This review summarizes various approaches for the analysis of low molecular weight (LMW) compounds by different laser desorption/ionization mass spectrometry techniques (LDI-MS). It is common to use an agent to assist the ionization, and small molecules are normally difficult to analyze by, e.g., matrix assisted laser desorption/ionization mass spectrometry (MALDI-MS) using the common matrices available today, because the latter are generally small organic compounds themselves. This often results in severe suppression of analyte peaks, or interference of the matrix and analyte signals in the low mass region. However, intrinsic properties of several LDI techniques such as high sensitivity, low sample consumption, high tolerance towards salts and solid particles, and rapid analysis have stimulated scientists to develop methods to circumvent matrix-related issues in the analysis of LMW molecules. Recent developments within this field as well as historical considerations and future prospects are presented in this review.

Proteomic methodologies for identification and analysis of biomarkers have gained more attention during recent years and have evolved rapidly. Identification and detection of disease biomarkers are important to foresee outbreaks of certain diseases thereby avoiding surgery and other invasive and expensive medical treatments for patients. Thus, more research into discovering new biomarkers and new methods for faster and more accurate detection is needed. It is often difficult to detect and measure biomarkers because of their low concentrations and the complexity of their respective matrices. Therefore it is hard to find and validate methods for accurate screening methods suitable for clinical use. The most recent developments during the last three years and also some historical considerations of proteomic methodologies for identification and validation of disease biomarkers are presented in this review.

The developed MALDI MS method for the quantitative determination of Ru(bpy)32+ in photooxidation reactions provides more reliable results than the wide-used spectrophotometric method.

A major obstacle for reusing and integrating existing data is finding the data that is most relevant in a given context. The primary metadata resource is the scientific literature describing the experiments that produced the data. To stimulate the development of natural language processing methods for extracting this information from articles, we have manually annotated 100 recent open access publications in Analytical Chemistry as semantic graphs. We focused on articles mentioning mass spectrometry in their experimental sections, as we are particularly interested in the topic, which is also within the domain of several ontologies and controlled vocabularies. The resulting gold standard dataset is publicly available and directly applicable to validating automated methods for retrieving this metadata from the literature. In the process, we also made a number of observations on the structure and description of experiments and open access publication in this journal.