Automation plays an integral role in our daily lives. From transportation to agriculture, we rely on robots and programs to assist in accomplishing tasks. Chemistry is no except with the deployment of high throughput screening and the recent machine-led reaction discovery, there is increased interest to integrate artificial intelligence and robotics beyond medicinal and synthetic organic chemistry. The addition of automation to mechanistic studies can improve the method in which reactions are understood experimentally and fundamentally. Chapter 1 introduces the basics of reaction chemistry. As we are interested in how the reaction occurs, for this work, there is a natural bias towards understanding kinetic behaviour. Chronograms obtained through mass spectrometry facilitate understanding of kinetics. The introduction of mass spectrometry in this chapter establishes the foundation of this technique for the subsequent experimental chemistry chapters. Chapter 2 investigates the reduction and subsequent oxidation of titanocene, generating a complex mixture of oxidized products. During this investigation, an interesting and rare methyl abstraction event occurred that led to the deuterium label study to understand a radical-based oxo-titanium reaction. This was made possible by Pressurized Sample Infusion Electrospray Ionization Mass Spectrometry (PSI-ESI-MS) coupled with a smartphone colorimetry technique developed herein known as ColorPixel. In Chapter 3 we explore the integration of machine learning with reaction monitoring. The attempt to classify reaction roles based on kinetic traces was done to automate the process of identifying important species in a reaction. Often there is a large amount of data from a PSI-ESI-MS experiment, but it is time-consuming to pick out the most important species. Implementing machine learning for reaction role classification can ease this process from taking three months to accomplish to one day. This chapter also outlines the development of Kendrick, an automated reaction sampler. Combined, these tools have the potential to impact reaction monitoring through robotic assistance and can speed up the process of reaction quantification through automated processing platforms to handle the streams of data. Chapter 4 starts with the implementation of a lightweight mass spectrometry library, Spectra.ly, that is suitable for any developers using python. This platform establishes a firm foundation that can enable developers to build complex programs using simple code. This chapter also describes the collaboration project PythoMS and the development process for this framework. In addition to the framework, the chapter also describes the development of two pieces of processing software: Sinatra – a cloud-ready EDESI processing platform, and AutoMRM – a cloud-based Multiple Reaction Monitoring method development web application. / Graduate

This diploma thesis deals with determination of carbohydrates in wine. Theoretical part attends to production of wine from grape to treatment and training of wine. It refers saccharides in wine, especially glucose and fructose. The study provides an overview of the available sources concerning the possibilities of determination of carbohydrates in wine by chromatographic methods and Skalar automated chemistry analyser. The study shortly refers simple analytical methods for determination of wine saccharides. The experimental part based on this search deals with analysis of saccharides by high performance liquid chromatography with refractive index detector and UV detector on aminoalkyl column and determination of reducting sugars by Skalar automated chemistry analyser. Finally, the results of these methods were compared.