blog – Christian L. Goueguel

Beyond Standard Boxplot: The Adjusted and Generalized Boxplots

Boxplots, also known as box-and-whisker plots, have been a cornerstone of data visualization since their introduction by John Tukey in the late 1970s. Despite their enduring utility, boxplot assumes a symmetrical mesokurtic distribution and might misrepresent datasets with skewness or heavy tails. Alternative approaches have been proposed to address these limitations.

Christian L. Goueguel

Robust Measures of Tail Weight in Distributions

Traditional measures like kurtosis have long been used to capture the weight of the tails of a distribution. However, kurtosis comes with limitations, particularly its sensitivity to outliers and lack of robustness. In this blog post, we will explore two robust measures for tail weight.

Christian L. Goueguel

Exploring Three Orthogonal Signal Correction (OSC) Algorithms

Orthogonal signal correction (OSC) is a powerful preprocessing technique frequently used to remove variation in spectral data that is orthogonal to the property of interest. Over the years, several implementations of OSC have emerged, with the most notable being those by Wold et al., Sjöblom et al., and Fearn. This post compares these three methods, exploring their algorithmic approaches and practical implications.

Christian L. Goueguel

Optical Breakdown Threshold in Water

Cascade (or avalanche) ionization and multiphoton ionization are the two primary mechanisms responsible for laser-induced plasma (LIP) formation in water. These absorption processes are influenced by the intensity of the laser pulse and the physical and chemical properties of the water itself. This post focuses on providing a concise overview of these key mechanisms.

Christian L. Goueguel

Chemometric Modeling with Tidymodels: A Tutorial for Spectroscopic Data

In this post, we demonstrate how to build robust chemometric models for spectroscopic data using the Tidymodels framework in R. This workflow is designed to cater to beginners and advanced practitioners alike, offering an end-to-end guide from data preprocessing to model evaluation and interpretation.

Christian L. Goueguel

Multivariate Outlier Detection in High-Dimensional Spectral Data

High-dimensional data are particularly challenging for outlier detection. Robust PCA methods have been developed to build models that are unaffected by outliers in high dimensions. These outliers are generally characterized by their deviation from the PCA subspace.

Christian L. Goueguel

An Overview of Orthogonal Partial Least Squares

Have you ever heard of Orthogonal Partial Least-Squares (OPLS)? This article aims to give you a clear and concise overview of OPLS and its advantages in the development of more efficient predictive models.

Christian L. Goueguel

The Pseudo-Voigt Function

In spectroscopy, especially laser spectroscopy, accurate modeling of spectral line shapes is essential for analyzing the physical and chemical properties of matter. A commonly used approximation is the pseudo-Voigt function, which serves as a simplified representation of the Voigt profile. The Voigt profile, defined as the convolution of a Gaussian function and a Lorentzian function, accurately describes the line shapes, but its calculation is often time consuming.

Christian L. Goueguel