A novel method for human gender classification using Raman spectroscopy of fingernail clippings

This paper illustrates a novel method for human gender classification by measuring the Raman spectrum of fingernail clippings. As Raman spectroscopy reveals the characteristics of vibrational frequencies of the fingernails, it provides unique chemical fingerprints that can be used to describe the molecular structure differences of fingernail between males and females. As the differences of Raman spectra of human fingernails are very subtle, they are enhanced by using a pattern recognition method. In the present study, a combination algorithm of principal component analysis (PCA) and support vector machines (SVM) was implemented to perform the data classification. This combined algorithm provides a classification accuracy of up to 90%. The success of this present method may be used as an alternative rapid tool to identify human gender in forensic applications.     

Sexing of turkey poults by Fourier transform infrared spectroscopy

Fourier transform infrared (FT-IR) spectroscopy was used to probe the molecular composition of germinal cells and to identify the gender of turkey poults. Germinal cells obtained from a feather pulp were characterized by FT-IR micro spectroscopy. The sample set consisted of growing contour feathers from 23 male and 23 female turkey poults. Significant spectral variations were observed in the range between 1,000 and 1,250 cm⁻¹. The spectra of male turkey poults exhibit a significantly higher content of RNA than those of female turkeys. Spectral classification was performed by a non-supervised method based on the principal component analysis. An evaluation of the first and third PCs led to a classification of female and male poults with an accuracy of more than 95%.     

核磁共振技术测试不同性别大鼠尿液代谢物的研究

采用核磁共振波谱技术测试不同性别大鼠尿液代谢物,分析性别因素对大鼠尿液代谢成分的影响.大鼠尿液核磁共振氢谱（1HNMR谱）结果采用主成分分析（principal component analysis,PCA）和正交偏最小二乘判别分析（orthogonal to partial least squares discriminant analysis,OPLS-DA）方法分析,得到不同性别大鼠尿液中的差异性代谢物.PCA分析结果显示2组尿液代谢成分有明显的差异,进一步进行OPLS-DA分析可以判别出2组尿液中具有差异性的代谢物.结果显示,雌性大鼠尿液中的丙氨酸、缬氨酸、鸟氨酸等氨基酸类以及乙酸、硫胺、氨基马尿酸、苯乙胺、氧氨嘧啶等代谢物含量高于雄性大鼠,差异有统计学意义（p〈0.05）.雄性大鼠尿液中的甲胺、二甲胺、三甲胺、肌酸酐、尿囊素、延胡索酸、甲酸等代谢物则明显高于雌性大鼠,差异有统计学意义（p〈0.05）.性别因素对大鼠尿液中的代谢成分有一定的影响.     

Metabolic profiling using principal component analysis, discriminant partial least squares, and genetic algorithms

The aim of this study was to evaluate evolutionary variable selection methods in improving the classification of ¹H nuclear magnetic resonance (NMR) metabonomic profiles, and to identify the metabolites that are responsible for the classification. Human plasma, urine, and saliva from a group of 150 healthy male and female subjects were subjected to ¹H NMR-based metabonomic analysis. The ¹H NMR spectra were analyzed using two pattern recognition methods, principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA), to identify metabolites responsible for gender differences. The use of genetic algorithms (GA) for variable selection methods was found to enhance the classification performance of the PLS-DA models. The loading plots obtained by PCA and PLS-DA were compared and various metabolites were identified that are responsible for the observed separations. These results demonstrated that our approach is capable of identifying the metabolites that are important for the discrimination of classes of individuals of similar physiological conditions.     

Kernel principal component analysis residual diagnosis (KPCARD): An automated method for cosmic ray artifact removal in Raman spectra

A new, fully automated, rapid method, referred to as kernel principal component analysis residual diagnosis (KPCARD), is proposed for removing cosmic ray artifacts (CRAs) in Raman spectra, and in particular for large Raman imaging datasets. KPCARD identifies CRAs via a statistical analysis of the residuals obtained at each wavenumber in the spectra. The method utilizes the stochastic nature of CRAs; therefore, the most significant components in principal component analysis (PCA) of large numbers of Raman spectra should not contain any CRAs. The process worked by first implementing kernel PCA (kPCA) on all the Raman mapping data and second accurately estimating the inter- and intra-spectrum noise to generate two threshold values. CRA identification was then achieved by using the threshold values to evaluate the residuals for each spectrum and assess if a CRA was present.     

基于非接触式拉曼光谱分析人血与犬血的PCA-LDA鉴别方法

将拉曼光谱分析法与数理统计方法有机结合,构建人血与犬血种属判别模型,实现了不同种属血液样本的高效无损鉴别.采用拉曼光谱的无损测试模式对血液样本进行测试,考察了抗凝管管材、聚焦位置及曝光时间等对血液样本拉曼光谱的影响,在激发波长为632.8 nm,光谱扫描范围为200~1800 cm-1,功率衰减率50%,曝光时间5 s及累加次数为2次的优化条件下,获得了无损检测条件下的血液样本拉曼光谱图.针对血液样本组分复杂、拉曼光谱信号基底背景高等问题,提出了基于小波变换去噪,进行分段多项式基线校正的预处理方法,有效解决了血液样本拉曼光谱谱图的高噪音和基线漂移问题.实验选择30例正常人血和33例比格犬血为样本训练集,5例正常人血和5例比格犬血为测试集,基于主成分分析法(PCA)联合线性判别法(LDA)模型,训练集分类正确率达到95.23%,盲测集分类正确率达90.00%.这种基于非接触式血液样本拉曼光谱和PCA-LDA判断模型的测试方法在进出口检验检疫等涉及血液无损鉴别的领域具有广泛的应用价值和前景.     

A sampling and analytical methodology for dental trace element analysis

The role of trace elements in human health and environmental pollution has developed into an extensive field of research. This study describes a sampling and analytical strategy to determine the trace element content of primary (deciduous) teeth and to assess their use in environmental health and nutrition studies. Exfoliated and extracted primary teeth were collected from 21 Ugandan and 27 UK children. The crown and root of the teeth were separated and the former digested and analysed for several elements by inductively coupled plasma mass spectrometry. The influence of country, tooth type, age and gender were statistically investigated in addition to within-person variation. A principal components analysis (PCA) was used to treat the data in a multivariate fashion and facilitated the moderation of outliers. The results demonstrated that country of origin has an important influence on the elemental composition of teeth and that tooth type should be controlled in these types of studies. Given such a restriction, the age and gender of the donor should have no effect and do not need to be controlled. In addition, where country of domicile, age and gender were controlled, the concentrations of most elements within a single tooth type were representative of an individual and therefore may be indicative of health status.     

Using the Method of “Optical Biopsy” of Prostatic Tissue to Diagnose Prostate Cancer

Simple SummaryAnalytical discrimination models of Raman spectra of prostate cancer tissue were constructed by using the projections onto latent structures data analysis (PLS-DA) method for different wavelengths of exciting radiation—532 and 785 nm. These models allowed us to divide the Raman spectra of prostate cancer and the spectra of hyperplasia sites for validation datasets with the accuracy of 70–80%, depending on the specificity value. Meanwhile, for the calibration datasets, the accuracy values reached 100% for the excitation of a laser with a wavelength of 785 nm. Due to the registration of Raman “fingerprints”, the main features of cellular metabolism occurring in the tissue of a malignant prostate tumor were confirmed, namely the absence of aerobic glycolysis, over-expression of markers, and a strong increase in the concentration of cholesterol and its esters, as well as fatty acids and glutamic acid.AbstractThe possibilities of using optical spectroscopy methods in the differential diagnosis of prostate cancer were investigated. Analytical discrimination models of Raman spectra of prostate tissue were constructed by using the projections onto latent structures data analysis(PLS-DA) method for different wavelengths of exciting radiation—532 and 785 nm. These models allowed us to divide the Raman spectra of prostate cancer and the spectra of hyperplasia sites for validation datasets with the accuracy of 70–80%, depending on the specificity value. Meanwhile, for the calibration datasets, the accuracy values reached 100% for the excitation of a laser with a wavelength of 785 nm. Due to the registration of Raman “fingerprints”, the main features of cellular metabolism occurring in the tissue of a malignant prostate tumor were confirmed, namely the absence of aerobic glycolysis, over-expression of markers (FASN, SREBP1, stearoyl-CoA desaturase, etc.), and a strong increase in the concentration of cholesterol and its esters, as well as fatty acids and glutamic acid. The presence of an ensemble of Raman peaks with increased intensity, inherent in fatty acid, beta-glucose, glutamic acid, and cholesterol, is a fundamental factor for the identification of prostate cancer.     

Surface enhanced Raman spectroscopy (SERS) for the discrimination of Arthrobacter strains based on variations in cell surface composition

Surface enhanced Raman spectroscopy (SERS) is a rapid and highly sensitive spectroscopic technique that has the potential to measure chemical changes in bacterial cell surface in response to environmental changes. The objective of this study was to determine whether SERS had sufficient resolution to differentiate closely related bacteria within a genus grown on solid and liquid medium, and a single Arthrobacter strain grown in multiple chromate concentrations. Fourteen closely related Arthrobacter strains, based on their 16S rRNA gene sequences, were used in this study. After performing principal component analysis in conjunction with Linear Discriminant Analysis, we used a novel, adapted cross-validation method, which more faithfully models the classification of spectra. All fourteen strains could be classified with up to 97% accuracy. The hierarchical trees comparing SERS spectra from the liquid and solid media datasets were different. Additionally, hierarchical trees created from the Raman data were different from those obtained using 16S rRNA gene sequences (a phylogenetic measure). A single bacterial strain grown on solid media culture with three different chromate levels also showed significant spectral distinction at discrete points identified by the new Elastic Net regularized regression method demonstrating the ability of SERS to detect environmentally induced changes in cell surface composition. This study demonstrates that SERS is effective in distinguishing between a large number of very closely related Arthrobacter strains and could be a valuable tool for rapid monitoring and characterization of phenotypic variations in a single population in response to environmental conditions.     

Qualitative study of ethanol content in tequilas by Raman spectroscopy and principal component analysis

Using Raman spectroscopy, with an excitation radiation source of 514.5 nm, and principal component analysis (PCA) was elaborated a method to study qualitatively the ethanol content in tequila samples. This method is based in the OH region profile (water) of the Raman spectra. Also, this method, using the fluorescence background of the Raman spectra, can be used to distinguish silver tequila from aged tequilas. The first three PCs of the Raman spectra, that provide the 99% of the total variance of the data set, were used for the samples classification. The PCA1 and PCA2 are related with the water (or ethanol) content of the sample, whereas the PCA3 is related with the fluorescence background of the Raman spectra.     

基于显微共聚焦拉曼光谱技术快速检测3种梭菌中肉毒梭菌

提出了一种基于显微共聚焦拉曼光谱技术的肉毒梭菌快速鉴别方法.利用共聚焦显微拉曼光谱技术(CRM)采集了肉毒梭菌、艰难梭菌和产气荚膜梭菌的拉曼光谱,比较了3种梭菌的平均拉曼光谱,采用基线校正、标准正态变换、Savitzky-Golay 5点平滑和最大最小值归一化预处理后,借助主成分分析(PCA)降维并提取特征变量,对样本...     

Micro-Raman spectroscopy of natural and synthetic indigo samples

In this work indigo samples from three different sources are studied by using Raman spectroscopy: the synthetic pigment and pigments from the woad (Isatis tinctoria) and the indigo plant (Indigofera tinctoria). 21 samples were obtained from 8 suppliers; for each sample 5 Raman spectra were recorded and used for further chemometrical analysis. Principal components analysis (PCA) was performed as data reduction method before applying hierarchical cluster analysis. Linear discriminant analysis (LDA) was implemented as a non-hierarchical supervised pattern recognition method to build a classification model. In order to avoid broad-shaped interferences from the fluorescence background, the influence of 1st and 2nd derivatives on the classification was studied by using cross-validation. Although chemically identical, it is shown that Raman spectroscopy in combination with suitable chemometric methods has the potential to discriminate between synthetic and natural indigo samples.     

偏最小二乘法在红外光谱识别茶叶中的应用

采用漫反射傅立叶变换红外光谱(FTIR)法结合主成分分析(PCA)、偏最小二乘法(PLS)、簇类的独立软模式(SIMCA)识别法对十三种茶叶进行了分类判别研究。研究结果表明,通过多元散射校正(MSC)对原始光谱进行预处理,可以提高模式识别技术的分类判别效果。在此基础上,选取1 900~900 cm-1波长范围内的茶叶红外光谱建立识别模型,三种方法都得到了满意的分类判别效果。在对检验集中全部130个样本的判别中,PCA仅有两类样本无法判别,SIMCA的识别率和拒绝率都在90%以上,而PLS的识别效果最佳,全部样本都得到了正确的归类。这一研究结果表明傅立叶变换红外光谱法与化学计量学方法相结合可以实现茶叶品种的快速鉴别,这为茶叶的客观评审提供了一种新思路。     

Permeability of Human Tooth Surfaces Studied In Vitro by Electrochemical Impedance Spectroscopy

Electrochemical Faradaic impedance spectroscopy was applied to evaluate dependence of the electrical resistance on human teeth. The experiments were performed using iodide anions as a redox probe to model permeability of teeth for fluoride upon an iontophoresis process. Tooth molars were used – as these are teeth most affected by tooth decay processes in vivo. Teeth compared included sound molars – with no evidence of pit and fissure decay, teeth with pits and fissures regarded ‘clinically’ as showing signs of decay, and teeth with crowns removed to present exposed dentin surfaces. A difference of more than an order of magnitude in electrical resistance was observed between sound molars and those regarded as showing evidence of tooth decay processes. Sound dentin, as expected from structural considerations demonstrated significantly lower resistance when compared to sound molars. Importantly, the difference in tooth resistance measured between carious and sound molars was shown to be much more representative of their structural integrity than comparison of digitally processed images of the teeth. The results support the utility of electrochemical Faradaic impedance spectroscopy for the development of understanding on how tooth electrical resistance may vary according to structural changes. This understanding may be useful to continued refinements in the use of electrical resistance measures as caries diagnostics and support generically the potential for iontophoretic processes in in‐office fluoride treatments of teeth.     

Raman spectroscopy in combination with background near-infrared autofluorescence enhances the in vivo assessment of malignant tissues

The diagnostic ability of optical spectroscopy techniques, including near-infrared (NIR) Raman spectroscopy, NIR autofluorescence spectroscopy and the composite Raman and NIR autofluorescence spectroscopy, for in vivo detection of malignant tumors was evaluated in this study. A murine tumor model, in which BALB/c mice were implanted with Meth-A fibrosarcoma cells into the subcutaneous region of the lower back, was used for this purpose. A rapid-acquisition dispersive-type NIR Raman system was employed for tissue Raman and NIR autofluorescence spectroscopic measurements at 785-nm laser excitation. High-quality in vivo NIR Raman spectra associated with an autofluorescence background from mouse skin and tumor tissue were acquired in 5 s. Multivariate statistical techniques, including principal component analysis (PCA) and linear discriminant analysis (LDA), were used to develop diagnostic algorithms for differentiating tumors from normal tissue based on their spectral features. Spectral classification of tumor tissue was tested using a leave-one-out, cross-validation method, and the receiver operating characteristic (ROC) curves were used to further evaluate the performance of diagnostic algorithms derived. Thirty-two in vivo Raman, NIR fluorescence and composite Raman and NIR fluorescence spectra were analyzed (16 normal, 16 tumors). Classification results obtained from cross-validation of the LDA model based on the three spectral data sets showed diagnostic sensitivities of 81.3%, 93.8% and 93.8%; specificities of 100%, 87.5% and 100%; and overall diagnostic accuracies of 90.6%, 90.6% and 96.9% respectively, for tumor identification. ROC curves showed that the most effective diagnostic algorithms were from the composite Raman and NIR autofluorescence techniques.     

Raman spectroscopic study on classification of cervical cell specimens

Cervix-cancer is the third most common female cancer worldwide. Papanicolaou (Pap) test, a well-recognized screening tool, is labor intensive, time consuming and prone to subjective interpretations. Optical spectroscopic methods, sensitive to molecular changes are being pursued as potential diagnostics tool. In this study we have explored Raman spectroscopic approach to differentiate exfoliated cell pellets using 94 cervical cell specimens (45-normal and 49-abnormal specimens). Study was carried out by two approaches. In the first approach, spectral data from 37 cell specimens were acquired and analyzed by Principal Component-Linear Discriminant Analysis (PC-LDA), which yielded classification efficiencies of 86% and 84% for normal and abnormal specimens, respectively. Mean and difference spectra suggest presence of blood in abnormal specimen as a major cause of discrimination. However, as tumor is vascular, bleeding was observed during abnormal sample collection. Hence, spectra of abnormal specimens show heme and fibrin features, and this can lead to false interpretations, as bleeding also occur in several non-cancerous conditions. Therefore, remaining 57 specimens were treated with Red Blood Corpuscles (RBC) lysis buffer in order to remove the RBC influence. PC-LDA resulted classification efficiency of about 79% and 78% for normal and abnormal smear, respectively – comparable to Pap test. Thus finding of the study suggests feasibility of Raman spectroscopic classification of normal and cancerous exfoliated cervical cell specimens.     

The determination for the three genotypes of D16S539 locus based on near-infrared spectroscopy and chemical pattern recognition

The paper has established an approach of typing short tandem repeats (STRs) based on the near-infrared spectroscopy (NIRS)-chemical pattern recognition. Taking the three genotypes 9-9, 9-11 and 11-11 of D16S539 locus as example, which have a middle degree of difference, DNA fragments containing the polymorphism sites were amplified by a pair of primers to obtain three genotypes samples; these samples were tested by the NIRS directly; using their spectra as recognition variables, the chemical pattern recognition models of the three genotypes were respectively established by using the principal discriminant variate (PDV) and support vector machine (SVM). The two models have a good fitting ability and strong prediction (i.e. the predicting accuracy was 100%). They are robust for these strong collinear spectra and the small number of the calibration samples. Without any preprocessing for the analyzed samples after PCR, the three genotypes of D16S539 locus could be indirectly determined by using the NIRS-s of the samples with the help of the models. This method is simple, rapid and low cost.     

In and ex vivo breast disease study by Raman spectroscopy

In this work, Raman spectra in the 900?C1,800?cm^?1 wavenumber region of in vivo and ex vivo breast tissues of both healthy mice (normal) and mice with induced mammary gland tumors (abnormal) were measured. In the case of the in vivo tissues, the Raman spectra were collected for both transcutaneous (with skin) and skin-removed tissues. To identify the spectral differences between normal and cancer breast tissue, the paired t-test was carried out for each wavenumber using the whole spectral range from both groups. Quadratic discriminate analysis based on principal component analysis (PCA) was also used to determine and evaluate differences in the Raman spectra for the various samples as a basis for diagnostic purposes. The differences in the Raman spectra of the samples were due to biochemical changes at the molecular, cellular and tissue levels. The sensitivity and specificity of the classification scheme based on the differences in the Raman spectra obtained by PCA were evaluated using the receiver operating characteristic (ROC) curve. The in vivo transcutaneous normal and abnormal tissues were correctly classified based on their measured Raman spectra with a discriminant proportion of 73%, while the in vivo skin-removed normal and abnormal tissues were correctly classified again based on their measured Raman spectra with a discriminant proportion of 86%. This result reveals a strong influence due to the skin of the breast, which decreased the specificity by 11%. Finally, the results from ex vivo measurements gave the highest specificity and sensitivity: 96 and 97%, respectively, as well as a largest percentage for correct discrimination: 94%. Now that the important bands have been experimentally determined in this and other works, what remains is for first principles molecular-level simulations to determine whether the changes are simply due to conformational changes, due to aggregation, due to changes in the environment, or complex interactions of all of the above.     

Discrimination of natural gas-related bacteria by means of micro-Raman spectroscopy

Methane-oxidizing bacteria (MOB) are a unique group of gram-negative bacteria that are proved to be biological indicator for gas prospecting since they utilize methane as a sole source of carbon and energy. Herein the feasibility of a novel and efficient gas prospecting method using Raman spectroscopy is studied. Confocal Raman spectroscopy is utilized to establish a Raman database of 11 species of methanotrophs and other closely related bacteria with similar morphology that generally coexist in the upper soil of natural gas. After strict and consistent spectral preprocessing, Raman spectra from the whole cell area are analyzed using the combination of principal component analysis (PCA) and Mahalanobis distance (MD) that allow unambiguous classification of the different cell types with an accuracy of 95.91%. The discrimination model based on multivariate analysis is further evaluated by classifying Raman spectra from independently cultivated bacteria, and achieves an overall accuracy of 94.04% on species level. Our approach using Raman spectroscopy in combination with statistical analysis of various gas reservoirs related bacteria provides rapid distinction that can potentially play a vital role in gas exploration.     

Rapid and Sensitive Determination of Aminocaproic Acid Injection by Raman Spectroscopy Combined with Chemometric Models

A quantitative approach for the determination of aminocaproic acid in commercial injections based on Raman spectroscopy and chemometrics has been developed. The Raman spectra of aminocaproic acid injections were analyzed by chemometric models including classical least squares (CLS), partial least squares (PLS), principal component regression (PCR), and stepwise multiple linear regression (SMLR). To compare the quantitative ability of the models, two key parameters, difference value and root mean square error, were calculated. The results indicated that the SMLR method was more efficient than the other methods. The difference value of the SMLR method was 90.5% and the root mean square error was 2.08. Raman determinations agreed with results obtained with a standard titration method (p < 0.05). The recovery was (99.7 ± 0.58)% and the repeatability was (99.2 ± 0.67)% by the SMLR method. These results show that the chemometric modeling of Raman spectra is a specific, rapid, and convenient alternative to quantify aminocaproic acid in injections.