Determination of freshness of UHT milk with L * a * b * color parameters measured by image processing

Document Type : Research Article

Authors

1 MSc Student of Agricultural Machinery, Faculty of Agriculture, Shahrekord University, Chahar Mahal and Bakhtiari

2 Assistant Professor, Department of Biosystems Mechanical Engineering, Faculty of Agriculture, Shahrekord University, Chahar Mahal and Bakhtiari

3 Associate Professor, Department of Biosystems Mechanical Engineering, Faculty of Agriculture, Shahrekord University, Chahar Mahal and Bakhtiari

4 Associate Professor, Department of Food Hygiene, Faculty of Veterinary Medicine, Shahrekord University, Chahar Mahal and Bakhtiari

Abstract

In this study the possibility of determining the shelf life of UHT milk with determininig the L * a * b * color indexes were investigated using image processing. Therefore purchased milk was stored in an incubator at ambient temperature (25±5˚ C) for six months. The imaging and image processing operations was performed to extract mean color parameter of the channel L, a * and b * of color system CIELab. The results indicate significant changes in mean color parameters of the three components of the L, a * and b * during storage of UHT milk. To determine the shelf life of UHT milk, extracted color features were applied as inputs to an artificial neural network linked with genetic algorithms. All programming for image processing and neural-genetic model was performed using Matlab software version R2013a. Neuro-genetic model with the correlation coefficient greater than 0.95 and the mean square error of 0.075 successfully tested in determining the freshness of UHT milk.

Keywords

Main Subjects

References

[1] کوشکی م. 1388. تکنولوژی شیر، گوشت و تخم‌مرغ. انتشارات سروا. تهران.
[2] کوشکی م. (1380). فناوری های نوین در صنایع شیر، جلد اول: پیشرفت­های جدید در فراوری شیر و مشتقات آن. انتشارات سازمان پژوهش های علمی و صنعتی ایران. تهران.
[3] فرهنودی ف. الف. (1377). صنعت شیر، جلد اول، انتشارات شرکت جهاد تحقیقات و آموزش تهران، ص 145تا 144.
[4] کریم گ. (1380). شیر و فراورده های آن (چاپ دوم). سپهر- موسسه فرهنگی هنری واقعه. تهران.
[5] Jackman, P., Sun, D.W. (2012). Recent advances in image processing using image texture features for food quality assessment. Trends in Food Science & Technology 29: 35-43.
[6] یعقوبی سوره الف.؛ علیزاده خالدآباد م. و رضازاد باری م. (1392). کاربرد پردازش تصویر برای تعیین شاخص‌های رنگی L*a*b* در سنجش رنگ غذاها. نشریه پژوهش‌های صنایع غذایی، جلد 23 (شماره3)، ص 411 تا 422.
[7] L Yam K., and E Papadakis S. (2004). A simple digital imaging method for measuring and analyzing color of food surfaces. Journal of Food Engineering 61: 137–142.
[8] Jackman P., Sun D. W., Du C. J., Allen P. and Downey G. (2008). Prediction of beef eating quality from colour, marbling and wavelet texture features. Meat Science 80(4): 1273-1281.
[9] Jackman P., Sun D.W., Du C.J. and Allen P. (2009). Prediction of beef eating qualities from colour, marbling and wavelet surface texture features using homogenous carcass treatment. Pattern Recognition 42(5): 751-763.
[10] Huang Z.K., Hou L.Y. and Li Z.H. (2013). Image Clustering Using Graph Cuts in LAB Color Space. International Journal of Digital Content Technology and its Applications (JDCTA). Volume 7, Number 12.
[11] Quevedo R.A., Diaz O., Caqueo A., Ronceros B. and  Aguilera J.M. (2009). Quantification of enzymatic browning kinetics in pear slices using non-homogenous L* color information from digital images. LWT-Food Science International 42(8): 1367-1373.
 [12] Brosnan T. and Sun D.W. (2004). Improving quality inspection of food products by computer vision-a review. Journal of Food Engineering 61: 3-16.
[13] Jelin´ski T., Du C.J., Sun D.W. and Fornal J. (2007). Inspection of the distribution and amount of ingredients in pasteurized cheese by computer vision. Journal of Food Engineering 83: 3–9.
[14] Du C.J. and Sun D.W. (2004). Recent developments in the applications of image processing techniques for food quality evaluation. Trends in Food Science & Technology 15(5): 230-249.
[15] خوش تقاضا م.؛ امیری چایجان ر.؛ منتظر غ. و مینایی س. (1386). پیش‌بینی ضریب تبدیل شلتوک به برنج سفید در خشک کردن به روش بستر ثابت به کمک شبکه های عصبی مصنوعی. مجله تحقیقات مهندسی کشاورزی، جلد 8 (شماره 2)، ص 135 تا 156.
[16] Ellis D. 2006. Speech & Audio Processing & Recognition. Columbia University.
[17] Cais-Sokolińska D., Pikul J. and Danków R. (2004). Measurement of color parameters as an index of the hydroxymethylfurfural content in the uht sterilised milk during its storage. Electronic journal of polish agricultural universities. Vol (7). Issue 2.
[18] Dmytrów I., Mituniewicz-Małek A. and Balejko J. (2010). Assessment of selected physicochemical parameters of uht sterilized goat’s milk. journal of polish agricultural universities. Vol (7). Issue 2.
[19] بهشتی مقدم ل. 1390. امکان سنجی تعیین چربی شیر با استفاده از ماشین بینایی. پایان‍نامه کارشناسی ارشد مکانیک ماشین‍های کشاورزی، دانشکده‍ی کشاورزی، دانشگاه تهران.
[20] Shafiee S., Minaei S., Moghaddam-Charkari N. and Barzegar M. (2014). Honey characterization using computer vision system and artificial neural networks. Food Chemistry 159 : 143–150.
[21] Popov-Raljić j.V. Lakić N.S. Laličić-Petronijević J.G. Barać M.Band Sikimić V.M. 2008. Color Changes of UHT Milk During Storage. Sensors. 8: 5961-5974.
 
 
Innovative Food Technologies
Volume 2, Issue 2
March 2015
Pages 105-113

Files

History

  • Receive Date: 19 October 2014
  • Revise Date: 11 January 2015
  • Accept Date: 24 February 2015

Share

How to cite

Statistics