A Biomimetic Sensor for the Classification of Honeys of Different Floral Origin and the Detection of Adulteration

Abstract

The major compounds in honey are carbohydrates such as monosaccharides and disaccharides. The same compounds are found in cane-sugar concentrates. Unfortunately when sugar concentrate is added to honey, laboratory assessments are found to be ineffective in detecting this adulteration. Unlike tracing heavy metals in honey, sugar adulterated honey is much trickier and harder to detect, and traditionally it has been very challenging to come up with a suitable method to prove the presence of adulterants in honey products. This paper proposes a combination of array sensing and multi-modality sensor fusion that can effectively discriminate the samples not only based on the compounds present in the sample but also mimic the way humans perceive flavours and aromas. Conversely, analytical instruments are based on chemical separations which may alter the properties of the volatiles or flavours of a particular honey. The present work is focused on classifying 18 samples of different honeys, sugar syrups and adulterated samples using data fusion of electronic nose (e-nose) and electronic tongue (e-tongue) measurements. Each group of samples was evaluated separately by the e-nose and e-tongue. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were able to separately discriminate monofloral honey from sugar syrup, and polyfloral honey from sugar and adulterated samples using the e-nose and e-tongue. The e-nose was observed to give better separation compared to e-tongue assessment, particularly when LDA was applied. However, when all samples were combined in one classification analysis, neither PCA nor LDA were able to discriminate between honeys of different floral origins, sugar syrup and adulterated samples. By applying a sensor fusion technique, the classification for the 18 different samples was improved. Significant improvement was observed using PCA, while LDA not only improved the discrimination but also gave better classification. An improvement in performance was also observed using a Probabilistic Neural Network classifier when the e-nose and e-tongue data were fused.

Practical procedure for discriminating monofloral honey with a broad pollen profile variability using an electronic tongue

“…The use of the meta-heuristic SA algorithm for variable selection enabled the identification and selection of a minimum set of sensors required to fully discriminate monofloral honey samples according to their floral origin, after colour honey classification. Moreover, compared with previous reported applications of E-tongue for floral origin classification of honey, the performance of the proposed potentiometric E-tongue is similar [10,22] or quite superior [6,20,21,26].…”

“…In general, all these approaches showed good discrimination capabilities, precision, accuracy and reliability, but they are in general destructive, time-consuming and expensive, being unsuitable for in situ monitoring [10]. To overcome these drawbacks other more simple and userfriendly methodologies have been proposed, namely the use of potentiometric [6,10,21,22], voltammetric [23,24] or impedance [25] electronic tongues (E-tongues). The results reported in these studies clearly show that all these electrochemical devices can be used as effective and practical tools to discriminate honey according to their botanical origin, allowing distinguishing among different monofloral and/or polyfloral samples and in some cases among different geographical origins.…”

“…Wei et al [21] used a commercial potentiometric E-tongue with 7 sensors to classify 192 Chinese honey samples of different floral origin (Acacia, Astragali, Buckwheat, Coptis, Data, Motherwort, Radix Changll and Vitex) from a Chinese geographical area and 120 samples from five other Chinese geographical origins, harvested in 2008, using threepattern recognitions techniques (PCA; Cluster Analysis, CA; and Artificial Neural Networks, ANN), with correct classification rates greater than 90%. Zakaria et al [22] showed that an electronic nose used together with a chalcogenide-based potentiometric E-tongue containing ion-selective sensors, was the best solution for classifying honey of different floral origin. In this study, 80 honey samples from 10 monofloral honey from Malaysia and New Zealand and 4 brands of polyfloral honey from Malaysia were used.…”

Electronic noses and tongues to assess food authenticity and adulteration

“…In the literature there are some examples in which multisensor data fusion 810 techniques have given rise to enhanced results for honey analysis (Maamor et al, 811 2014;Subari et al, 2012;Ulloa et al, 2013;Zakaria et al, 2011). The combination of 812 data obtained from both an e-nose and an e-tongue to discriminate between different 813 honeys, sugar syrups, and sugar adulterated honey samples has also been proposed 814 (Zakaria et al, 2011).…”

“…The combination of 812 data obtained from both an e-nose and an e-tongue to discriminate between different 813 honeys, sugar syrups, and sugar adulterated honey samples has also been proposed 814 (Zakaria et al, 2011). Samples were analyzed by means of the Cyranose320 TM e-815 nose (32 non-selective sensors of different types of polymer matrix, blended with 816 carbon black), as well as by a potentiometric e-tongue consisting of seven 817 chalcogenide-based ion selective electrodes (ISEs).…”