Detecting Adulteration in Lotus Honey Using a Machine Olfactory System

Abstract

Introduction Honey is a supersaturated sugar and viscose solution taken from the nectar of flowers, collected and modified by honeybees. Many producers of honey add some variety of sugars in honey that make difficulties with detection of adulterated and pure honey. Flavor is one of the most important parameters in the classification of honey samples and the smell emitted by the honey depending on the different flowers and constituents that could be different. This causes using an electronic nose system to detect honey adulteration. Materials and Methods Honey samples used in this study were lotus honey that was supplied from a market in Karaj city, Alborz province, Iran. Adulterated honey, along with percentages of fraud (by weight) of zero, 20, 35 and 50 percent, was prepared by mixing sugar syrup. Each group of samples, nine times were tested by the electronic nose system. The proposed system, consists of six metal oxide semiconductor sensors, sensor chamber, sample chamber, data acquisition systems, power supply, electric valves, and pumps. Electronic nose is planned for three-phase system baseline correction, the smell of sample injection and cleaning of the sensor and sample chambers with clean air (Oxygen). Responses of the sensors were collected and stored in 420 seconds by a data acquisition system and LabView ver 2012 software. We used fractional method in this study, in order to improve the quality of the information available and to optimize the array output before passing it on to the pattern recognition system. Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA) and Artificial neural network (ANN) were the methods used for analyzing and recognizing pattern of electronic nose signals. Data processing was carried out using Microsoft Excel, neuralsolution 5 and Unscrambler X 10.3 (CAMO AS, Norway). Results and Discussion PCA Results PCA reduces the complexity of the data-set and is performed with no information on the classification of samples. It is based on the variance of the data-set. For PCA analysis, overall PC1 and PC2 explained 91% of the total variance among Lotus honey samples and the adulterations (PC1=80% and PC2=11%). The results indicate that it is clearly possible to recognize Lotus honey with adulterant using electronic nose systems. LDA Results The LDA method for the detection of adulterated honey samples using leave-one-out validation was estimated. The method is most widely used as a method of classification that maximizes variance between the clusters and minimizes variance of within classes. By applying LDA on the collected data, 100% accurate classification for detecting of honey and their adulterations was obtained. It can also be concluded that this method could recognize adulterated honey samples properly. ANN Results The back propagation multilayer perceptron algorithm was used to classify and to detect honey and adulterated types. Performance evaluations of each designed networks were compared by mean square error (MSE) and correlation coefficient (r).The data were divided to three subsets: 60% was used for training, 20% for testing and the remaining 20% were kept for cross validation.After network training and validation using optimized ANN model, i.e. 6-8-4 structure, success rate for 4 outputs (0, 20, 35 and 50% adulterated levels)were found to be 100%.After detecting adulteration, e-nose system accompanied with ANN can accurately classify honey from honey mixtures with fraud materials. Conclusions An electronic nose based on six metal oxide semiconductor sensors was used to detect adulterated honey samples. Electronic nose system can successfully classify between original honey and the adulterated one by pattern recognition method. The PCA, LDA and ANN techniques and analyzes of the electronic nose were very useful for evaluating the quality of the lotus honey. The results of these methods were used to classify the fraud in Lotus honey. However, there is a need to do more researches on the detection of adulteration in other agricultural and food products by electronic nose system.

Keywords

• adulterated honey
• electronic nose
• principle component analysis
• sugar