#chemometrics — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #chemometrics, aggregated by home.social.
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📣 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.
🆓 Free registration
🏛️ Tutorial and research talks (including guest speakers and workshop sessions)
📅 Monthly or fortnightly schedule
💻 Fully online
If it sound like you may be interested, please read more and register your interest here
https://nirpyresearch.com/nirpy-webinars/ -
📣 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.
🆓 Free registration
🏛️ Tutorial and research talks (including guest speakers and workshop sessions)
📅 Monthly or fortnightly schedule
💻 Fully online
If it sound like you may be interested, please read more and register your interest here
https://nirpyresearch.com/nirpy-webinars/ -
📣 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.
🆓 Free registration
🏛️ Tutorial and research talks (including guest speakers and workshop sessions)
📅 Monthly or fortnightly schedule
💻 Fully online
If it sound like you may be interested, please read more and register your interest here
https://nirpyresearch.com/nirpy-webinars/ -
📣 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.
🆓 Free registration
🏛️ Tutorial and research talks (including guest speakers and workshop sessions)
📅 Monthly or fortnightly schedule
💻 Fully online
If it sound like you may be interested, please read more and register your interest here
https://nirpyresearch.com/nirpy-webinars/ -
📣 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.
🆓 Free registration
🏛️ Tutorial and research talks (including guest speakers and workshop sessions)
📅 Monthly or fortnightly schedule
💻 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 🔬
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.
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If your #spectroscopy dataset is small, deterministic data subdivision into training and test sets may be the way to go 🔬
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.
-
If your #spectroscopy dataset is small, deterministic data subdivision into training and test sets may be the way to go 🔬
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.
-
If your #spectroscopy dataset is small, deterministic data subdivision into training and test sets may be the way to go 🔬
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.
-
If your #spectroscopy dataset is small, deterministic data subdivision into training and test sets may be the way to go 🔬
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.
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It took a while, but I'm finally back to writing my blog 😎
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/
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It took a while, but I'm finally back to writing my blog 😎
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/
-
It took a while, but I'm finally back to writing my blog 😎
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/
-
It took a while, but I'm finally back to writing my blog 😎
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/
-
It took a while, but I'm finally back to writing my blog 😎
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/
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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
📖 https://cthoyt.com/2025/08/26/chembl-history.html
#chembl #chemistry #chemometrics #chemoinformatics #cheminformatics #rdkit #cdk #proteochemometrics
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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
📖 https://cthoyt.com/2025/08/26/chembl-history.html
#chembl #chemistry #chemometrics #chemoinformatics #cheminformatics #rdkit #cdk #proteochemometrics
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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
📖 https://cthoyt.com/2025/08/26/chembl-history.html
#chembl #chemistry #chemometrics #chemoinformatics #cheminformatics #rdkit #cdk #proteochemometrics
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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
📖 https://cthoyt.com/2025/08/26/chembl-history.html
#chembl #chemistry #chemometrics #chemoinformatics #cheminformatics #rdkit #cdk #proteochemometrics
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An #introduction: I am a doctoral candidate at Rutgers with a focus on using #spectroscopy, especially #VibrationalSpectroscopy, #Chemometrics and data tools to understand chemical reaction systems.
At home, I am interested in #homelab, #birdphotography, and #3dprinting. I used to have other interests, but the PhD consumed them. Whenever I post, it'll probably be small things for funsies and work I do in #Python and LaTeX. -
An #introduction: I am a doctoral candidate at Rutgers with a focus on using #spectroscopy, especially #VibrationalSpectroscopy, #Chemometrics and data tools to understand chemical reaction systems.
At home, I am interested in #homelab, #birdphotography, and #3dprinting. I used to have other interests, but the PhD consumed them. Whenever I post, it'll probably be small things for funsies and work I do in #Python and LaTeX. -
An #introduction: I am a doctoral candidate at Rutgers with a focus on using #spectroscopy, especially #VibrationalSpectroscopy, #Chemometrics and data tools to understand chemical reaction systems.
At home, I am interested in #homelab, #birdphotography, and #3dprinting. I used to have other interests, but the PhD consumed them. Whenever I post, it'll probably be small things for funsies and work I do in #Python and LaTeX. -
An #introduction: I am a doctoral candidate at Rutgers with a focus on using #spectroscopy, especially #VibrationalSpectroscopy, #Chemometrics and data tools to understand chemical reaction systems.
At home, I am interested in #homelab, #birdphotography, and #3dprinting. I used to have other interests, but the PhD consumed them. Whenever I post, it'll probably be small things for funsies and work I do in #Python and LaTeX. -
Development and validation of a new method by MIR-FTIR and chemometrics for the early diagnosis of leprosy and evaluation of the treatment effect.
Chemometrics and Intelligent Laboratory Systems
Volume 254, 15 November 2024, 105248
https://doi.org/10.1016/j.chemolab.2024.105248
#infrared #chemometrics #leprosy -
Development and validation of a new method by MIR-FTIR and chemometrics for the early diagnosis of leprosy and evaluation of the treatment effect.
Chemometrics and Intelligent Laboratory Systems
Volume 254, 15 November 2024, 105248
https://doi.org/10.1016/j.chemolab.2024.105248
#infrared #chemometrics #leprosy -
Development and validation of a new method by MIR-FTIR and chemometrics for the early diagnosis of leprosy and evaluation of the treatment effect.
Chemometrics and Intelligent Laboratory Systems
Volume 254, 15 November 2024, 105248
https://doi.org/10.1016/j.chemolab.2024.105248
#infrared #chemometrics #leprosy -
Development and validation of a new method by MIR-FTIR and chemometrics for the early diagnosis of leprosy and evaluation of the treatment effect.
Chemometrics and Intelligent Laboratory Systems
Volume 254, 15 November 2024, 105248
https://doi.org/10.1016/j.chemolab.2024.105248
#infrared #chemometrics #leprosy -
📚 New post from me | Genetic Algorithm for Wavelength Selection Using NumPy
TL;DR A simplified implementation of a genetic algorithm (GA) for wavelength selection, using only @numpy and @sklearn
📝 Key Points:
🔸 The basics, including population, fitness function, crossovers, and mutations.
🔸 A step-by-step implementation of the GA for wavelength selection, with clear Python code examples.
🔸An example using NIR spectroscopy data to demonstrate the algorithm's application in selecting optimal wavelengths for predicting soil properties.
Improved regression results when comparing the performance before and after optimization.This post may be valuable for spectroscopists, chemometricians, and data scientists looking to optimise feature selection in #spectroscopy datasets using evolutionary algorithms.
🌐 Full post available here
https://nirpyresearch.com/genetic-algorithm-wavelength-selection-numpy/ -
📚 New post from me | Genetic Algorithm for Wavelength Selection Using NumPy
TL;DR A simplified implementation of a genetic algorithm (GA) for wavelength selection, using only @numpy and @sklearn
📝 Key Points:
🔸 The basics, including population, fitness function, crossovers, and mutations.
🔸 A step-by-step implementation of the GA for wavelength selection, with clear Python code examples.
🔸An example using NIR spectroscopy data to demonstrate the algorithm's application in selecting optimal wavelengths for predicting soil properties.
Improved regression results when comparing the performance before and after optimization.This post may be valuable for spectroscopists, chemometricians, and data scientists looking to optimise feature selection in #spectroscopy datasets using evolutionary algorithms.
🌐 Full post available here
https://nirpyresearch.com/genetic-algorithm-wavelength-selection-numpy/ -
📚 New post from me | Genetic Algorithm for Wavelength Selection Using NumPy
TL;DR A simplified implementation of a genetic algorithm (GA) for wavelength selection, using only @numpy and @sklearn
📝 Key Points:
🔸 The basics, including population, fitness function, crossovers, and mutations.
🔸 A step-by-step implementation of the GA for wavelength selection, with clear Python code examples.
🔸An example using NIR spectroscopy data to demonstrate the algorithm's application in selecting optimal wavelengths for predicting soil properties.
Improved regression results when comparing the performance before and after optimization.This post may be valuable for spectroscopists, chemometricians, and data scientists looking to optimise feature selection in #spectroscopy datasets using evolutionary algorithms.
🌐 Full post available here
https://nirpyresearch.com/genetic-algorithm-wavelength-selection-numpy/ -
📚 New post from me | Genetic Algorithm for Wavelength Selection Using NumPy
TL;DR A simplified implementation of a genetic algorithm (GA) for wavelength selection, using only @numpy and @sklearn
📝 Key Points:
🔸 The basics, including population, fitness function, crossovers, and mutations.
🔸 A step-by-step implementation of the GA for wavelength selection, with clear Python code examples.
🔸An example using NIR spectroscopy data to demonstrate the algorithm's application in selecting optimal wavelengths for predicting soil properties.
Improved regression results when comparing the performance before and after optimization.This post may be valuable for spectroscopists, chemometricians, and data scientists looking to optimise feature selection in #spectroscopy datasets using evolutionary algorithms.
🌐 Full post available here
https://nirpyresearch.com/genetic-algorithm-wavelength-selection-numpy/ -
📚 New post from me | Genetic Algorithm for Wavelength Selection Using NumPy
TL;DR A simplified implementation of a genetic algorithm (GA) for wavelength selection, using only @numpy and @sklearn
📝 Key Points:
🔸 The basics, including population, fitness function, crossovers, and mutations.
🔸 A step-by-step implementation of the GA for wavelength selection, with clear Python code examples.
🔸An example using NIR spectroscopy data to demonstrate the algorithm's application in selecting optimal wavelengths for predicting soil properties.
Improved regression results when comparing the performance before and after optimization.This post may be valuable for spectroscopists, chemometricians, and data scientists looking to optimise feature selection in #spectroscopy datasets using evolutionary algorithms.
🌐 Full post available here
https://nirpyresearch.com/genetic-algorithm-wavelength-selection-numpy/ -
New publication: a novel quantitative method to validate accurate scale-up of chemical reactions in real time.
Discover the full methodology at https://oa.eu/E4xb6X
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New publication: a novel quantitative method to validate accurate scale-up of chemical reactions in real time.
Discover the full methodology at https://oa.eu/E4xb6X
-
New publication: a novel quantitative method to validate accurate scale-up of chemical reactions in real time.
Discover the full methodology at https://oa.eu/E4xb6X
-
New publication: a novel quantitative method to validate accurate scale-up of chemical reactions in real time.
Discover the full methodology at https://oa.eu/E4xb6X
-
New publication: a novel quantitative method to validate accurate scale-up of chemical reactions in real time.
Discover the full methodology at https://oa.eu/E4xb6X
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☕ Here's a bit of technical content from me - today a deep dive on #baseline correction methods.
📈 Baseline correction is a preprocessing technique to remove background signal and isolate peaks in hashtag#spectroscopy data.
📝 In my recent post I discuss two methods:
1. Wavelet transform (WT) - Decomposes signal into components at different frequencies. Lowest frequency component represents baseline and can be removed.
2. Asymmetric least squares (ALS) - Fits a smooth baseline function, penalising positive deviations more than negative ones.TL;DR: WT method is intuitive but can distort peaks. ALS produces better results.
🔎 Both methods are applied on a #Raman spectrum and an X-ray fluorescence (#XRF) spectrum. ALS gives a cleaner baseline correction and it's effective for removing broad, slowly varying background while preserving sharper spectral features.
#chemometrics #Python #MachineLearning #wavelets #regression
https://nirpyresearch.com/two-methods-baseline-correction-spectral-data/
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☕ Here's a bit of technical content from me - today a deep dive on #baseline correction methods.
📈 Baseline correction is a preprocessing technique to remove background signal and isolate peaks in hashtag#spectroscopy data.
📝 In my recent post I discuss two methods:
1. Wavelet transform (WT) - Decomposes signal into components at different frequencies. Lowest frequency component represents baseline and can be removed.
2. Asymmetric least squares (ALS) - Fits a smooth baseline function, penalising positive deviations more than negative ones.TL;DR: WT method is intuitive but can distort peaks. ALS produces better results.
🔎 Both methods are applied on a #Raman spectrum and an X-ray fluorescence (#XRF) spectrum. ALS gives a cleaner baseline correction and it's effective for removing broad, slowly varying background while preserving sharper spectral features.
#chemometrics #Python #MachineLearning #wavelets #regression
https://nirpyresearch.com/two-methods-baseline-correction-spectral-data/
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☕ Here's a bit of technical content from me - today a deep dive on #baseline correction methods.
📈 Baseline correction is a preprocessing technique to remove background signal and isolate peaks in hashtag#spectroscopy data.
📝 In my recent post I discuss two methods:
1. Wavelet transform (WT) - Decomposes signal into components at different frequencies. Lowest frequency component represents baseline and can be removed.
2. Asymmetric least squares (ALS) - Fits a smooth baseline function, penalising positive deviations more than negative ones.TL;DR: WT method is intuitive but can distort peaks. ALS produces better results.
🔎 Both methods are applied on a #Raman spectrum and an X-ray fluorescence (#XRF) spectrum. ALS gives a cleaner baseline correction and it's effective for removing broad, slowly varying background while preserving sharper spectral features.
#chemometrics #Python #MachineLearning #wavelets #regression
https://nirpyresearch.com/two-methods-baseline-correction-spectral-data/
-
☕ Here's a bit of technical content from me - today a deep dive on #baseline correction methods.
📈 Baseline correction is a preprocessing technique to remove background signal and isolate peaks in hashtag#spectroscopy data.
📝 In my recent post I discuss two methods:
1. Wavelet transform (WT) - Decomposes signal into components at different frequencies. Lowest frequency component represents baseline and can be removed.
2. Asymmetric least squares (ALS) - Fits a smooth baseline function, penalising positive deviations more than negative ones.TL;DR: WT method is intuitive but can distort peaks. ALS produces better results.
🔎 Both methods are applied on a #Raman spectrum and an X-ray fluorescence (#XRF) spectrum. ALS gives a cleaner baseline correction and it's effective for removing broad, slowly varying background while preserving sharper spectral features.
#chemometrics #Python #MachineLearning #wavelets #regression
https://nirpyresearch.com/two-methods-baseline-correction-spectral-data/
-
☕ Here's a bit of technical content from me - today a deep dive on #baseline correction methods.
📈 Baseline correction is a preprocessing technique to remove background signal and isolate peaks in hashtag#spectroscopy data.
📝 In my recent post I discuss two methods:
1. Wavelet transform (WT) - Decomposes signal into components at different frequencies. Lowest frequency component represents baseline and can be removed.
2. Asymmetric least squares (ALS) - Fits a smooth baseline function, penalising positive deviations more than negative ones.TL;DR: WT method is intuitive but can distort peaks. ALS produces better results.
🔎 Both methods are applied on a #Raman spectrum and an X-ray fluorescence (#XRF) spectrum. ALS gives a cleaner baseline correction and it's effective for removing broad, slowly varying background while preserving sharper spectral features.
#chemometrics #Python #MachineLearning #wavelets #regression
https://nirpyresearch.com/two-methods-baseline-correction-spectral-data/
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I had not see this #chemistry journal before: https://www.sciencedirect.com/journal/artificial-intelligence-chemistry That's a lot of #cheminformatics #chemometrics journals we have now. #elsevier
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I had not see this #chemistry journal before: https://www.sciencedirect.com/journal/artificial-intelligence-chemistry That's a lot of #cheminformatics #chemometrics journals we have now. #elsevier
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I had not see this #chemistry journal before: https://www.sciencedirect.com/journal/artificial-intelligence-chemistry That's a lot of #cheminformatics #chemometrics journals we have now. #elsevier
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I had not see this #chemistry journal before: https://www.sciencedirect.com/journal/artificial-intelligence-chemistry That's a lot of #cheminformatics #chemometrics journals we have now. #elsevier
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I had not see this #chemistry journal before: https://www.sciencedirect.com/journal/artificial-intelligence-chemistry That's a lot of #cheminformatics #chemometrics journals we have now. #elsevier
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JEOL NMR users: you can now do chemometrics within Delta using the ChemoSpec package #chemometrics #NMR #NMRchat
https://chemospec.org/posts/2023-08-23-CS-Delta/CS-Delta.html -
JEOL NMR users: you can now do chemometrics within Delta using the ChemoSpec package #chemometrics #NMR #NMRchat
https://chemospec.org/posts/2023-08-23-CS-Delta/CS-Delta.html -
JEOL NMR users: you can now do chemometrics within Delta using the ChemoSpec package #chemometrics #NMR #NMRchat
https://chemospec.org/posts/2023-08-23-CS-Delta/CS-Delta.html