AIMC Topic: Spectroscopy, Fourier Transform Infrared

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Improved Classification Performance of Bacteria in Interference Using Raman and Fourier-Transform Infrared Spectroscopy Combined with Machine Learning.

Molecules (Basel, Switzerland)
The rapid and sensitive detection of pathogenic and suspicious bioaerosols are essential for public health protection. The impact of pollen on the identification of bacterial species by Raman and Fourier-Transform Infrared (FTIR) spectra cannot be ov...

Iterative Regression of Corrective Baselines (IRCB): A New Model for Quantitative Spectroscopy.

Journal of chemical information and modeling
In this work, a new model with broad utility for quantitative spectroscopy development is reported. A primary objective of this work is to create a novel modeling procedure that may allow for higher automation of the model development process. The fu...

Lipids balance as a spectroscopy marker of diabetes. Analysis of FTIR spectra by 2D correlation and machine learning analyses.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
The number of people suffering from type 2 diabetes has rapidly increased. Taking into account, that elevated intracellular lipid concentrations, as well as their metabolism, are correlated with diminished insulin sensitivity, in this study we would ...

Study on breast cancerization and isolated diagnosis in situ by HOF-ATR-MIR spectroscopy with deep learning.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Mid-infrared (MIR) spectroscopy can characterize the content and structural changes of macromolecular components in different breast tissues, which can be used for feature extraction and model training by machine learning to achieve accurate classifi...

Reagent-free detection of Plasmodium falciparum malaria infections in field-collected mosquitoes using mid-infrared spectroscopy and machine learning.

Scientific reports
Field-derived metrics are critical for effective control of malaria, particularly in sub-Saharan Africa where the disease kills over half a million people yearly. One key metric is entomological inoculation rate, a direct measure of transmission inte...

Sorption Behavior of Azo Dye Congo Red onto Activated Biochar from Waste: Gradient Boosting Machine Learning-Assisted Bayesian Optimization for Improved Adsorption Process.

International journal of molecular sciences
This work aimed to describe the adsorption behavior of Congo red (CR) onto activated biochar material prepared from waste (). The carbon precursor was soaked with phosphoric acid, followed by pyrolysis to convert the precursor into activated biochar...

Vibrational spectroscopy coupled with machine learning sheds light on the cellular effects induced by rationally designed TLR4 agonists.

Talanta
In this work, we present the potential of Fourier transform infrared (FTIR) microspectroscopy to compare on whole cells, in an unbiased and untargeted way, the capacity of bacterial lipopolysaccharide (LPS) and two rationally designed molecules (FP20...

Integrating mid-infrared spectroscopy, machine learning, and graphical bias correction for fatty acid prediction in water buffalo milk.

Journal of the science of food and agriculture
BACKGROUND: Buffalo milk, constituting 15% of global production, has higher fatty acids content than Holstein milk. Fourier-transform mid-infrared (FT-MIR) spectroscopy is widely used for dairy analysis, but its application to buffalo milk, with larg...

Clustering honey samples with unsupervised machine learning methods using FTIR data.

Anais da Academia Brasileira de Ciencias
This study utilizes Fourier transform infrared (FTIR) data from honey samples to cluster and categorize them based on their spectral characteristics. The aim is to group similar samples together, revealing patterns and aiding in classification. The p...