Mahmad Nor Jaafar

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.

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.

Comparative Performance Analysis of Wireless RSSI in Wireless Sensor Networks Motes in Tropical Mixed-crop Precision Farm

To provide reliable and adequate network coverage whilst minimizing the cost of wireless sensor network (WSN) deployments, detailed knowledge of wireless signal propagation within the specific environments is required. There are many WSN devices on the market that have been developed using proprietary systems and therefore have different performances, although implementing similar standards. This paper presents a comparative performance measurement and analysis of three types of WSN devices evaluated for application in a mixed-crop farm. The results show that the Xbee-PRO maintains very strong RSSI values in open field measurements that are sometime 15 dBm higher than those obtained from the IRIS and Microchip motes. Overall, two important factors that influence WSN node performances are antenna height and the type of antenna used. Whip omni-directional antenna has been shown to double the range of the WSN node compared to a patch antenna. Results also show that the log-distance propagation model is a more flexible model that can be used to model a variety of channels, although it lacks standard global parameter values.

A real-time greenhouse monitoring system for mango with Wireless Sensor Network (WSN)

Harumanis or its scientific name as Mangifera indica is a popular mango in Malaysia due to its unique aroma and taste, despite its expensive price. The high demand for this mango and its potential in export has been the reason why this tropical fruit being a national agenda for the Malaysian government to classify it as the specialty fruit from Perlis (smallest state in Malaysia). As the sole university in Perlis, University of Malaysia Perlis (UniMAP) has taken the initiative to develop greenhouse specifically for Harumanis mango. To support this, a real-time greenhouse monitoring system has been proposed. The system was developed based on Wireless Sensor Networks technology which consists of three parts: sensing module, radio communication module and gateway module. This system is able to provide real time monitoring of the important factors in plant growth such as the carbon dioxide, temperature, humidity level in the greenhouse. The performance result shows that the temperature inside the greenhouse is slightly higher compared to the open field; which meets the crop requirements. At night, the greenhouse microclimate drops and equilibrates to the surrounding temperature and humidity. This condition ensures good flowering and fruiting of sweet and juicy mangoes.

Water quality classification and monitoring using e-nose and e-tongue in aquaculture farming

Aquaculture is an important to national food security. Productivity of aquaculture farms hinges on water quality. Lack of appropriate instrumentation for measurement of water quality is a hindrance to the industry. This experiment proposed and verify the application of e-nose and e-tongue for water quality parameters for shrimp farming. Results indicated it has the potential but required additional analytical techniques. Thus, by using sensor array technologies, e-nose and e-tongue has been employed in classification of different type of water that has been used in aquaculture farming. E-nose consists of 10 metal oxide sensors meanwhile e-tongue consists of 13 working electrodes and one reference electrode. Linear Discriminant Analysis (LDA) was used as data classifier. The e-nose and e-tongue was able to classify different type of water with the accuracy up to 95%. These results show the potential use of e-nose and e-tongue to classify the different type of water used in aquaculture industry.

Bio-inspired taste assessment of pure and adulterated honey using multi-sensing technique

Current studies document the effectiveness of multi-sensing technique implementation to mimic or to complement human senses. This work demonstrated the successful application of multi-sensing techniques such electronic tongue (e-tongue), electronic nose (e-nose) and Fourier Transform Infrared Spectroscopy (FTIR). The fusion of these modalities enhance the classification of pure Tualang honey using Linear Discriminant Analysis (LDA), Probabilistic Neural Network (PNN), Support Vector Machine (SVM) and k-Nearest Neighbour (KNN). KNN and PNN are able to classify between pure and adulterated honey samples, outperform LDA and SVM. By performing data fusion, SVM and LDA classifier can achieved more than 80% accuracy, while KNN and PNN obtained greater precision, up to 96% correct classification. The findings confirmed that, multi-sensing technique; either KNN or PNN was significantly superior compared to SVM and LDA classification methods. Thus, both analyses are able to discriminate between pure and adulterated honey.

Data Mining on Climatic Factors for Harumanis Mango Yield Prediction

Yield Prediction is an essential task to be achieved in order to implement effective forward marketing. Forward marketing is a contract that will be signed between supplier and client based on the amount of delivery and the price of delivery in future. To be able to sign such a contract the supplier should be very confident that the yield could be achieved. The yield sustainability is a challenging process in agriculture. Mango cultivar Harumanis is one of the best table tropical fruit due to its aroma and sweetness. Despite its overwhelming local demand in Malaysia and also internationally, the fruit supply never meets the demand. The flowering phase is identified as an important stage as plant reproductive physiology. Currently, Harumanis mango flowering only happens once a year that restricts the yield. In this paper, data mining is used to quantify the climatic effects on Harumanis mango yield to enable yield prediction.

P2.9.27 Development of a Decision Support System for Brackish Aquafarm Management Using Electronic Tongue

This paper presents the findings in developing a decision support in assessing brackish water quality using an electronic taste sensing system made up of sensor array and signal processing tools. Seven ionic sensors and precision centigrade temperature sensor fused together as an array. Developed graphical user interface displays real time measurement and pattern based prediction. Results of periodic test on prepared brackish water solutions showed that the system differentiates samples of varying salinity (0.01 to 0.5 Mol of NaCl per liter), supporting the system usage as salinity detection instrument in aquafarms. Incorporation of temperature sensor increases system stability to temperature drift. System robustness showed that three ionic sensors sufficiently differentiate sixdecade dilution of brackish solution.