بررسی خصیصه‌های رنگی و بافتی دونات کم کالری حاوی مالتودکسترین با استفاده از سامانه بینایی ماشین

نویسندگان
علی گنجلو
محسن زندی
ماندانا بی مکر
گروه علوم و مهندسی صنایع غذایی، دانشکده کشاورزی، دانشگاه زنجان، زنجان38791- 45371، ایران
10.22034/FSCT.19.125.11
چکیده
در مطالعه حاضر برای ارزیابی خصیصه‌های رنگی و بافتی مغز دونات کم کالری یک روش پردازش تصویر ساده بر اساس تصاویر RGB با استفاده از سیستم بینایی ماشین طراحی شد. بر این اساس اثر جایگزینی مالتودکسترین با مارگارین در سطوح 25، 50 ، 75 و 100 درصد (وزنی/وزنی) بر خصوصیات بینایی ارزیابی گردید. این خصیصه‌ها شامل رنگ مغز دونات (L*، a* و b*)، کسر مساحت منافذ، تعداد سلول در سانتی‌متر مربع، شکل منافذ، عدد اولر، بعد برخالی مرزهای منافذ و بافت مغز (کنتراست، آنتروپی، همبستگی، انرژی و همگنی) بود. نتایج نشان داد که افزودن مالتودکسترین به‌طور معنی‌دار سبب افزایش روشنایی (L*) و کاهش زردی (a*) مغز دونات می‌گردد. کنتراست، آنتروپی و عدد اولر مغز دونات حاوی 75 و 100 درصد مالتودکسترین به‌طور قابل توجهی بالاتر از نمونه‌های شاهد، 25 و 50 درصد بود. همبستگی، انرژی، همگنی، متوسط سطح و قطر حفره‌ها، تعداد سلول (حفره) در هر سانتی‌متر مربع و نسبت سطحی منافذ پس از افزودن مالتودکسترین (75 یا 100 درصد) کاهش یافت اما هیچ تفاوت معنی‌داری بین نمونه‌های شاهد، 25 و 50 درصد مشاهده نشد (05/0>p). مقدار بعد برخالی مرز منافذ در دونات حاوی 100 درصد مالتودکسترین بیشترین مقدار بود که نشان می‌دهد افزودن مالتودکسترین باعث ایجاد مرزهای متخلخل نامنظم و پر پیچ و خم می‌گردد.
کلیدواژه‌ها
پردازش تصویر
خصیصه‌های بافتی
خصیصه‌های رنگی
دونات کم کالری
مالتودکسترین

موضوعات

عنوان مقاله English

Evaluation of Color and Texture Features of Low-calorie Doughnut Containing Maltodextrin using Machine Vision System

نویسندگان English

Ali Ganjloo
Mohsen Zandi
Mandana Bimakr
Department of Food Science and Engineering, Faculty of Agriculture, University of Zanjan, Zanjan 45371-38791, Iran
چکیده English

In the current study, a simple image processing method based on a RGB images using machine vision system has been designed to evaluate the color and texture features of low-calorie doughnut crumb. The effect of maltodextrin substitution with margarine at the levels of 25, 50, 75 and 100% (w/w) was evaluated on vision properties. These features were crumb color (L*, a* and b*), pore area fraction, number of cells/cm2, pore shape, Euler number, fractal dimension of pore boundaries and crumb texture (contrast, entropy, correlation, energy and homogeneity). Results revealed that the addition of maltodextrin increased L* value and decrease a* value of crumb color significantly. The contrast, entropy and Euler number of doughnut crumb containing 75 and 100% maltodextrin were considerably higher than control, 25% and 50% samples. Correlation, energy, homogeneity, mean pore area and diameter, number of cells per square centimeter and pore area fraction decreased after maltodextrin addition (75 or 100%) but no significant difference observed between control, 25% and 50% samples. The fractal dimension value of pore boundaries in doughnut containing 100% maltodextrin were the highest which indicates that the addition of maltodextrin caused more irregular and tortuosity porous boundaries.

کلیدواژه‌ها English

Image processing
Textural features
color features
Low-calorie doughnut
Maltodextrin
[1] Brosnan, T. and Sun, D.-W. 2004. Improving quality inspection of food products by computer vision––a review. Journal of Food Engineering, 61(1): 3-16.
[2] Pordarbani, R., Gasemzadeh, H.R., Golzadeh, A.A. and Behfar, H. 2009. Feasibility study of apple quality grading using image processing. Journal of Food Research, 19(1): 75-85.
[3] Ganjloo, A., Zandi, M., Bimakr, M. and Ghareh Baghi, A. 2021. Evaluation the effect of Farsi gum containing hemp seed oil coating on mass and volume changes of grape during cold storage using machine vision and machine learning systems. Journal of Food Science and Technology (Iran), 18(113): 157–172.
[4] Ganjloo, A., Zandi, M., Bimakr, M. and Monajem, S. 2021. Visual grading of cherry tomatoes coated with aloe vera gel containing hemp seed oil using principal component analysis, artificial neural network and adaptive neuro-fuzzy inference system methods. Journal of Food Science and Technology (Iran), 18(116), pp. 143–159
[5] Gonzales-Barron, U. and Butler, F. 2008. Discrimination of crumb grain visual appearance of organic and non-organic bread loaves by image texture analysis. Journal of Food Engineering, 84(3): 480-488.
[6] Shahidi, F., Mohebbi, M. and Ehtiai, A. 2011. Image analysis of crumb digital images in Barbary bread enriched with soy flour. Iranian Food Science and Technology Research Journal, 6(4): 247-253.
[7] Rahimi, J., Baur, J. and Singh, A. 2020. Digital imaging as a tool to study the structure of porous baked foods. Journal of Cereal Science, 95: 103084.
[8] Nouri, M., Nasehi, B. and Abdanan Mehdizadeh, S. 2017. Modeling the effects of Persian gum and carrot pomace powder addition on visual features of donut using response surface methodology. Food Science and Technology, 14(65): 285-299.
[9] Zolfaghari, Z., Mohebbi, M. and Haddad khodaparast, M.H. 2013. Quality changes of donuts as influenced by leavening agent and hydrocolloid coating. Journal of Food Processing and Preservation, 37: 34-45.
[10] Chronakis, I.S., 1998. On the molecular characteristics, compositional properties, and structural-functional mechanisms of maltodextrins: A Review. Critical Review in Food Science, 38: 599-673.
[11] Sibel, R. and Sylvia, A.J. 1996. Handbook of fat replacers. CRC Press, Boca Raton.
[12] Ghandehari Yazdi, A.P., Hojjat aleslami, M., Keramat, J. and Jahadi, M. 2014. Study on the effect of replacing sucrose with sucralose-maltodextrin on the rheological properties and the amount of calories in Ghotab- A traditional confectionary. Innovative Food Technologies, 1(2): 49-58.
[13] Najafi, S. and Salehifar, M. 2016. Optimization of production low-calorie muffin with natural sweetener stevia and maltodextrin. Journal of Food Research (Agricultural Science), 26(4): 715-724.
[14] Nadian, N., Mosafa, L., Hojjatoleslamy, M. and Adib, S.H. 2017. Effect of isomalt and maltodextrin on physical, textural and sensory properties of sugar-free biscuit with oat flour. Journal of Food Science and Technology, 69(14): 181-193.
[15] Witczak, M., Korus, J., Ziobro, R. and Juszczak, L. 2010. The effects of maltodextrins on gluten-free dough and quality of bread. Journal of Food Engineering, 96: 258–265.
[16] Rahmati, M., Hashemiravan, M. and Khani, M.R. 2018. Effect of hydrocolloids kappa-carrageenan and maltodextrin as fat substitutes on the production of low-fat cream cheese. Food Science and Technology, 14(73): 121-131.
[17] Amiri Aghdaei, S.S., Aalami, M. and Rezaei, R. 2011. Feasibility of gum tragacanth and maltodextrin used as a fat replacer in mayonnaise Electronic Journal of Food Processing and Preservation, 2(3): 1-18.
[18] Sultan, W.J. 1969. Practical Baking, Second Ed. AVI Publishing Company Inc., Westport, CT.
[19] Arakeri, M.P. and Lakshmana, L. 2016. Computer vision based fruit grading system for quality evaluation of tomato in agriculture industry. Procedia Computer Science, 79: 426-433.
[20] Ireri, D., Belal, E., Okinda, C., Makange, N. and Ji, C. 2019. A computer vision system for defect discrimination and grading in tomatoes using machine learning and image processing. Artificial Intelligence in Agriculture, 2: 28-37.
[21] Ganjloo, A., Zandi, M., Bimakr, M. and Monajem, S. 2020. Ripening stages control of cherry tomato coated with aloe vera gel using artificial vision system. Iranian Journal of Food Science and Technology, 17(105): 135-149.
[22] Moreira, I.C., 1999. In Complexidade e Caos, Ed. by H.M Nussenzveig (Editora UFRJ/COPEA, Rio de Janeiro). 51–82
[23] Barros Filho, M. and Sobreira, F. 2005. Assessing texture pattern in slum across scales: an unsupervised approach, Vol. (CASA Working Papers Series). 87
[24] Ganjloo, A., Bimakr, M. and Shiekhi, Z. 2021. Effect of maltodextrin-sucralose mixture on physical, chemical and sensory properties of reduced-calorie doughnut. Journal of Innovation in Food Science and Technology. In Press.
[25] Jackman, P. and Sun, D.-W. 2013. Recent advances in image processing using image texture features for food quality assessmenT Trends in Food Science & Technology, 29(1): 35-43.
[26] Nouri, M., Nasehi, B. and Abdanan Mehdizadeh, S. 2017. Modeling the effects of Persian gum and carrot pomace powder addition on visual features of donut using response surface methodology. Iranian Journal of Food Science and Technology, 35(14): 285-299.
[27] Naji-Tabasi, S. and Mohebbi, M. 2015. Evaluation of cress seed gum and xanthan gum effect on macrostructure properties of gluten-free bread by image processing. Journal of Food Measurement and Characterization, 9(1): 110–119.