2014
DOI: 10.5935/0103-5053.20140140
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Discrimination of Black Pen Inks on Writing Documents Using Visible Reflectance Spectroscopy and PLS-DA

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Cited by 10 publications
(8 citation statements)
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“…13,15,18,20 PLS-DA is performed using binary coding, in which a dummy discrete response vector y is attributed to the data set, such as 0 for counterfeit samples and 1 for original samples. 14,17,21,22 In the training stage, the method is trained to assign "membership values", one for each class; a test sample is then assigned to a specific class if its y value surpasses a specific prediction threshold that may be estimated by establishing confidence limits for each sample classified. 17,21,23 Therefore, estimated values in y are approximations of 0 or 1, and a good discrimination is obtained when the distributions of the estimated values belonging to classes 1 and 0 are not overlapped.…”
Section: Partial Least Squares -Discriminant Analysis (Pls-da)mentioning
confidence: 99%
See 1 more Smart Citation
“…13,15,18,20 PLS-DA is performed using binary coding, in which a dummy discrete response vector y is attributed to the data set, such as 0 for counterfeit samples and 1 for original samples. 14,17,21,22 In the training stage, the method is trained to assign "membership values", one for each class; a test sample is then assigned to a specific class if its y value surpasses a specific prediction threshold that may be estimated by establishing confidence limits for each sample classified. 17,21,23 Therefore, estimated values in y are approximations of 0 or 1, and a good discrimination is obtained when the distributions of the estimated values belonging to classes 1 and 0 are not overlapped.…”
Section: Partial Least Squares -Discriminant Analysis (Pls-da)mentioning
confidence: 99%
“…17,21,23 Therefore, estimated values in y are approximations of 0 or 1, and a good discrimination is obtained when the distributions of the estimated values belonging to classes 1 and 0 are not overlapped. 22 In this study, PLS-DA was applied using the same spectral regions and pre-processing methods as with PCA. The number of latent variables for the model was defined using the smallest root mean squared error of crossvalidation (RMSECV) determined by full cross-validation (leave-one-out approach) in the training set.…”
Section: Partial Least Squares -Discriminant Analysis (Pls-da)mentioning
confidence: 99%
“…A Video Spectral Comparator (VSC) imaging device allows a document examiner to analyze inks, visualize hidden security features, and reveal alterations in a document [9]. It allows for the non-destructive visualization of security elements and the acquisition of reflectance measurements in both visible and shortwave near infrared region at focused areas in the document [10].…”
Section: Introductionmentioning
confidence: 99%
“…O PLS-DA é um método supervisionado utilizado para classificar uma amostra desconhecida como pertencente ou não a uma classe. 15,16,116 __________________________________________________________________3-Fundamentação Teórica…”
Section: Mínimos Quadrados Parciais Para Análise Discriminante (Pls-da)unclassified
“…Já nos métodos não destrutivos, a análise é realizada diretamente sobre o documento, preservando a integridade deste durante todo o processo. 11 Nos últimos anos, após o aprimoramento de técnicas analíticas modernas, novos estudos [12][13][14][15][16] vêm sendo divulgados evidenciando o potencial de técnicas tais como: espectroscopia no infravermelho, [17][18][19][20][21][22][23] espectroscopia raman, [24][25][26][27] espectrometria de massas, [28][29][30][31][32] como poderosas ferramentas não destrutivas para a discriminação de tintas. Mais recentemente, a utilização de técnicas não destrutivas associadas a Análise Multivariada [33][34][35][36][37][38] Ressalta-se que a utilização de dados espectroscópicos usando o VSC ® 6000 e Quimiometria, no melhor do nosso conhecimento, consiste de uma abordagem inédita na literatura.…”
Section: Introductionunclassified