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Spectrophotometry, Infrared

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Determining soil particle-size distribution from infrared spectra using machine learning predictions: Methodology and modeling.

PloS one
Accuracy of infrared (IR) models to measure soil particle-size distribution (PSD) depends on soil preparation, methodology (sedimentation, laser), settling times and relevant soil features. Compositional soil data may require log ratio (ilr) transfor...

Predicting pregnancy status from mid-infrared spectroscopy in dairy cow milk using deep learning.

Journal of dairy science
Accurately identifying pregnancy status is imperative for a profitable dairy enterprise. Mid-infrared (MIR) spectroscopy is routinely used to determine fat and protein concentrations in milk samples. Mid-infrared spectra have successfully been used t...

Infrared Metasurface Augmented by Deep Learning for Monitoring Dynamics between All Major Classes of Biomolecules.

Advanced materials (Deerfield Beach, Fla.)
Insights into the fascinating molecular world of biological processes are crucial for understanding diseases, developing diagnostics, and effective therapeutics. These processes are complex as they involve interactions between four major classes of b...

AI-based spectroscopic monitoring of real-time interactions between SARS-CoV-2 and human ACE2.

Proceedings of the National Academy of Sciences of the United States of America
The novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), invades a human cell via human angiotensin-converting enzyme 2 (hACE2) as the entry, causing the severe coronavirus disease (COVID-19). The interactions between hACE...

Machine learning algorithms to control concentrations of carbon nanocomplexes in a biological medium via optical absorption spectroscopy: how to choose and what to expect?

Applied optics
A solution of spectroscopic inverse problems, implying determination of target parameters of the research object via analysis of spectra of various origins, is an overly complex task, especially in case of strong variability of the research object. O...

Feature selection of infrared spectra analysis with convolutional neural network.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Data-driven deep learning analysis, especially for convolution neural network (CNN), has been developed and successfully applied in many domains. CNN is regarded as a black box, and the main drawback is the lack of interpretation. In this study, an i...

Machine Learning Methods of Regression for Plasmonic Nanoantenna Glucose Sensing.

Sensors (Basel, Switzerland)
The measurement and quantification of glucose concentrations is a field of major interest, whether motivated by potential clinical applications or as a prime example of biosensing in basic research. In recent years, optical sensing methods have emerg...

Rapid Quantitative Analysis of IR Absorption Spectra for Trace Gas Detection by Artificial Neural Networks Trained with Synthetic Data.

Sensors (Basel, Switzerland)
Infrared absorption spectroscopy is a widely used tool to quantify and monitor compositions of gases. The concentration information is often retrieved by fitting absorption profiles to the acquired spectra, utilizing spectroscopic databases. In compl...

Colon Cancer Grading Using Infrared Spectroscopic Imaging-Based Deep Learning.

Applied spectroscopy
Tumor grade assessment is critical to the treatment of cancers. A pathologist typically evaluates grade by examining morphologic organization in tissue using hematoxylin and eosin (H&E) stained tissue sections. Fourier transform infrared spectroscopi...

Atomistic Simulations for Reactions and Vibrational Spectroscopy in the Era of Machine Learning─

The journal of physical chemistry. B
Atomistic simulations using accurate energy functions can provide molecular-level insight into functional motions of molecules in the gas and in the condensed phase. This Perspective delineates the present status of the field from the efforts of othe...