Prediction mapping of physicochemical properties in mango by hyperspectral imaging

被引:66
|
作者
Rungpichayapichet, Parika [1 ]
Nagle, Marcus [1 ]
Yuwanbun, Pasinee [2 ]
Khuwijitjaru, Pramote [2 ]
Mahayothee, Busarakorn [2 ]
Mueller, Joachim [1 ]
机构
[1] Univ Hohenheim, Inst Agr Engn, Trop & Subtrop Grp, Garbenstr 9, D-70599 Stuttgart, Germany
[2] Silpakorn Univ, Fac Engn & Ind Technol, Dept Food Technol, Nakhon Pathom 73000, Thailand
关键词
Mangifera indica; Fruit quality; Hyperspectral imaging; Chemometrics; Spectral mapping; vis-NIR spectroscopy; MANGIFERA-INDICA L; SOLUBLE-SOLIDS; NONDESTRUCTIVE DETERMINATION; DRY-MATTER; FRUIT; QUALITY; IMAGES; FIRMNESS; SUGARS; CONSTITUENTS;
D O I
10.1016/j.biosystemseng.2017.04.006
中图分类号
S2 [农业工程];
学科分类号
0828 ;
摘要
Hyperspectral imaging (HSI) techniques using a newly-developed frame camera were applied to determine internal properties of mango fruits including firmness, total soluble solids (TSS) and titratable acidity (TA). Prediction models were developed using spectral data from relative surface reflectance of 160 fruits in the visible and near infrared (vis/NIR) region of 450-998 nm analysed by PLS regression. For data reduction, MLR analysis showed 16 significant factors for firmness, 17 for TA, and 20 for TSS. The results of MLR did not substantially affect the prediction performance as compared to PLS. An original approach with combined chemometric and HSI data analyses was applied using R programming. Significant correlations were found between HSI data and firmness (R-2 = 0.81 and RMSE = 2.83 N) followed by TA (R-2 = 0.81 and RMSE = 0.24%) and TSS (R-2 = 0.5 and RMSE = 2.0%). Prediction maps of physicochemical qualities were achieved by applying the prediction models to each pixel of HSI to visualise their spatial distribution. The variation of firmness, TSS, and TA within the fruit indicated fruit ripening started from shoulder toward to tip part. From these results, HSI can be used as a non-destructive technique for determining the quality of fruits which could potentially enhance grading capabilities in the industrial handling and processing of mango. (C) 2017 IAgrE. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码:109 / 120
页数:12
相关论文
共 50 条
  • [21] Hyperspectral imaging of beet seed germination prediction
    Zhou, Shuang
    Sun, Laijun
    Xing, Wang
    Feng, Guojun
    Ji, Yamin
    Yang, Jun
    Liu, Shuangcai
    INFRARED PHYSICS & TECHNOLOGY, 2020, 108
  • [22] Application of Hyperspectral Imaging for Prediction of Textural Properties of Maize Seeds with Different Storage Periods
    Wang, Lu
    Pu, Hongbin
    Sun, Da-Wen
    Liu, Dan
    Wang, Qijun
    Xiong, Zhenjie
    FOOD ANALYTICAL METHODS, 2015, 8 (06) : 1535 - 1545
  • [23] Application of Hyperspectral Imaging for Prediction of Textural Properties of Maize Seeds with Different Storage Periods
    Lu Wang
    Hongbin Pu
    Da-Wen Sun
    Dan Liu
    Qijun Wang
    Zhenjie Xiong
    Food Analytical Methods, 2015, 8 : 1535 - 1545
  • [24] Physicochemical properties of mango kernel fats extracted from different mango varieties cultivated in Sabah, Malaysia
    Jahurul, Aanada H.
    Ru, Chan Y.
    Norazlina, Ridhwan
    Hasmadi, Mamat
    Sharifudin, Md Shaarani
    Patricia, Matanjun
    Lee, Jau Shya
    Shihabul, Awal
    Shariff, Amir H. M.
    Roslan, Jumardi
    Ab Wahab, Noorakmar
    Karim, Rezaul
    JOURNAL OF FOOD PROCESSING AND PRESERVATION, 2020, 44 (10)
  • [25] Pigment Mapping of the Scream (1893) Based on Hyperspectral Imaging
    Deborah, Hilda
    George, Sony
    Hardeberg, Jon Yngve
    IMAGE AND SIGNAL PROCESSING, ICISP 2014, 2014, 8509 : 247 - 256
  • [26] AUTOMATIC MAPPING OF HYDROCARBON POLLUTION BASED ON HYPERSPECTRAL IMAGING
    Achard, Veronique
    Elin, Christopher
    2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019), 2019, : 5768 - 5771
  • [27] Mapping the Pungency of Green Pepper Using Hyperspectral Imaging
    Anisur Rahman
    Hoonsoo Lee
    Moon S. Kim
    Byoung-Kwan Cho
    Food Analytical Methods, 2018, 11 : 3042 - 3052
  • [28] Mapping the Pungency of Green Pepper Using Hyperspectral Imaging
    Rahman, Anisur
    Lee, Hoonsoo
    Kim, Moon S.
    Cho, Byoung-Kwan
    FOOD ANALYTICAL METHODS, 2018, 11 (11) : 3042 - 3052
  • [29] Underwater Hyperspectral Imaging for seafloor and benthic habitat mapping
    Foglini, Federica
    Chimienti, Giovanni
    Meroni, Agostino N.
    Prampolini, Mariacristina
    Badalamenti, Fabio
    Martorelli, Eleonora
    Angeletti, Lorenzo
    Grande, Valentina
    Marchese, Fabio
    Taviani, Marco
    Corselli, Cesare
    Savini, Alessandra
    Bracchi, Valentina
    Hansen, Ingrid Myrnes
    Mercorella, Alessandra
    Vertino, Agostina
    Erdal, Ivar
    2018 IEEE INTERNATIONAL WORKSHOP ON METROLOGY FOR THE SEA; LEARNING TO MEASURE SEA HEALTH PARAMETERS (METROSEA), 2018, : 201 - 205
  • [30] Mapping retinal non-perfusion with hyperspectral imaging
    Dang, Darvy
    Hadoux, Xavier
    van Wijngaarden, Peter
    CLINICAL AND EXPERIMENTAL OPHTHALMOLOGY, 2022, 49 (08): : 967 - 967