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008 180719s2018 my a f a m 000 0 eng d
020 _aTHE0008051(Local)
039 9 _a201906181320
_bamy2
_c201808131529
_dfateeha
_y201807191610
_zfateeha
040 _aUMP
_beng
_cUMP
_erda
090 _aFKEE .N87 2018 r Thesis
100 0 _aNurdiyana Zahed,
_eauthor.
245 1 0 _aIntelligent odour profile classification of honey using Case Based Reasoning and K-Nearest Neighbors /
_cNurdiyana Zahed
264 1 _aKuantan, Pahang :
_bUMP,
_c2018
300 _axii, 102 pages :
_billustrations (some color) ;
_c30 cm. +
_e1 CD ROM
336 _atext
_2rdacontent
336 _atext
_2rdacontent
337 _aunmediated
_2rdamedia
337 _acomputer
_2rdamedia
338 _avolume
_2rdacarrier
338 _acomputer disc
_2rdacarrier
347 _atext file
_bPDF
_2rda
500 _aFaculty of Electrical and Electronics Engineering
502 _aThesis (Master of Science) -- Universiti Malaysia Pahang – 2018
504 _aIncludes bibliographical references
520 3 _aHoney produced by sting bee contains several important nutrients that can be used as food additive, medicinal food and food preservative. In Malaysia, three most common types of pure honey are Gelam, Acacia, and Tualang which can be verified from Department of Agriculture Malaysia. Honey can be classified using several methods and one of them is by referring to its botanical origin (botanical place where the honey is collected) which influence the quality of honey, its market price and benefits. Various techniques can be found in classifying honey where odour profile is amongst them. In order to establish honey odour profile, electronic nose (E-nose) is one of the significant instrument that can be used for honey odour profile measurement and detection. E-nose comprises of three major components; chamber, microcontroller and sensor arrays that capable of establishing odour profile pattern. In order to get E-nose response measurement, it has been validated using Gas Chromatography Mass Spectrometry (GC-MS) which was pre-processed using Solid Phase Micro-extraction (SPME). Three datasets of three different groups of honey from E-nose were measured in electrical signal for odour detection. The measured resistance data was pre-processed using normalization technique to standardize the scale of sample features. Mean values were extracted as selected feature and statistical methods using proximity matrix and boxplot data pattern graphical representation according to types of honey. Mean features for Case-based Reasoning (CBR) cases that have been extracted were employed into two types of classifiers; intelligent classification using CBR and K-Nearest Neighbors (KNN). Both CBR and KNN performances have been evaluated. The results have shown 100% rate of accuracy, sensitivity and specificity of classification from CBR classifier and KNN using weigh (k=1), ratio 90:10 and Euclidean distance.
610 2 0 _aFaculty of Electrical and Electronics Engineering
_xDissertations
650 0 _aUniversities and colleges
_xDisertations
650 0 _aTheses
999 _aVIRTUA40
_c8800
_d8806
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