水果成熟度近红外光谱及高光谱成像无损检测研究进展

胡逸磊 姜洪喆 周宏平 王影

胡逸磊,姜洪喆,周宏平,等. 水果成熟度近红外光谱及高光谱成像无损检测研究进展[J]. 食品工业科技,2021,42(20):377−383. doi:  10.13386/j.issn1002-0306.2020070074
引用本文: 胡逸磊,姜洪喆,周宏平,等. 水果成熟度近红外光谱及高光谱成像无损检测研究进展[J]. 食品工业科技,2021,42(20):377−383. doi:  10.13386/j.issn1002-0306.2020070074
HU Yilei, JIANG Hongzhe, ZHOU Hongping, et al. Research Progress on Nondestructive Detection of Fruit Maturity by Near Infrared Spectroscopy and Hyperspectral Imaging[J]. Science and Technology of Food Industry, 2021, 42(20): 377−383. (in Chinese with English abstract). doi:  10.13386/j.issn1002-0306.2020070074
Citation: HU Yilei, JIANG Hongzhe, ZHOU Hongping, et al. Research Progress on Nondestructive Detection of Fruit Maturity by Near Infrared Spectroscopy and Hyperspectral Imaging[J]. Science and Technology of Food Industry, 2021, 42(20): 377−383. (in Chinese with English abstract). doi:  10.13386/j.issn1002-0306.2020070074

水果成熟度近红外光谱及高光谱成像无损检测研究进展

doi: 10.13386/j.issn1002-0306.2020070074
基金项目: 国家重点研发计划项目(2016YFD0701501)
详细信息
    作者简介:

    胡逸磊(1996−),男,硕士研究生,研究方向:光谱与食品检测技术方面的研究,E-mail:916104064@qq.com

    通讯作者:

    周宏平(1964−),男,硕士,教授,研究方向:植保机械装备与技术方面的研究,E-mail:hpzhou@njfu.edu.cn

  • 中图分类号: TS255.1

Research Progress on Nondestructive Detection of Fruit Maturity by Near Infrared Spectroscopy and Hyperspectral Imaging

  • 摘要: 成熟度作为一项水果品质重要评价指标,与水果的采收、储存、加工、运输、销售等环节息息相关,也是其产量和质量的关键影响因素之一。本文综述了国内外近十年来利用近红外光谱和高光谱成像技术检测水果成熟度的研究现状。从水果成熟度定性判别和成熟度参数定量预测两个方面入手,详细分析了光谱仪器工作波段、光谱采集方式、光谱采样区域、成熟度表征因子、单一成熟度参数、多元成熟度指数对最终检测模型精度和稳定性的影响,最后展望了近红外光谱和高光谱成像技术在水果成熟度检测方向的未来发展趋势,以期为相关领域研究工作提供科学依据和技术参考。
  • 表  1  不同水果的光谱采样区域及分类结果

    Table  1.   Spectral sampling regions and classification results of different fruits

    检测对象光谱采样区域建模方法结果参考文献
    荔枝整个果面PLSDA准确率:光谱集Ⅰ为90.63%,光谱集Ⅱ为96.88%[16]
    果实赤道处着色和未着色两面FDA识别准确率:“Bergarouge”品种为92%,“Harostar”品种为89%[24]
    柑橘柑橘表面划分6个区域,每个区域采集一次PSO分类准确率为70.5%[25]
    石榴果实赤道处等间距的四个点PCA分类准确率为93.25%[26]
    樱桃整个果面LDA分类准确率为96.4%[27]
    梨赤道处采集12次光谱数据PLSDA识别率为97.22%[28]
    哈密瓜果实赤道处每隔120°采集一个区域的光谱数据SVM分类准确率为94%[29]
    西瓜西瓜赤道处的三个点KNN识别率为91.67%[32]
    柿子柿子正反两面各四个方形区域LDA识别率为95.3%[33]
    下载: 导出CSV

    表  2  不同水果的成熟度参数定量预测方法及结果

    Table  2.   Quantitative prediction methods and results of maturity parameters of different fruits

    检测对象成熟度参数建模方法结果参考文献
    石榴TSS、pH、硬度PLSRRMSEP分别为0.22,0.038,0.68[15]
    甜瓜ERPLSRRMSEC=0.047;RMSEP=0.041[17]
    SSC、硬度、糖含量PLSR${r_p}$分别是0.653,0.609,0.8971[18,35]
    榴莲DMCPLSR${r_{cv}}$ 为0.82,RMSECV为2.68[37]
    SSC、TAPLSR${r_p}$分别为0.92,0.88;RMSEP分别为0.98,3.62[38]
    柑橘SSC/TAMPLSRRPD为1.21[39]
    火龙果第一主成分PLSRRPD为3.26[40]
    芒果${{\rm{I}}_{\rm{m}}}$PLSR${r_c}$为0.74,${r_p}$为0.68[41]
    西瓜SSC、水分、番茄红素PLSR${r_p}$分别为0.862,0.939,0.751;RPD分别为1.83,2.79,1.13[22,42]
    苹果SSC、淀粉含量、硬度PLSR$R_p^2$分别为0.83,0.79,0.38[36,43]
    葡萄总酚、糖、TA、pHMPLSRSEP分别为1.97、1.61、3.89、0.18[44-45]
    哈密瓜果肉颜色a*b*C*h*MPLSR相关系数分别是0.96、0.85、0.82、0.96[46]
    桃子硬度、WSPPLSRRPD分别为1.67、1.31[47-48]
    蓝莓SSC、硬度、花青素、鲜重相关性分析${{\rm{I}}_{{\rm{AD}}} }$与蓝莓理化指标有较高相关性[49]
    牛油果DMCPLSRRMSEP为1.53[50]
    甘蔗蔗糖含量PLSR$R_c^2$为0.94,RMSEC为0.7[51]
    注:SSC:可溶性固形物;TA:可滴定酸度;TSS:总可溶性固形物;WSP:水溶性果胶含量;PLSR:偏最小二乘回归;MPLSR:多元偏最小二乘回归;DMC:干物质含量;${{\rm{I}}_{{\rm{AD}}} }$:吸光度指数;${{\rm{I}}_{\rm{m}}}$:成熟度指数;ER:食用比;${r_c}$:校正集相关系数;${r_p}$:预测集相关系数;${r_{cv}}$:交叉验证相关系数;RMSEC:校正集均方根误差;RMSEP:预测集均方根误差;RMSECV:交叉验证均方根误差;$R_c^2$:校正集决定系数;$R_p^2$:预测集决定系数;RPD:性能偏差比;SEP:标准预测误差。
    下载: 导出CSV
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  • 收稿日期:  2020-07-08
  • 网络出版日期:  2021-08-02
  • 刊出日期:  2021-10-11

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