Agarwood classification based on odor profile using intelligent signal processing technique / Muhammad Sharfi Najib

This thesis presents the classification of Agarwood from Malaysia and Indonesia regions based on signal processing technique. Signal processing for the Agarwood classification is a new area and has yet been actively implemented. In this thesis, the Agarwood has been pre-identified by experts using 32 sensor arrays to measure the Agarwood odor profile. General Agarwood pattern has been plot in 2D diagram. The odor profile from different samples have been normalized and pre-processed and visualized in 3D and 2D plot to find unique patterns. The variation of patterns that has been visualized has been marked as different group samples

E-Nose peranti yang boleh mengesan kemeruapan bau

Pekan, 30 Ogos- Bau merupakan salah satu fenomena yang tidak boleh disukat seperti mana kita menyukat berat, jarak, masa dan pelbagai lagi kuantiti fizikal yang lain tetapi boleh dibuat kalibrasi dengan peranti berpiawai. Bau adalah salah satu kesan yang terhasil daripada tindak balas yang berlaku daripada volatile organic compound (VOCs) atau kompaun organik yang meruap. Tindakbalas ini boleh berlaku daripada kompaun yang berada di dalam bentuk pepejal, cecair atau gas

The study of groundwater source by using KNN classification

This study was focused on assessing the groundwater as a source using odor by electronic nose (E-nose). Water is a finite resource that essential for humans and ecosystem existence. The suitable quality water resources need to be paid attention since it controlled by naturalistic activities such as geology, motion of groundwater, and water-rock interaction. In general, it is tasteless, odorless, and nearly colorless liquid but in other aspect, it also fulfills the need of minerals in human body up to a certain limit. The anthropogenic activities had caused an imbalance of these minerals in water that result in degradation of its quality. The aim of this study to apply an E-nose in classification of water and to identify odor pattern. It consists of sensor array which mimic the olfactory receptor in human nose that ability to sniff volatile odor that usually undetectable by human nose. K-Nearest Neighbor (KNN) is applied in performing the intelligent classification with mean feature data as an input. The finding results shows that the E-nose sensitivity, specificity and accuracy indicates at 100% for Euclidean distance

Air pollution monitoring system in semi enclosed building for agricultural sector

This paper describes the development of an optical sensor system to monitor ammonia emission in the agricultural sector. At initial stage, an open path optical technique where a cylindrical chamber is used to detect low ammonia concentration within the Ultraviolet region. The methodology describing the operation of the sensor with wavelengths combination technique to optimize the measurement is presented. The results show the sensor is best measuring ammonia concentration at combination wavelengths (around 212 nm) with the Lower Detection Limit of 2.25 ppm and 1 s response time is achieved. Then the system is tested to monitor ammonia pollution in the cattle barn in Tipperary, Ireland. It shows that the developed system is able to detect and measure very low ammonia concentration which is less than 2 ppm

Gravitational search algorithm: R is better than R2?

ravitational Search Algorithm (GSA) is a metaheuristic population-based optimization algorithm inspired by the Newtonian law of gravity and law of motion. Ever since it was introduced in 2009, GSA has been employed to solve various optimization problems. Despite its superior performance, GSA has a fundamental problem. It has been revealed that the force calculation in GSA is not genuinely based on the Newtonian law of gravity. Based on the Newtonian law of gravity, force between two masses in the universe is inversely proportional to the square of the distance between them. However, in the original GSA, R is used instead of R2. In this paper, the performance of GSA is re-evaluated considering the square of the distance between masses, R2. The CEC2014 benchmark functions for real-parameter single objective optimization problems are employed in the evaluation. An important finding is that by considering the square of the distance between masses, R2, significant improvement over the original GSA is observed provided a large gravitational constant should be used at the beginning of the optimization process. © 2006-2016 Asian Research Publishing Network (ARPN)

Intelligent classification of cocoa bean using E-nose

Cocoa bean (Theobrama cacao) is an essential raw material in the manufacture of chocolate, and their classification is crucial for the synthesis of good chocolate flavour. Cocoa beans appear to be very similar to one another when visualised. Hence, an electronic device named the electronic nose (E-Nose) is used to classify the odor of cocoa beans to give the best cocoa bean quality. E-nose is a set of an array of chemical sensors used to sense the gas vapours produced by the cocoa bean and the raw data collected was kept in Microsoft Excel, and the classification took place in Octave. They then underwent normalisation technique to increase classification accuracy, and their features were extracted using mean calculation. The features were classified using CBR, and the similarity value is obtained. The results show that CBR's classification accuracy, specificity and sensitivity are all 100%

The classification of meat odor-profile using K-nearest neighbors (KNN)

Meat is a type of food that humans consume and it is an important part of their diet. In recent years, there are several cases involving meat product fraud have come to public attention. There have been numerous reports that meat labelled, certified or sold as halal may not be and that some butchers in the market mix beef and pork meat. This is causing problems for customers, particularly Muslim customers. Meat can be distinguishhed using human sensors such as vision and smell. The limitation is that meat alterations cannot be clearly distinguished by visual evaluation. In addition, unreliable reliance on the human nose to detect odor is highly risky and hazardous to human health. Electronic Nose (Enose) was proposed in this study in order to work as well as a human sensor that is made up of four Metal Oxide Sensor (MOS) gas sensors to collect the raw data from the beef and pork meat samples. The raw data were then pre-processed and the data was extracted using the mean feature to produce the odor-profile. Finally, the K-Nearest Neighbors (KNN) method was used to classify the data. KNN was then evaluated using a performance measure. As a result, the classification using KNN has 99.24 % highest accuracy at training and testing ratio 70:30 using weight K=1 at Euclidean distance and all rules

Intelligent clasification sewage treatment plant (STP) using E-nose

The environment is an invaluable gift. In an era of progress, economic activities and development projects are often carried out to improve living standards and keep pace with other developed countries. However, this activity has had a negative impact on the environment because some parties still fail to control the disposal of waste that can cause environmental pollution. The effects of this pollution can give to discomfort and disruption to the life of the community. It is because the effects of toxic emissions have caused air pollution to spread foul odors. Therefore, this study was conducted to classify air odor and water odor from the treatment plant in the area of Universiti Malaysia Pahang, Gambang Campus. The classification of air and water odors was done using case-based reasoning

Inspection of crude oil condition using electronic nose (E-nose)

Oil and gas production and distribution processes technologies are highly complex and capital-intensive. Crude oil is a high demand commodity in Malaysia and across the world. Physical and chemical properties are used to classify crude oil in oil and gas industries. The human's nose cannot distinguish the difference of smell among various crude oils grade. Conventional approaches to detect odour are expensive and difficult to operate. Due to declining production and increasing demand, using E-nose technologies to inspect the odour condition of crude oil might be a significant change in the industries. The Case-Based Reasoning (CBR) classification method also is utilised in this project to classify crude oil conditions. As a result, all crude oil samples have their odour profile pattern extracted through the normalisation of data. The performance accuracy of the CBR classifier achieved a high rate, which is 99.31% on average. Hence, the using of E-nose and utilising CBR are excellent methods in investigating odour