Korean Journal of Agricultural Science (농업과학연구)

Institute of Agricultural Science, CNU (충남대학교 농업과학연구소)
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Hyperspectral imaging technique to evaluate the firmness and the sweetness index of tomatoes

  • Rahman, Anisur (Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University) ;
  • Park, Eunsoo (Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University) ;
  • Bae, Hyungjin (Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University) ;
  • Cho, Byoung-Kwan (Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University)
  • Received : 2018.06.14
  • Accepted : 2018.09.27
  • Published : 2018.12.31
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Abstract

The objective of this study was to evaluate the firmness and the sweetness index (SI) of tomatoes with a hyperspectral imaging (HSI) technique within the wavelength range of 1000 - 1550 nm. The hyperspectral images of 95 tomatoes were acquired with a push-broom hyperspectral reflectance imaging system, from which the mean spectra of each tomato were extracted from the regions of interest. The reference firmness and sweetness index of the same sample was measured and calibrated with their corresponding spectral data by partial least squares (PLS) regression with different preprocessing methods. The calibration model developed by PLS regression based on the Savitzky-Golay second-derivative preprocessed spectra resulted in a better performance for both the firmness and the SI of the tomatoes compared to models developed by other preprocessing methods. The correlation coefficients ($R_{pred}$) were 0.82, and 0.74 with a standard error of prediction of 0.86 N, and 0.63, respectively. Then, the feature wavelengths were identified using a model-based variable selection method, i.e., variable importance in projection, from the PLS regression analyses. Finally, chemical images were derived by applying the respective regression coefficients on the spectral image in a pixel-wise manner. The resulting chemical images provided detailed information on the firmness and the SI of the tomatoes. The results show that the proposed HSI technique has potential for rapid and non-destructive evaluation of firmness and the sweetness index of tomatoes.

Keywords

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Fig. 1. The average relative reflectance spectra and standard deviation (SD) with resulting 2nd derivative spectral profle for tomato.

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Fig. 2. Selection of feature wavelengths by VIP scores for (a) frmness and (b) SI of tomatoes. VIP, variable importance in projection; SI, sweetness index.

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Fig. 3. Measured versus predicted (a) frmness and (b) SI estimated by PLS regression model using selected wavelengths. SI, sweetness index; a.u., arbitrary unit.

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Fig. 3. Prediction maps showing the distribution of (a) frmness, and (b) SI in tomato samples.

Table 1. Statistics of quality parameters for tomato measured by standard methods.

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Table 2. Results of PLS regression for frmness and SI with diferent preprocessing techniques.

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Table 3. Results of PLS, and PLS-VIP based on Savitzky-Golay (S-G) 2nd derivative preprocessing spectra.

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