Preliminary Courses
As part of CAC2026 , we are pleased to offer participants the opportunity to attend four specialized Preliminary Courses designed to deepen knowledge in key areas related to the congress topics.
The courses will be held on Monday, 29 June, before the Opening Ceremony of the congress. They are organized into two morning sessions and two afternoon sessions, allowing participants to register for one morning course and/or one afternoon course according to their interests and availability.
Registration for the Preliminary Courses requires an additional fee of €75 per course for accredited students (€80 for Full Participants). This amount will be added during the congress registration process.
These courses provide an excellent opportunity to strengthen practical skills, explore advanced chemometric methodologies, and engage directly with leading experts in the field. Once the fee covering one or two pre-courses has been paid, an email will be sent with a link to select which pre-course you wish to attend.
Session 1 – Morning
1.1 – Open-source chemometrics for real-world NIR handheld spectroscopy
An applied introduction to open‑source chemometric tools and workflows for processing and interpreting data obtained from handheld NIR spectrometers in real‑world scenarios.
1.2 – Multivariate Curve Resolution (MCR): Basic concepts and application to massive MS-based data
A foundational course covering the principles of MCR and its practical application to large-scale mass spectrometry datasets, with emphasis on data decomposition and chemical interpretation.
Session 2 – Afternoon
A beginner‑friendly overview of ANOVA‑Simultaneous Component Analysis (ASCA), focusing on experimental design, factor interpretation, and its advantages in complex multivariate studies.
2.2 – Non-linear Machine Learning for Calibration and Classification
A practical introduction to non‑linear machine learning approaches applied to chemometric calibration and classification problems, including model choice, validation strategies, and performance assessment.
