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.
Highlights
A large number of Asian countries are highly dependent on their agricultural sectors
EC-pH level and honey dilution were enough to discriminate between honeys of different floral origin, they were still unable to discriminate between adulterated samples and pure honey
The brix, refractometer and pH measurements were unable to discriminate the different varieties of honey samples from syrup and adulterated samples, as the measurements show no distinct readings between the samples
Summary
A large number of Asian countries are highly dependent on their agricultural sectors. Rapid growth of the agro-based industry and the lack of quality assessment have become a cause for concern. The agro-based industry covers a broad spectrum of products, ranging from fresh farm produce to processed foods, herbal products and beverages. Malaysia is a tropical country rich in natural forest resources such as herbs, medicinal plants, spices and honey. These traditional foods are one of the main sources of income for the Malaysian agricultural industry, but some of these traditional products, especially those produced by small scale industries, have not been screened or undergone strict quality assessments. Current quality assessment or screening methods using analytical instruments are generally time consuming and often operator dependant. With the limited number of testing laboratories available, such assessments are unable to meet the demand of the increasing number of these traditional products
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