Comparison of the Laser Backscattering and Digital Imaging Techniques on Detection of α-Solanine in Potatoes

Document Type : Research Article-en

Authors

• S. Babazadeh ¹
• P. Ahmadi Moghaddam ¹
• A. Sabatyan ²
• F. Sharifian ¹

¹ Department of Mechanical Engineering of Biosystems, Faculty of Agriculture, Urmia University, Urmia, Iran

² Department of Physics, Faculty of Sciences, Urmia University, Urmia, Iran

https://doi.org/10.22067/jam.v10i1.79800

Abstract

The overall objective of this research is to check the abilities of two non-destructive techniques, the digital imaging (DI) and laser light backscattering imaging (LLBI), on detection of α-solanine toxicant in potatoes. Potato samples were classified in healthy and toxic categories based on the amount of α-solanine. For quantifying α-solanine in potato tubers, high-performance liquid chromatography (HPLC) has been used. The results of classification showed that single layer perceptron neural networks can classify potatoes with the accuracies of 94.28% and 98.66% by DI and LLBI systems (Donald cultivar), respectively. It can be said that LLBI systems might take precedent over DI systems due to their high accuracy, rapidity, and industrial capability.

Keywords

• Backscattering imaging
• Digital imaging
• Glycoalkaloids
• Liquid chromatography
• Quality inspection

20.1001.1.22286829.1399.10.1.5.1

Open Access

©2019 The author(s). This article is licensed under Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source.

References

1. Espinoza, M. A., G. Istamboulie, A. Chira, T. Noguer, M. Stoytcheva, and J. L. Marty. 2014. Detection of glycoalkaloids using disposable biosensors based on genetically modified enzymes. Analytical Biochemistry. Article in press. DOI: 10.1016/j.ab.2014.04.005.

2. FAOSTAT. 2008. International year of the potato. New light on a hidden treasure. An end-of-year review. Rome.

3. FAO/WHO. 2003. Joint FAO/WHO expert committee on food additives. Sixty-first meeting. Summary and conclusions. 52: 22. Retrieved from ftp://ftp.fao.org/es/esn/jecfa/jecfa61sc.pdf.

4. Ji, X., L. Rivers, Z. Zielinski, M. Xu, E. MacDougall, J. Stephen, and J. Zhang. 2012. Quantitative analysis of phenolic components and glycoalkaloids from 20 potato clones and in vitro evaluation of antioxidant, cholesterol uptake, and neuroprotective activities. Food Chemistry 133 (4): 1177-1187.

5. Kaveh, M., Y. Abbaspour-Gilandeh, R. Amiri Chayjan, and R. Mohammadigol. 2019. Comparison of mathematical modeling, artificial neural networks and fuzzy logic for predicting the moisture ratio of garlic and shallot in a fluidized bed dryer. Journal of Agricultural Machinery 9 (1): 99-112. (In Farsi).

6. E. Rytel. 2012. Changes in the Levels of Glycoalkaloids and Nitrates after the Dehydration of Cooked Potatoes. American Journal of Potato Research 89 (6): 501-507.

7. Tavakoli, M., and M. Najafzadeh. 2015. Application of the image processing techniques for separating sprouted potatoes in the sorting line. Journal of Applied Environmental and Biological Sciences 4 (11S): 223-227.

8. Wu, D., and D. W. Sun. 2013a. Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: A review- Part I: Fundamentals. Innovative Food Science and Emerging Technologies 19: 01-14.

9. Wu, D., and D. W. Sun. 2013b. Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: A review- Part II: Applications. Innovative Food Science and Emerging Technologies 19: 15-28.

10. Gao, Y., Q. Li, X. Rao, and Y. Ying. 2018. Precautionary analysis of sprouting potato eyes using hyperspectral imaging technology. International Journal of Agricultural and Biological Engineering 11 (2): 153-157.

11. Ebrahimi, E., K. Mollazade, and A. Arefi. 2011. Detection of greening in potatoes using image processing techniques. Journal of American Science 7 (3): 243-247.

12. Udomkun, P., M. Nagle, B. Mahayothee, and J. Müller. 2014. Laser-based imaging system for non-invasive monitoring of quality changes of papaya during drying. Food Control 42: 225-233.

13. Mollazade, K., M. Omid, F. A. Tab, Y. R. Kalaj, S. S. Mohtasebi, and M. Zude. 2013. Analysis of texture-based features for predicting mechanical properties of horticultural products by laser light backscattering imaging. Computers and Electronics in Agriculture 98: 34-45.

14. Geng, J., L. Xiao, X. He, and X. Rao. 2019. Discrimination of clods and stones from potatoes using laser backscattering imaging technique. Computers and Electronics in Agriculture 160: 108-116.

15. Maestresalas, A. L., J. C. Keresztes, M. Goodarzi, S. Arazuri, C. Jaren, and W. Saeys. 2016. Non-destructive detection of blackspot in potatoes by Vis-NIR and SWIR hyperspectral imaging. Food Control. Article in press. DOI: 10.1016/j.foodcont.2016.06.001.

16. Ji, Y., L. Sun, Y. Li, J. Li, S. Liu, X. Xie, and X. Yuantong. 2019. Non-destructive classification of defective potatoes based on hyperspectral imaging and support vector machine. Infrared Physics and Technology. Article in press. DOI: 10.1016/j.infrared.2019.04.007.

17. Ye, D., L. Sun, W. Tan, W. Che, and M. Yang. 2018. Detecting and classifying minor bruised potato based on hyperspectral imaging. Chemometrics and Intelligent Laboratory Systems. Article in press. DOI: 10.1016/j.chemolab.2018.04.002.