I'm presenting a webinar on
"Class modelling and outlier detection in NIR spectroscopy"
We have two sessions scheduled
πΈ Session 1 (best for the Americas): Jun 16th, 06:00 - 07:00 (UTC+10)
πΈ Session 2 (best for EMEA - APAC): Jun 16th, 18:00 - 19:00 (UTC+10)
Registration is free, but spots are limited.
Details and abstract are on the registration page π
https://www.trybooking.com/DMSTO
#NIR #spectroscopy #NearInfrared #Chemometrics #Python #MachineLearning
π£ I'm planning to organize an online series of webinars/workshops, focused on #chemometrics and #MachineLearning for #spectroscopy (using #Python )
I'd like it to be beginner-friendly and informal.
π Free registration
ποΈ Tutorial and research talks (including guest speakers and workshop sessions)
π Monthly or fortnightly schedule
π» Fully online
If it sound like you may be interested, please read more and register your interest here
https://nirpyresearch.com/nirpy-webinars/
If your #spectroscopy dataset is small, deterministic data subdivision into training and test sets may be the way to go π¬
The SPXY algorithm is an extension of the Kennard-Stone method that selects training samples to maximize coverage of both spectra (X) and response variable (Y) at the same time.
Here's a primer, with a #Python implementation for #NIR spectroscopy.
π’ Pre-conference workshop:
Open-source chemometrics for real-world NIR handheld spectroscopy
A hands-on course at CAC 2026 (International Conference on Chemometrics in Analytical Chemistry):
https://congressos.urv.cat/cac-2026/preliminary-courses
π
29 June β 3 July 2026
π Tarragona, Spain (sunny and Mediterranean)
Fee: only 75 EUR, so reserve your seat asap
If you work with NIR, chemometrics, or deploy models in practice, this may be relevant.
#Chemometrics #NIRSpectroscopy #OpenScience #AnalyticalChemistry #soil
π¨ #publication alert!
Just a few days into the new year and we already have a published #paper first-authored by our PhD candidate Sarah. π
It is the first study on the volatile emissions of Synchytrium endobioticum (potato wart disease) on potatoes. We identified seven possible marker compounds using TD-GC-MS and #chemometrics.
S. endobioticum is a highly potent fungus classified as a quarantine pathogen in the EU, since it can affect #potato fields for more than 40 years.
Our research offers new insights into the volatilome and may enable future screening methods for potato diseases.
Read more β‘οΈ https://link.springer.com/article/10.1007/s41348-025-01216-9
#voc #planthealth #gcms #analyticalchemistry #potatowartdisease
Volatile organic compounds (VOCs) can serve as sensitive indicators of plant health and pathogen infection. In this study, gas chromatographyβmass spectrometry combined with multivariate chemometric analysis was applied to identify VOC patterns specific to potato wart disease caused by the pathogen Synchytrium endobioticum. Healthy and artificially infected potato tubers were analyzed under controlled conditions, and the resulting chromatographic data were processed using a Python-based workflow integrating data merging, preprocessing, principal component analysis, and linear discriminant analysis. The chemometric models successfully distinguished infected from healthy tubers. Seven compounds, 1-methoxy-3-methylbutane, 3-methyl-1-butanol, 2-methyl-1-butanol, 2,3-butanediol, prenyl ethyl ether, styrene, and solavetivone, were identified as indicative for infection. In addition, a mass-specific evaluation demonstrated that discrimination is possible using selected ion fragments alone, providing a basis for simplified on-site applications. This study presents the first characterization of a volatile fingerprint for S. endobioticum infection and establishes a robust, time-efficient workflow for non-invasive detection of quarantine pathogens in potato crops.
It took a while, but I'm finally back to writing my blog π
The first installment for 2026 is an easy introduction to calculating information #entropy for optical spectra (or for any signal, really).
In my blog, I focus on #data analysis (#chemometrics, machine learning) applied to optical and near-infrared #spectroscopy Smoothing, or denoising, is one of the most common steps to work with spectroscopy data, and information entropy can be used as a criterion to guide the smoothing process.
Better still, the entropy of the derivative of a signal can help with that, because it accounts for the shape of the signal more naturally.
Read more at https://nirpyresearch.com/information-entropy-spectra/
I used chembl-downloader to create some nice charts on how the number of compounds, assays, activities, and other entities in ChEMBL have grown over time
π https://cthoyt.com/2025/08/26/chembl-history.html
#chembl #chemistry #chemometrics #chemoinformatics #cheminformatics #rdkit #cdk #proteochemometrics
Iβve recently submitted an article to the Journal of Open Source Software (JOSS) describing chembl-downloader, a Python package for automating downloading and using ChEMBL data in a reproducible way. In this post, I use chembl-downloader to show how the number of compounds, assays, activities, and other entities in ChEMBL have changed over time.
Our first step towards a semi-automated approach for finding relevant VOC profiles of plant pests and pathogens has recently been published in Scientific Reports. π₯Όπ¦ π¬
It's the first publication of our PhD-student Sarah! ππ₯³
She developed a multivariate evaluation method of GC-MS data and found a few more possible VOC markers for ALB infestation in maple trees compared to a manual evaluation.
Read more: https://rdcu.be/exF4r
#science #publication #research #volatiles #PlantProtection #ALB #AsianLonghornedBeetle #QuarantinePests #chemometrics
Daniel Pelliccia
Leo Ramirez-Lopez
Markus Witzler
Charles Tapley Hoyt
Jakub Konkol
CLIRSPEC