With the increasing importance of hand-held devices with touch displays, the need for flat panel displays (FPDs) will likely increase in the future. Glass is the most important substrate for FPD manufacturing, where both its bulk and surface properties are critical for its performance. Many properties of the glass used in FPDs are controlled by its surface chemistry. Surface hydroxyls are the most important functional groups on a glass surface, which control processes that occurs on oxide surfaces, including wetting, adhesion, electrostatic charging and discharge, and the rate of contamination. In this dissertation, I present a new approach for determining surface silanol densities on planar surfaces. This methodology consists of tagging surface silanols using atomic layer deposition (ALD) followed by low energy ion scattering (LEIS) analysis of the tags. The LEIS signal is limited to the outermost atomic layer, i.e., LEIS is an extremely surface sensitive technique. Quantification in LEIS is straightforward in the presence of suitable reference materials. An essential part of any LEIS measurement is the preparation and characterization of the sample and appropriate reference materials that best represent the samples. My tag-and-count method was applied to chemically and thermally treated fused silica. In this work, I determined the silanol density of a fully hydroxylated fused silica surface to be 4.67 OH/nm2. This value agrees with the literature value for high surface area silica powder. My methodology should be important in future glass studies. Surface Science Spectra (SSS) is an important, peer-reviewed database of spectra from surfaces. Recently, SSS has been expanding to accept spectra from new surface techniques. I created the first SSS submission form for LEIS spectra (see appendix 5), and used it to create the first SSS LEIS paper (on CaF2 and Au reference materials, see chapter 3). I also show LEIS reference spectra for ZnO, and copper in the appendix 1. The rest of my dissertation focuses on my chemometrics/informatics and data analysis work. For example, I showed the performance and capabilities of a series of summary statistics as new tools for unsupervised exploratory data analysis (EDA) (see chapter 4). Unsupervised EDA is often the first step in understanding complex data sets because it can group, and even classify, samples according to their spectral similarities and differences. Pattern recognition entropy (PRE) and other summary statistics are direct methods for analyzing data - they are not factor-based approaches like principal component analysis (PCA) or multivariate curve resolution (MCR). I show that, in general, PRE outperforms the other summary statistics, especially in image analysis, although I recommend a suite of summary statistics be used in exploring complex data sets. In addition, I introduce the concept of divided spectrum-PRE (DS-PRE) as a new EDA method and use it to analyze multiple data sets. DS-PRE increases the discrimination power of PRE. I have also prepared a guide that discusses the vital aspects and considerations for chemometrics/informatics analyses of XPS data along with specific EDA tools that can be used to probe XPS data sets, including PRE, PCA, MCR, and cluster analysis (see chapter 5). I emphasize the importance of an initial evaluation/plotting of raw data, data preprocessing, returning to the original data after a chemometrics/informatics analysis, and determining the number of abstract factors to keep in an analysis, including reconstructing the data using PCA. In my thesis, I also show the analysis of commercial automotive lubricant oils (ALOs) with various chemometrics techniques (see chapter 6). Using these methods, the ALO samples were readily differentiated according to their American Petroleum Institute (API) classification and base oil types: mineral, semi-synthetic, and synthetic.