Characterisation and classification of automotive clear coats with Raman spectroscopy and chemometrics for forensic purposes

The clear coats from a collection of automotive paint samples of 139 vehicles, covering a range of Australian and international vehicle manufacturers and sold in Western Australia, were characterised using FT‐Raman spectroscopy. Principal component analysis (PCA) revealed 19 distinct classes that were associated with the vehicles' manufacturer and model, and in the case of Australian manufacturers, the years of manufacture. Linear discriminant analysis based on the PCA groupings gave excellent discrimination between the groups with 96.9% of the calibration set and 97.6% of the validation set being correctly classified. Although the sample set comprised only vehicles available in Australia, the methodology used is universal and hence applicable in any jurisdiction that is willing and able to generate a statistically significant data set and maintain and update it as new vehicles appear on the market. A FT‐Raman spectroscopy‐based database would rapidly provide information regarding vehicle origin and manufacture and hence generate investigative leads for questioned paint samples found at incident sites. Copyright © 2016 John Wiley & Sons, Ltd.     

Organic and inorganic discrimination of ballpoint pen inks by ToF-SIMS and multivariate statistics

Surface analysis by ToF-SIMS analysis of ballpoint pen ink markings was performed for discrimination. ToF-SIMS provided non-destructive analysis of ink's organic and inorganic components directly off paper with no interference from the paper substrate. Organic and inorganic information were collected simultaneously and processed with PCA, discriminating 41 out of 45 pairs (91%) of pens analysed. Minimal sample preparation and analysis time, the simultaneous acquisition of organics and metals, and ability to analyse trace amounts gives this technique advantages over others currently utilised in the forensic field. Simultaneous acquisition of organics and inorganics has not been presented before for the characterisation of these materials. It was indicated that pens from the same manufacturer, but discrete batches, can be significantly different.     

On the examination of raw,pasteurized, powdered,and adulterated milk samples and their multivariate classification: applications in food and forensic science

Abstract

In the present study, we have utilized attenuated total reflectance Fourier transform infrared spectroscopy for the examination of raw, pasteurized and adulterated milk samples. Principal component analysis and discriminant analysis have been applied for discrimination and classification purposes. According to the observations and the model of “goodness of fit”, the obtained results explain 100% of the original classification of the dataset and leave one out cross-validation provides 93.74% of accurate classification.     

Near infrared Raman spectroscopic characterization of blood plasma of normal,oral premalignant and malignant conditions—a pilot study

The metabolic end products from cells/tissues that are released into the circulating blood stream and any changes in their level because of pathological conditions may be used as markers in disease diagnosis. Raman spectroscopy has been exploited to characterize the biomolecules present in the blood plasma of clinically confirmed normal group, premalignant (Oral Sub Mucous Fibrosis) and malignant (Oral Squamous Cell Carcinoma) at 784.15 nm. Raman spectral signatures show relatively less intense Raman bands of phenylalanine, lipid and antioxidant beta carotene but higher intense bands for proteins, DNA base components and amino acids (tyrosine and tryptophan) for malignant group than that of normal group. However premalignant group possess high intense Raman bands for amino acids (tyrosine and tryptophan) at 830, 1020 and 1620 cm⁻¹ and protein peaks at 913, 978 and 1646 cm⁻¹ when compared to that of malignant and normal group. Principal component analysis coupled with linear discriminant analysis (PCA‐LDA) yielded a diagnostic sensitivity of 96.3% and 91.2%, and a specificity of 80.0% and 96.7% in the classification of normal from premalignant and normal from malignant, respectively. This indicates that Raman spectroscopy of blood plasma has the potential in classifying normal and oral malignancy conditions. Copyright © 2015 John Wiley & Sons, Ltd.     

Classification of iron‐based inks by means of micro‐Raman spectroscopy and multivariate data analysis

In this work, multivariate data analysis methods were applied to the analysis and interpretation of micro‐Raman spectra, collected from a broad set of historical iron‐based ink samples, previously characterised for the content of organic acids (gallic acid, ellagic acid and protocatechuic acid). The proposed method relies on principal component analysis of the noisy spectra typically obtained on original, degraded, organic samples, where fluorescence could affect the Raman signal. The signal components could be distinguished from the noise components and then used to build a linear discriminant analysis (LDA) model, achieving separation of the spectra into three classes. Selection of pure signal factors also improved effectiveness and performances of partial least square regression (PLS) algorithms, allowing quantification of condensed tannic acid residuals. Application of multivariate methods to discriminate signal from noise removes the need for spectral data manipulation (filtering, smoothing and differentiating). The obtained classification method for discrimination of historic inks and the regression method for determination of condensed tannic acid residuals supports the use of Raman analysis of fluorescing organic materials, and may provide information to scholars on ink composition and potentially on its provenance. Copyright © 2013 John Wiley & Sons, Ltd.     

主成分分析排序和模糊线性判别分析的生菜近红外光谱分类

贮存时间是影响生菜品质的一项重要因素,传统的贮存时间鉴别方法主要依靠人工经验,但是这种方法的准确率和可信度并不高。研究的目标是建立一种基于模糊识别的模型进行生菜光谱分析以实现生菜贮存时间的鉴别,并与其他鉴别方法作比较。为此,在当地超市购买60份新鲜生菜样品,存放于冰箱中待用。首先,通过AntarisⅡ近红外光谱检测仪采集生菜样品的近红外光谱数据,每隔12小时检测一次,每个样本检测重复三次,并取三次平均值作为实验数据。其次,利用多元散射校正(MSC)减少近红外光谱中的冗余信息。为了进一步去除近红外光谱中的无用信息以及简化随后的数据分类过程,分别运用主成分分析(PCA)和排序主成分分析(PCA Sort)。其中,PCA Sort通过改进对主成分的排序方法能提高分类准确率,同时便于模糊线性鉴别分析(FLDA)进一步提取特征。PCA和PCA Sort的计算仅运用了前15个主成分(能充分反映光谱的主要信息)。最后,利用模糊线性鉴别分析算法(FLDA)和K近邻算法(KNN)进一步分类所得的低维数据。基于PCA和KNN算法的模型鉴别准确率达到43%,而基于PCA, FLDA和KNN算法的模型鉴别准确...     

Fixed-point algorithms for constrained ICA and their applications in fMRI data analysis

Constrained independent component analysis (CICA) eliminates the order ambiguity of standard ICA by incorporating prior information into the learning process to sort the components intrinsically. However, the original CICA (OCICA) and its variants depend on a learning rate, which is not easy to be tuned for various applications. To solve this problem, two learning-rate-free CICA algorithms were derived in this paper using the fixed-point learning concept. A complete stability analysis was provided for the proposed methods, which also made a correction to the stability analysis given to OCICA. Variations for adding constraints either to the components or to the associated time courses were derived too. Using synthetic data, the proposed methods yielded a better stability and a better source separation quality in terms of higher signal-to-noise-ratio and smaller performance index than OCICA. For the artificially generated brain activations, the new CICAs demonstrated a better sensitivity/specificity performance than standard univariate general linear model (GLM) and standard ICA. Original CICA showed a similar sensitivity/specificity gain but failed to converge for several times. Using functional magnetic resonance imaging (fMRI) data acquired with a well-characterized sensorimotor task, the proposed CICAs yielded better sensitivity than OCICA, standard ICA and GLM in all the target functional regions in terms of either higher t values or larger suprathreshold cluster extensions using the same significance threshold. In addition, they were more stable than OCICA and standard ICA for analyzing the sensorimotor fMRI data.     

哈尔滨市城区大气重金属沉降特征和来源研究

鉴于大气重金属降尘对城市居民健康存在一定的威胁,针对哈尔滨市从未开展过重金属沉降特征研究的现状,采用被动采样技术共收集大气降尘样品46份,用AFS-230E原子荧光光度计和ICP-AES全谱直读电感耦合等离子发射光谱法测量样品中的重金属元素含量,计算各元素年沉降通量,利用Pearson系数法、主成分分析法、富集因子法进行重金属沉降特征和来源分析。结果表明,Mn和Co元素主要来自于自然源,其余各元素沉降主要由人为源构成,且人为源中占主导地位的是燃煤尘、汽车尘和金属冶金尘。     

Multifractal detrended fluctuation analysis for clustering structures of electricity price periods

A new model is proposed to investigate the structure of electricity price in different time periods. A popular method — the multifractal detrended fluctuation analysis (MF-DFA) method is employed to analyze the features achieved from three types of electricity price data after filtering some trends by Fourier detrended fluctuation function. Twelve multifractal parameters are calculated and selected as the characteristic indicators for comparison. Moreover, the minimum number of indicators is determined so that the discriminant accuracy reaches maximum based on Fisher’s linear discriminant algorithm (Fisher’s LDA) for each time period. These indicators form a multi-dimensional space, in which each point represents a price time series. This allows us to cluster the three price time periods, namely, the low price time periods, the average price time periods and the peak price time periods. Fisher’s LDA is employed to evaluate the discriminant accuracy on these three kinds of time periods. Our analysis is then applied to the data in California1999–2000 and PJM2001–2002 electricity markets to demonstrate the applicability of our methods.     

水下噪声听觉属性的主观评价与分析

为探求人耳感知水下目标类型的声学因素,研究了水下噪声听觉属性空间的维度数及其各维度的物理解释.首先通过词汇聚类分析和问卷调查确定评价水下噪声听觉属性的汉语描述词,然后完成基于成对比较法和语义细分法的主观评价实验,获得听觉属性的不相似性矩阵及各样本在不同听觉属性下的主观评价分值.最后,利用多维尺度分析确定水下噪声听觉属性空间由五个维度组成,再利用主成分分析得到独立的五个主成分,进而利用相关系数和压力值确定五个主成分分别表示听觉属性空间的五个维度,根据各个主成分对应的汉语描述词所反映的听觉属性对其进行物理解释 关键词： 听觉属性 多维尺度分析 主成分分析     

Comparison of near infrared laser excitation wavelengths and its influence on the interrogation of seized drugs‐of‐abuse by Raman spectroscopy

The laser excitation wavelength is an important parameter in obtaining Raman spectra from drugs‐of‐abuse. This article compares the effect of near infrared wavelengths, 785 nm, using both benchtop and portable instrumentation and benchtop 1064 nm on the Raman spectra of seized drugs‐of‐abuse, including cocaine hydrochloride, cocaine freebase (crack), methylenedioxymethamphetamine (‘ecstasy’), amphetamine, diamorphine (heroin) and cannabis. The significant benefit of using 1064 nm for the interrogation of this type of sample is highlighted. Copyright © 2009 John Wiley & Sons, Ltd.     

分数阶微分预处理及PCA-SRDA的多模型融合对红富士苹果产地溯源

苹果产地溯源具有重要的应用价值和现实意义。为了探寻苹果产地溯源新方法,以红富士品种为研究对象,以新疆阿克苏、山东烟台、陕西洛川三个产地671个红富士苹果样本为试材,分别采集其590～1 250 nm的近红外透射光谱,然后基于分数阶微分(FD)及主成分分析(PCA)-谱回归判别分析(SRDA)进行多模型融合,构建红富士苹果产地溯源的集成学习模型。首先,将经过光谱校正后的光谱数据划分为训练集和测试集,并利用分数阶微分预处理训练集光谱,获取不同阶次(取0～2阶,步长为0.1)的分数阶微分光谱;结合不同阶次的分数阶微分光谱及PCA-SRDA算法构建基学习器,将基学习器预测结果构成一个新训练集,并通过决策树算法完成模型融合,得到最终分类预测模型;随后,采用对应阶次的分数阶微分预处理测试集光谱,并基于已建立的基学习器,获得测试集相应的预测结果;最后,将预测结果构成一个新测试集,并基于已建立的分类预测模型,输出最终的预测结果。按7∶3比例随机划分样本集,并进行200次重复实验。结果表明,结合不同阶次的分数阶微分预处理及线性判别分析(LDA)、 SRDA、 PCA-LDA、 PCA-SRDA算法建立多...     

代谢组学数据分析方法及在糖尿病研究中的应用

对NMR波谱数据的统计分析是基于NMR代谢组学研究的关键问题之一. 鉴于NMR波谱信号可以近似为样品中各种成分谱信号的线性叠加,本文将非负矩阵分解(NMF)方法引入基于NMR代谢组学的数据处理中,并与代谢组学中常用的统计方法--主成分分析(PCA)进行比较. 通过NMF和PCA两种方法对健康志愿者与2型糖尿患者血液和尿液的NMR谱图的统计分析,对所获取的特征代谢物进行比较和验证,并探讨了PCA方法可能存在的不足之处及其原因;阐明了NMF方法是基于NMR的代谢组学研究中较理想的数据分析方法. 最后,讨论了基于NMR代谢组学在糖尿病研究中的前景.     

基于光谱分析技术的黄瓜与茎叶识别研究

为了能够快速实时地识别温室中的黄瓜,研究了黄瓜和其茎叶的近红外反射光谱特性。利用近红外光谱仪在室内共采集138个样本(黄瓜46个,茎46个,叶46个)的反射光谱,进行Savitzky-Golay平滑后,抽取光谱中的108个样本作为校正集,采用偏差权重法选择信息量较大的光谱波段690~950 nm进行研究。在主成分分析(PCA)的基础上,结合马氏距离建立识别模型,剔除了7个异常样本。用剩余的101个样本进行偏最小二乘法建模,对校正集之外的30个样本进行预测。结果显示预测值和实际值的相关性达0.994 1,正确识别率达100%。说明黄瓜、茎和叶的近红外反射光谱特性之间有一定差异,可以用近红外光谱技术进行鉴别,为黄瓜识别提供了一种新的方法和思路。     

Quantitative statistical analysis, optimization and noise reduction of atomic resolved electron energy loss spectrum images

In this work we investigate methods of statistical processing and background fitting of atomic resolution electron energy loss spectrum image (SI) data. Application of principal component analysis to SI data has been analyzed in terms of the spectral signal-to-noise ratio (SNR) and was found to improve both the spectral SNR and its standard deviation over the SI, though only the latter was found to improve significantly and consistently across all data sets analyzed. The influence of the number of principal components used in the reconstructed data set on the SNR and resultant elemental maps has been analyzed and the experimental results are compared to theoretical calculations.     

Synthesis,structural characterization,Hirshfeld surface analysis,spectroscopic and ferroelectric properties studies of 4-methylbenzylammonium sulfate

Single crystals of 4-methylbenzylammonium sulfate were grown in an aqueous solution at room temperature. The grown compound is characterized by spectroscopic, thermal, and dielectric studies and its structure was determined by single-crystal X-ray diffraction analysis. Its crystal structure is described as a three-dimensional network where the sulfate anions (HSO₄^?) are interconnected through H-bonds to form anionic layers between which the 4-methylbenzylammonium cations are located. The hydrogen bonding network connecting the different components is given. Hirshfeld surface analysis was performed to visualize, explore and quantify intermolecular interactions in the crystal lattice. This analysis revealed the presence of H…C/H…C, C…O/O…C intermolecular interactions and O…O, H…H short contacts in the crystal. X-ray, structural and electrical results are correlated. The kind of the observed conduction is protonic by translocation. Differential scanning calorimetry (DSC) shows that this material presents a reversible phase transition at 390 K, confirmed by the dielectric permittivity study.     

A comparison of noise models in a hybrid reference spectrum and principal components analysis algorithm for Raman spectroscopy

Raman spectroscopy exploits the Raman scattering effect to analyze chemical compounds with the use of laser light. Raman spectra are most commonly analyzed using the ordinary least squares (LS) method. However, LS is known to be sensitive to variability in the spectra of the analyte and background materials. In a previous paper, we addressed this problem by proposing a novel algorithm that models expected variations in the analyte as well as background signals. The method was called the hybrid LS and principal component analysis (HLP) algorithm and used an unweighted Gaussian distribution to model the noise in the measured spectra. In this paper, we show that the noise in fact follows a Poisson distribution and improve the noise model of our hybrid algorithm accordingly. We also approximate the Poisson noise model by a weighted Gaussian noise model, which enables the use of a more efficient solver algorithm. To reflect the generalization of the noise model, we from hereon call the method the hybrid reference spectrum and principal components analysis (HRP) algorithm. We compare the performance of LS and HRP with the unweighted Gaussian (HRP‐G), Poisson (HRP‐P), and weighted Gaussian (HRP‐WG) noise models. Our experiments use both simulated data and experimental data acquired from a serial dilution of Raman‐enhanced gold‐silica nanoparticles placed on an excised pig colon. When the only signal variability was zero‐mean random noise (as examined using simulated data), HRP‐P consistently outperformed HRP‐G and HRP‐WG, with the latter coming in as a close second. Note that in this scenario, LS and HRP‐G were equivalent. In the presence of random noise as well as variations in the mean component spectra, the three HRP algorithms significantly outperformed LS, but performed similarly among themselves. This indicates that, in the presence of significant variations in the mean component spectra, modeling such variations is more important than optimizing the noise model. It also suggests that for real data, HRP‐WG provides a desirable trade‐off between noise model accuracy and computational speed. Copyright © 2013 John Wiley & Sons, Ltd.     

Multivariate analysis of combined Raman and fibre‐optic reflectance spectra for the identification of binder materials in simulated medieval paints

The non‐invasive identification of paint materials used in works of art is essential, both for preserving and restoring them, and also for understanding and verifying the history surrounding their creation. As such, the development of suitable non‐invasive techniques has received much interest in recent years. We have investigated the use of Fourier transform (FT)‐Raman spectroscopy and fibre‐optic reflectance spectroscopy (FORS), together with multivariate principal‐component analysis (PCA) techniques, in order to identify the pigment and binding materials used in made‐up samples representative of real artwork. We demonstrate that both types of spectroscopy provide complementary information which can be used to identify the pigments and binders in paint samples. We show that PCA with FT‐Raman spectra can be used to assist in the identification of oil‐based binders, and that the additional data provided by FORS spectra enables PCA on combined spectra to identify more complex proteinaceious and polysaccharide‐based binding media. The results presented here demonstrate that multivariate analyses of lead‐based paints, using data measured by FT‐Raman and FORS in conjunction, have much potential for identifying individual pigments and binders in paint samples. This provides a path towards computer‐assisted characterisation of paint materials on artwork. Copyright © 2013 John Wiley & Sons, Ltd.     

Precise (m,k)-Zipf diagram analysis of mathematical and financial time series when m=6, k=2

For studying short-range time correlations in financial signals, we have envisaged to combine the Zipf method and the i-variability diagrams (VD) as useful tools. The 2-VD describes the local curvature short-range correlations. We have resulted into ranking the 2-VD data according to their frequency of occurrence. After having tested the ideas and estimated the error bars on a Brownian motion signal, we have examined two stocks, i.e. SGP and OXHP closing price and volume of transaction long series. A precise (m,k)-Zipf diagram analysis when m=6, k=2 has been shown to lead to a non-immediate information on the signal behavior, even taking into account error bars. The set of curvatures (translated into “words”) indicates a Brownian motion-like set for the closing price local curvature of such signals over a 6 day span. Moreover, it has been shown that the conjecture about a simple relationship between the Hurst exponent H and the ζ exponent of Zipf plots does not seem to be substantiated here.     

强泵浦下掺Yb3+双包层光纤激光器输出特性数值分析和实验研究

对强泵浦下线形腔掺Yb3+双包层光纤激光器输出特性进行了理论和实验研究。通过数值模拟,分析了泵浦光及激光在光纤中的分布、输出功率与泵浦功率的关系、光纤长度及腔镜反射率对激光输出功率的影响。在实验中,利用D型掺Yb3+双包层光纤获得了输出功率10 6W的光纤激光输出,斜率效率达86%。测量了在不同输出耦合条件下的输出功率、阈值泵浦功率和斜率效率,理论分析与实验结果基本一致,为进一步提高光纤激光器功率提供了理论和实验依据。