Nondestructive Evaluation for the Viability of Watermelon (Citrullus lanatus) Seeds Using Fourier Transform Near Infrared Spectroscopy

  • Lohumi, Santosh (Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University) ;
  • Mo, Changyeun (National Academy of Agricultural Science, Rural Development Administration) ;
  • Kang, Jum-Soon (Department of Horticultural Bioscience, Pusan National University) ;
  • Hong, Soon-Jung (Rural Human Resource Development Center) ;
  • Cho, Byoung-Kwan (Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University)
  • Received : 2013.10.05
  • Accepted : 2013.11.04
  • Published : 2013.12.01


Purpose: Conventional methods used to evaluate seeds viability are destructive, time consuming, and require the use of chemicals, which are not feasible to implement to process plant in seed industry. In this study, the effectiveness of Fourier transform near infrared (FT-NIR) spectroscopy to differentiate between viable and nonviable watermelon seeds was investigated. Methods: FT-NIR reflectance spectra of both viable and non-viable (aging) seeds were collected in the range of 4,000 - 10,000 $cm^{-1}$ (1,000 - 2,500 nm). To differentiate between viable and non-viable seeds, a multivariate classification model was developed with partial least square discrimination analysis (PLS-DA). Results: The calibration and validation set derived from the PLS-DA model classified viable and non-viable seeds with 100% accuracy. The beta coefficient of PLS-DA, which represented spectral difference between viable and non-viable seeds, showed that change in the chemical component of the seed membrane (such as lipids and proteins) might be responsible for the germination ability of the seeds. Conclusions: The results demonstrate the possibility of using FT-NIR spectroscopy to separate seeds based on viability, which could be used in the development of an online sorting technique.



  1. Albishri, H. M. and A. O. Almaghrabi. 2013. Characterization and chemical composition of fatty acid content of watermelon and muskmelon cultivars in Saudi Arabia using gas chromatography/mass spectroscopy 9(33): 58-66.
  2. Chung H. and H. J. Kim. 2000. Near-infrared spectroscopy principles. Analytical science technology 13(1):1-14 (In Korean).
  3. International Seed Testing Association. 1985. International rules for seed testing. Seed science and technology 13:300-520.
  4. Lee, J. H. and M. Choung. 2011. Nondestructive discrimination of herbicide-resistant genetically modified soybean seeds using near-infrared reflectance spectroscopy. Food chemistry 126(1):368-373.
  5. Lugo, I. B. and A. C. Leopold. 1991. Changes in soluble carbohydrates during seed storage. Plant physiology 98(3):1207-1210.
  6. Mazliak, P. 1983. Plant membrane lipids: changes and alterations during aging and senescence. In Liebermann M. (Ed) Postharvest Physiology and Crop Preservation. pp. 123-140, Plenum Press, New York, USA.
  7. McDonald M. B. 1999. Seed deterioration: physiology, repair and assessment. Seed Science and Technology 27(1):177-237.
  8. Min, T. G. 2000. A nondestructive system for detection deteriorated crop seeds by amino acid leakage. J. Kor. Soc. Hort. Sci. 41:576-578.
  9. Moore, R. P. 1976. Tetrazolium seed testing developments in North America. J. Seed Technol. 1:17-30.
  10. Osborne, B. G., T. Fearn and P. T. Hindle. 1993. Practical NIR spectroscopy with applications in food and beverage analysis. 2nd edition. Longman Scientific and Technical, Singapore.
  11. Tigabu, M. and P. C. Oden. 2002. Discrimination of viable and empty seeds of Pinus patula Schiede & Deppe with NIR spectroscopy. New forest 25:163-176, 2003.
  12. Wilson, D. O. and M. B. McDonald. 1986. The lipid peroxidation model of seed aging. Seed science and technology 14:269-300.

Cited by

  1. Large-Scale Screening of Intact Tomato Seeds for Viability Using Near Infrared Reflectance Spectroscopy (NIRS) vol.9, pp.4, 2017,
  2. Spatial assessment of soluble solid contents on apple slices using hyperspectral imaging vol.159, 2017,
  3. Development of Non-Destructive Sorting Technique for Viability of Watermelon Seed by Using Hyperspectral Image Processing vol.36, pp.1, 2016,
  4. Near-infrared hyperspectral imaging system coupled with multivariate methods to predict viability and vigor in muskmelon seeds vol.229, 2016,
  5. Location of Sampling Points in Optical Reflectance Measurements of Chinese Cabbage and Kale Leaves vol.40, pp.2, 2015,
  6. Non-destructive technique for determining the viability of soybean (Glycine max ) seeds using FT-NIR spectroscopy 2017,
  7. Non-Destructive Quality Evaluation of Pepper (Capsicum annuum L.) Seeds Using LED-Induced Hyperspectral Reflectance Imaging vol.14, pp.12, 2014,
  8. A review of vibrational spectroscopic techniques for the detection of food authenticity and adulteration vol.46, pp.1, 2015,
  9. Detection of Starch Adulteration in Onion Powder by FT-NIR and FT-IR Spectroscopy vol.62, pp.38, 2014,
  10. Separation of viable and non-viable tomato ( Solanum lycopersicum L.) seeds using single seed near-infrared spectroscopy vol.142, 2017,
  11. Rapid monitoring of the fermentation process for Korean traditional rice wine ‘Makgeolli’ using FT-NIR spectroscopy vol.73, 2015,
  12. Comparative nondestructive measurement of corn seed viability using Fourier transform near-infrared (FT-NIR) and Raman spectroscopy vol.224, 2016,
  13. Assessment of seed quality using non-destructive measurement techniques: a review vol.26, pp.04, 2016,
  14. Development of On-line Sorting System for Detection of Infected Seed Potatoes Using Visible Near-Infrared Transmittance Spectral Technique vol.35, pp.1, 2015,
  15. Single-Kernel FT-NIR Spectroscopy for Detecting Supersweet Corn (Zea mays L. Saccharata Sturt) Seed Viability with Multivariate Data Analysis vol.18, pp.4, 2018,
  16. Near-infrared spectroscopy used to predict soybean seed germination and vigour vol.28, pp.3, 2018,
  17. Detection of Oil Chestnuts Infected by Blue Mold Using Near-Infrared Hyperspectral Imaging Combined with Artificial Neural Networks vol.18, pp.6, 2018,