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Non-destructive quality prediction of truss tomatoes using hyperspectral reflectance imagery

초분광 영상을 이용한 송이토마토의 비파괴 품질 예측

  • Kim, Dae-Yong (Department of Biosystems Machinery Engineering, Chungnam National University) ;
  • Cho, Byoung-Kwan (Department of Biosystems Machinery Engineering, Chungnam National University) ;
  • Kim, Young-Sik (Department of Plant Industry Engineering, Sangmyung University)
  • 김대용 (충남대학교 바이오시스템기계공학과) ;
  • 조병관 (충남대학교 바이오시스템기계공학과) ;
  • 김영식 (상명대학교 식물산업공학과)
  • Received : 2012.06.20
  • Accepted : 2012.09.25
  • Published : 2012.09.30

Abstract

Spectroscopic measurement method based on visible and near-infrared wavelengths was prominent technology for rapid and non-destructive evaluation of internal quality of fruits. Reflectance measurement was performed to evaluate firmness, soluble solid content, and acid content of truss tomatoes by hyperspectral reflectance imaging system. The Vis/NIR reflectance spectra was acquired from truss tomatoes sorted by 6 ripening stages. The multivariable analysis based on partial least square (PLS) was used to develop regression models with several preporcessing methods, such as smoothing, normalization, multiplicative scatter correction (MSC), and standard normal variate (SNV). The best model was selected in terms of coefficient of determination of calibration ($R_c^2$) and full cross validation ($R_{cv}^2$), and root mean standard error of calibration (RMSEC) and full cross validation (RMSECV). The results of selected models were 0.8976 ($R_p^2$), 6.0207 kgf (RMSEP) with gaussian filter of smoothing, 0.8379 ($R_p^2$), $0.2674^{\circ}Bx$ (RMSEP) with the mean of normalization, and 0.7779 ($R_p^2$), 0.1033% (RMSEP) with median filter of smoothing for firmness, soluble solid content (SSC), and acid content, respectively. Results show that Vis / NIR hyperspectral reflectance imaging technique has good potential for the measurement of internal quality of truss tomato.

Keywords

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