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

Classification for ammonia in water by specific concentration using artificial neural network (ANN)

Water pollution caused by poor management of waste water release or dump need to be monitored. This paper present to monitor on ammonia release by industry which can caused death to plant worker. This monitoring was a combination between E-Nose and Classification techniques which is ANN. ANN the most common retrieval method that used in industry nowadays. Furthermore, ANN classification successful to classify 100% accuracy for specific concentration of Ammonia which is using Lavernberg-Marquardt (LM) algorithm with supervised learning and fast convergence Back Propagation (BP) method

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

kNN: Classification of agarwood types in oil and wooden using E-nose

Agarwood is one of the most expensive woods existed that has been used in many fields such as ceremony, religion, medical and more. There are many spe-cies of agarwood which give different quality. The most high-demand species were A. Malaccensis and A. Crassna. However, there is hard to differentiate be-tween both species either in oil or wooden medium. There is still no analytic standard method available to differentiate them. This study introduces a method of determining the types of agarwood specifically an A. Malaccensis and A. Crassna in oil and in the wooden medium using e-nose with k-Nearest Neighbour (kNN) analysis. In other to achieve that purpose, the objectives of this study were to develop the odor profile of A. Malaccensis and A. Crassna in oil and wooden medium, to classify A. Malaccensis and A. Crassna in oil and wooden medium using kNN classifier, and to measure the performance of kNN classifier on A. Malaccensis and A. Crassna on oil and wooden medium. As the result, the introduced method was able to classify both types of agarwood in both mediums with a high classification rate which is 94.5 percent accurate

The study of raw water based on quality parameter using smell-print sensing device

Water is a renewable natural resource and comprises about 70% of earth whilst the balance is land. Cleanliness and purity of drinking water is important for human health worldwide, thus it is important to know the water body source content so that consumption of it does not give any risk to human body’s health. This study focuses on establishing a case library profile and classification of water based on recommended by Ministry of Health (MOH). This study water quality parameters such as iron (Fe) and pH is obtained using Electronic nose (E-nose). E-nose is an instrument that mimics human nose that has the ability to sniff in advance for volatile odor. However, colourless and odourless chemical usually undetectable by normal eyes or noses. Case Based Reasoning (CBR) is used in performing the intelligent classification that involved CBR computation, voting and performance measure. The similarity result shows that the technique accomplished to classify with 97.5% accuracy, 88.0% specificity and 92.2% accuracy

Lubricant oil odor-profile classification using case based reasoning intelligent classification method

Lubricant Oil is one of the products from the crude petroleum refinery process. The implementation of lubricating oil automotive sector is very crucial to make sure the smoothness of moving parts in the vehicles engine. The smoothness of engine of vehicles influence the performance of vehicle at the highest level. Common method used by public to determine the aging level of lubricant oil is by checking the mileage meter mounted on the vehicles dashboard. In the world of research, researchers used various methods and instruments such as ICP-MS, AAS and so on. However, these methods involved the complex sample preparation, complicated procedures and costly for installation and maintenance. In order to avoid these difficulties, e-nose is used in order to classify the aging level of the lubricant oil with simpler sample preparation, less experimental procedures and lower cost compare to other instruments. The signal processing technique is implemented in order to process the raw data in order to make sure the data in a very good condition for features extraction phase. The important information that known as odor-profile then will be used for classification using Casebased Reasoning Intelligent Classification method. From this research, 100% classification result is obtained

The investigation of meat classification based on significant authentication features using odor-profile intelligent signal processing approach

Meat is the flesh or another edible part of an animal and includes uncooked meat prepared or otherwise but does not include meat products. Meat is the most valuable livestock product and for many people serves as their first-choice source of animal protein. Fraud meat products are causing annoyance to consumer’s, especially Muslim users. There are many cases that have been brought to the public attention regarding fraud on meat products such as incidences of meat that is labeled, certified or sold as halal may not be so. This project sets out to identify two types of different meat which is beef meat and pork meat. Therefore, the significant authentication features using odor-profile intelligent signal processing approach which is Electronic Nose (E-nose) was used to measure odor-profile from meat. E-nose is one of the chemical-based sensor arrays instruments which have a capability to measure odor-profile based sample data. The data measurement of odor-profile for different meat samples was collected based on the designated experimental procedure. Then, the normalized and their unique features were extracted using statistical tools for feature extraction. The input of features will be inserting into Case-Based Reasoning (CBR) library and intelligently classified using CBR method and will be validated based specific performance measure. From the CBR performance measures result, it is observed that the classification of CBR is 100%

Classification of agarwood types (malaccensis and crassna) between oil and smoke using E-nose with CBR classifier

The issue of quality of agarwood quality among sellers and buyers is still ongoing due to manual olfactory methods. This study purpose classification of Malaccensis and Crassna agarwood in oil and smoke by electronic nose using Case-based Reasoning classifier. The CBR performance measurement shows that classification of agarwood Malaccensis and Crassna for both oil and smoke using CBR technique can achieve 100% classification success

Classification of lubricant oil geometrical odor-profile using cased-based reasoning

The Lubricant oil is one of the petroleum refinery product. The lubricant oil usage is very important in order to make sure the operation of vehicle engine at the highest performance. In determining the lubricant oil adulteration level, there were so many method of classification using various instrument such as ICP-MS, AAS and Dielectric Spectroscopy. E-nose is one of the significant instrument using odor approach to classify the odor of the sample. The purpose of this study is to classify the lubricant oil degradation level based on odor-pattern that extracted from the odor data that collected using electronic nose. The lubricant oil sample consist of 4 level of lubricant oil adulteration level which are virgin lube oil, 3000KM, 7000KM and 10000KM lubricant oil sample. Pre-processing technique were applied by implementing normalization formulation in order to standardize the odor raw data. Normalized data very beneficial in features extraction process, so that the significant odor-patterns can be established. In this study, geometry average calculation method was applied in order to establish the odor-profile for lubricant oil sample. The odor-pattern then were classified using case-based reasoning classifier. Based on the classification results, it shows that the accuracy of the classification is100% correct classification