Modeling and Evaluation of Honey Adulteration Based on Image Processing of Water-Soluble Samples

Document Type : Research Paper

Authors

Department of Mechanical Engineering of Biosystems, Faculty of Agriculture, Razi University, Kermanshah, Iran

Abstract

Abstract
Adulteration, especially industrial type, is caused by the direct addition of natural or fermented syrups to honey. In this study, fennel honey was supplied from beekeepers located in Kangavar city.After ensuring the authenticity of honey, 39 samples of counterfeit honey were prepared by adding and stirring sucrose, fructose and a combination of 0.9 fructose-glucose syrups in natural honey at different levels of 0 to 100 wt%.Different samples were dissolved in water and their images were recorded using a camera.In order to process the images in each 33 monochrome channels, 15 parameters (495 parameters in total) were measured.  Few parameters were selected by sensitivity analysis using ANFIS fuzzy neural inference network, ANN artificial neural network and RSM response level. The explanation coefficient of the presented models for water-soluble samples was 0.9512, 0.9882 and 0.9904, respectively.Considering all the statistical error valuesof the RSM model, it was introduced as the best model to determine the amount of honey fraud in this method by the desirability function.
 

Keywords


Adelkhani, A., Beheshti, B., Minaei, S., and Javadikia, P. (2012). Optimization of lighting conditions and camera height for citrus image processing. World Applied Sciences Journal, 18(10), 1435-1442.
Agila, A., and Barringer, S. (2013). Effect of adulteration versus storage on volatiles in unifloral honeys from different floral sources and locations. Journal of Food Science, 78(2), C184-C191.
Anonymous. (2012). Honey- Specification and Test methods. Iranian National Standarddization Organization, 92, 34. (In Persian).
Asadi, M. (2006). Beet-sugar handbook: John Wiley and  Sons,
Bidin, N., Zainuddin, N. H., Islam, S., Abdullah, M., Marsin, F. M., and Yasin, M. (2016). Sugar Detection in Adulterated Honey via Fiber Optic Displacement Sensor for Food Industrial Applications. IEEE Sensors Journal, 16(2), 299-305.
Bogdanov, S. (2009). Authenticity of Honey and Other Bee Products: State of the Art. Bulletin of University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca. Animal Science and Biotechnologies, 64(1-2).
Bogdanov, S., Ruoff, K., and Oddo, L. P. (2004). Physico-chemical methods for the characterisation of unifloral honeys: a review. Apidologie, 35 (Suppl. 1), S4-S17.
Cotte, J.F., Casabianca, H., Chardon, S., Lheritier, J., and Grenier-Loustalot, M.F. (2003). Application of carbohydrate analysis to verify honey authenticity. Journal of Chromatography A, 1021(1), 145-155.
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.
 
Ebrahimi, M. (2012). Comparison of Artificial Neural Network and Time Series Approach for Forecasting Electricity Cansumption in Agricultural Sector. Journal of Agricultural Economics Research, 4(1 (13)), (In Persian).
Fairchild, G. F., Capps, O., and Nichols, J. P. (2000). Impacts of economic adulteration on the US honey Industry: Food and Resource Economics Department, Institute of Food and Agricultural Sciences, University of Florida,
Guelpa, A., Marini, F., du Plessis, A., Slabbert, R., and Manley, M. (2017). Verification of authenticity and fraud detection in South African honey using NIR spectroscopy. Food Control, 73, 1388-1396.
Hashemi, M. (2002). Comprehensive book of honey therapy: nutritional, medicinal and therapeutic properties of bee products (honey, pollen ...) (First ed.). Tehran: Jamea Culture, (In Persian).
Kvaal, K., Wold, J. P., Indahl, U. G., Baardseth, P., and Næs, T. (1998). Multivariate feature extraction from textural images of bread. Chemometrics and Intelligent Laboratory Systems, 42(1), 141-158.
Myers, R. H., Montgomery, D. C., and Anderson-Cook, C. M. (2016). Response surface methodology: process and product optimization using designed experiments: John Wiley & Sons,
Ou, W.-J., Meng, Y.-Y., ZHANG, X.-Y., and KONG, M. (2011). Application of UV visible absorption spectroscopy and principal components back propagation artifical neural network to identification of authentic and adulterated honeys. Chinese Journal of Analytical Chemistry, 7.
Özbalci, B., Boyaci, İ. H., Topcu, A., Kadılar, C., and Tamer, U. (2013). Rapid analysis of sugars in honey by processing Raman spectrum using chemometric methods and artificial neural networks. Food Chemistry, 136(3), 1444-1452.
Ramzi, M., Kashaninejad, M., Sadeghi Mahoonak, A. R., and Razavi, S. M. A. (2015). Comparison of physico-chemical and rheological characteristics of natural honeys with adulterated and sugar honeys. Iranian Food Science and Technology Research Journal, 11(4), 407-392. (In Persian).
Razzaghy Kamrody, M., Shariat Panahi, M., Nazarian, H., and Ghlichnia, H. (2010) Identification the honey exist pollen in Mazandaran province (Noor–rood watershed). Pajouhsh & Sazandegi, 72, 74-83. (In Persian).
Shafiee, S., Minaei, S., Moghaddam-Charkari, N., Ghasemi-Varnamkhasti, M., and Barzegar, M. (2013). Potential application of machine vision to honey characterization. Trends in Food Science & Technology, 30(2), 174-177.
Shafiee, S., Polder, G., Minaei, S., Moghadam-Charkari, N., Van Ruth, S., and Kuś, P. M. (2016). Detection of Honey Adulteration using Hyperspectral Imaging. IFAC-Papers OnLine, 49(16), 311-314.
Tajik, H., Shokouhi Sabet Jalali, F., and Valehi, S. (2007). Assesment of Antimicrobial Efficacy of Commercial Urmia's Honeys. IRANIAN JOURNAL OF FOOD SCIENCE AND TECHNOLOGY, 4(2), 39-45 (In Persian).
Zumla, A., and Lulat, A. (1989). Honey--a remedy rediscovered. Journal of the Royal Society of Medicine, 82(7), 384.