DOI QR코드

DOI QR Code

Prediction of Soluble Solids Content of Chestnut using VIS/NIR Spectroscopy

  • Park, Soo Hyun (Department of Biosystems and Biomaterials Science and Engineering, Seoul National University) ;
  • Lim, Ki Taek (Department of Biosystems and Biomaterials Science and Engineering, Seoul National University) ;
  • Lee, Hoyoung (Environmental Microbiology and Food Safety Laboratory, Animal and Natural Resources Institute, Agricultural Research Service, U.S. Department of Agriculture) ;
  • Lee, Soo Hee (Life & Technology CO. LTD.) ;
  • Noh, Sang Ha (Department of Biosystems and Biomaterials Science and Engineering, Seoul National University)
  • Received : 2013.07.17
  • Accepted : 2013.08.14
  • Published : 2013.09.01

Abstract

Purpose: The present study focused on the estimation of soluble solids content (SSC) of chestnut using reflectance and transmittance spectra in range of VIS/NIR. Methods: Four species intact/peeled chestnuts were used for acquisition of spectral data. Transmittance and reflectance spectra were used to develop the best PLS model to estimate SSC of chestnut. Results: The model developed with the transmitted energy spectra of peeled chestnuts rather than intact chestnuts and with range of NIR rather than VIS performed better. The best $R^2$ and RMSEP of cross validation were represented as 0.54 and $1.85^{\circ}Brix$. The results presented that the reflectance spectra of peeled chestnuts by species showed the best performance to predict SSC of chestnut. $R^2$ and RMSEP were 0.55 and $1.67^{\circ}Brix$. Conclusions: All developed models showed RMSEP around $1.44{\sim}2.54^{\circ}Brix$, which is considered not enough to estimate SSC accurately. It was noted that $R^2$ of cross validation that we found were not high. For all that, grading of the fruits in two or three classes of SSC during postharvest handling seems possible with an inexpensive spectrophotometer. Furthermore, the development of estimation of SSC by each chestnut species could be considered in that SSC distribution is clustering in different range by species.

Keywords

References

  1. Chen, P and Z. Sun. 1991. A review of non-destructive methods for quality evaluation and sorting of agricultural products. Journal of Agricultural Engineering Research 49:85-98. https://doi.org/10.1016/0021-8634(91)80030-I
  2. Cubeddu, R., P. Pfifferi, P. Taroni, G. Valentini, A. Torricelli,... and C. Valero. 2001. Ortiz Nondestructive quantification of chemical and physical properties of fruits by timeresolved reflectance spectroscopy in the wavelength range 650-1000 nm. Applied Optics 40(4):538-543. https://doi.org/10.1364/AO.40.000538
  3. Donis-González, I. R., D. E. Guyer, A. Pease and D. W. Fulbright. 2012. Relation of computerized tomography Hounsfield unit measurements and internal components of fresh chestnuts (Castanea spp.). Postharvest Biology and Technology 64(1):74-82. https://doi.org/10.1016/j.postharvbio.2011.09.018
  4. ElMasry, G., N. Wang, C. Vigneault, J. Qiao and A. ElSayed. 2008. Early detection of apple bruises on different background colors using hyperspectral imaging. LWT-Food Science and Technology 41(2):337-345. https://doi.org/10.1016/j.lwt.2007.02.022
  5. Food and Agriculture Organization. 2011. FAO online. Food and Agriculture Organization of the United Nations. Available at: http://faostat.fao.org/ (verified July 05, 2013).
  6. Gunasekaran, S. 2001. Nondestructive Food Evaluation: Techniques to Analyze Properties and Quality Vol. 105. CRC Press.
  7. Khodabux, K., M. S. S. L'Omelette, S. Jhaumeer-Laulloo, P. Ramasami and P. Rondeau. 2007. Chemical and nearinfrared determination of moisture, fat and protein in tuna fishes. Food chemistry 102(3):669-675. https://doi.org/10.1016/j.foodchem.2006.05.057
  8. Liu, J., X. Li, P. Li, W. Wang, J. Zhang, W. Zhou and Z. Zhou. 2011. Non-destructive measurement of sugar content in chestnuts using near-infrared spectroscopy. Computer and Computing Technologies in Agriculture IV 347:246-254. https://doi.org/10.1007/978-3-642-18369-0_28
  9. Norris, K. H and J. R. Hart. 1965. Direct spectrophotometric determination of moisture content of grain and seeds, In: "Humidity and Moisture," Vol. 4, "Principles and Methods of Measuring Moisture in Liquids and Solids," Reinhold Publishing Corp., New York.
  10. Roy, S., R. C. Anantheswaran, J. S. Shenk, M. O. Westerhaus and R. B. Beelman. 1993. Determination of moisture content of mushrooms by Vis/NIR spectroscopy. Journal of the Science of Food and Agriculture 63(3):355-360. https://doi.org/10.1002/jsfa.2740630314
  11. Suh, S. R., S. H. Yu and S. N. Yoo. 2012. Agricultural process and food engineering; A melon fruit grading machine using a miniature VIS/NIR spectrometer: 2. Design factors for optimal interactance measurement setup. Journal of Biosystems Engineering 37(3):177-183. https://doi.org/10.5307/JBE.2012.37.3.177
  12. Wang, L. 2010. Near infrared reflectance spectroscopy (NIRS) and its application in the determination for the quality of animal feed and products. Spectroscopy and Spectral Analysis 30(6):1482-1487.
  13. Zude, M., B. Herold, J. M. Roger, V. Bellon-Maurel and S. Landahl. 2006. Non-destructive tests on the prediction of apple fruit flesh firmness and soluble solids content on tree and in shelf life. Journal of Food Engineering 77(2):254-260. https://doi.org/10.1016/j.jfoodeng.2005.06.027

Cited by

  1. Determination of the sample number for optical reflectance measurement of vegetable leaf vol.112, 2015, https://doi.org/10.1016/j.compag.2015.01.004