AIMC Topic: Spectrum Analysis

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Diffuse reflectance spectroscopy based rapid coal rank estimation: A machine learning enabled framework.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
This research aims at studying the ability of using diffuse reflectance spectroscopy (DRS) for discriminating or classifying coal samples into different ranks. Spectral characteristics such as the shape of the spectral profile, slope, absorption inte...

SmartSpectrometer-Embedded Optical Spectroscopy for Applications in Agriculture and Industry.

Sensors (Basel, Switzerland)
The ongoing digitization of industry and agriculture can benefit significantly from optical spectroscopy. In many cases, optical spectroscopy enables the estimation of properties such as substance concentrations and compositions. Spectral data can be...

Quantitative analysis of lithium in brine by laser-induced breakdown spectroscopy based on convolutional neural network.

Analytica chimica acta
In this study, a simple and effective method for accurate determination of lithium in brine samples was developed by the combination of laser induced breakdown spectroscopy (LIBS) and convolutional neural network (CNN). Our results clearly demonstrat...

Machine Learning Strategy for Soil Nutrients Prediction Using Spectroscopic Method.

Sensors (Basel, Switzerland)
The research presented in this paper is based on the hypothesis that the machine learning approach improves the accuracy of soil properties prediction. The correlations obtained in this research are important for understanding the overall strategy fo...

Spectroscopic and deep learning-based approaches to identify and quantify cerebral microhemorrhages.

Scientific reports
Cerebral microhemorrhages (CMHs) are associated with cerebrovascular disease, cognitive impairment, and normal aging. One method to study CMHs is to analyze histological sections (5-40 μm) stained with Prussian blue. Currently, users manually and sub...

Mapping soil salinity using a combined spectral and topographical indices with artificial neural network.

PloS one
Monitoring the status of natural and ecological resources is necessary for conservation and protection. Soil is one of the most important environmental resources in agricultural lands and natural resources. In this research study, we used Landsat 8 a...

Exploring photoacoustic spectroscopy-based machine learning together with metabolomics to assess breast tumor progression in a xenograft model ex vivo.

Laboratory investigation; a journal of technical methods and pathology
In the current study, a breast tumor xenograft was established in athymic nude mice by subcutaneous injection of the MCF-7 cell line and assessed the tumor progression by photoacoustic spectroscopy combined with machine learning tools. The advancemen...

Rapid and non-destructive spectroscopic method for classifying beef freshness using a deep spectral network fused with myoglobin information.

Food chemistry
A simple, novel, rapid, and non-destructive spectroscopic method that employs the deep spectral network for beef-freshness classification was developed. The deep-learning-based model classified beef freshness by learning myoglobin information and ref...

Reconstructing Undersampled Photoacoustic Microscopy Images Using Deep Learning.

IEEE transactions on medical imaging
One primary technical challenge in photoacoustic microscopy (PAM) is the necessary compromise between spatial resolution and imaging speed. In this study, we propose a novel application of deep learning principles to reconstruct undersampled PAM imag...

Novel Method Based on Hollow Laser Trapping-LIBS-Machine Learning for Simultaneous Quantitative Analysis of Multiple Metal Elements in a Single Microsized Particle in Air.

Analytical chemistry
Elemental identification of individual microsized aerosol particles is an important topic in air pollution studies. However, simultaneous and quantitative analysis of multiple constituents in a single aerosol particle with the noncontact in situ mann...