Mohd Noor Ahmad

A biomimetic sensor for the classification of honeys of different floral origin and the detection of adulteration.

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.

Classification of Agarwood Oil Using an Electronic Nose

Presently, the quality assurance of agarwood oil is performed by sensory panels which has significant drawbacks in terms of objectivity and repeatability. In this paper, it is shown how an electronic nose (e-nose) may be successfully utilised for the classification of agarwood oil. Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA), were used to classify different types of oil. The HCA produced a dendrogram showing the separation of e-nose data into three different groups of oils. The PCA scatter plot revealed a distinct separation between the three groups. An Artificial Neural Network (ANN) was used for a better prediction of unknown samples.

Improved Maturity and Ripeness Classifications of Magnifera Indica cv. Harumanis Mangoes through Sensor Fusion of an Electronic Nose and Acoustic Sensor

In recent years, there have been a number of reported studies on the use of non-destructive techniques to evaluate and determine mango maturity and ripeness levels. However, most of these reported works were conducted using single-modality sensing systems, either using an electronic nose, acoustics or other non-destructive measurements. This paper presents the work on the classification of mangoes (Magnifera Indica cv. Harumanis) maturity and ripeness levels using fusion of the data of an electronic nose and an acoustic sensor. Three groups of samples each from two different harvesting times (week 7 and week 8) were evaluated by the e-nose and then followed by the acoustic sensor. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were able to discriminate the mango harvested at week 7 and week 8 based solely on the aroma and volatile gases released from the mangoes. However, when six different groups of different maturity and ripeness levels were combined in one classification analysis, both PCA and LDA were unable to discriminate the age difference of the Harumanis mangoes. Instead of six different groups, only four were observed using the LDA, while PCA showed only two distinct groups. By applying a low level data fusion technique on the e-nose and acoustic data, the classification for maturity and ripeness levels using LDA was improved. However, no significant improvement was observed using PCA with data fusion technique. Further work using a hybrid LDA-Competitive Learning Neural Network was performed to validate the fusion technique and classify the samples. It was found that the LDA-CLNN was also improved significantly when data fusion was applied.

Disposable E-Tongue for the Assessment of Water Quality in Fish Tanks

A disposable screen-printed e-tongue based on sensor array and pattern recognition that is suitable for the assessment of water quality in fish tanks is described. The characteristics of sensors fabricated using two kinds of sensing materials, namely (i) lipids (referred to as Type 1), and (ii) alternative electroactive materials comprising liquid ion-exchangers and macrocyclic compounds (Type 2) were evaluated for their performance stability, sensitivity and reproducibility. The Type 2 e-tongue was found to have better sensing performance in terms of sensitivity and reproducibility and was thus used for application studies. By using a pattern recognition tool i.e. principal component analysis (PCA), the e-tongue was able to discriminate the changes in the water quality in tilapia and catfish tanks monitored over eight days. E-tongues coupled with partial least squares (PLS) was used for the quantitative analysis of nitrate and ammonium ions in catfish tank water and good agreement were found with the ion-chromatography method (relative error, ±1.04- 4.10 %).

Signal propagation in aquaculture environment for wireless sensor network applications

This paper presents results of signal propagation studies for wireless sensor network planning in aquaculture environment for water quality and changes in water characteristics monitoring. Some water pollutants can cause widespread damage to marine life within a very short time period and thus wireless sensor network reliability is more critical than in crop farming. This paper shows that network coverage models and assumptions over land do not readily apply in tropical aquaculture environment where high temperatures are experienced during the day. More speci¯cally, due to high humidity caused by evaporation, network coverage at 15 cm antenna height is better than at 5m antenna heights due to the presence of a super-refraction (ducting) layer. For a 69m link, the di®erence between the signal strength measured over several days is more than 7dBm except under anomaly conditions. In this environment, the two-ray model has been found to provide high accuracy for signal propagation over water where there are no objects in close proximity to the propagation path. However, with vegetation in close proximity, accurate signal variation predication must consider contributions from scattered and diffused components, taking into account frequency selective fading characteristics to represent the temporal and spatial signal variations.

Signal Propagation Analysis for Low Data Rate Wireless Sensor Network Applications in Sport Grounds and on Roads

This paper presents results of a study to characterise wire-less point-to-point channel for wireless sensor networks applications in sport hard court arenas, grass fields and on roads. Antenna height and orientation effects on coverage are also studied and results show that for omni-directional patch antenna, node range is reduced by a factor of 2 when the antenna orientation is changed from vertical to horizontal. The maximum range for a wireless node on a hard court sport arena has been determined to be 70m for 0dBm transmission but this reduces to 60m on a road surface and to 50m on a grass field. For horizontal antenna orientation the range on the road is longer than on the sport court which shows that scattered signal components from the rougher road surface combine to extend the communication range. The channels investigated showed that packet error ratio (PER) is dominated by large-scale, rather than small-scale, channel fading with an abrupt transition from low PER to 100% PER. Results also show that large-scale received signal power can be modelled with a 2nd order log-distance polynomial equation on the sport court and road, but a 1st order model is sufficient for the grass field. Small-scale signal variations have been found to have a Rice distribution for signal to noise ratio levels greater than 10 dB but the Rice K-factor exhibits significant variations at short distances which can be attributed to the influence of strong ground reflections.

Paraquat sensors based on cyclotetrasiloxanes

A solvent polymer membrane electrode that exhibits a significant potentiometric response toward paraquat ion is described. The membrane is prepared by incorporation of neutral octamethylcyclotetrasiloxane (I) or its phenyl derivative (II) and potassium tetrakis(4-chlorophenyl)borate as membrane additive in a thin film of poly(vinyl chloride) plasticized with o-nitrophenyl octyl ether. The electrodes exhibit near-Nernstian slopes, fast dynamic response times of about 20 s and a useful pH range of 4–11 and display an insignificant or low response towards common inorganic cations, copper(II), glucose, urea and diquat ions. Determinations of 12.9 and 25.7 mg1−1 of paraquat dichloride in each of water, electrolyte solution (consisting of 0.14 M sodium chloride, 1 mM calcium chloride, 1 mM magnesium chloride and 5 mM potassium chloride in Tris buffer, pH 7.0) and normal artificial serum, using an electrode based on I were satisfactory. Electrochemical features of these electrodes versus those based on the dibenzo-30-crown-10 system reported earlier are compared.

Chloroquine polymeric membrane electrodes: Development and applications.

Three main types of PVC solvent polymeric membrane ion-selective electrodes for chloroquine are described. They are based on three ion-pairing agents namely dipicrylamine (DPA), tetraphenylborate (TPB) or tetrakis(4-chlorophenyl)borate (TCPB) with either dioctylphenyl phosphonate (DOPP) or trioctyl phosphate (TOP) solvent mediator. All electrodes exhibit Nernstian responses, fast dynamic response times and a wide useful pH range. The best all-round electrode is based on TPB and TOP plasticizing solvent mediators with a limit of detection of 7.1 x 10(-6)M and was utilized for the assay of chloroquine in tablets. Direct potentiometric determinations with either the analyte addition method or the normal calibration method gave results comparable to the official method.

Solvent extraction of lanthanum(III), europium(III) and lutetium(III) with fluorinated 1-phenyl-3-methyl-4-benzoyl-5-pyrazolones into chloroform

A series of chelating reagents, 1-phenyl-3-methyl-4-(2-fluorobenzoyl)-5-pyrazolone, 1-phenyl-3-methyl-4-(3-fluorobenzoyl)-5-pyrazolone and 1-phenyl-3-methyl-4-(4-fluorobenzoyl)-5-pyrazolone, has been synthesized. The extraction of Ln(III), (Ln = La, Eu and Lu) into chloroform with these reagents at 30 +/- 1 degrees has been studied. The composition of the complexes extracted has been determined by the slope method, and the extraction constants K(ex), were measured. The presence of the fluorine atom in the reagents does not make the K(ex), values much different from those obtained with the parent pyrazolone.

5.2.1 Development of Highly Selective Interdigitated Electrode (IDE) Sensor Array using Molecular Imprinted Polymer (MIP) for Detection of Mango Fruit Ripeness

An electronic nose using of MIP (Molecularly Imprinted Polymer) array is developed to give a unique fingerprint of mango volatiles when ripe. Volatiles such as α-pinene, γ-terpinene and terpinolene are found to be released during different stages of fruit maturity. Utilizing these different volatiles, MIPs templates were then synthesized and deposited onto the Interdigitated Electrode (IDE) platform to acquire a specific fingerprint of fruit maturity. It was found that the best composition ratio (Template: MAA: EGDMA) for IDE MIP Sensor is (1:5:20). When exposed to a specific mango volatiles, the IDE-MIP sensor array exhibited a specific sensitivity and high selectivity. These studies provide a potential solution for agriculture sector to improve the quality of harvest.