Study of color and textural feature variation of carp meat using image processing

Document Type : Research Article

Authors

1 MSc student of Mechanics of Biosystems Engineering Department, Faculty of Agricultural Engineering and Rural Development, Khuzestan Agricultural Sciences and Natural Resources University.

2 Assistant professor of Khuzestan Agricultural Sciences and Natural Resources University

3 Graduated MSc of Mechanics of Biosystems Engineering Department, Faculty of Agricultural Engineering and Rural Development, Khuzestan Agricultural Sciences and Natural Resources University.

Abstract

Fish quality is affected by terms of handling, maintenance and storage time. These terms make chemical changes in fish and accelerate the deterioration its of tissue and make it dangerous for human body. There are several methods use for assessment of fish freshness, most of them are costly and destructive., Therefore, in this paper a non-destructive machine vision system based on gill and eye color and textural features is proposed. . Accordingly, after segmentation of region of interest in the images (eyes and gills), the color and textural properties of the images were extracted and the most suitable ones were selected using Fisher's selection algorithm and QDA and LDA classification methods were applied. For the QDA classifier , the V_HSV (extracted from the gills), the energy and the contrast (extracted from the fish's eye) and for the LDA classifier, the energy (extracted from the eye), the contrast )extraction from the eye) ,V_HSV (extracted from the gills) ,homogeneity (extracted from the eye) and H_HSV (extracted from the gills) were extracted. The classification accuracy for QDA and LDA were 93% and 96%, respectively.

Graphical Abstract

Study of color and textural feature variation of carp meat using image processing

Highlights

  • The image processing method was used to evaluate the freshness of the fish.
  • The color and textural features of the eye and gill images were extracted and the most suitable ones were selected using the Fisher's feature selection algorithm.
  • For prediction of freshness, the QDA and LDA classifications were used and the accuracy of 93 and 96% were acquired, respectively.

Keywords

Main Subjects

References

[1] Hammond, J., Marquis, B., Michaels, R., Oickle, B., Segee, B., Vetelino, J., Bushway, A., Camire, M.E. and Davis-Dentici, K. )2002(. A semiconducting metal-oxide array for monitoring fish freshness. Sens. Actuators., B,  84(2-3),113-122.
[2] . Zhai, X., Shi, J., Zou, X., Wang, S., Jiang, C., Zhang, J., Huang, X., Zhang, W., Holmes, M. (2017). Novel colorimetric films based on starch/polyvinyl alcohol incorporated with roselle anthocyanins for fish freshness monitoring. Food Hydrocoll., 69,308-317.
[3] . Goon, S., Bipasha, M., Islam, M. S., Hossain, M. B. (2014). Fish marketing status with formalin treatment in Bangladesh. Int. J. Public Health Sci., 3(2), 95-100.
[4] . Issac, A., Dutta, M. K., Sarkar, B. (2017). Computer vision based method for quality and freshness check for fish from segmented gills. Comput Electron Agric., 139, 10-21.
[5] . WHO. World Health Organization, 2005. Avoiding heart attacks and strokes: don't be a victim-protect yourself. World Health Organization.Available from: http://www.who.int/cardiovascular _diseases/resources /avoid _heart _attack_ report/ en/index.html. Accessed Aug 7, 2012.
[6] . Neale, E. P., NOLAN‐CLARK, D., Probst, Y. C., Batterham, M. J., Tapsell, L. C. (2012). Comparing attitudes to fish consumption between clinical trial participants and non‐trial individuals. Nutr Diet., 69(2), 124-129.
[7] . Olsen, S. O. (2004). Antecedents of seafood consumption behavior: An overview. J. Aquat Food prod Technol., 13(3), 79-91.
[8] . Verbeke, W., Vackier, I. (2005). Individual determinants of fish consumption: application of the theory of planned behaviour. Appetite., 44(1), 67-82.
[9] . Alasalvar, C., Miyashita, K., Shahidi, F., Wanasundara, U. (2011). (Eds.), Handbook of seafood quality, safety and health applications. John Wiley., Sons.
[10] . Hassoun, A. and Karoui, R. (2015). Front-face fluorescence spectroscopy coupled with chemometric tools for monitoring fish freshness stored under different refrigerated conditions. Food Control., 54, 240-249
[11] . Chung, W. Y., Le, G. T., Tran, T. V., Nguyen, N. H. (2017). Novel proximal fish freshness monitoring using batteryless smart sensor tag. Sens. Actuators., B , 248, 910-916.
[12] . McCaig, T. N. (2002). Extending the use of visible/near-infrared reflectance spectrophotometers to measure colour of food and agricultural products. Food Res Int., 35(8), 731-736.
[13] . Wang, F., Zang, Y., Wo, Q., Zou, C., Wang, N., Wang, X., Li, D. (2013). Fish freshness rapid detection based on fish-eye image. In PIAGENG 2013: Image Processing and Photonics for Agricultural Engineering., 8761,  87610A.
[14] . Zion, B., Alchanatis, V., Ostrovsky, V., Barki, A., Karplus, I. (2007). Real-time underwater sorting of edible fish species. Comput Electron Agric., 56(1), 34-45.
[15] . Dutta, Malay Kishore, Singh, Anushikha, Ghosal, Sabari. (2015). A computer vision based technique for identification of acrylamide in potato chips. Comput. Electron. Agric., 119, 40–50.
[16] . Camargo, A., Smith, J.S. (2009). An image-processing based algorithm to automatically identify plant disease visual symptoms. Biosys. Eng., 102 (1), 9–21.
[17] . Dutta, Malay Kishore, Issac, Ashish, Minhas, Navroj, Sarkar, Biplab. (2016). Image processing based method to assess fish quality and freshness. J. Food Eng., 177, 50–58.
[18] . Gowen, A.A., O’Donnell, C.P., Cullen, P.J., Downey, G., Frias, J.M. (2007). Hyperspectral imaging – an emerging process analytical tool for food quality and safety control. Trends Food Sci. Technol., 18 (12), 590–598.
[19] . نعمتی‌نیا، الف؛ آبدانان مهدی‌زاده، س؛ ناصحی، ب. (1396) 'اندازه‌گیری پارامترهای رنگ در اسپاگتی با استفاده از سیستم بینایی ماشین، علوم و صنایع غذایی ایران، جلد 14، شماره 73، ص 71-81.
[20] .سلطانی کاظمی، م؛ آبدانان مهدی‌زاده، س. (1395) ساخت، توسعه و ارزیابی سامانه جداکننده توت فرنگی با استفاده از تکنولوژی بینایی، نشریه پژوهشهای مکانیک ماشینهای کشاورزی، جلد 6، شماره 1، ص 61-69.
[21] . Gosselin, P. H., Murray, N., Jégou, H., Perronnin, F. (2014). Revisiting the fisher vector for fine-grained classification. Pattern recognit. Let., 49, 92-98.
[22] . آبدانان مهدی زاده، س، سلطانی کاظمی، م. (1395) ساخت و ارزیابی سامانه تشخیص تراکم توده زنبور درون کندو با استفاده از بینایی ماشین، مهندسی بیوسیستم ایران، جلد 47، شماره 1، ص 21-29.
[23] .Fedikow, M. A. F., Parbery, D.,  Ferreira, K. J. (1991). Geochemical target selection along the Agassiz Metallotect utilizing stepwise discriminant function analysis. Econ Geol., 86(3), 588-599.
 
[24] . Wu, W., Mallet, Y., Walczak, B., Penninckx, W., Massart, D. L., Heuerding, S., Erni, F. (1996). Comparison of regularized discriminant analysis linear discriminant analysis and quadratic discriminant analysis applied to NIR data. Anal. Chim. Acta., 329(3), 257-265.
[25] .Qiao, M., Fletcher, D. L., Smith, D. P., Northcutt, J. K. (2001). The effect of broiler breast meat color on pH, moisture, water-holding capacity, and emulsification capacity. Poult. Sci., 80(5), 676-680.
[26] .Wu, W., Mallet, Y., Walczak, B., Penninckx, W., Massart, D. L., Heuerding, S. Erni, F. (1996). Comparison of regularized discriminant analysis linear discriminant analysis and quadratic discriminant analysis applied to NIR data. Anal. Chim. Acta., 329(3), 257-265.
[27] . Masniyom, P. )2011(. Deterioration and shelf-life extension of fish and fishery products by modified atmosphere packaging. J.Sci. Techno.l, 33 (2), 181–192.
[28] . Lie, O.  )2001(. Flesh quality-the role of nutrition. Aquacult Res, 32 (s1), 341–348.
[29] . Thanonkaew, A., Benjakul, S., Vissessanguan, W., Decker, E.A. )2006(. The effect of metal ions on lipid oxidation, color and physicochemical properties of cuttlefish (Sepia pharaonis) subjected to multiple freeze-thaw cycles. Food Chem., 95 (4), 591–599.
[30] . آبدانان مهدی زاده، س، نوری، م، سلطانی کاظمی، م،  امرایی، س. (1395). بررسی غیرمخرب فاکتورهای کیفی آبمیوه مرکبات در خلال انبارمانی با کمک پردازش تصویر.پژوهش‌های علوم و صنایع غذایی ایران، پژوهش‌های علوم و صنایع غذایی ایران، جلد 13، شماره 2، ص 262-272
[31] . Guzmán, E., Baeten, V., Fernández Pierna, J. A., García-Mesa, J. A. (2015) Determination of the Olive Maturity Index of Intact Fruits Using Image Analysis. Food Sci. Technol., 52 (3), 1462–1470.
[32] . Quevedo, R., Jaramillo, M., Diaz, O., Pedreschi, F., Aguilera, J. M.) 2009(. Quantification of enzymatic browning in apple slices applying the fractal texture Fourier image. J. Food Eng., 95(2), 285-290.
[33] . Quevedo, R., Valencia, E., Bastías, J. M., Cárdenas, S. (2013, October). Description of the enzymatic browning in avocado slice using GLCM image texture. In Pacific-Rim Symposium on Image and Video Technology pp 93-101. Springer, Berlin, Heidelberg.
[34] . Gerrard, D. E., Gao, X., Tan, J. (1996). Beef marbling and color score determination by image processing. Food Sci., 61(1), 145-148.
[35] . Tan, J. (2004). Meat quality evaluation by computer vision. J. Food Eng., 61(1), 27-35.
[36] Castro, P., Millan, R., Penedo, J. C., Sanjuan, E., Santana, A., Caballero, M. J. (2012). Effect of storage conditions on total volatile base nitrogen determinations in fish muscle extracts. J. Aquat Food prod Technol., 21(5), 519-523
[37] . . Dowlati, M., Mohtasebi, S. S., Omid, M., Razavi, S. H., Jamzad, M., De La Guardia, M. (2013). Freshness assessment of gilthead sea bream (Sparus aurata) by machine vision based on gill and eye color changes. J. Food Eng., 119(2), 277-287.
[38]  Muhamad, F., Hashim, H., Jarmin, R., Ahmad, A. (2009, December). Fish freshness classification based on image processing and fuzzy logic. In Proceedings of the 8th WSEAS International Conference on Circuits, Systems, Electronics, Control. 109-115.
[39] . Dufour, É, Frencia, J. P., Kane, E. (2003). Development of a rapid method based on front-face fluorescence spectroscopy for the monitoring of fish freshness. Food Res Int., 36(5), 415-423.
[40] . Khojastehnazhand, M., Khoshtaghaza, M. H., Mojaradi, B., Rezaei, M., Goodarzi, M., Saeys, W. (2014). Comparison of visible–near infrared and short wave infrared hyperspectral imaging for the evaluation of rainbow trout freshness. Food Res Int., 56, 25-34.
 
Innovative Food Technologies
Volume 6, Issue 2
February 2019
Pages 257-276

Files

History

  • Receive Date: 04 June 2018
  • Revise Date: 26 July 2018
  • Accept Date: 02 September 2019

Share

How to cite

Statistics