Czech J. Food Sci., 2008, 26(2):109-116 | DOI: 10.17221/1/2008-CJFS

Image vision technology for the characterisation of shape and geometrical properties of two varieties of lentil grown in Turkey

Ebru Firatligil-Durmuş, Evľen ©árka, Zdeněk Bubník
Department of Carbohydrate Chemistry and Technology, Faculty of Food and Biochemical Technology, Institute of Chemical Technology in Prague, Prague, Czech Republic

Geometrical features of lentil seeds (Lens culinaris Medik) were analysed using the image analysis LUCIA system Ver. 3.52. The values of the weight of 1000 kernels, kernel density, specific volume, specific surface area, and surface area of 1000 kernels of red and green lentils were determined as 66.61 and 138.56 g, 1504.5 and 1376.4 kg/m3, 0.6647 and 0.7265 cm3/g, 0.594 and 0.579 m2/kg, 395.4 and 801.9 cm2,, respectively. The lentil volume was simulated by an oblate spheroid and two sphere segments and the volumes obtained with both models were compared with that obtained by pycnometric method. Percentage differences of the two sphere segment approximation for red and green lentils were 4.4% and 4.2%, respectively. The height (thickness) of lentils was constant and practically the same with both varieties (2.6 mm) and therefore it was possible to simplify the geometrical models. Thus, 2D image analysis is suitable for a quick evaluation of the specific volume and surface area of grains on the basis of the projected area (equivalent diameter) without the measurement of the height. Image processing provides a simple, rapid, and non-invasive methodology to estimate lentil geometric features and engineering parameters.

Keywords: image analysis; lentil; legumes; size parameters; geometrical model

Published: April 30, 2008  Show citation

ACS AIP APA ASA Harvard Chicago Chicago Notes IEEE ISO690 MLA NLM Turabian Vancouver
Firatligil-Durmuş E, ©árka E, Bubník Z. Image vision technology for the characterisation of shape and geometrical properties of two varieties of lentil grown in Turkey. Czech J. Food Sci. 2008;26(2):109-116. doi: 10.17221/1/2008-CJFS.
Share...
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. Altuntas E., Yildiz M. (2007): Effect of moisture content on some physical and mechanical properties of faba bean (Vicia faba L.) grains. Journal of Food Engineering, 78: 174-183. Go to original source...
    2. Bhattacharya S., Narasimha H.V., Bhattacharya S. (2005): The moisture dependent physical and mechanical properties of whole lentil pulse and split cotyledon. International Journal of Food Science and Technology, 40: 213-221. Go to original source...
    3. Bhatty R.S. (1988): Composition and quality of lentil (Lens culinaris Medik) - a review. Canadian Institute of Food Science and Technology Journal - Journal de l'Institut Canadien de Science et Technologie Alimentaires, 21: 144-160. Go to original source...
    4. Bubník Z., Kadlec P., Starhova H., Pour V., Hinková A. (2000): Application of computer image analysis for evaluation and control of the sucrose crystallization process. Czech Journal of Food Sciences, 18: 23-28.
    5. Dalen G. (2004): Determination of the size distribution and percentage of broken kernels of rice using flatbed scanning and image analysis. Food Research International, 37: 51-58. Go to original source...
    6. Fernandez L., Castillero C., Aguilera J.M. (2005): An application of image analysis to dehydration of apple discs. Journal of Food Engineering, 67: 185-193. Go to original source...
    7. Isik E., Unal H. (2007): Moisture-dependent physical properties of white speckled red kidney bean grains. Journal of Food Engineering, 82: 209-216. Go to original source...
    8. Kadlec P., Dostálová J., Houąka M., Strohalm J., Bubník Z. (2006a): Evaluation of α-galactosides decrease during storage of germinated pea seeds treated by high pressure. Journal of Food Engineering, 77: 364-367. Go to original source...
    9. Kadlec P., Skulinová M., ©árka E., Fořt I. (2006b): Microwave and vacuum microwave drying of germinated pea seeds. In: Proceedings 17th International Congress of Chemical and Process Engineering CHISA 2006, Prague (CD ROM).
    10. Koc A.B. (2007): Determination of watermelon volume using ellipsoid approximation and image processing. Postharvest Biology and Technology, 45: 366-371. Go to original source...
    11. Kumar A., Mathew A. (2003): Method for estimating the surface area of ellipsoidal food materials. Biosystems Engineering, 85: 1-5. Go to original source...
    12. Lee H.C., Htoon A.K., Uthayakumaran S., Paterson J.L. (2007): Chemical and functional quality of protein isolated from alkaline extraction of Australian lentil cultivars: Matilda and Digger. Food Chemistry, 102: 1199-1207. Go to original source...
    13. Mendoza F., Dejmek P., Aguilera J.M. (2007): Colour and image texture analysis in classification of commercial potato chips. Food Research International, 40: 1146-1154. Go to original source...
    14. Mezreb K., Goullieux A., Ralainirina R., Queneudec M. (2003): Application of image analysis to measure screw speed influence on physical properties of corn and wheat extrudates. Journal of Food Engineering, 57: 145-152. Go to original source...
    15. Nikolopoulou D., Grigorakis K., Stasini M., Alexis M.N., Iliadis K. (2007): Differences in chemical composition of field pea (Pisum sativum) cultivars: Effects of cultivation area and year. Food Chemistry, 103: 847-852. Go to original source...
    16. Sakai N., Yonekawa S., Matsuzaki A. (1996): Twodimensional image analysis of the shape of rice and its application to separating varieties. Journal of Food Engineering, 27: 397-407. Go to original source...
    17. Shouche S.P., Rastogi R., Bhagwat S.G., Sainis J.K. (2001): Shape analysis of grains of Indian wheat varieties. Computers and Electronics in Agriculture, 33: 55-76. Go to original source...
    18. Soral-Smietana M., Krupa L. (2005): Changes in the macrocomponents and microstructure of white bean seeds upon mild hydrothermal treatment. Czech Journal of Food Sciences, 23: 74-83. Go to original source...
    19. ©árka E., Bubník Z., Hinková A. (2006a): Changes in particle size of carbonation slurry during preliming observed by the image analysis method. Zuckerindustrie, 113: 551-557.
    20. ©árka E., Bubník Z., Veselá A. (2006b): Distribuce velikosti částic saturačního kalu a moľnosti jejího ovlivnění. Listy cukrovarnické a řepařské, 122: 277-280.
    21. ©vec I., Hruąková M. (2004): Image data of bread crumb structure from Czech spring wheat cultivars. Czech Journal of Food Sciences, 22: 133-142. Go to original source...
    22. Tahir A.R., Neethirajan S., Jayas D.S., Shahin M.A., Symons S.J., White N.D.G. (2007): Evaluation of the effect of moisture content on cereal grains by digital image analysis. Food Research International, 40: 1140-1145. Go to original source...
    23. Tanska M., Rotkiewicz D., Kozirok W., Konopka I. (2005): Measurement of the geometrical features and surface colour of rapeseeds using digital image analysis. Food Research International, 38: 741-750. Go to original source...
    24. Tukiendorf M., Krótkiewicz M., Boss J. (2003): The application of image analysis as a method of evaluation of granular blend's quality. Polish Journal of Food and Nutrition Sciences, 22/53: 27-30.
    25. Venora G., Grillo O., Shahin M.A., Symons S.J. (2007): Identification of Sicilian landraces and Canadian cultivars of lentil using an image analysis system. Food Research International, 40: 161-166. Go to original source...
    26. Yadav B.K., Jindal V.K. (2007a): Dimensional changes in milled rice (Oryza sativa L.) kernel during cooking in relation to its physicochemical properties by image analysis. Journal of Food Engineering, 81: 710-720. Go to original source...
    27. Yadav B.K., Jindal V.K. (2007b): Modeling changes in milled rice (Oryza sativa L.) kernel dimensions during soaking by image analysis. Journal of Food Engineering, 80: 359-369. Go to original source...
    28. Yang C.C., Chao K.L., Chen Y.R. (2005): Development of multispectral image processing algorithms for identification of wholesome, septicemic, and inflammatory process chickens. Journal of Food Engineering, 69: 225-234. Go to original source...
    29. Zheng C., Sun D.W., Zheng L. (2006): Recent developments and applications of image features for food quality evaluation and inspection - a review. Trends in Food Science & Technology, 17: 642-655. Go to original source...

    This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY NC 4.0), which permits non-comercial use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return to the content