Combined Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA): an efficient chemometric approach in aged gel inks discrimination

Asri, Muhammad; Desa, Wan Nur Syuhaila Mat; Ismail, Dzulkiflee

doi:10.1080/00450618.2018.1466913

Cited by 16 publications

(10 citation statements)

References 9 publications

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“…Then, the second principal component that is independent of the first principal component and has the smallest square sum of vertical distances from n points is found. Analogously, until m principal components are obtained, the value of m is usually such that the variance of the first few principal components accounts for more than 85% of the total variance [ 46 ].…”

Section: Methodsmentioning

confidence: 99%

Water quality assessment and source identification of the Shuangji River (China) using multivariate statistical methods

et al. 2021

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Multivariate statistical techniques, including cluster analysis (CA), discriminant analysis (DA), principal component analysis (PCA) and factor analysis (FA), were used to evaluate temporal and spatial variations in and to interpret large and complex water quality datasets collected from the Shuangji River Basin. The datasets, which contained 19 parameters, were generated during the 2 year (2018–2020) monitoring programme at 14 different sites (3192 observations) along the river. Hierarchical CA was used to divide the twelve months into three periods and the fourteen sampling sites into three groups. Discriminant analysis identified four parameters (CODMn, Cu, As, Se) loading more than 68% correct assignations in temporal analysis, while seven parameters (COD, TP, CODMn, F, LAS, Cu and Cd) to load 93% correct assignations in spatial analysis. The FA/PCA identified six factors that were responsible for explaining the data structure of 68% of the total variance of the dataset, allowing grouping of selected parameters based on common characteristics and assessing the incidence of overall change in each group. This study proposes the necessity and practicality of multivariate statistical techniques for evaluating and interpreting large and complex data sets, with a view to obtaining better information about water quality and the design of monitoring networks to effectively manage water resources.

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Section: Methodsmentioning

confidence: 99%

Water quality assessment and source identification of the Shuangji River (China) using multivariate statistical methods

et al. 2021

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“…The bands included in the 700–1300 cm −1 range correspond to different types of C–H bends. [ 32,33 ]…”

Section: Resultsmentioning

confidence: 99%

“…The extensive review of the scientific protocols developed in the last decade based on infrared and Raman spectroscopy combined with chemometrics for forensic studies of questioned documents were reported by Silva et al [ 28 ] Several chemometric approaches are used with the combinations of analytical techniques. [ 29–34 ] For example, the linear discriminant analysis based on the FTIR–ATR data (Fourier transform infrared/attenuated total reflectance spectroscopy) was successfully applied to the classification of blue pen ink according to types and brands. [ 26 ] The method named DATUVINK [ 8 ] based on diffuse reflectance UV–VIS–NIR spectroscopy combined with partial least squares (PLS) analysis could be useful for dating naturally aged Inoxcrom‐type inks.…”

Section: Introductionmentioning

confidence: 99%

Application of chemometric methods for the determination of fading and age determination of blue ballpoint inks

Łydżba‐Kopczyńska

Czaja

Cieśla

et al. 2020

J Raman Spectroscopy

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This study presents preliminary results for applicability of micro‐Raman and VIS–NIR reflectance spectroscopy in age determination of blue ballpoints inks containing phthalocyanine and crystal violet. Based on chemometric methods, 74 different random blue pens were classified to various types of inks. Then handwritten samples stored in different light conditions were subjected to the analysis. The first set of samples was stored for 7 years at room temperature in an envelope without direct exposure to the light, whereas the second set of samples was aged by exposing them to natural light cycle for 85 days. The L, a, b values were determined on the basis of recorded Raman and VIS–NIR spectra and then used for chemometric calculation. Based on those values, partial least squares models were built, allowing to obtain correlations between ∆E and Raman and reflectance spectra. Relative standard errors of prediction RSEP(%) for obtained models were calculated in the range 1.97–5.13 and 1.20–2.27 for Raman and VIS–NIR spectra, respectively. The proposed nondestructive method allows to estimate the age of daylight exposed samples.

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“…The papers surveyed cover a range of application domains including food and agriculture (30 papers) [7][8][9][12][13][14][16][17][18][20][21][22][24][25][26]28,[34][35][36][37]39,41,44,45,47,48,54], biomedical (15 papers) [10,11,15,19,24,25,30,32,38,40,42,[51][52][53]56], industrial (3 papers) [29,43,49], and forensics (3 paper) [23,31,55].…”

Section: Methodsmentioning

confidence: 99%

Cluster Analysis for IR and NIR Spectroscopy: Current Practices to Future Perspectives

Crase¹,

Hall

Thennadil³

2021

Computers, Materials &Amp; Continua

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Supervised machine learning techniques have become well established in the study of spectroscopy data. However, the unsupervised learning technique of cluster analysis hasn't reached the same level maturity in chemometric analysis. This paper surveys recent studies which apply cluster analysis to NIR and IR spectroscopy data. In addition, we summarize the current practices in cluster analysis of spectroscopy and contrast these with cluster analysis literature from the machine learning and pattern recognition domain. This includes practices in data pre-processing, feature extraction, clustering distance metrics, clustering algorithms and validation techniques. Special consideration is given to the specific characteristics of IR and NIR spectroscopy data which typically includes high dimensionality and relatively low sample size. The findings highlighted a lack of quantitative analysis and evaluation in current practices for cluster analysis of IR and NIR spectroscopy data. With this in mind, we propose an analysis model or workflow with techniques specifically suited for cluster analysis of IR and NIR spectroscopy data along with a pragmatic application strategy.

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Combined Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA): an efficient chemometric approach in aged gel inks discrimination

Cited by 16 publications

References 9 publications

Water quality assessment and source identification of the Shuangji River (China) using multivariate statistical methods

Water quality assessment and source identification of the Shuangji River (China) using multivariate statistical methods

Application of chemometric methods for the determination of fading and age determination of blue ballpoint inks

Cluster Analysis for IR and NIR Spectroscopy: Current Practices to Future Perspectives

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