Detection of tobacco blend ratio based on hyperspectral imaging

Abstract

Keywords:
• hyperspectral imaging technology,
• blend ratio,
• band selection,
• machine learning,
• regression analysis

References

1. [1]

   丁美宙,刘欢,刘强,等.梗丝形态对细支卷烟加工及综合质量的影响[J].食品与机械,2017,33(9):197-202.
   

2. [2]

   陈帅伟,胡苏林,崔宁,等.“三丝” 掺配比例对卷烟理化指标的影响研究[J].西南农业学报,2015,28(6):2742-2745.
   

3. [3]

   徐成龙,宋朝鹏,戴亚,等.梗丝与膨胀丝及薄片丝对卷烟主流烟气中3种有害成分释放量的影响[J].湖南农业大学学报(自然科学版),2013,39(1):23-25.
   

4. [4]

   胡立中,张胜军,余小平,等.均匀设计-PLS-NIR法预测卷烟配方烟丝中梗丝及薄片丝含量[J].中国烟草学报,2010,16(2):26-30.
   

5. [5]

6. [6]

   温若愚,席年生,张大波,等.不同混丝模式对烟丝掺配效果的影响[J].烟草科技,2008,41(9):13-16.
   

7. [7]

   刘栋,陈越立,李华杰,等.取样量、取样次数对烟丝混合均匀性检测的影响[J].郑州轻工业学院学报(自然科学版),2013,28(1):45-49.
   

8. [8]

   寇霄腾,张勇,张卉,等.基于RGB图像特征的卷烟梗丝掺配比例检测[J].食品与机械,2021,37(9):78-82.
   

9. [9]

10. [10]

    LU Y Z,SAEYS W,KIM M,et al.Hyperspectral imaging technology for quality and safety evaluation of horticultural products:A review and celebration of the past 20-year progress[J].Postharvest Biology and Technology,2020,170:111318.

11. [11]

    ZHANG C,GUO C T,LIU F,et al.Hyperspectral imaging analysis for ripeness evaluation of strawberry with support vector machine[J].Journal of Food Engineering,2016,179:11-18.

12. [12]

    GOMEZ C,GHOLIZADEH A,BORU。VKA L,et al.Using legacy data for correction of soil surface clay content predicted from VNIR/SWIR hyperspectral airborne images[J].Geoderma,2016,276:84-92.

13. [13]

    NEITTAANMÄKI-PERTTU N,GRÖNROOS M,TANI T,et al.Detecting field cancerization using a hyperspectral imaging system[J].Lasers in Surgery and Medicine,2013,45(7):410-417.

14. [14]

    张卫正,张伟伟,张焕龙,等.基于高光谱成像技术的甘蔗茎节识别与定位方法研究[J].轻工学报,2017,32(5):95-102.
    

15. [15]

    郭文孟,薛宇毅,罗靖,等.基于高光谱成像的烟叶泛青特征分析与表征[J].烟草科技,2023,56(7):84-91.
    

16. [16]

    朱亚昆,梅吉帆,郭文孟,等.基于PPF投影算法和高光谱技术的卷烟牌号识别模型[J].轻工学报,2024,39(4):118-126.
    

17. [17]

    陶发展,杨栋,洪伟龄,等.基于高光谱成像的烟丝中梗签分类识别研究[J].河南科技大学学报(自然科学版),2024,45(3):32-42
    ,5.

18. [18]

    梅吉帆,李智慧,李嘉康,等.基于高光谱成像技术的配方烟丝组分判别[J].分析测试学报,2021,40(8):1151-1157.
    

19. [19]

    LI B,WANG Y Q,LI L S,et al.Research on apple origins classification optimization based on least-angle regression in instance selection[J].Agriculture,2023,13(10):1868.

20. [20]

    WANG J G,TIAN T,WANG H J,et al.Improving the estimation accuracy of rapeseed leaf photosynthetic characteristics under salinity stress using continuous wavelet transform and successive projections algorithm[J].Frontiers in Plant Science,2023,14:1284172.

21. [21]

    ZHU L,GU W Z,SONG T Q,et al.Coal seam in situ inorganic analysis based on least angle regression and competitive adaptive reweighted sampling algorithm by XRF-visNIR fusion[J].Scientific Reports,2022,12(1):22365.

22. [22]

    褚小立,袁洪福,王艳斌,等.遗传算法用于偏最小二乘方法建模中的变量筛选[J].分析化学,2001,29(4):437-442.
    

23. [23]

    令峰,邢富康,韦克苏,等.基于高光谱成像技术和深度学习的烤后烟叶品种分类判别[J].中国烟草科学,2024,45(4):83-92.
    

24. [24]

    MISHRA P,NORDON A,TSCHANNERL J,et al.Near-infrared hyperspectral imaging for non-destructive classification of commercial tea products[J].Journal of Food Engineering,2018,238:70-77.

25. [25]

    吴龙国,王松磊,何建国.基于高光谱技术的土壤水分无损检测[J].光谱学与光谱分析,2018,38(8):2563-2570.
    

26. [26]

    FU P,MEACHAM-HENSOLD K,GUAN K Y,et al.Estimating photosynthetic traits from reflectance spectra:A synthesis of spectral indices,numerical inversion,and partial least square regression[J].Plant,Cell & Environment,2020,43(5):1241-1258.

27. [27]

    ZHANG P P,SHEN B B,JI H W,et al.Nondestructive prediction of mechanical parameters to apple using hyperspectral imaging by support vector machine[J].Food Analytical Methods,2022,15(5):1397-1406.

Proportional views