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基于光源发射接收一体化探头的糖度分选线改造研究

刘燕德 饶宇 孙旭东 肖怀春 姜小刚 徐海 李雄 徐佳 王观田

刘燕德, 饶宇, 孙旭东, 肖怀春, 姜小刚, 徐海, 李雄, 徐佳, 王观田. 基于光源发射接收一体化探头的糖度分选线改造研究[J]. 中国光学(中英文), 2020, 13(4): 795-804. doi: 10.37188/CO.2019-0165
引用本文: 刘燕德, 饶宇, 孙旭东, 肖怀春, 姜小刚, 徐海, 李雄, 徐佳, 王观田. 基于光源发射接收一体化探头的糖度分选线改造研究[J]. 中国光学(中英文), 2020, 13(4): 795-804. doi: 10.37188/CO.2019-0165
LIU Yan-de, RAO Yu, SUN Xu-dong, XIAO Huai-chun, JIANG Xiao-gang, XU Hai, LI Xiong, XU Jia, WANG Guan-tian. Modification of Soluble Solids Content sorting line based on light source transmitting and receiving integrated probe[J]. Chinese Optics, 2020, 13(4): 795-804. doi: 10.37188/CO.2019-0165
Citation: LIU Yan-de, RAO Yu, SUN Xu-dong, XIAO Huai-chun, JIANG Xiao-gang, XU Hai, LI Xiong, XU Jia, WANG Guan-tian. Modification of Soluble Solids Content sorting line based on light source transmitting and receiving integrated probe[J]. Chinese Optics, 2020, 13(4): 795-804. doi: 10.37188/CO.2019-0165

基于光源发射接收一体化探头的糖度分选线改造研究

doi: 10.37188/CO.2019-0165
基金项目: 国家自然科学基金(No. 31760344);江西省杰出青年人才资助计划(No. 20171BCB23060);水果光电检测技术能力提升项目(No. S2016-90)
详细信息
    作者简介:

    刘燕德(1967—),女,江西泰和人,教授,博士生导师,2006年于浙江大学获得博士学位,现为华东交通大学机电学院院长,主要从事光机电检测技术与装备方面的研究,E-mail:jxliuyd@163.com

    饶 宇(1993—),男,江西吉安人,硕士研究生,主要从事水果无损检测装备方面的研究,E-mail:caeser-rao108@foxmail.com

  • 中图分类号: S24

Modification of Soluble Solids Content sorting line based on light source transmitting and receiving integrated probe

Funds: Supported by National Natural Science Foundation of China (No.31760344); Subsidize Scheme for Outstanding Youth Talents of Jiangxi Province (No. 20171BCB23060); Photoelectric Detection Technology of Fruits’ Capacity Improvement Project (No. S2016-90)
More Information
  • 摘要: 传统外部品质分选目前已经无法满足人们对水果口感品质的需求,对传统外部品质分选线进行糖度分选改造,实现糖度分选,对确保水果的口感品质具有重要意义。分别采集两种不同检测方式下获取的脐橙的近红外漫反射光谱,其中环形发射与接收漫反射方式的光谱能量要比多点发射与接收漫反射方式强,波峰与波谷位置大致相同。近红外漫反射光谱经基线校正、多元散射校正、一阶、二阶导数等方法进行光谱数据预处理,以减少杂散光和噪声的影响。分别建立脐橙在两种不同漫反射检测方式下的糖度偏最小二乘(PLS)模型进行对比分析。实验结果表明,采用基线校正预处理方法获得的结果最优;环形发射与接收漫反射检测方式下的糖度模型预测相关系数为0.81,预测均方根误差为0.46°Brix,多点发射与接收漫反射检测方式下的糖度模型预测相关系数为0.76,预测均方根误差为0.53°Brix。研究表明,应用PLS建模结合近红外漫反射光谱对传统外部品质分选线中的糖度分选线功能进行升级改造是可行的。

     

  • 图 1  实验装置图

    Figure 1.  Experimental device

    图 2  近红外漫反射在线检测装置示意图

    Figure 2.  Schematic diagram of online detection device of near infrared diffuse reflection

    图 3  光纤不同角度示意图

    Figure 3.  Schematic diagram of fiber with different angles

    图 4  光纤在不同出射角度下的光强图

    Figure 4.  Light intensity charges of optical fiber at different exit angles

    图 5  两种不同漫反射式及相应检测光纤探头的发射接收端

    Figure 5.  Two kinds of diffuse reflection detection modes and corresponding optical fiber probes

    图 6  两种光纤探头下参比球光谱的标准偏差

    Figure 6.  Standard deviations of reference sphere spectra under two different kinds of fiber probes

    图 7  两种不同检测方式下的可见近红外漫反射光谱

    Figure 7.  Visible near-infrared diffuse reflectance spectra with two different kinds of detection modes

    图 8  两种不同检测方式下脐橙实果与脐橙果皮的近红外漫反射光谱

    Figure 8.  Visible near-infrared diffuse reflectance spectra of navel orange and peel with two different kinds of detection modes

    图 9  环形发射与接收检测方式下基线校正前后光谱图

    Figure 9.  Spectra obtained by ring transmission and diffuse reflection mode before and after pretreatment with baseline correction

    图 10  多点发射与接收检测方式下基线校正前后光谱对比

    Figure 10.  Spectra obtained by multi-point transmission and diffuse reflection mode before and after pretreatment with baseline correction

    图 11  两种漫反射方式下预测集的糖度模型主成分因子图

    Figure 11.  Principal component factor charts of prediction set obtained with two kinds of diffuse reflection methods

    图 12  两种不同漫反射方式下的回归系数图

    Figure 12.  Regression coefficients obtained with two different kinds of diffuse reflection methods

    图 13  环形发射与接收模式下偏最小二乘模型预测散点图

    Figure 13.  Predicted scatter plot with partial least squares model using ring transmission and diffuse reflection mode

    图 14  多点发射与接收模式下偏最小二乘模型预测散点图

    Figure 14.  Predicted scatter plot with partial least squares model using multi-point transmission and diffuse reflection mode

    表  1  两种不同近红外漫反射光谱检测方式结合不同预处理方法时的脐橙糖度PLS建模结果

    Table  1.   PLS modeling results of navel orange sugar content using two different near-infrared diffuse reflectance spectroscopy methods combined with different pretreatment methods

    检测方式预处理方法RcRMSECRpRMSEPPCs
    环形发射和接收无预处理0.890.390.820.4312
    MSC0.890.410.750.5110
    Baseline0.900.380.810.4611
    1st-90.830.480.800.469
    2st-130.950.260.730.5815
    SG-50.860.450.770.5115
    多点发射和接收无预处理0.920.310.770.519
    MSC0.910.330.720.598
    Baseline0.940.280.760.5310
    1st-30.870.400.790.506
    2st-130.840.450.750.536
    SG-50.820.480.810.476
    下载: 导出CSV
  • [1] 陈亚平, 杨保华. 食品动态检重秤控制器设计[J]. 衡器,2018,47(1):44-46. doi: 10.3969/j.issn.1003-5729.2018.01.012

    CHEN Y P, YANG B H. Food dynamic check weighing scale controller design[J]. Weighing Instrument, 2018, 47(1): 44-46. (in Chinese) doi: 10.3969/j.issn.1003-5729.2018.01.012
    [2] 陈孝照. 柑桔重量式分选机自动控制系统设计[J]. 福建农机,2015(3):9-12. doi: 10.3969/j.issn.1004-3969.2015.03.003

    CHEN X ZH. Design of automatic control system for citrus weight sorting machine[J]. Fujian Agricultural Machinery, 2015(3): 9-12. (in Chinese) doi: 10.3969/j.issn.1004-3969.2015.03.003
    [3] 张剑一. 动态称重数据处理算法及其在禽蛋和类球形水果分选中的应用研究[D]. 杭州: 浙江大学, 2017.

    ZHANG J Y. Dynamic weighing data processing algorithm and its application to poultry egg and spherical fruits sorting[D]. Hangzhou: Zhejiang University, 2017. (in Chinese)
    [4] 高升, 王巧华, 李庆旭, 等. 基于近红外光谱的红提维生素C含量、糖度及总酸含量无损检测方法[J]. 分析化学,2019,47(6):941-949.

    GAO SH, WANG Q H, LI Q Y, et al. Non-destructive detection of vitamin C, sugar content and total acidity of red globe grape based on near-infrared spectroscopy[J]. Chinese Journal of Analytical Chemistry, 2019, 47(6): 941-949. (in Chinese)
    [5] 李龙, 彭彦昆, 李永玉. 苹果内外品质在线无损检测分级系统设计与试验[J]. 农业工程学报,2018,34(9):267-275. doi: 10.11975/j.issn.1002-6819.2018.09.033

    LI L, PENG Y K, LI Y Y. Design and experiment on grading system for online non-destructive detection of internal and external quality of apple[J]. Transactions of the Chinese Society of Agricultural Engineering, 2018, 34(9): 267-275. (in Chinese) doi: 10.11975/j.issn.1002-6819.2018.09.033
    [6] 赵环, 宦克为, 石晓光, 等. 基于自加权变量组合集群分析法的近红外光谱变量选择方法研究[J]. 分析化学,2018,46(1):136-142.

    ZHAO H, HUAN K W, SHI X G, et al. A variable selection method of near-infrared spectroscopy based on automatic weighting variable combination population analysis[J]. Chinese Journal of Analytical Chemistry, 2018, 46(1): 136-142. (in Chinese)
    [7] 谢越, 李飞跃, 范行军, 等. 基于近红外光谱技术的生物炭组分分析[J]. 分析化学,2018,46(4):609-615.

    XIE Y, LI F Y, FAN X J, et al. Component analysis of biochar based on near infrared spectroscopy technology[J]. Chinese Journal of Analytical Chemistry, 2018, 46(4): 609-615. (in Chinese)
    [8] 赵娟, 全朋坤, 马敏娟, 等. 富士苹果采收成熟度光谱无损预测模型对比分析[J]. 农业机械学报,2018,49(12):347-354. doi: 10.6041/j.issn.1000-1298.2018.12.041

    ZHAO J, QUAN P K, MA M J, et al. Comparative analysis of harvest maturity model for Fuji apple based on visible/near spectral nondestructive detection[J]. Transactions of the Chinese Society for Agricultural Machinery, 2018, 49(12): 347-354. (in Chinese) doi: 10.6041/j.issn.1000-1298.2018.12.041
    [9] 胡士成, 白凯伦, 毛丽婷, 等. 苹果糖度的光谱模型温度补偿设计[J]. 食品安全质量检测学报,2018,9(11):2716-2721. doi: 10.3969/j.issn.2095-0381.2018.11.028

    HU SH CH, BAI K L, MAO L T, et al. Design of spectral model for temperature compensation in measurement of sugar content in apples[J]. Journal of Food Safety &Quality, 2018, 9(11): 2716-2721. (in Chinese) doi: 10.3969/j.issn.2095-0381.2018.11.028
    [10] 杨卫梅, 刘刚, 欧全宏, 等. 红外光谱结合曲线拟合对自然老化豆类种子的研究[J]. 分析化学,2019,47(12):2004-2011.

    YANG W M, LIU G, OU Q H, et al.. Study on natural aging legume seeds by infrared spectroscopy combined with curve fitting[J]. Chinese Journal of Analytical Chemistry, 2019, 47(12): 2004-2011. (in Chinese)
    [11] 李梦依, 仓世龙, 孔寒, 等. 近红外光谱分析技术在玉米品质分析中的应用[J]. 山东化工,2019,48(3):68-71.

    LI M Y, CANG SH L, KONG H, et al. The application of near infrared spectroscopy in corn quality analysis[J]. Shandong Chemical Industry, 2019, 48(3): 68-71. (in Chinese)
    [12] 史云颖, 李敬岩, 褚小立. 多元校正模型传递方法的进展与应用[J]. 分析化学,2019,47(4):479-487.

    SHI Y Y, LI J Y, CHU X L. Progress and application of multivariate calibration model transfer method[J]. Chinese Journal of Analytical Chemistry, 2019, 47(4): 479-487. (in Chinese)
    [13] SHAO W H, LI Y J, DIAO S F, et al. Rapid classification of Chinese quince (Chaenomeles speciosa Nakai) fruit provenance by near-infrared spectroscopy and multivariate calibration[J]. Analytical and Bioanalytical Chemistry, 2017, 409(1): 115-120. doi: 10.1007/s00216-016-9944-7
    [14] 崔丰娟, 闸建文. 近红外透射苹果运动速度模型适用性的研究[J]. 农机化研究,2010,32(11):170-173. doi: 10.3969/j.issn.1003-188X.2010.11.042

    CUI F J, XIA J W. Study of the applicability of the apple speed model by near-infrared transmission[J]. Journal of Agricultural Mechanization Research, 2010, 32(11): 170-173. (in Chinese) doi: 10.3969/j.issn.1003-188X.2010.11.042
    [15] 章海亮, 孙旭东, 郝勇, 等. 近红外漫反射无损检测赣南脐橙中可溶性固形物和总酸[J]. 食品科学,2011,32(6):151-154.

    ZHANG H L, SUN X D, HAO Y, et al. Determination of soluble solids and total acidity in Gannan navel orange by near infrared diffuse reflection spectroscopy[J]. Food Science, 2011, 32(6): 151-154. (in Chinese)
    [16] 刘燕德, 吴明明, 李轶凡, 等. 苹果可溶性固形物和糖酸比可见/近红外漫反射与漫透射在线检测对比研究[J]. 光谱学与光谱分析,2017,37(8):2424-2429.

    LIU Y D, WU M M, LI Y F, et al. Comparison of reflection and diffuse transmission for detecting solid soluble contents and ratio of sugar and acid in apples by on-line Vis/NIR spectroscopy[J]. Spectroscopy and Spectral Analysis, 2017, 37(8): 2424-2429. (in Chinese)
    [17] SARANWONG S I, SORNSRIVICHAI J, KAWANO S. Performance of a portable near infrared instrument for Brix value determination of intact mango fruit[J]. Journal of Near Infrared Spectroscopy, 2003, 11(3): 175-181. doi: 10.1255/jnirs.364
    [18] WALSH K B, GOLIC M, GREENSILL C V. Sorting of fruit using near infrared spectroscopy: application to a range of fruit and vegetables for soluble solids and dry matter content[J]. Journal of Near Infrared Spectroscopy, 2004, 12(1): 141-148.
    [19] GREENSILL C V, WALSH K B. A remote acceptance probe and illumination configuration for spectral assessment of internal attributes of intact fruit[J]. Measurement Science and Technology, 2000, 11(12): 1674-1684. doi: 10.1088/0957-0233/11/12/304
    [20] TSUTA M, YOSHIMURA M, KASAI S, et al. Prediction of internal flesh browning of “Fuji” apple using Visible-Near Infrared spectra acquired by a fruit sorting machine[J]. Japan Journal of Food Engineering, 2019, 20(1): 7-14. doi: 10.11301/jsfe.18530
    [21] DASZYKOWSKI M, SERNEELS S, KACZMAREK K, et al. TOMCAT: A MATLAB toolbox for multivariate calibration techniques[J]. Chemometrics and Intelligent Laboratory Systems, 2007, 85(2): 269-277. doi: 10.1016/j.chemolab.2006.03.006
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出版历程
  • 收稿日期:  2019-08-12
  • 修回日期:  2019-09-29
  • 刊出日期:  2020-08-01

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