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Spectroscopy, Fourier Transform Infrared

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Machine learning prediction of stalk lignin content using Fourier transform infrared spectroscopy in large scale maize germplasm.

International journal of biological macromolecules
Lignin has been recognized as a major factor contributing to lignocellulosic recalcitrance in biofuel production and attracted attentions as a high-value product in the biorefinery field. As the traditional wet chemical methods for detecting lignin c...

Determination of spectroscopy marker of atherosclerotic carotid stenosis using FTIR-ATR combined with machine learning and chemometrics analyses.

Nanomedicine : nanotechnology, biology, and medicine
Atherosclerotic carotid stenosis (ACS) is a recognized risk factor for ischemic stroke. Currently, the gold diagnostic standard is Doppler ultrasound, the results of which do not provide certainty whether a given person should be qualified for surger...

Renal Cell Carcinoma Discrimination through Attenuated Total Reflection Fourier Transform Infrared Spectroscopy of Dried Human Urine and Machine Learning Techniques.

International journal of molecular sciences
Renal cell carcinoma (RCC) is the sixth most common cancer in men and is often asymptomatic, leading to incidental detection in advanced disease stages that are associated with aggressive histology and poorer outcomes. Various cancer biomarkers are f...

High-throughput prediction of stalk cellulose and hemicellulose content in maize using machine learning and Fourier transform infrared spectroscopy.

Bioresource technology
Cellulose and hemicellulose are key cross-linked carbohydrates affecting bioethanol production in maize stalks. Traditional wet chemical methods for their detection are labor-intensive, highlighting the need for high-throughput techniques. This study...

A new approach to assess post-mortem interval: A machine learning-assisted label-free ATR-FTIR analysis of human vitreous humor.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
A crucial issue in forensics is determining the post-mortem interval (PMI), the time between death and the finding of a body. Despite various methods already employed for its estimation, only approximate values are currently achievable. Vitreous humo...

Innovative label-free lymphoma diagnosis using infrared spectroscopy and machine learning on tissue sections.

Communications biology
The diagnosis of lymphomas is challenging due to their diverse histological presentations and clinical manifestations. There is a need for inexpensive tools that require minimal expertise and are accessible for routine laboratories. Contrastingly, cu...

Chemical composition alterations in rat brain hypothalamus induced by irisin administration using spectroscopic and machine learning techniques.

Analytical biochemistry
This study employed Fourier transform infrared (FTIR) spectroscopy to determine the chemical composition of brain tissues and the changes induced by irisin at doses of 50 mg and 100 mg. Brain tissues were collected from control rats and those adminis...

A novel approach to the cause of death identification-multi-strategy integration of multi-organ FTIR spectroscopy information using machine learning.

Talanta
Identifying the cause of death has always been a major focus and challenge in forensic practice and research. Traditional techniques for determining the causes of death are time-consuming, labor-intensive, have high professional barriers, and are vul...

Machine learning assisted rapid approach for quantitative prediction of biochemical parameters of blood serum with FTIR spectroscopy.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
This study develops regression models for predicting blood biochemical data using Fourier-transform infrared spectroscopy (FTIR) analysis. Absorption at specific wavelengths of blood serum is revealed to have strong correlations with biochemical para...

Molecular profiling of blood plasma-derived extracellular vesicles derived from Duchenne muscular dystrophy patients through integration of FTIR spectroscopy and machine learning reveals disease signatures.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
PURPOSE: To identify and monitor the FTIR spectral signatures of plasma extracellular vesicles (EVs) from Duchenne Muscular Dystrophy (DMD) patients at different stages with Healthy controls using machine learning models.