Quantitative analysis of infrared spectroscopy data for soil and plant sciences (SPEC02)
24 October 2023 - 26 October 2023
£365.00
Event Date
Tuesday, October 24th, 2023
COURSE FORMAT
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
TIME ZONE
TIME ZONE – `Central European Standard Time – however all sessions will be recorded and made available allowing attendees from different time zones to follow.
Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you.
About This Course
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. 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.
By the end of the course, participants should be able to:
- Select the best pre-processing techniques for their own raw infrared spectral data.
- Apply data exploration techniques and avoid the common pitfalls in tackling a data analysis of infrared spectral data.
- Select the optimal sample size and the best sampling design to subset spectral data and send the samples for laboratory analysis.
- Understand and apply approaches for spectral data outlier detection.
- Apply statistical multivariate modelling methods to infrared spectroscopy data and validate the model predictions.
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:
- Graduate, post-graduate or post-doctoral level researchers who wish to learn how to analyse their own infrared data in R.
- Applied researchers and analysts in the environmental or ecological sector with a role in handling and analysing infrared spectroscopy data.
Course Details
Time zone – CET
Availability – TBC
Duration – 3 days
Contact hours – Approx. 20 hours
ECT’s – Equal to 2 ECT’s
Language – 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 knowledge
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 requirements
A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open-source software for PCs, Macs, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/.
All the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed, and a full list of required packages will be made available to all attendees prior to the course.
A working webcam is desirable for enhanced interactivity during the live sessions, we encourage attendees to keep their cameras on during live zoom sessions.
Although not strictly required, using a large monitor or preferably even a second monitor will improve the learning experience
PLEASE READ – CANCELLATION POLICY
Cancellations/refunds are accepted as long as the course materials have not been accessed,.
There is a 20% cancellation fee to cover administration and possible bank fess.
If you need to discuss cancelling please contact oliverhooker@prstatistics.com.
If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com
COURSE PROGRAMME
Day 1
Classes from 09:00 to 17:00
Introduction to spectral inference in soil and plant sciences
Handling spectral data
Practical
The pre-processing of raw spectra
Practical
Exploratory spectral analysis
Practical
Day 2
Spectral similarity analysis
The detection of outliers
Practical
Selecting the samples for laboratory analysis
Practical
Day 3
Estimating properties from spectra
Multivariate statistical models
Practical
Validation of the predictions
Practical
Bring your own data! – OR large exercise estimating properties from raw spectra
Discussion & questions
Alexandre Wadoux
Works at: The University of Sydney (until March 2023) and French National Institute for Agronomy and Environment (INRAE) from April 2023
Alexandre Wadoux is a Research Associate in digital soil mapping at the University of Sydney and recently moved to the French National Institute for Agronomic and Environmental Research in Montpellier (France) to work on his Marie-Curie Fellowship. He has an undergraduate degree from the University of Angers in France, a MSc in soil science from the University of Tubingen in Germany, a Master in epistemology of sciences from the University of Nantes in France and a PhD in applied geostatistics from Wageningen University in the Netherlands. He has made contributions to several quantitative aspects of soil and environmental science through the development of methods for spatial sampling, mapping and assessment using geostatistics, statistical learning algorithms and spectroscopy. He is the author of the book “Soil Spectral Inference with R” published in 2021 with Springer.