Journal of Biosystems Engineering

Korean Society for Agricultural Machinery (한국농업기계학회)
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A Melon Fruit Grading Machine Using a Miniature VIS/NIR Spectrometer: 1. Calibration Models for the Prediction of Soluble Solids Content and Firmness

  • Suh, Sang-Ryong (Department of Rural and Biosystems Engineering, Chonnam National University) ;
  • Lee, Kyeong-Hwan (Department of Rural and Biosystems Engineering, Chonnam National University) ;
  • Yu, Seung-Hwa (Department of Rural and Biosystems Engineering, Chonnam National University) ;
  • Shin, Hwa-Sun (Department of Rural and Biosystems Engineering, Chonnam National University) ;
  • Choi, Young-Soo (Department of Rural and Biosystems Engineering, Chonnam National University) ;
  • Yoo, Soo-Nam (Department of Rural and Biosystems Engineering, Chonnam National University)
  • Received : 2012.05.30
  • Accepted : 2012.06.29
  • Published : 2012.06.30
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Abstract

Purpose: This study was conducted to investigate the potential of interactance mode of NIR spectroscopy technology for the estimation of soluble solids content (SSC) and firmness of muskmelons. Methods: Melon samples were taken from local greenhouses in three different harvesting seasons (experiments 1, 2, and 3). The fruit attributes were measured at the 6 points on an equator of each sample where the spectral data were collected. The prediction models were developed using the original spectral data and the spectral data sets preprocessed by 20 methods. The performance of the models was compared. Results: In the prediction of SSC, the highest coefficient of determination ($R_{cv}{^2}$) values of the cross-validation was 0.755 (standard error of prediction, SEP=$0.89^{\circ}Brix$) with the preprocessing of normalization with range in experiment 1. The highest coefficient of determination in the robustness tests, $R_{rt}{^2}$=0.650 (SEP=$1.03^{\circ}Brix$), was found when the best model of experiment 3 was evaluated with the data set of experiment 2. The best $R_{cv}{^2}$ for the prediction of firmness was 0.715 (SEP=3.63 N) when no preprocessing was applied in experiment 1. The highest $R_{rt}{^2}$ was 0.404 (SEP=5.30 N) when the best model of experiment 3 was applied to the data set of experiment 1. Conclusions: From the test results, it can be concluded that the interactance mode of VIS/NIR spectroscopy technology has a great potential to measure SSC and firmness of thick-skinned muskmelons.

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References

  1. Armstrong, P. R., M. L. Stone and G. H. Brusewitz. 1997. Nondestructive acoustic and compression measurements of watermelon for internal damage detection. Applied Engineering in Agriculture 13(5):641-645. https://doi.org/10.13031/2013.21638
  2. Bobelyn, E., A. S. Serban, M. Nicu, J. Lammertyn, B. M. Nicolai and W. Saeys. 2010. Postharvest quality of apple predicted by NIR-spectroscopy: study of the effect of biological variability on spectra and model performance. Postharvest Biology and Technology 55(3): 133-143. https://doi.org/10.1016/j.postharvbio.2009.09.006
  3. Diener, R. G., J. P. Mitchell and M. L. Rhoten. 1970. Using an X-ray image scan to sort bruised apples. Agricultural Engineering 51(6):356-361.
  4. Diezma-Iglesias, B., M. Ruiz-Altisent and P. Barreiro, 2004. Detection of internal quality in seedless watermelon by acoustic impulse response. Biosystems Engineering 88(2):221-230. https://doi.org/10.1016/j.biosystemseng.2004.03.007
  5. Fan, G. Q., J. W. Zha, R. Du and L. Gao. 2009. Determination of soluble solids and firmness of apples by Vis/NIR transmittance. Journal of Food Engineering 93(4):416- 420. https://doi.org/10.1016/j.jfoodeng.2009.02.006
  6. Guthrie, J., B. Wedding and K. Walsh. 1998. Robustness of NIR calibrations for soluble solids in intact melon and pineapple. Journal of Near Infrared Spectroscopy 6: 259-265. https://doi.org/10.1255/jnirs.145
  7. Jamal, N., Y. Ying, J. Wang and X, Rao. 2005. Finite element models of watermelon and their applications. Transactions of the CSAE 21(1):17-22.
  8. Lammertyn, J., T. Dresselaers, P. H. Van, P. Jancsòk, M. Wevers and B. M. Nicolai. 2003. MRI and X-ray CT study of spatial distribution of core breakdown in 'Conference' pears. Magnetic Resonance Imaging 21: 805-815. https://doi.org/10.1016/S0730-725X(03)00105-X
  9. Lee, K. H., N. Zhang, W. B. Kuhn and G. J. Kluitenberg. 2007. A frequency-response permittivity sensor for simultaneous measurement of multiple soil properties: part 1. the frequency-response method. Transactions of the ASABE 50(6):2315-2326. https://doi.org/10.13031/2013.24084
  10. Liu, Y., X. Sun and A. Ouyang. 2010. Nondestructive measurement of soluble solid content of navel orange fruit by visible-NIR spectrometric technique with PLSR and PCA-BPNN. LWT-Food Science and Technology 43: 602-607. https://doi.org/10.1016/j.lwt.2009.10.008
  11. Liu, Y. D., X. M. Chen and A. G. Ouyang. 2008. Nondestructive determination of pear internal quality indices by visible and near-infrared spectrometry. LWT - Food Science and Technology 41(9):1720-1725. https://doi.org/10.1016/j.lwt.2007.10.017
  12. Lu, R. 2004. Multispectral imaging for predicting firmness and soluble solids content of apple fruit. Postharvest Biology and Technology 31:147-157. https://doi.org/10.1016/j.postharvbio.2003.08.006
  13. McGlone, V. A., C. J. Clark and R. B. Jordan. 2007. Comparing density and VNIR methods for predicting quality parameters of yellow-fleshed kiwifruit (Actinidia chinensis). Postharvest Biology and Technology 46 (1):1-9. https://doi.org/10.1016/j.postharvbio.2007.04.003
  14. Nelson, S. O., S. Trabelsi and S. J. Kays. 2006. Dielectric spectroscopy of honeydew melons from 10 MHz to 1.8 GHz for quality sensing. Transactions of the ASABE 49(6):1977-1981. https://doi.org/10.13031/2013.22278
  15. Nelson, S. O., W. Guo, S. Trabelsi and S. J. Kays. 2007. Dielectric spectroscopy of watermelons for quality sensing. Measurement Science and Technology 18: 1887-1892. https://doi.org/10.1088/0957-0233/18/7/014
  16. Nelson, S. O., S. Trabelsi and S. J. Kays. 2008. Dielectric spectroscopy of melons for potential quality sensing. Transactions of the ASABE 51(6):2209-2214. https://doi.org/10.13031/2013.25384
  17. Nicolai, B. M., K. Beullens, E. Bobelyn, A. Peirs, W. Saeys, K. I. Theron and J. Lammertyn. 2007. Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review. Postharvest Biology and Technology 46:99-118. https://doi.org/10.1016/j.postharvbio.2007.06.024
  18. Ozer, N., B. A. Engel and J. E. Simon. 1998. A multiple impact approach for nondestructive measurement of fruit firmness and maturity. Transactions of the ASAE 41(3):871-876. https://doi.org/10.13031/2013.17204
  19. Penchaiya, P., E. Bobelyn, B. E. Verlinden, B. M. Nicolai and W. Saeys. 2009. Non-destructive measurement of firmness and soluble solids content in bell pepper using NIR spectroscopy. Journal of Food Engineering 94: 267-273. https://doi.org/10.1016/j.jfoodeng.2009.03.018
  20. Saeys, W., K. Beullens, J. Lammertyn, H. Ramon and T. Naes. 2008. Increasingrobustness against changes in the interferent structure by incorporating priorinformation in the augmented classical least-squares framework. Anal. Chem. 80(13):4951-4959. https://doi.org/10.1021/ac800155n
  21. Stone, M. L., P. R. Armstrong, X. Zhang, G. H. Brusewitz and D. D. Chen. 1996. Watermelon maturity determination in the field using acoustic impulse impedance techniques. Transactions of the ASAE 39(6):2325-2330. https://doi.org/10.13031/2013.27743
  22. Sugiyama, J., T. Katsural, J. Hong, H. Koyama and K. Mikuriya. 1998. Melon ripeness monitoring by a portable firmness tester. Transactions of the ASAE 41(1):121-127. https://doi.org/10.13031/2013.17135
  23. Suh, S. R., K. H. Lee, S. H. Yu, S. N. Yoo and Y. S. Choi. 2011. Comparison of performance of measuring of VIS/NIR spectroscopic spectrum to predict soluble solids content and 'Shingo' pear. Journal of Biosystems Engineering 36(2):130-139. https://doi.org/10.5307/JBE.2011.36.2.130
  24. Suh, S. R., K. H. Lee, S. H. Yu, H. S. Shin, S. N. Yoo and Y. S. Choi. 2012. A melon fruit grading machine using a miniature VIS/NIR spectrometer: 2. Design factors for optimal interactance measurement setuo. Journal of Biosystems Engineering 37(3):177-183. https://doi.org/10.5307/JBE.2012.37.3.177
  25. Sun, T., K. Huang, H. Xu and Y. Ying. 2010. Research advances in nondestructive determination of internal quality in watermelon/melon: A review. Journal of Food Engineering 100:569-577. https://doi.org/10.1016/j.jfoodeng.2010.05.019
  26. Tollner, E. W., R. D. Gitaitis, K. W. Seebold and B. W. Maw. 2005. Experiences with a food product X-ray inspection system for classifying onions. Applied Engineering in Agriculture 21(5):907-912. https://doi.org/10.13031/2013.19695
  27. Wang, S., Q. Jiao and J. Ji. 1999. An impulse response method of nondestructive inspection of the ripeness of watermelon. Transactions of the CSAE 15(3):241- 245.

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