الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
Chemometrics is the science of modelling spectral data to perform material quantitative and qualitative analysis. Near Infrared spectroscopy is widely used in generating spectral signals that carries a chemical and physical signature for different materials. Miniaturiza-tion technologies are adopted to develop handheld spectrometers enabling a cheap, non-invasive, and accurate spectral measurements. The recent widespread use of handheld spectrometers facilitates the access to large datasets and empowers chemometricians to de-velop accurate models.
Nevertheless, new challenges arise with the increase of the data size and the number of in-cluded sensors in model building. Among these challenges are the need for model devel-opment automation, model globalization across different sensors and higher coverage of sample conditions. Calibration robustness and scalability must be validated thoroughly.
In this thesis, a study of modern chemometrics challenges is discussed. A survey of pro-posed methodologies in literature is presented. We propose model development automation processes. Techniques to increase chemometrics models’ robustness using automated pre-processing, variable selection and feature engineering are introduced. A novel semi-supervised deep learning framework is proposed to enhance models’ scalability across sen-sor and sample spaces. The proposed processes and techniques are applied and validated on seven datasets resembling different data and application problems.
This thesis is organized in six chapters as follows:
Chapter 1: gives a brief introduction of the motivation, objective, major contributions, and organization of the thesis.
Chapter 2: presents common chemometrics modelling challenges, traditional and modern solutions introduced in literature.
Chapter 3: proposes modelling pipelines to treat data and modelling challenges in an auto-mated manner guaranteeing optimized model parameters and fast data analysis.
Chapter 4: studies the application of proposed techniques on seven datasets including sol-id, liquid and gas samples. Different dataset problems and modelling challenges are ex-plored.
Chapter 5: proposes a semi-supervised deep learning framework for chemometrics model-ling aiming at increasing a model’s scalability across sample space and sensor space.
Chapter 6: gives the conclusion of this thesis and introduces recommendations for future work.