Macro-classification of meteorites by portable energy dispersive X-ray fluorescence spectroscopy (pED-XRF), principal component analysis (PCA) and machine learning algorithms

Allegretta, Ignazio; Marangoni, Bruno; Manzari, Paola; Porfido, Carlo; Terzano, Roberto; Pascale, O. De; Senesi, Giorgio S.

doi:10.1016/j.talanta.2020.120785

Cited by 42 publications

(20 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The findings revealed that the Cubic Support Vector Machine (CSVM), Fine Kernel Nearest Neighbor (FKNN), Subspace Discriminant-Ensemble Classifiers SD-EC, and SKNN algorithms on uniform spectra obtained in the key instrumental energy spectrum achieved 100 percent precision in classifying meteorites. These first findings confirm that a mixture of X-ray fluorescence spectroscopy XRF spectra and machine learning algorithms is a very powerful and promising solution to identifying and classifying every actual or supposed meteorite [80].…”

Section: Review For Pca Algorithmsupporting

confidence: 58%

A Review of Principal Component Analysis Algorithm for Dimensionality Reduction

Hasan

Abdulazeez

2021

JSCDM

123

View full text Add to dashboard Cite

Big databases are increasingly widespread and are therefore hard to understand, in exploratory biomedicine science, big data in health research is highly exciting because data-based analyses can travel quicker than hypothesis-based research. Principal Component Analysis (PCA) is a method to reduce the dimensionality of certain datasets. Improves interpretability but without losing much information. It achieves this by creating new covariates that are not related to each other. Finding those new variables, or what we call the main components, will reduce the eigenvalue /eigenvectors solution problem. (PCA) can be said to be an adaptive data analysis technology because technology variables are developed to adapt to different data types and structures. This review will start by introducing the basic ideas of (PCA), describe some concepts related to (PCA), and discussing. What it can do, and reviewed fifteen articles of (PCA) that have been introduced and published in the last three years.

show abstract

Section: Review For Pca Algorithmsupporting

confidence: 58%

A Review of Principal Component Analysis Algorithm for Dimensionality Reduction

Hasan

Abdulazeez

2021

JSCDM

123

View full text Add to dashboard Cite

show abstract

“…Other machine learning approaches have inferred unusual properties from XRF spectra including water 20 and carbon/nitrogen 38 from organic fertilizers, organic carbon from sedimentary cores 21 , and meteorite classification 39 . While these approaches do not use 1D CNN models, they do highlight functions that are not typically considered for XRF.…”

Section: Resultsmentioning

confidence: 99%

Rapid identification of wood species using XRF and neural network machine learning

Shugar

Drake

Kelley

2021

Sci Rep

View full text Add to dashboard Cite

An innovative approach for the rapid identification of wood species is presented. By combining X-ray fluorescence spectrometry with convolutional neural network machine learning, 48 different wood specimens were clearly differentiated and identified with a 99% accuracy. Wood species identification is imperative to assess illegally logged and transported lumber. Alternative options for identification can be time consuming and require some level of sampling. This non-invasive technique offers a viable, cost-effective alternative to rapidly and accurately identify timber in efforts to support environmental protection laws and regulations.

show abstract

Section: Interpreting a Pca Modelmentioning

confidence: 99%

“…It is common practice to exploit such discrimination by inputting the PC scores into some form of classifier to produce a mathematical model allowing prediction of group membership. 10,39,40 Examining the associated loadings for PC1 and PC2 allows an association to be made between spectral features and the score plot trends. These loadings are shown in Fig.…”

Section: Interpreting a Pca Modelmentioning

confidence: 99%

Exploration of Principal Component Analysis: Deriving Principal Component Analysis Visually Using Spectra

Beattie¹,

Esmonde-White²

2021

Appl Spectrosc

154

View full text Add to dashboard Cite

Spectroscopy rapidly captures a large amount of data that is not directly interpretable. Principal component analysis is widely used to simplify complex spectral datasets into comprehensible information by identifying recurring patterns in the data with minimal loss of information. The linear algebra underpinning principal component analysis is not well understood by many applied analytical scientists and spectroscopists who use principal component analysis. The meaning of features identified through principal component analysis is often unclear. This manuscript traces the journey of the spectra themselves through the operations behind principal component analysis, with each step illustrated by simulated spectra. Principal component analysis relies solely on the information within the spectra, consequently the mathematical model is dependent on the nature of the data itself. The direct links between model and spectra allow concrete spectroscopic explanation of principal component analysis , such as the scores representing “concentration” or “weights". The principal components (loadings) are by definition hidden, repeated and uncorrelated spectral shapes that linearly combine to generate the observed spectra. They can be visualized as subtraction spectra between extreme differences within the dataset. Each PC is shown to be a successive refinement of the estimated spectra, improving the fit between PC reconstructed data and the original data. Understanding the data-led development of a principal component analysis model shows how to interpret application specific chemical meaning of the principal component analysis loadings and how to analyze scores. A critical benefit of principal component analysis is its simplicity and the succinctness of its description of a dataset, making it powerful and flexible.

show abstract

Macro-classification of meteorites by portable energy dispersive X-ray fluorescence spectroscopy (pED-XRF), principal component analysis (PCA) and machine learning algorithms

Cited by 42 publications

References 37 publications

A Review of Principal Component Analysis Algorithm for Dimensionality Reduction

A Review of Principal Component Analysis Algorithm for Dimensionality Reduction

Rapid identification of wood species using XRF and neural network machine learning

Exploration of Principal Component Analysis: Deriving Principal Component Analysis Visually Using Spectra

Contact Info

Product

Resources

About