Quantitative analysis of infrared spectroscopy data for soil and plant sciences

Dec 16, 2025 - Dec 18, 2025

3 Days

About

This 3-day short course is aimed at providing an introduction to the analysis infrared spectroscopy data using the R programming language. Infrared spectroscopy is a high-throughput, non-destructive, and cheap sensing method that has a large range of applications in agricultural, plant and environmental sciences. Theory underpinning the visible, near and mid-infrared reflectance will be discussed, as well as interpretation of the wavelengths corresponding to specific molecular vibrations and the pre-processing of the raw spectra (day 1). We will then cover chemometric methods for exploratory spectral analysis with principal component analysis. We will have the opportunity to detect outlier spectra as well as to select the samples for laboratory analysis using the spectral data (day 2). Finally, we will introduce methods for building accurate multivariate models. Multivariate models will be explained and tested, including machine learning and conventional statistical algorithms (day 3). Sessions will be a blend of interactive demonstrations/practical and lectures, where learners will have the opportunity to ask questions throughout. Prior to the course, attendees will receive R script and datasets and a list of R packages to install.

You can also join this program via the mobile app. Go to the app

Instructors

Alexandre Wadoux

Price

€415.00

Course information

INTENDED AUDIENCES

This course is aimed at anyone who wishes to introduce into the analysis of visible, near and mid-infrared spectral data for plant and soil sciences. It is particularly suited for 1) graduate, post-graduate or post-doctoral level researchers who wish to learn how to analyse their own infrared data in R and 2) applied researchers and analysts in the environmental or ecological sector with a role in handling and analysing infrared spectroscopy data.

COURSE DETAILS

The time zone is CET. The duration is three days, encompassing approximately 20 hours. This program is equivalent to 2 ECTs and will be conducted in English.

TEACHING FORMAT

This course will comprise a mixture of taught theory and practical examples. Data and analytical approaches will be presented in a lecture format to introduce key concepts. Statistical analyses will then be presented using R. All R script that the instructor uses during these sessions will be shared with participants, and R script will be presented and explained.

ASSUMED QUANTITATIVE KNOWLEGDE

Understanding of basic concept of sensing in the infrared range of the electromagnetic spectrum and prior knowledge of basic statistical techniques (e.g. linear regression).

ASSUMED COMPUTER BACKGROUND

Prior basic experience with performing statistical analyses using R and R Studio will be assumed, but is not a requirement.

EQUIPMENT AND SOFTWARE REQUIREMENT

A laptop with a working version of R or RStudio is required for this course. Both R and RStudio are free and open-source software available for PCs, Macs, and Linux computers. You can download R from the R Project website and RStudio from the RStudio website. During the workshop, all necessary R packages will be available for download and installation as needed, and a complete list of required packages will be shared with attendees in advance. While not mandatory, having a working webcam is encouraged to enhance interactivity during live sessions, and we recommend keeping cameras on during Zoom meetings. Additionally, using a large monitor or a second monitor can significantly improve the learning experience.

Course programme

Day 1

Classes run from 09:00 to 17:00 CET on Day 1, which will cover an introduction to spectral inference in soil and plant sciences, handling spectral data, a practical session on data handling, pre-processing of raw spectra, a practical session on pre-processing, exploratory spectral analysis, and a practical session on exploratory analysis.

Day 2

Classes run from 09:00 to 17:00 CET on Day 2, which will focus on spectral similarity analysis, the detection of outliers, a practical session on outlier detection, selecting samples for laboratory analysis, and a practical session on sample selection.

Day 3

Classes run from 09:00 to 17:00 CET on Day 3, which will include estimating properties from spectra, an introduction to multivariate statistical models, a practical session on statistical modeling, validation of predictions, a practical session on validation, and an opportunity to bring your own data or participate in a large exercise estimating properties from raw spectra, followed by a discussion and questions.