تحسين لون زيت مخلفات اسماك  الكارب Cyprinus carpio المستخلص بالأشعة تحت الحمراء بطريقة سطح الاستجابة

Sabah M. H. Al-Shatty; Asaad R.S. Al–Hilphy; Atheer A.A. Al–Mtury

doi:10.59743/jmset.v5i2.60

Authors

Sabah M. H. Al-Shatty Department of Food Science, College of Agriculture, University of Basrah, Basrah, Iraq.
Asaad R.S. Al–Hilphy Department of Food Science, College of Agriculture, University of Basrah, Basrah, Iraq.
Atheer A.A. Al–Mtury Department of Food Science, College of Agriculture, University of Basrah, Basrah, Iraq.

DOI:

https://doi.org/10.59743/jmset.v5i2.60

Keywords:

Fish oil, Image processing, Carp fish waste, Oil color, Infrared

Abstract

Common carp (Cyprinus carpio) fish wastes oil was extracted by using infrared rays. The study aimed to measure one of the important sensory characteristics, which is the colour characteristic using the image processing method. The parameters of colour were lightness (L^*), redness/greenness (a^*), yellowness/blueness (b^*), and hue angle (h). The optimization process was carried out in a response surface methodology using the central composite design for determining the optimal conditions for obtaining the best colour components of the extracted oil. Quadratic polynomial regression models have been utilized to predict the colour properties values. The results showed that the highest values of practical and predictive to L^*, a^*, b^*, and h were 62.35, 39.15, 0.94, 1.12, 50.82, 33.89, 1.15, and 0.096 respectively. The statistical analysis results revealed that there is a significant effect (P <0.05) for the temperature, power, distance, and interaction between them in a^*, while L^*, b^*, and h were not significantly affected by these treatments. Optimization results showed that the optimum conditions were 66.14 ºC, 215.94 watts, and 24.02 cm of the temperature, power, and distance respectively to produce the best colour characteristics.

References

قائمة المراجع باللغة العربية:

المطوري، أثير عبد الامير عبد الجبار (2019). دراسة كفاءة استخلاص زيت مخلفات الاسماك باستعمال جهاز مصمم محليا يعمل بالأشعة تحت الحمراء. رسالة ماجستير، قسم علوم الاغذية، كلية الزراعة، جامعة البصرة، العراق.

المطوري، أثير عبد الامير عبد الجبار؛ الشطي، صباح مالك حبيب؛ والحلفي، أسعد رحمان سعيد (2018). تأثير نوع العبوة على بعض الدلائل الكيميائية لزيت مخلفات اسماك الكارب Cyprinus carpio المستخلص بالأشعة تحت الحمراء. مجلة علوم البحار والتقنيات البيئية، 4(2): 1-16.

قائمة المراجع باللغة الإنجليزية:

Al-Hilphy A.R.S. (2017). Food processing engineering by using infrared ray. Noor Publishing, Germany.

Abdullah M.Z., Guan L.C., Lim K.C., and Karim A.A. (2004). The applications of computer vision and tomographic radar imaging for assessing physical properties of food. Journal of Food Engineering,61(1): 125-135.

Ayala- Silva T., Schnell R.J., Meerow A.W., Winterstein M., Cervantes C., and Brown J.S. (2005). Determination of color and fruit traits of half-sib families of mango (Magnifera indica L.). Proceedings of the Florida State Horticultural Society, 118: 253-257.

Costa C., Antonucci F., Pallottino F., Aguzzi J., Sun D., and Menesatti P. (2011). Shape analysis of agricultural products: A review of recent research advances and potential application to computer vision. Food and Bioprocess Technology, 4(5): 673–692.

Du C., and Sun D. (2004). Recent developments in the applications of image processing techniques for food quality evaluation. Trends in Food Science and Technology, 15(5): 230-249.

Gulrajani M.L. (2010). Color measurement principles advances and industrial applications. Woodhead Publishing Limited, Cambridge, UK.

Hatcher D.W., Symons S.J., and Manivannan U. (2004). Developments in the use of image analysis for the assessment of oriental noodle appearance and color. Journal of Food Engineering, 61(1): 109-117.

Latscha T. (1989). The role of astaxanthin in shrimp pigmentation. Advances in Tropical Aquaculture, Workshop at Tahiti, French Polynesia, 20 Feb - 4 Mar 1989, Actes de colloques Ifremer, Tahiti, French Polynesia, 20 Feb-4 Mar 1989, n°9, chap. 31, pp: 319-325.

Leon K., Mery D., Pedreschi F., and Leon J. (2006). Color measurement in L* a* b* units from RGB digital images. Food Research International, 39(10): 1084–1091.

McGuire R.G. (1992). Reporting of objective colour measurements. Horticultural Science, 27(12): 1254-1255.

Papadakis S.E., Abdul-Malek S., Kamdem R.E., and Yam K.L. (2000). A versatile and inexpensive technique for measuring color of foods. Food Technology, 54(12): 48-51.

Pathare P.B., Opara U.L., and Al-Said F.A. (2012). Colour measurement and analysis in fresh and processed foods: A review. Food and Bioprocess Technology, 6(1): 36-60.

Pedisic S., Levaj B., Dragovic-Uzelac V., Skevin D., and Skendrovic-Babo M. (2009). Color parameters and total anthocyanins of sour cherries (Prunus cerasus L.) during ripening. Agriculturae Conspectus Scientificus, 74 (3):259-262.

Pedreschi F., Aguilera J.M., and Brown C.A. (2000). Characterization of food surfaces using scale-sensitive fractal analysis. Journal of Food Process Engineering, 23(2): 127-143.

Sahin S., and Sumnu S.G. (2006). Physical properties of foods. Springer Science Business Media, LLC. USA.

Segnini S., Dejmek P., and Öste R. (1999). A low cost video technique for colour measurement of potato chips. Lebensmittel-Wissenschaft und Technologie, 32(4): 216–222.

Voss D.H. (1992). Relating colorimeter measurement of plant color to the royal horticultural society colour chart. Horticultural Science, 27(12): 1256–1260.

Yam K.L., and Papadakis S. (2004). A simple digital imaging method for measuring and analyzing color of food surfaces. Journal of Food Engineering, 61(1): 137-142.