Development and Evaluation of an Image Processing Algorithm for Gradation of White Sugar Crystals

Document Type : Research Article

Authors

1 Department of Biosystems Engineering, Faculty of Agriculture, Shahid Chamran University of Ahvaz,, Ahvaz, Iran.

2 Department of Biosystems Engineering, Faculty of Agriculture, Shahid Chamran University of Ahvaz,, َAhvaz, Iran

3 Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

Abstract

Sieve test is a standard method widely used in sugar factories to determine the size of sugar particles. Needing for relatively large samples, time-consuming, limited measured parameters, being not automatic and offline are main disadvantages of the sieve test. Image processing technique can determine the parameters related to size and shape of sugar particles, quickly, automatically and instantly and is a good alternative to the sieve test. The objective of this study was presenting a suitable image processing algorithm for online determination of white sugar crystals size. First, the sugar mass crystals were sorted according to size using the standard sieve test (with 7 sieves at 10 replications). Then, the crystals images were taken by a digital microscope with 5 megapixels image sensor. Three marking methods, including foreground-background (FB), ultimate erosion (UE) and distance transform (DT), were used for images segmentation and determination of the crystals morphological parameters in the image processing toolbox of MATLAB software. The analysis of variance of mean aperture (MA) values was performed based on factorial experiment in a completely randomized design and the mean of MA values was compared with Duncan's multiple range test. The effects of both sieve size and segmentation method factors as well as their interaction on the MA value were significant. The MA of FB and UE markers were not significantly different from the reference MA value that obtained manually at 1% level. Since, the UE marker showed the best performance in MA determination due to a lower error (equal 10.13%), so it is recommended for online determination of white sugar particles size by image processing.

Graphical Abstract

Development and Evaluation of an Image Processing Algorithm for Gradation of White Sugar Crystals

Highlights

  • A suitable image processing algorithm was presented for gradation and size determination of white sugar crystals.
  • The effects of both sieve size and segmentation method factors as well as their interaction on the mean aperture (MA) index were significant.
  • The MA of FB and UE markers were not significantly different from the reference MA value that obtained manually at 1% level.
  • The UE marker is recommended for online determination of white sugar particles size by image processing due to a lower error in MA determination.

Keywords

Main Subjects

References

[1]  ICUMSA (The International Commission for Uniform Methods of Sugar Analysis Ltd.). Particle Size Distribution of White Sugar and Plantation White Sugar by Sieving. ICUMSA Method GS 2/9-37, 2007. URL http://www.icumsa.org . Accessed 01.05.17.
[2]  Bennár, M., Betoret, E., Bojňanská, T., Brňo, D., Hambálková, J., & Richter, A. (2012). Optimal particle size distribution of white sugar. Listy Cukrovarnické a Řepařské, 128(12), 385-389.
[3]  Khalili, Kh., & Emam, S.M. (2018). Dimensional control of sand particles based on the Iranian National Standard Organization using image processing technique. Iran. J. Manuf. Eng., 5(3), 52-62. [In Persian]
[4]     Miller, K.F. & Beath, A.C. (2000). The measurement of raw sugar crystal size by sieving and laser diffraction. In: Proceeding of Aust. Soc. Sug. Cane Technol., (22, pp. 393-398), Brisbane, Australia.
[5]  Argaw, G.A. (2007). Sugar Crystal Size Characterization Using Digital Image Processing. Durban, South Africa: University of KwaZulu-Natal, School of Physics, Ph.D. dissertation.
[6]  Argaw, G.A., Alport, M.J. & Malinga, S.B. (2006). Automatic measurement of crystal size distribution using image processing. In: Proceeding of the Cong. of S. Afr. Sug. Technol. Ass., (80, pp. 399-411), Durban, South Africa.
[7]  Mhlongo, A.Z. & Alport, M.J. (2002). Application of artificial neural network techniques for measuring grain sizes during sugar crystallization. In: Proceeding of the Cong. of S. Afr. Sug. Technol. Ass., (76, pp. 460-468), Durban, South Africa.
[8]  Wang, X.Z., Roberts, K.J., & Ma, C. (2008). Crystal growth measurement using 2D and 3D imaging and the perspectives for shape control. Chem. Eng. Sci., 63, 1173-1184.
[9]  Wang, L.M., Zhu, M.R., & Fan, C.L. (2009). Application of image recognition in sugar crystal size measurement. Computer Simul., 26: 294-297.
[10]   Patience, D.B., & Rawlings, J.B. (2001). Particle-shape monitoring and control in crystallization processes. AIChE J., 47: 2125-2130.
[11]   De Anda, J.C., Wang, X.Z., & Roberts, K.J. (2005).  Multi-scale segmentation image analysis for the in-process monitoring of particle shape with batch crystallisers. Chem. Eng. Sci., 60: 1053-1065.
[12]   Dharmayat, S., De Anda, J.C., Hammond, R.B., Lai, X.J., Roberts, K.J., & Wang, X.Z. (2006). Polymorphic transformation of L-glutamic acid monitored using combined on-line video microscopy and X-ray diffraction. J. Crystal Growth, 294: 35-40.
[13]   Schumann, G.T. & Thakur, C.S. (1993). The use of video camera and PC for crystal image analysis. In: Proceedings of S. Afr. Sug. Technol. Ass., (67, pp. 135-139), Durban, South Africa.
[14] Palenzuela, E.S.G. & Cruz, P.I.V. (1996). Techniques for classifying sugar crystallization images based on spectral analysis and the use of neural networks. In: Proceedings of IWISP '96, (pp. 641-645), Manchester, United Kingdom.
[15]   Daliziel, S.M., Tan, S.Y., White, E.T. & Broadfoot, R. (1999). An image analysis system for sugar crystal sizing. In: Proceeding of Aust. Soc. Sug. Cane Technol., (21, pp. 366-372), Brisbane, Australia.
[16]   Merkus, H.G. (2009). Particle Size Measurements: Fundamentals, Practice, Quality. Springer publishing.
[17]   Otsu, N. (1979). A threshold selection method from grey level histograms. IEEE Transactions on Syst., Man, and Cybernetics, 9(1): 62-66.
[18]   Venkataraman, S., Allison, D.P., Qi, H., Morrell-Falvey, J.L., Kallewaard, N.L., Crowe Jr., J.E., & Doctaycz, M.J. (2006). Automated image analysis of atomic force microscopy images of rotavirus particles. Ultra microscopy, 106(8–9): 829-837.
Innovative Food Technologies
Volume 8, Issue 1
November 2020
Pages 97-110

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History

  • Receive Date: 05 May 2020
  • Revise Date: 17 July 2020
  • Accept Date: 26 August 2020

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