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.     

激光显微共聚焦拉曼光谱仪对红色墨迹的研究

目的利用激光显微共聚焦拉曼光谱仪对文书中常见的红色墨迹材料进行表征,研究该方法对红色墨迹材料的区分能力。方法在785nm激发波长,50倍物镜条件下,对49种红色印文,以及9种彩色喷墨打印和13种彩色激光打印的红色墨迹材料进行拉曼光谱表征。结果通过对71种墨迹样品的谱图进行分析,可以发现,红色印文墨迹、喷墨打印红色墨迹及激光打印红色墨迹的拉曼光谱间均存在差异,同时,拉曼光谱可将这三种墨迹材料分别进一步区分。结论显微共聚焦激光拉曼光谱可对红色墨迹材料进行有效表征和区分。这一方法可对红色印文墨迹进行识别,并且可实现对伪造印文文件的鉴别。     

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.     

Discrimination of phenethylamine regioisomers and structural analogues by Raman spectroscopy

In this study, the Raman spectra of 21 phenethylamines were obtained using far‐red excitation (785 nm). The distinguishing ability of Raman for phenethylamines, especially for phenethylamine regioisomers and structural analogues, was investigated. Here, the evaluation of a cross section of Raman spectra demonstrated that all types of phenethylamines were distinguishable, even for certain structural analogues with high spectrum similarity. Raman exhibited high distinguishing ability for phenethylamine regioisomers that differ in the substitution position of halogen, methoxy, alkyl, or other substituted groups; as well as for structural analogues containing different groups, such as furanyl, 2,3‐dihydrofuranyl, halogen, and alkyl substituted at the same position. The Raman spectra for homologues with differences in only a methyl group were found to be highly similar; however, their spectra demonstrated small but detectable differences. Four analogue mixtures and 59 seized samples were also analyzed to study the practical use of the Raman method in forensic field. 95% of the seized samples were correctly identified, which significantly validated the ability of Raman method in identifying the correct isomers. Accordingly, this study provides a non‐destructive, high‐throughput and minimal sample preparation technique for the discrimination of phenethylamines.     

The Discrimination of Colored Acrylic,Cotton, and Wool Textile Fibers Using Micro‐Raman Spectroscopy. Part 1: In situ Detection and Characterization of Dyes

Raman spectroscopy has been applied to characterize fiber dyes and determine the discriminating ability of the method. Black, blue, and red acrylic, cotton, and wool samples were analyzed. Four excitation sources were used to obtain complementary responses in the case of fluorescent samples. Fibers that did not provide informative spectra using a given laser were usually detected using another wavelength. For any colored acrylic, the 633‐nm laser did not provide Raman information. The 514‐nm laser provided the highest discrimination for blue and black cotton, but half of the blue cottons produced noninformative spectra. The 830‐nm laser exhibited the highest discrimination for red cotton. Both visible lasers provided the highest discrimination for black and blue wool, and NIR lasers produced remarkable separation for red and black wool. This study shows that the discriminating ability of Raman spectroscopy depends on the fiber type, color, and the laser wavelength.     

Evaluation and classification of fentanyl-related compounds using EC-SERS and machine learning

Multiple analytical techniques for the screening of fentanyl-related compounds exist. High discriminatory methods such as GC–MS and LC–MS are expensive, time-consuming, and less amenable to onsite analysis. Raman spectroscopy provides a rapid, inexpensive alternative. Raman variants such as electrochemical surface-enhanced Raman scattering (EC-SERS) can provide signal enhancements with 10¹⁰ magnitudes, allowing for the detection of low-concentration analytes, otherwise undetected using conventional Raman. Library search algorithms embedded in instruments utilizing SERS may suffer from accuracy when multicomponent mixtures involving fentanyl derivatives are analyzed. The complexing of machine learning techniques to Raman spectra demonstrates an improvement in the discrimination of drugs even when present in multicomponent mixtures of various ratios. Additionally, these algorithms are capable of identifying spectral features difficult to detect by manual comparisons. Therefore, the goal of this study was to evaluate fentanyl-related compounds and other drugs of abuse using EC-SERS and to process the acquired data using machine learning—convolutional neural networks (CNN). The CNN was created using Keras v 2.4.0 with Tensorflow v 2.9.1 backend. In-house binary mixtures and authentic adjudicated case samples were used to evaluate the created machine-learning models. The overall accuracy of the model was 98.4 ± 0.1% after 10-fold cross-validation. The correct identification for the in-house binary mixtures was 92%, while the authentic case samples were 85%. The high accuracies achieved in this study demonstrate the advantage of using machine learning to process spectral data when screening seized drug materials comprised of multiple components.     

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.     

Secondary ion mass spectrometric characterization of nail polishes and paint surfaces

A variety of paint and fingernail polish samples, which were visually similar, but had different chemical compositions and formulations, was analyzed using quadrupole static secondary ion mass spectrometry (SIMS). Coating distinction was easily achieved in many cases because of the presence of dominant ions derived from the components of the coating, which could be observed in the SIMS spectra. In other instances, coating distinction was difficult within a product line because of spectral complexity; for this reason and because of the large numbers of spectra generated in this study, multivariate statistical techniques were employed, which allowed the meaningful classification and comparison of spectra. Partial Least Squares (PLS) and Principal Component Analysis (PCA) were applied to quadrupole SIMS data. PCA showed distinct spectral differences between most spectral groups, and also emphasized the reproducibility of the SIMS spectra. When using PLS analysis, reasonably accurate coating identification was achieved with the data. Overall, the PLS model is more than 90% effective in identifying the spectrum of a particular coating, and nearly 100% effective at telling which coating components represented in the PLS models are not present in a spectrum. The level of spectral variation caused by sample bombardment in the SIMS analysis was investigated using Fourier transform infrared spectroscopy (FT-IR) and quadrupole static SIMS. Changes in the FT-IR spectra were observed and were most likely a result of a number of factors involving the static SIMS analysis. However, the bulk of the sample is unaltered and may be used for further testing.     

On the discrimination between facial creams of different brands using Raman Spectroscopy and partial least squares discriminant analysis for forensic application

Determining the origin of cosmetic traces is an important aspect of forensic investigations, that helps linking a suspect to a crime. Such type of evidence can help further narrow down the undergoing investigations. This paper reports the first use of Raman Spectroscopy (RS) coupled with the exploratory principal component analysis (PCA) and supervised partial least squares-discriminant analysis (PLS-DA) in facial creams. 40 facial cream samples of 8 different brands were studied in this work. Preliminary assessments through visual inspection of their Raman spectra revealed the presence of oxides, titanium dioxide, castor seed oil, and beeswax. Also, the peaks of alkyne groups were indicative of the presence of talc or mica compounds. The exploratory PCA correctly segregated the samples into 8 clusters and the supervised PLS-DA model correctly classified them into 8 classes. Further evaluation of the performance of the trained PLS-DA model resulted in perfect classification shown by the receiver operating characteristic (ROC) curves. The PLS-DA model also resulted in 100% accuracy of correctly assigning the brand on the face wipes on each of the five substrates viz. cotton, dry and wet tissue paper, nylon substrate, and polyester. This validation was done treating these samples as unknowns. The study has a potential for use under actual forensic casework conditions.     

The application of chemometrics on Infrared and Raman spectra as a tool for the forensic analysis of paints

The aim of this work is to evaluate the capabilities and limitations of chemometric methods and other mathematical treatments applied on spectroscopic data and more specifically on paint samples. The uniqueness of the spectroscopic data comes from the fact that they are multivariate - a few thousands variables - and highly correlated. Statistical methods are used to study and discriminate samples. A collection of 34 red paint samples was measured by Infrared and Raman spectroscopy. Data pretreatment and variable selection demonstrated that the use of Standard Normal Variate (SNV), together with removal of the noisy variables by a selection of the wavelengths from 650 to 1830 cm(-1) and 2730-3600 cm(-1), provided the optimal results for infrared analysis. Principal component analysis (PCA) and hierarchical clusters analysis (HCA) were then used as exploratory techniques to provide evidence of structure in the data, cluster, or detect outliers. With the FTIR spectra, the Principal Components (PCs) correspond to binder types and the presence/absence of calcium carbonate. 83% of the total variance is explained by the four first PCs. As for the Raman spectra, we observe six different clusters corresponding to the different pigment compositions when plotting the first two PCs, which account for 37% and 20% respectively of the total variance. In conclusion, the use of chemometrics for the forensic analysis of paints provides a valuable tool for objective decision-making, a reduction of the possible classification errors, and a better efficiency, having robust results with time saving data treatments.     

Analysis of glass fragments by laser ablation-inductively coupled plasma-mass spectrometry and principal component analysis

Laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) is used to differentiate glass samples with similar optical and physical properties based on trace elemental composition. Laser ablation increases the number of elements that can be used for differentiation by eliminating problems commonly associated with dissolution and contamination. In this study, standard residential window and tempered glass samples that could not be differentiated by refractive index or density were successfully differentiated by LA-ICP-MS. The primary analysis approach used is Principal Component Analysis (PCA) of the complete mass spectrum. PCA, a multivariate analysis technique, provides rapid analysis of samples without time-consuming pair-wise comparison of calibrated analyses or prior knowledge of the elements present in the samples. Probabilities for positive association of the individual samples are derived from PCA. Utilization of the Q-statistic with PCA allowed us to distinguish all samples within the set to a certainty greater than the 99% confidence interval.     

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.     

Characterization of Blue Pigments Used in Automotive Paints by Raman Spectroscopy

Micro‐Raman spectroscopy was applied to forensic identification of pigments in paint chips and provided differentiation between paint samples. Sixty‐six blue automotive paint samples, 26 solid and 40 metallic were examined. It was found that the majority of the collected Raman spectra provided information about the pigments present. However, in some cases, fluorescence precluded pigment identification. Using laser excitation at longer wavelengths or pretreatment to effect photobleaching often resulted in reduced fluorescence, particularly for solid color samples, and allowed pigment identification. The examined samples were compared pairwise taking into account number, location, and intensity of absorption bands in their infrared spectra. The estimated discrimination power ranged from 97% for solid paint samples to 99% for metallic paint samples.     

Comparison of feature selection and data fusion of Fourier transform infrared and Raman spectroscopy for identifying watercolor ink

The identification of different kinds of watercolor inks is an important work in the field of forensic science. Four different kinds of watercolor ink Spectroscopy data fusion strategies (Fourier Transform Infrared spectroscopy and Raman spectroscopy) combined with a non-linear classification model (Extreme Learning Machine) were used to identify the brand of watercolor inks. The study chose Competitive Adaptive Reweighted Sampling (CARS), Random Frog (RF), Variable Combination Population Analysis-Genetic Algorithm (VCPA-GA), and Variable Combination Population Analysis-Iteratively Retains Informative Variables (VCPA-IRIV) to extract characteristic variables for mid-level data fusion. The Cuckoo Search (CS) algorithm is used to optimize the extreme learning machine classification model. The results showed that the classification capacity of the mid-level fusion spectra model was more satisfactory than that of single Infrared spectroscopy or Raman spectroscopy. The CS-ELM models based on infrared spectroscopy used to recognize the watercolor ink according to brands (ZHENCAI, DELI, CHENGUANG, and STAEDTLER) obtained an accuracy of 66.67% in the test set using all spectral datasets. The accuracy of CS-ELM models based on Raman spectroscopy was 67.39%. The characteristic wavelength selection algorithms effectively improved the accuracy of the CS-ELM models. The classification accuracy of the mid-level spectroscopy fusion model combined with the VCPA-IRIV algorithm was 100%. The data fusion method increased effectively spectral information. The method could satisfactorily identify different brands of watercolor inks and support the preservation of artifacts, paintings, and forensic document examination.     

The spectroscopic detection of exogenous material in fingerprints after development with powders and recovery with adhesive lifters

The application of powders to fingerprints has long been established as an effective and reliable method for developing latent fingerprints. The powders adhere to the ridge pattern of the fingerprint only, thus allowing the image to be visualised. Fingerprints developed in situ at a crime scene routinely undergo lifting with specialist tapes to facilitate subsequent laboratory analysis. As with all recovered evidence these samples would be stored in evidence bags to allow secure transit from the scene to the laboratory and also to preserve the chain of evidence. In this paper, the application of Raman spectroscopy for the analysis of exogenous material in latent fingerprints is reported for contaminated fingerprints that had been treated with powders and also subsequently lifted with adhesive tapes. A selection of over the counter (OTC) analgesics were used as samples for the analysis and contaminated fingerprints were deposited on clean glass slides. The application of aluminium or iron based powders to contaminated fingerprints did not interfere with the Raman spectra obtained for the contaminants. In most cases background fluorescence attributed to the sebaceous content of the latent fingerprint was reduced by the application of the powder thus reducing spectral interference. Contaminated fingerprints developed with powders and then lifted with lifting tapes were also examined. The combination of these two techniques did not interfere with the successful analysis of exogenous contaminants by Raman spectroscopy. The lifting process was repeated using hinge lifters. As the hinge lifters exhibited strong Raman bands the spectroscopic analysis was more complex and an increase in the number of exposures to the detector allowed for improved clarification. Raman spectra of developed and lifted fingerprints recorded through evidence bags were obtained and it was found that the detection process was not compromised in any way. Although the application of powders did not interfere with the detection process the time taken to locate the contaminant was increased due to the physical presence of more material within the fingerprint. The presence of interfering Raman bands from lifting tapes is another potential complication. This, however, could be removed by spectral subtraction or by the choice of lifting tapes that have only weak Raman bands.     

Raman Spectroscopy as a Simple,Rapid, Nondestructive Screening Test for Methamphetamine in Clandestine Laboratory Liquids

Raman spectroscopy has found increased use in the forensic controlled substances laboratory in recent years due to its rapid and nondestructive analysis capabilities. Here, Raman spectroscopy as a screening test for methamphetamine in clandestine laboratory liquid samples is discussed as a way to improve the efficiency of a laboratory by identifying the most probative samples for further workup among multiple samples submitted for analysis. Solutions of methamphetamine in ethanol, diethyl ether, and Coleman fuel were prepared in concentrations ranging from 0.5% to 10% w/v, and Raman spectra of each were collected. A concentration‐dependant Raman peak was observed at 1003 per cm in each solution in 4% w/v and greater solutions. Case samples were analyzed and also found to reliably contain this diagnostic peak when methamphetamine was present. The use of this diagnostic indicator can save the forensic controlled substances laboratory time and materials when analyzing clandestine laboratory liquid submissions.     

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.     

Characterization of condom lubricant components using Raman spectroscopy and Raman chemical imaging

A condom can be described as a protective sheath used as a contraceptive or to protect against sexually transmitted diseases. However, individuals also use condoms during the commission of sexual assaults to prevent identification through deposited biological material. Raman spectroscopy offers a novel approach to identifying the presence of condom lubricant components. Furthermore, Raman chemical imaging expands on conventional Raman spectroscopy to characterize multiple condom lubricant components simultaneously in a manner that effectively demonstrates heterogeneous sample mixtures both spectrally and spatially. Known reference materials, liquid and solid lubricant components of common condom brands were successfully characterized using Raman dispersive spectroscopy and Raman chemical imaging without extensive sample preparation inherent to other analytical methods. The characterization of these materials demonstrates the potential of this technique to become a routine screening method for condom lubricants. This preliminary investigation provides a basis for future studies to determine the feasibility of Raman spectroscopy and Raman chemical imaging for condom lubricant trace detection in case type samples.