Gas Chromatography (GC) is an analytical technique in the chemistry field widely used to separate compounds present in a sample mixture. Conventional GC systems are an extremely versatile and powerful tool to perform complex separations. However, these systems come with the cost of being bulky and requiring a high amount of power for operation. With considerable research for over 40 years, the advent of Micro-Gas Chromatography (µGC) made it possible for miniaturized, compact, low-power, and field portable GC systems. This thesis presents a portable µGC system that enables real-time analysis of complex compound separations, made possible with the use of multiple separation columns and a novel multidimensional compound identification algorithm. The system architecture and the software design with multiple features enabling portability of the µGC system are discussed. A set of microfabricated separation columns (µSCs) and photoionization detectors (PIDs) are integrated to realize a fully functional µGC system that is tested with different types of complex compound mixtures. An in-depth analysis of processing the output chromatograms obtained from the setup for signal filtering and peak detection is described in this thesis. A multidimensional analysis for compound identification in complex mixtures is presented. / Master of Science / Volatile organic compounds (VOCs) are generally chemicals that have high vapor pressure and low boiling points used and produced in the processing of petroleum products, paint, refrigerants, pharmaceuticals, and adhesives. VOCs are emitted as gases from certain solids or liquids, some of which may have short- and long-term adverse health effects even with minute exposure. Gas Chromatography (GC) is a common analytical technique used to detect, identify, and quantify VOCs in the environment, and conventional GC Systems have been utilized for this purpose. The separation of compounds occurs inside an analytical column that has selective interaction between the column and the analytes passing through. However, these systems are expensive, bulky, consume high power, and require expertise to operate. Recently, advancements in the Microelectromechanical systems (MEMS) field has paved the way to create Micro-Gas Chromatography (µGC) systems with improved performance when compared to traditional systems. Active research is ongoing to improve the portability of µGC systems for reliable and quick on-field analysis. In this thesis, we present a µGC system that has a robust and scalable design that allows the development of a portable µGC system. The compound separation of complex mixtures is showcased using the portable µGC system setup. The output chromatograms obtained from the µGC system are pre-processed, which involves noise filtering and peak detection, followed by an analysis using a multidimensional compound identification algorithm.

The Web is a vast repository of data, and information on almost any subject can be found with the aid of search engines. Although the Web is international, the majority of research on finding of information has a focus on languages such as English and Chinese. In this thesis, we investigate information retrieval techniques for Indonesian. Although Indonesia is the fourth most populous country in the world, little attention has been given to search of Indonesian documents. Stemming is the process of reducing morphological variants of a word to a common stem form. Previous research has shown that stemming is language-dependent. Although several stemming algorithms have been proposed for Indonesian, there is no consensus on which gives better performance. We empirically explore these algorithms, showing that even the best algorithm still has scope for improvement. We propose novel extensions to this algorithm and develop a new Indonesian stemmer, and show that these can improve stemming correctness by up to three percentage points; our approach makes less than one error in thirty-eight words. We propose a range of techniques to enhance the performance of Indonesian information retrieval. These techniques include: stopping; sub-word tokenisation; and identification of proper nouns; and modifications to existing similarity functions. Our experiments show that many of these techniques can increase retrieval performance, with the highest increase achieved when we use grams of size five to tokenise words. We also present an effective method for identifying the language of a document; this allows various information retrieval techniques to be applied selectively depending on the language of target documents. We also address the problem of automatic creation of parallel corpora --- collections of documents that are the direct translations of each other --- which are essential for cross-lingual information retrieval tasks. Well-curated parallel corpora are rare, and for many languages, such as Indonesian, do not exist at all. We describe algorithms that we have developed to automatically identify parallel documents for Indonesian and English. Unlike most current approaches, which consider only the context and structure of the documents, our approach is based on the document content itself. Our algorithms do not make any prior assumptions about the documents, and are based on the Needleman-Wunsch algorithm for global alignment of protein sequences. Our approach works well in identifying Indonesian-English parallel documents, especially when no translation is performed. It can increase the separation value, a measure to discriminate good matches of parallel documents from bad matches, by approximately ten percentage points. We also investigate the applicability of our identification algorithms for other languages that use the Latin alphabet. Our experiments show that, with minor modifications, our alignment methods are effective for English-French, English-German, and French-German corpora, especially when the documents are not translated. Our technique can increase the separation value for the European corpus by up to twenty-eight percentage points. Together, these results provide a substantial advance in understanding techniques that can be applied for effective Indonesian text retrieval.