Characterization of gunpowder samples using time-of-flight secondary ion mass spectrometry (TOF-SIMS)

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) was utilized to obtain characteristic mass spectra from three different smokeless powders and six different black powder samples. In these mass spectra, peaks indicative of both the organic and inorganic additive constituents in the gunpowders were observed. TOF-SIMS was able to successfully differentiate between the different black and smokeless gunpowder samples analyzed with the aid of principal components analysis (PCA), a multivariate statistical analysis approach often used to reduce the dimensionality of complex data. TOF-SIMS was also used to obtain information about the spatial distribution of the various additives contained within the gunpowder samples. SIMS imaging demonstrated that that the samples could potentially be characterized by their 2-D structure, which varied from sample to sample. These results clearly demonstrate the feasibility of utilizing TOF-SIMS as a tool for the characterization and differentiation of gunpowder samples for general forensic applications.     

Classification of narcotics in solid mixtures using principal component analysis and Raman spectroscopy

Eighty-five solid samples consisting of illegal narcotics diluted with several different materials were analyzed by near-infrared (785 nm excitation) Raman spectroscopy. Principal Component Analysis (PCA) was employed to classify the samples according to narcotic type. The best sample discrimination was obtained by using the first derivative of the Raman spectra. Furthermore, restricting the spectral variables for PCA to 2 or 3% of the original spectral data according to the most intense peaks in the Raman spectrum of the pure narcotic resulted in a rapid discrimination method for classifying samples according to narcotic type. This method allows for the easy discrimination between cocaine, heroin, and MDMA mixtures even when the Raman spectra are complex or very similar. This approach of restricting the spectral variables also decreases the computational time by a factor of 30 (compared to the complete spectrum), making the methodology attractive for rapid automatic classification and identification of suspect materials.     

红外光谱结合化学计量学方法在激光打印原装黑色墨粉分析中的应用研究

目的 采用红外光谱比较分析不同原装黑色墨粉（31个样本）的差异,探讨化学计量学方法在墨粉种类识别中的适用性.方法 对打印样本纸张上的黑色墨粉进行烫熨提取后直接分析检测.IR分析条件：显微红外光谱仪,常温透射模式,扫描范围为4 000～400 cm-1,分辨率为16cm-1,累计扫描次数为64次.结果 墨粉树脂主要为聚苯乙烯/甲基丙烯酸甲酯和聚对苯二甲酸/环氧树脂.采用主成分析（Principal Component Analysis,PCA）与系统聚类法（Hierarchical Cluster Analysis,HCA）对不同种类墨粉进行聚类分析与建模预报.基于前两主成分得分值（53.3％）不同树脂成分的墨粉样本聚类效果明显,同时在第三个主成分方向上黑白与彩色激光墨粉样本获得良好区分.结论 红外光谱法结合化学计量学方法用于不同种类树脂及打印类型原装黑色墨粉的聚类分析与建模预报方法可行,结果准确可靠.     

Fourier transform infrared reflectance spectra of latent fingerprints: a biometric gauge for the age of an individual

To determine if changes in fingerprint infrared spectra linear with age can be found, partial least squares (PLS1) regression of 155 fingerprint infrared spectra against the person's age was constructed. The regression produced a linear model of age as a function of spectrum with a root mean square error of calibration of less than 4 years, showing an inflection at about 25 years of age. The spectral ranges emphasized by the regression do not correspond to the highest concentration constituents of the fingerprints. Separate linear regression models for old and young people can be constructed with even more statistical rigor. The success of the regression demonstrates that a combination of constituents can be found that changes linearly with age, with a significant shift around puberty.     

基于拉曼光谱的血液遗留时间研究与模型预测

目的针对犯罪现场血液遗留时间的判断问题,实验以不同遗留时间的血液为研究对象,建立基于拉曼光谱的血液遗留时间的辨别方法。方法采集遗留时间为0.5h~240h的指尖血液样本,获取其拉曼光谱数据,经校正和平滑处理后,对数据进行归一化。利用RSD值评价光谱的稳定性,选取前10个主成分和6个重要波段分别建立模型,利用预测集测试模型效果。结果相同遗留时间血液的拉曼光谱具有较好的稳定(RSD<0.2),不同遗留时间血液的拉曼光谱在680cm^-1等6个波段有明显的强度变化。前10个主成分建立的PLSR模型,其r²=0.9927。6个重要波段建立的PLSR模型,预测集效果r²=0.8797。结论利用拉曼光谱结合建模分析是一种无损快速的评估血液遗留时间的方法。     

Investigation of the new possibility of mathematical processing of Raman spectra for dating documents

The use of spectral analysis methods to determine the age of writing inks is an important forensic task. However, the use of spectral data for this purpose has a number of limitations and difficulties. This paper considers the application of the Raman spectroscopy method to an urgent forensic task. The known mechanisms of dye degradation are analyzed; Raman bands are identified that are related to the age of the sample. In a sample of 5 randomly selected writing inks, temporary markers were identified. Narrow sections of Raman spectra containing characteristic lines were used for analysis. It was shown that processing narrow sections of the Raman spectra using the PCA chemometric method allowed the separation of writing inks into groups (clusters) corresponding to different creation intervals.     

A method of age estimation using Raman microspectrometry imaging of the human dentin

The aim of this work is to establish a correlation between ageing and Raman spectra imaging of human teeth. Raman spectrum of an analysed sample is characteristic of its chemical components. By exploring the different dentinal areas on each tooth, we sought to find a correlation between noticeable variability on Raman spectra and the stages of dentinal evolution with advancing age. A study on 30 teeth analysed by Raman microspectrometry was used to select predictors of age, and it allowed us to determine a regression formula, with age as a dependent variable. Our data were computed by means of a statistical method called partial least squares (PLS) regression. This method was specially adapted for this kind of situation, where we have numerous predictors and relatively few observations. The regression formula was performed on a test sample of four more teeth, and we obtained a correct age estimation, with a mean error of more or less than 5 years. The main benefits of this method was a minimal and non-destructive tooth preparation, which led to an efficient age prediction, for any age group.     

Forensic Discrimination of Dyed Hair Color: II. Multivariate Statistical Analysis*,†

Abstract: This research is intended to assess the ability of UV–visible microspectrophotometry to successfully discriminate the color of dyed hair. Fifty‐five red hair dyes were analyzed and evaluated using multivariate statistical techniques including agglomerative hierarchical clustering (AHC), principal component analysis (PCA), and discriminant analysis (DA). The spectra were grouped into three classes, which were visually consistent with different shades of red. A two‐dimensional PCA observations plot was constructed, describing 78.6% of the overall variance. The wavelength regions associated with the absorbance of hair and dye were highly correlated. Principal components were selected to represent 95% of the overall variance for analysis with DA. A classification accuracy of 89% was observed for the comprehensive dye set, while external validation using 20 of the dyes resulted in a prediction accuracy of 75%. Significant color loss from successive washing of hair samples was estimated to occur within 3 weeks of dye application.     

Elemental Characterization and Discrimination of Nontoxic Ammunition Using Scanning Electron Microscopy with Energy Dispersive X‐Ray Analysis and Principal Components Analysis

Concerns over the toxic by‐products produced by traditional ammunition have led to an increase in popularity of nontoxic ammunition. In this work, the chemical composition of six brands of nontoxic ammunition was investigated and compared to that of a road flare, which served as an environmental source with similar composition. Five rounds of each brand were fired while a further five were disassembled and the primer alone was fired. Particles collected from all samples, including the road flare, were analyzed by scanning electron microscopy with energy dispersive X‐ray analysis. Common elements among the different ammunition brands included aluminum, potassium, silicon, calcium, and strontium. Spectra were then subjected to principal components analysis in which association of the primer to the intact ammunition sample was generally possible, with distinction among brands and from the road flare sample. Further, PCA loadings plots indicated the elements responsible for the association and discrimination observed.     

Quantification of the amphetamine content in seized street samples by Raman spectroscopy

A Raman spectroscopy method for determining the drug content of street samples of amphetamine was developed by dissolving samples in an acidic solution containing an internal standard (sodium dihydrogen phosphate). The Raman spectra of the samples were measured with a CDD-Raman spectrometer. Two Raman quantification methods were used: (1) relative peak heights of characteristic signals of the amphetamine and the internal standard; and (2) multivariate calibration by partial least squares (PLS) based on second derivative of the spectra. For the determination of the peak height ratio, the spectra were baseline corrected and the peak height ratio (h(amphetamine at 994 cm(-1) )/h(internal standard at 880 cm(-1) )) was calculated. For the PLS analysis, the wave number interval of 1300-630 cm(-1) (348 data points) was chosen. No manual baseline correction was performed, but the spectra were differentiated twice to obtain their second derivatives, which were further analyzed. The Raman results were well in line with validated reference LC results when the Raman samples were analyzed within 2 h after dissolution. The present results clearly show that Raman spectroscopy is a good tool for rapid (acquisition time 1 min) and accurate quantitative analysis of street samples that contain illicit drugs and unknown adulterants and impurities.     

Application of multivariate chemometrics in forensic soil discrimination based on the UV-Vis spectrum of the acid fraction of humus

Forty-four soil samples from five different areas were examined on the basis of the UV-Vis spectrum of the acid fraction of humus with a view to achieving good discrimination between them. Fulvic and humic acids were extracted from the samples into an alkaline aqueous solution and absorbance values, after appropriate transformations, were subjected to a K-mean cluster analysis (CA) over the objects (samples) for an initial feature reduction (20 variables retained). This was followed by principal component analysis (PCA) for the removal of outliers (four samples removed). The same statistical technique was used on the remaining samples to decide how many variables to enter into the linear discriminant function analysis (DA) and whether original variables or component scores should be used. It was found that the scree test was a good criterion for deciding on the number of components to extract (nine components extracted) and that the use of component scores instead of original variables led to a lower average redundancy (20.6%) of the variables in the discriminant model. From the components entered into the model and their loadings, it was concluded that the discrimination achieved was due to the relative concentration of aromatic groups and other fragments in the samples as well as the degree of soil humification. An overall 85% correct classification of the training dataset was observed (Wilks' lambda = 0.0420) and the method was judged satisfactory for supporting exclusionary forensic purposes.     

Profiling of counterfeit medicines by vibrational spectroscopy

Counterfeit pharmaceutical products have become a widespread problem in the last decade. Various analytical techniques have been applied to discriminate between genuine and counterfeit products. Among these, Near-infrared (NIR) and Raman spectroscopy provided promising results. The present study offers a methodology allowing to provide more valuable information for organisations engaged in the fight against counterfeiting of medicines. A database was established by analyzing counterfeits of a particular pharmaceutical product using Near-infrared (NIR) and Raman spectroscopy. Unsupervised chemometric techniques (i.e. principal component analysis - PCA and hierarchical cluster analysis - HCA) were implemented to identify the classes within the datasets. Gas Chromatography coupled to Mass Spectrometry (GC-MS) and Fourier Transform Infrared Spectroscopy (FT-IR) were used to determine the number of different chemical profiles within the counterfeits. A comparison with the classes established by NIR and Raman spectroscopy allowed to evaluate the discriminating power provided by these techniques. Supervised classifiers (i.e. k-Nearest Neighbors, Partial Least Squares Discriminant Analysis, Probabilistic Neural Networks and Counterpropagation Artificial Neural Networks) were applied on the acquired NIR and Raman spectra and the results were compared to the ones provided by the unsupervised classifiers. The retained strategy for routine applications, founded on the classes identified by NIR and Raman spectroscopy, uses a classification algorithm based on distance measures and Receiver Operating Characteristics (ROC) curves. The model is able to compare the spectrum of a new counterfeit with that of previously analyzed products and to determine if a new specimen belongs to one of the existing classes, consequently allowing to establish a link with other counterfeits of the database.     

Discriminating red spray paints by optical microscopy, Fourier transform infrared spectroscopy and X-ray fluorescence

Red spray paints from different European suppliers were characterised to determine the discriminating power of a sequence of analysing techniques. A total of 51 red spray paints were analysed with the help of three techniques: (1) optical microscopy, (2) Fourier transform infrared spectrometry and (3) X-ray fluorescence. Infrared spectra were classified according to binder type, filler and pigment composition and a searchable spectral library was created. Due to the difference in the elemental composition of spray paints, a further discrimination was possible. The microscopic analysis was not taken into consideration for classification purposes. The structure of the substrate under a paint coating strongly affects the surface characteristics of this spray paint. Together with the spectral library, a database of information of spray paints was build.     

Source Determination of Red Gel Pen Inks using Raman Spectroscopy and Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy combined with Pearson's Product Moment Correlation Coefficients and Principal Component Analysis

The potential combination of two nondestructive techniques, that is, Raman spectroscopy (RS) and attenuated total reflectance–fourier transform infrared (ATR‐FTIR) spectroscopy with Pearson's product moment correlation (PPMC) coefficient (r) and principal component analysis (PCA) to determine the actual source of red gel pen ink used to write a simulated threatening note, was examined. Eighteen (18) red gel pens purchased from Japan and Malaysia from November to December 2014 where one of the pens was used to write a simulated threatening note were analyzed using RS and ATR‐FTIR spectroscopy, respectively. The spectra of all the red gel pen inks including the ink deposited on the simulated threatening note gathered from the RS and ATR‐FTIR analyses were subjected to PPMC coefficient (r) calculation and principal component analysis (PCA). The coefficients r = 0.9985 and r = 0.9912 for pairwise combination of RS and ATR‐FTIR spectra respectively and similarities in terms of PC1 and PC2 scores of one of the inks to the ink deposited on the simulated threatening note substantiated the feasibility of combining RS and ATR‐FTIR spectroscopy with PPMC coefficient (r) and PCA for successful source determination of red gel pen inks. The development of pigment spectral library had allowed the ink deposited on the threatening note to be identified as XSL Poppy Red (CI Pigment Red 112).     

Characterization and dating of blue ballpoint pen inks using principal component analysis of UV-Vis absorption spectra, IR spectroscopy, and HPTLC

The ink of pens and ink extracted from lines on white photocopier paper of 10 blue ballpoint pens were subjected to ultraviolet-visible (UV-Vis) spectroscopy, infrared (IR), and high-performance thin-layer liquid chromatography (HPTLC). The R(f) values and color tones of the bands separated by thin-layer chromatography (TLC) analysis used to classify the writing inks into three groups. The principal component analysis (PCA) investigates the pen responsible for a piece of writing, and how time affects spectroscopy of written ink. PCA can differentiate between pen ink and ink line indicates the influence of solvent extraction process on the results. The PCA loadings are useful in individualization of a questioned ink from a database. The PCA of ink lines extracted at different times can be used to estimate the time at which a questioned document was written. The results proved that the UV-Vis spectra are effective tool to separate blue ballpoint pen ink in most cases rather than IR and HPTLC.     

Progress Toward the Determination of Correct Classification Rates in Fire Debris Analysis,,

Principal components analysis (PCA), linear discriminant analysis (LDA), and quadratic discriminant analysis (QDA) were used to develop a multistep classification procedure for determining the presence of ignitable liquid residue in fire debris and assigning any ignitable liquid residue present into the classes defined under the American Society for Testing and Materials (ASTM) E 1618‐10 standard method. A multistep classification procedure was tested by cross‐validation based on model data sets comprised of the time‐averaged mass spectra (also referred to as total ion spectra) of commercial ignitable liquids and pyrolysis products from common building materials and household furnishings (referred to simply as substrates). Fire debris samples from laboratory‐scale and field test burns were also used to test the model. The optimal model's true‐positive rate was 81.3% for cross‐validation samples and 70.9% for fire debris samples. The false‐positive rate was 9.9% for cross‐validation samples and 8.9% for fire debris samples.     

Postmortem Diagnosis of Fatal Hypothermia by Fourier Transform Infrared Spectroscopic Analysis of Edema Fluid in Formalin-Fixed,Paraffin-Embedded Lung Tissues

The goal of this study was to investigate whether pulmonary edema could become a specific diagnostic marker for fatal hypothermia using Fourier transform infrared (FTIR) spectroscopy in combination with chemometrics. The spectral profile analysis indicated that hypothermia fatalities associated with pulmonary edema fluid contained more β-sheet protein conformational structures than the control causes of death, which included sudden cardiac death, brain injury, cerebrovascular disease, mechanical asphyxiation, intoxication, and drowning. Subsequently, the results of principal component analysis (PCA) further revealed that the content of β-sheet protein conformational structures in the pulmonary edema fluid was the main discriminatory marker between fatal hypothermia and the other causes of death. Ultimately, a robust postmortem diagnostic model for fatal hypothermia using a partial least-squares discriminant analysis (PLS-DA) algorithm was constructed. Pulmonary edema fluid spectra collected from eight new forensic autopsy cases that did not participate in the construction of the diagnostic model were predicted using the model. The results showed the causes of death of all these eight cases were correctly classified. In conclusion, this preliminary study demonstrates that FTIR spectroscopy in combination with chemometrics could be a promising approach for the postmortem diagnosis of fatal hypothermia.     

氨基树脂漆的傅立叶变换红外光谱分析

根据红外谱图的相似程度决定两种涂料是否属同一种类的涂料是最基本也是最初级的检验方法,不可避免地带有极大的盲目性,只有把握了特征峰所代表的基团的振动形式,才能准确地识谱,正确地鉴别.本文介绍了氨基树脂漆的傅立叶变换红外光谱分析及谱图解释.     

Chemical Imaging of Human Fingermark by X-ray Photoelectron Spectroscopy (XPS)

X-ray photoelectron spectroscopy (XPS) is a widely used technique to characterize the surface chemistry of materials. It plays a crucial role in accessing qualitative and quantitative information and in detecting the presence of chemical functional groups on the surface of any material. The forensic methods available to detect and identify elements and organic/inorganic compounds are often destructive, so evidence cannot be re-analyzed. However, XPS allows rapid analysis of samples without damaging them. Recently, an increasing number of forensic researchers have begun to study certain chemical information on fingermarks. In this study, the authors aimed to present the applicability and power of XPS imaging in fingermark analysis which can also provide specific information about the fingermark chemical composition. Herein, monochromated X-ray (Al Kα) spot size was fixed at 50 μm. XPS mapping resulted in the acquisition of spectra at each pixel, in an array of 41 × 30 pixels with a step size of 50 μm. Then, a simple discussion has been made about how the scanned surface spectrum and basic snapshot spectra are used to identify different components at a fingertip of a scanned surface area (~3 mm²). Hence, a fingermark pattern contaminated with caffeine, TiO₂, and Pb/PbO deposited on the silicon wafer can be chemically mapped and visualized by XPS using principal component analysis (PCA). Thus, the present study showed the possible applicability of XPS for the identification of illicit drugs of abuse, gunshot residue, and skin care products on latent fingermark by mimicking a crime scene evidence.