Now showing items 1-4 of 4
A Machine Learning Application for Classification of Chemical Spectra
This paper presents a software package that allows chemists to analyze spectroscopy data using innovative machine learning (ML) techniques. The package, designed for use in conjunction with lab-based spectroscopic ...
Classification of a Target Analyte in Solid Mixtures using Principal Component Analysis, Support Vector Machines and Raman Spectroscopy
The quantitative analysis of illicit materials using Raman spectroscopy is of widespread interest for law enforcement and healthcare applications. One of the difficulties faced when analysing illicit mixtures is the ...
Qualitative and Quantitative Analysis of Chlorinated Solvents using Raman Spectroscopy and Machine Learning
The unambiguous identification and quantification of hazardous materials is of increasing importance in many sectors such as waste disposal, pharmaceutical manufacturing, and environmental protection. One particular ...
The Effect of Principal Component Analysis on Machine Learning Accuracy with High Dimensional Spectral Data
The classi¿cation of high dimensional data, such as images, gene-expression data and spectral data, poses an interesting challenge to machine learning, as the presence of high numbers of redundant or highly correlated ...