Comparison of several variants of principal component analysis (PCA) on forensic analysis of paper based on IR spectrum

Lee, Loong Chuen; Liong, Choong Yeun; Osman, Khairul; Jemain, Abdul Aziz

doi:10.1063/1.4954617

Cited by 9 publications

(5 citation statements)

References 8 publications

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“…Unfortunately, chemical methods usually cause some damage to samples, and are therefore less popular compared to the non-destructive techniques. The major techniques that follow the non-destructive paradigm are; Fourier transform infrared (FTIR) [ 3 ], Raman spectroscopy [ 4 ], video spectral comparator (VSC) [ 5 ], multi-spectral imaging and hyperspectral imaging (HSI) [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

“…The HSI data contains redundant information, and it requires an efficient method to extract the most interesting and useful information [ 13 ]. When considering hyperspectral dimensionality reduction, a favorite method is the well-known PCA approach [ 14 ], as outlined in several papers [ 3 , 15 , 16 ]. Other traditional techniques such as Independent Component Analysis (ICA) [ 17 ] and Linear Discriminant Analysis (LDA) [ 18 ] as well as statistical methods [ 19 , 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

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Unsupervised Clustering of Hyperspectral Paper Data Using t-SNE

2020

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For a suspected forgery that involves the falsification of a document or its contents, the investigator will primarily analyze the document’s paper and ink in order to establish the authenticity of the subject under investigation. As a non-destructive and contactless technique, Hyperspectral Imaging (HSI) is gaining popularity in the field of forensic document analysis. HSI returns more information compared to conventional three channel imaging systems due to the vast number of narrowband images recorded across the electromagnetic spectrum. As a result, HSI can provide better classification results. In this publication, we present results of an approach known as the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm, which we have applied to HSI paper data analysis. Even though t-SNE has been widely accepted as a method for dimensionality reduction and visualization of high dimensional data, its usefulness has not yet been evaluated for the classification of paper data. In this research, we present a hyperspectral dataset of paper samples, and evaluate the clustering quality of the proposed method both visually and quantitatively. The t-SNE algorithm shows exceptional discrimination power when compared to traditional PCA with k-means clustering, in both visual and quantitative evaluations.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Unsupervised Clustering of Hyperspectral Paper Data Using t-SNE

2020

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“…This study demonstrates the ability to classify samples using element signatures, which element signatures come from the spectra of LIBS. Principal component analysis (PCA) is a relatively simple machine learning technique that uses PCA to analyze these signatures, using this method to analyze signatures from the spectrum [9] . As is an unsupervised method that requires no prior knowledge of the classes and can be used for data dimensionality reduction.…”

Section: Introductionmentioning

confidence: 99%

In-situ monitoring and characterization of the component analysis of steel at elevated temperature environment of a steel industry using stand-off LIBS

Peng,

Song,

Zhang

2024

Sixth Conference on Frontiers in Optical Imaging and Technology: Imaging Detection and Target Recognition

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Chemical component analysis is an indispensable part of steel analysis. At present, the remote intelligent detection of the component analysis of steel needs to be further developed. In this study, laser‐induced breakdown spectroscopy (LIBS), a sensitive optical technique that can rapidly analyze various elements, is applied to real‐time detection and steel analysis of steel at elevated temperature environment of a steel industry. Representative steel samples are selected for spectral analysis. The characteristic spectral lines of different elements observed in the spectra. By comparing the spectra of different samples, the differences among them discussed. First, principal component analysis (PCA) is used for dimensionality reduction. With the help of PCA, the data are distributed in two dimensional and three-dimensional space and different kinds of the constituent elements of steel are classified successfully. Then, combined with error back propagation training artificial neural network, the steel identification model was established, and the recognition rate can be improved. The results show that LIBS is a powerful tool for remote intelligent real-time detection and classification, and has great application prospects in the chemical component analysis of steel at elevated temperature environment in the future.

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“…Uguz [14] presented a Genetic Algorithm (GA) based hybrid approach and information gain for selecting an optimal feature set from a dataset transformed by PCA. Further, Lee et al [15] examined the three PCA variants: sparse PCA, independent PCA, and sparse independent PCA. A randomized PCA has been proposed by Rokhlin et al [16] to identify principal components that maximize the variance.…”

Section: Introductionmentioning

confidence: 99%