒大米平均准确率
    DT1008010010095
    下载: 导出CSV

    表  5  不同核函数分类准确率

    Table  5.   Classification accuracy of different kernel functions

    不同核函数测试集准确率(%)
    河南红米珍珠糯米黑米富硒大米平均准确率
    RBF10060852567.5
    linear100100807588.75
    Sigmoid10040802561.25
    下载: 导出CSV

    表  6  不同模型的分类准确率

    Table  6.   Classification accuracy of different models

    模型平均准确率(%)
    LC80.75
    SVM88.75
    SD-iPLS-DT95
    下载: 导出CSV
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  • 收稿日期:  2021-12-27
  • 网络出版日期:  2022-10-19
  • 刊出日期:  2022-11-23

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