title: 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.
authors.affiliation: Sensor Technology and Applications Group, Universiti Malaysia Perlis, 01000 Kangar, Perlis, Malaysia. sag.unimap@gmail.com
authors.forename: Ammar
authors.initials: A
authors.lastname: Zakaria
authors.forename: Ali Yeon Md
authors.initials: AY
authors.lastname: Shakaff
authors.forename: Maz Jamilah
authors.initials: MJ
authors.lastname: Masnan
authors.forename: Mohd Noor
authors.initials: MN
authors.lastname: Ahmad
authors.forename: Abdul Hamid
authors.initials: AH
authors.lastname: Adom
authors.forename: Mahmad Nor
authors.initials: MN
authors.lastname: Jaafar
authors.forename: Supri A
authors.initials: SA
authors.lastname: Ghani
authors.forename: Abu Hassan
authors.initials: AH
authors.lastname: Abdullah
authors.forename: Abdul Hallis Abdul
authors.initials: AH
authors.lastname: Aziz
authors.forename: Latifah Munirah
authors.initials: LM
authors.lastname: Kamarudin
authors.forename: Norazian
authors.initials: N
authors.lastname: Subari
authors.forename: Nazifah Ahmad
authors.initials: NA
authors.lastname: Fikri
mesh_terms: D032701:Biomimetics; D015374:Biosensing Techniques; D002623:Chemistry Techniques, Analytical; D004581:Electronics; D035264:Flowers; D005504:Food Analysis / Q000379:methods; D008401:Gas Chromatography-Mass Spectrometry / Q000379:methods; D006722:Honey / Q000032:analysis*; D006863:Hydrogen-Ion Concentration; D016014:Linear Models; D008296:Malaysia; D016571:Neural Networks, Computer; D009520:New Zealand; D011199:Potentiometry / Q000379:methods; D017550:Spectroscopy, Fourier Transform Infrared / Q000379:methods