Loong Chuen Lee

Nondestructive classification and identification of ballpoint pen inks by Raman spectroscopy for forensic document examinations

As a non-destructive analytical method, Raman spectroscopy often provides insufficient information to identify or differentiate the ink used for the preparation of a questioned document. In this study, blue and black ballpoint pen inks deposited on paper substrate were examined in situ by conventional Raman spectroscopy. Inks were successfully classified based on the total number of prominent bands in Raman spectra. It was found that more than 90% of the samples of the same type and color could be differentiated visually using only Raman spectra, i.e. 94 and 95% for blue and black inks, respectively. As a result of this study, a flow chart has been constructed for blue and black ballpoint pen inks allowing their systematic identification. Raman spectroscopy proved to be a fast and precise technique for forensic ink analysis.

Effects of scatter-correction pre-processing methods and spectral derivative algorithms on forensic classification of paper

Infrared (IR) spectral data are always influenced by undesired random and systematic variations. As such, pre-processing of spectral data is normally required before chemometric modeling. Two most widely used pre-processing techniques, i.e. scatter-correction methods and spectral derivatives, were used to pre-process 150 IR spectral data of paper. The algorithms investigated in this preliminary study are Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), Savitzky-Golay (SG) and Gap-Segment (GS). The visual examination of the clustering among three studied varieties of paper, i.e. IK Yellow, One Paper and Save Pack, is accomplished via Principal Component Analysis (PCA). Overall, separation of the three varieties of paper is greatly enhanced after pre-processing. The most significant improvement is obtained with pre-processing via 1st derivative using SG algorithms.

The effects of column-wise manipulations on accuracy of classical classifiers with high-dimensional spectral data

Column-wise manipulations (CWM), a group of data pre-processing (DP) techniques composed of mean-centering, Pareto scaling (PS), variance scaling and auto-scaling; are often applied individually or in combination. It has been applied like a norm without thoughtful considerations partly attributed to its simplicity and ease of applications. Theoretically, all variables in IR spectrum are measured on the same scale and seldom have different means and as such rarely require CWM as compared to normalization. This preliminary paper aims to investigate the real needs of each aforementioned CWM in infrared (IR) spectroscopic dataset that is derived from white copy paper. The untreated and pre-processed IR data is then processed with Principal Component Analysis plus Linear Discriminant Analysis (PCA-DA). The impact of CWM on test accuracy of the different PCA-DA models is then compared according to different IR wavenumber intervals. Error of the predictive models is determined via nonparametric bootstrap. Result...

Forensic differentiation of paper by ATR-FTIR spectroscopy technique and partial least-squares-discriminant analysis (PLS-DA)

The infrared (IR) spectra of different white copy paper types tend to appear indifferent. Discrimination between white copy papers could lead to the solution of forgery cases. In this preliminary study, three varieties of white paper were purchased from local stationery shops in Kuala Lumpur, Malaysia. The papers were classified according to their manufacturers using Partial Least Squares-Discriminant Analysis (PLS-DA) and sparse PLS-DA (sPLS-DA) models. The error rate for the two models on the training and the test data sets were estimated and compared. Results show that the performance of the two models are comparable. By including the first six latent variables in both models, classification accuracy as high as 100% can be achieved.

Genetic algorithms for wavenumber selection in forensic differentiation of paper by linear discriminant analysis

Selection of the most significant variables, i.e. the wavenumber, from an infrared (IR) spectrum is always difficult to be achieved. In this preliminary paper, the feasibility of genetic algorithms (GA) in identifying most informative wavenumbers from 150 IR spectra of papers was investigated. The list of selected wavenumbers was then employed in Linear Discriminant Analysis (LDA). GA procedure was repeated 30 times to get different lists of variables. Then the performances of LDA models were estimated via leave-one-out cross-validation. A total of six to eight wavenumbers were identified to be valuable variables in the GA procedures. All the 30 LDA models achieve correct classification rates between 97.3% to 100.0%. Therefore the GA-LDA model could be a suitable tool for differentiating white papers that appeared to be highly similar in their IR fingerprints.

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

Principal Component Analysis (PCA) is a commonly used unsupervised exploratory analysis technique. It is also frequently used in dimensionality reduction. This preliminary paper investigates the feasibility of three variants of PCA, i.e. independent PCA (iPCA), sparse PCA (sPCA), and sparse independent PCA (siPCA) on forensic classification of paper based on their IR spectral data. After that, Linear Discriminant Analysis (LDA) models were built using the Principal Components (PCs) produced by the PCA and the three aforementioned variants. The performances of all these four LDA models, i.e. PCA-DA, iPCA-DA, sPCA-DA and siPCA-DA, were evaluated via leave-one-out cross-validation on the data set. The results obtained show that iPCA-DA and siPCA-DA are the most effective models with 100.0% classification accuracy. Then, the effectiveness of siPCA and iPCA models was evaluated based on posterior probability used for predictions of class membership that were derived from leave-one-out cross-validation. As a conclusion, siPCA is identified as the best classification model.

Statistical discrimination of black ballpoint pen inks using ultra-performance liquid chromatography with principal component analysis

The aim of this study is to propose an approach for the analysis of black ballpoint pen writing inks based on ultra-performance liquid chromatography (UPLC) combined with principal component analysis (PCA). A total of twelve varieties of black ballpoint pens available in the Malaysian market were examined by an UPLC that coupled with a photodiode array detection (PDA). Chromatograms of ink samples were extracted at 279, 370 and 400 nm. Chromatographic data obtained were subjected to PCA after normalization. Seven principal components were produced from a total of 15 raw peaks. The new set of variables was then used for running one-way ANOVA to differentiate 66 pen-pair formed from twelve varieties of black ballpoint pen. The approach proposed here has successfully differentiated all pen-pair thus achieving 100% discrimination power.

Iterative random vs. Kennard-Stone sampling for IR spectrum-based classification task using PLS2-DA

External testing (ET) is preferred over auto-prediction (AP) or k-fold-cross-validation in estimating more realistic predictive ability of a statistical model. With IR spectra, Kennard-stone (KS) sampling algorithm is often used to split the data into training and test sets, i.e. respectively for model construction and for model testing. On the other hand, iterative random sampling (IRS) has not been the favored choice though it is theoretically more likely to produce reliable estimation. The aim of this preliminary work is to compare performances of KS and IRS in sampling a representative training set from an attenuated total reflectance – Fourier transform infrared spectral dataset (of four varieties of blue gel pen inks) for PLS2-DA modeling. The ‘best’ performance achievable from the dataset is estimated with AP on the full dataset (APF, error). Both IRS (n = 200) and KS were used to split the dataset in the ratio of 7:3. The classic decision rule (i.e. maximum value-based) is employed for new sample ...

Q-mode versus R-mode principal component analysis for linear discriminant analysis (LDA)

Many literature apply Principal Component Analysis (PCA) as either preliminary visualization or variable con-struction methods or both. Focus of PCA can be on the samples (R-mode PCA) or variables (Q-mode PCA). Traditionally, R-mode PCA has been the usual approach to reduce high-dimensionality data before the application of Linear Discriminant Analysis (LDA), to solve classification problems. Output from PCA composed of two new matrices known as loadings and scores matrices. Each matrix can then be used to produce a plot, i.e. loadings plot aids identification of important variables whereas scores plot presents spatial distribution of samples on new axes that are also known as Principal Components (PCs). Fundamentally, the scores matrix always be the input variables for building classification model. A recent paper uses Q-mode PCA but the focus of analysis was not on the variables but instead on the samples. As a result, the authors have exchanged the use of both loadings and scores plots in which cluster...