A comprehensive tutorial on data‐driven SIMCA: Theory and implementation in web
Sergey Kucheryavskiy, Oxana Ye. Rodionova, Alexey L. Pomerantsev
Abstract
Understanding and applying data-driven soft independent modeling of class analogy (DD-SIMCA) is crucial for chemometricians who want to get the most out of one-class classification techniques.This tutorial goes beyond the usual complex theories and provides a clear, step-by-step guide that helps users learn and master this method effectively.It is designed for both experienced data scientists and those new to this area, breaking down complex math concepts into simple steps supplementing the theory with easy-to-understand examples.To tackle the common problems of software limitations and ease of access, this tutorial introduces a new web-based application that simplifies the use of DD-SIMCA.The application is free to use, works perfectly in any modern browser without needing any extra software, and can even operate offline.Illustrated with examples from the Oregano dataset, the application demonstrates each concept in action.Additionally, a set of detailed video tutorials on the project's YouTube channel helps further explain each operation.This combination of interactive and practical learning ensures that users not only grasp the techniques but can also apply them effectively in a variety of scientific and business tasks.