Qualitative analysis of desi ghee,edible oils,and spreads using Raman spectroscopy

Keeping in view the importance of dietary fats in modulating disease risk, a study was planned to compare edible oils, spreads, and desi ghee based on fatty acid composition through Raman spectroscopy. The double bonds in unsaturated oils tend to react more with oxygen causing oxidative stress in living cells; therefore, the excessive use of processed vegetable oils may pose risk for human health. In the spectral analysis, Raman peaks at 1063 and 1127 cm⁻¹ represent out‐of‐phase and in‐phase aliphatic C C stretch for saturated fatty acids. The peak at 1300 cm⁻¹, labeled for alkane, decreases with increase in the double bond contents (unsaturation). Further, the Raman peak at 1655 cm⁻¹ showed a monotonic increase as a function of unsaturation. The double bond contents in the Raman spectra from 1650–1657 cm⁻¹ represent unsaturated fatty acids that changes during the synthesis of spreads and banaspati ghee. Desi ghee, extracted from cow and buffalo milk, showed distinctive Raman peaks at 1650 and 1655 cm⁻¹, which originates because of isomers of conjugated linoleic acid. These Raman shifts differentiated desi ghee from other artificially produced banaspati ghee, spreads, and oils. Conjugated linoleic acid has proved to be anti‐carcinogenic, anti‐inflammatory, and anti‐allergic properties; therefore, the limited use of desi ghee may reduce the risk of cardiac diseases. Principal component analysis has been applied on the Raman spectra that clearly differentiated desi ghee, mono‐unsaturated extra virgin olive oil, and extra virgin olive oil spread from other oils, oil mixtures, spreads, and ghee. In addition, principal component analysis has been blindly applied successfully on 13 unknown samples to classify them with reference to the known ghee sample. Copyright © 2016 John Wiley & Sons, Ltd.     

Simultaneous qualitative and quantitative analysis of counterfeit and unregistered medicines using Raman spectroscopy

Raman spectroscopy was used to classify a group of seized counterfeit medications associated with erectile dysfunction. Using appropriate data preprocessing, principal component analysis and the classification method soft independent modelling of class analogy (SIMCA), it was possible to classify genuine from unregistered generic and counterfeit Cialis® batches. However, SIMCA did not effectively classify samples on the basis of their active pharmaceutical ingredient (API). Partial least squares discriminant analysis, principal component regression and support vector machines effectively distinguished between the API of the samples but were unable to correctly distinguish all samples as genuine or generic/counterfeit. This study highlights the importance of choosing the correct preprocessing procedures and classification method for the data set. Despite diverse tablet contents in addition to the drug, it was possible to quantify the levels of drug in the medicines as high, medium or low (within ±20 mg g⁻¹ tablet concentration). Overall, the potential for Raman spectroscopy combined with multivariate analysis for qualitative and semiquantitative analysis of counterfeit medicines was demonstrated, and the approach may be used to determine the potential level of harm in counterfeit medicines on the basis of API identity and amount. Copyright © 2013 John Wiley & Sons, Ltd.     

Surface‐enhanced Raman scattering characterization of monohydroxylated polymethoxyflavones

Polymethoxyflavones (PMFs) belong to a unique class of flavonoids mainly found in citrus fruits. Characterization of different PMFs is important to further understand and apply these compounds as functional ingredients in food. The objective of this study is to characterize three monohydroxylated PMFs using surface‐enhanced Raman spectroscopy (SERS) and to determine the role of hydroxylation in their SERS behaviors. Serial concentrations of 3′‐hydroxylnobiletin (3HN), 4′‐hydroxylnobiletin (4HN), and 5‐hydroxylnobiletin (5HN) were incubated with silver dendrites for SERS analysis. Results demonstrated that three PMFs exhibited significantly different SERS behaviors. 5HN produced saturation peak intensity at relative low concentration (0.05 mM), while 3HN and 4HN produced saturation peak intensity at much higher concentrations (0.5 and 1 mM, respectively) according to principal component analysis. Below saturation, 5HN had the highest peak intensity, while 3HN had the lowest peak intensity. After reaching saturation, 4HN and 5HN had similar relative peak intensities that were much greater than 3HN. The HPLC analysis revealed that 36.13 ± 1.06% of 5HN, 18.40 ± 3.31% of 4HN, and 9.66 ± 0.94% of 3HN were bound to silver. Based on these results, we speculated that different positions of hydroxylation of PMFs were critical for determining spatial conformation of PMFs on binding sites, resulting in different binding affinities and saturation points, therefore their SERS behaviors. This study first reported that the position of hydroxylation in the monohydroxylated PMFs was crucial for their interactions with silver dendrites and provided valued information for further applying SERS for molecular characterization of flavonoids. Copyright © 2016 John Wiley & Sons, Ltd.     

Surface‐enhanced Raman scattering spectroscopy for potential noninvasive nasopharyngeal cancer detection

Combining membrane electrophoresis with surface‐enhanced Raman scattering (SERS) spectroscopy, the serum proteins were first purified and then mixed with silver nanoparticles to perform SERS spectral analysis. Therefore, the spectral signatures were enhanced to high‐fidelity SERS signatures because of the purification procedure of the first step. We used the method to analyze blood plasma samples from nasopharyngeal cancer patients (n = 43) and healthy volunteers (n = 33) for cancer detection. Principle component analysis of the SERS spectra revealed that the data points for the cancer group and the normal group form distinct, completely separated clusters with no overlap. Therefore, the nasopharyngeal cancer group can be unambiguously discriminated from the normal group, i.e., with both diagnostic sensitivity and specificity of 100%. These results are very promising for developing a label‐free, noninvasive, and reliable clinical tool for rapid cancer detection and screening. Copyright © 2011 John Wiley & Sons, Ltd.     

Rapid Identification of Bacterial Species by Fluorescence Spectroscopy and Classification Through Principal Components Analysis

This work presents the development of a method for rapid bacterial identification based on the autofluorescence spectrum. It was demonstrated differences in the autofluorescence spectrum in three bacterial species and the subsequent separation, through the Principal Components Analysis (PCA) technique, in groups with high likeness, that could identify the bacteria in less than 10 min. Fluorescence spectra of 60 samples of 3 different bacterial species (Escherichia coli, EC, Enterococcus faecalis, EF and Staphylococcus aureus, SA), previously identified by automated equipment Mini API, were collected in 10 excitation wavelengths from 330 to 510 nm. The PCA technique applied to the fluorescence spectra showed that bacteria species could be identified with sensitivity and specificity higher than 90% according to differences that occur within the spectra with excitation of 410 nm and 430 nm. This work presented a method of bacterial identification of three more frequent and more clinically significant species based on the autofluorescence spectra in the excitation wavelengths of 410 and 430 nm and the classification of the spectra in three groups using PCA. The results demonstrated that the bacterial identification is very efficient with such methodology. The proposed method is rapid, ease to perform and low cost compared to standard methods.     

Raman spectroscopic analysis and fast identification of several saturated monohydroxy alcohols

The Raman spectra of nine monohydroxy alcohols have been obtained by confocal Raman spectrometer at room temperature. Based on the Raman spectra, the density functional theory was used to analyze the characteristic Raman bands of monohydric alcohols. Through the discussion of the characteristic Raman bands and their corresponding assignment, four major Raman bands were selected to identify nine monohydric alcohols using principal component analysis and Euclidean distance. Finally, nine saturated monohydroxy alcohols can be distinguished exactly, and the recognition rate is 100%.     

Study of both fingerprint and high wavenumber Raman spectroscopy of pathological nasopharyngeal tissues

High wavenumber (HW) Raman spectroscopy has weaker fluorescence background compared with fingerprint (FP) region. This study aims to evaluate the discrimination feasibility of nasopharyngeal non‐cancerous and nasopharyngeal cancer (NPC) tissue with both FP and HW Raman spectroscopy. HW Raman spectra of nasopharyngeal tissue were obtained for the first time. Raman spectra were collected to differentiate nasopharyngeal non‐cancerous (n = 37) from NPC (n = 41) tissues in FP (800–1800cm⁻¹), HW (2700–3100cm⁻¹), and integrated FP/HW region. First, to assess the utility of this method, the averaged Raman spectral intensities and intensity ratios of corresponding Raman bands were analyzed in HW and FP regions, respectively. The results show that intensities as well as the ratios of specific Raman peaks might be helpful in distinguishing nasopharyngeal non‐cancerous from NPC tissue with the HW Raman spectroscopy, as with FP Raman reported before. The multivariate statistical method based on the combination of principal component analysis–liner discriminant analysis (PCA‐LDA), together with leave‐one‐patient‐out, cross‐validation diagnostic algorithm, was used for discriminating nasopharyngeal non‐cancerous from NPC tissue, generating sensitivities of 87.8%, 85.4%, and 95.1% and specificities of 86.5%, 91.9%, and 89.2%, respectively, with Raman spectroscopy in the FP, HW, and integrated FP/HW regions. The posterior probability of classification results and receiver operating characteristic curves were utilized to evaluate the discrimination of PCA‐LDA algorithm, verifying that HW Raman spectroscopy has a positive effect on the differentiation for the diagnosis of NPC tissue by integrated FP/HW Raman spectroscopy. What's more, the potential of Raman spectroscopy used for differentiating different pathology NPC tissues was also discussed. The results demonstrate that both FP and HW Raman spectroscopy have the potential for diagnosis and detection in early nasopharyngeal carcinoma, and HW Raman spectroscopy may improve the discrimination of NPC tissue compared with FP region alone, providing a promising diagnostic tool for the diagnosis of NPC tissue. Copyright © 2015 John Wiley & Sons, Ltd.     

激光拉曼光谱法无损鉴别人参及其伪品

利用激光拉曼光谱技术并结合二阶导数拉曼光谱,对人参及其拟伪品峨参、北沙参、桔梗进行了鉴别。人参及其伪品均在拉曼光谱中出现了1 460,1 130,1 086,942,483cm-1等拉曼振动峰,根据这些拉曼位移可以判断出在人参及其伪品中都含有糖类物质。北沙参的拉曼谱图中出现了不同于其他三种药材的2 206cm-1的拉曼特征峰。峨参中出现了1 050和1 869cm-1相对应的链状酯类化合物的拉曼振动峰。桔梗中出现了1 227,600,691cm-1等明显不同于其他三种药材的拉曼振动峰。利用这些拉曼振动峰的差异可将人参及其伪品进行很好的区分。再利用二阶导数拉曼光谱图对人参及其伪品的鉴别结果进行进一步的补充说明。此鉴别方法与常规的光谱法相比具有更直接、快速,并且具有不破坏样品的原性质的特点。     

基于LGSPP-Bayes的故障检测与辨识方法

针对主元分析(Principal component analysis, PCA)和局部保持投影(Locality preserving projections, LPP)方法在降维过程中分别只能保留数据集的整体信息和局部信息,提出一种基于局部整体结构保持投影的贝叶斯故障检测与辨识方法(Local and global structure preserving projections and bayes, LGSPP-Bayes)。首先,将正常工况操作下的原始数据通过局部整体结构保持投影方法投影到低维特征空间,得到高维到低维的数据转换矩阵;然后通过设计贝叶斯分类器来进行故障检测;最后当检测到故障后通过计算贝叶斯分类函数的大小来识别故障种类。将LGSPP-Bayes方法应用于TE过程,仿真结果表明对故障的检测优于其他方法,并且可以很好地将故障种类识别出来。     

基于激光诱导击穿光谱的洋葱在线原位检测

激光诱导击穿光谱(Laser-induced breakdown spectroscopy, LIBS)是一种分析多元素的光学技术,可用于新鲜蔬菜的快速检测.以洋葱为例,采用LIBS对其含有的元素进行了在线原位检测.用乙酸铅溶液污染洋葱以模拟大气湿沉降的重金属污染现象,并进行重金属Pb元素检测.使用主成分分析(principal components analysis, PCA)和反向传播人工神经网络(back-propagating artificial neutral network, BP-ANN),以洋葱、大蒜、小葱为样品进行区分检测.洋葱光谱中的特征谱线包括Si、Fe、C、Mg、Al、Ca、Ti、Sr、Ba、Na、Li和K等元素,以及N、H、O的谱线和CN分子谱带.不同浓度梯度乙酸铅溶液污染的洋葱样品中都能检测出Pb元素,其相对强度与溶液浓度成比例.此外,PCA的结果表明洋葱、小葱、大蒜的区分效果明显,BP-ANN交叉验证的识别率为89.47%.结果证明LIBS在对元素进行快速分析时具有较好的识别能力,是检测新鲜蔬菜的有效手段.     

Effect of the target positions on the rapid identification of aluminum alloys by using filament-induced breakdown spectroscopy combined with machine learning

Filament-induced breakdown spectroscopy (FIBS) combined with machine learning algorithms was used to identify five aluminum alloys. To study the effect of the distance between focusing lens and target surface on the identification accuracy of aluminum alloys, principal component analysis (PCA) combined with support vector machine (SVM) and K-nearest neighbor (KNN) was used. The intensity and intensity ratio of fifteen lines of six elements (Fe, Si, Mg, Cu, Zn, and Mn) in the FIBS spectrum were selected. The distances between the focusing lens and the target surface in the pre-filament, filament, and post-filament were 958 mm, 976 mm, and 1000 mm, respectively. The source data set was fifteen spectral line intensity ratios, and the cumulative interpretation rates of PC1, PC2, and PC3 were 97.22%, 98.17%, and 95.31%, respectively. The first three PCs obtained by PCA were the input variables of SVM and KNN. The identification accuracy of the different positions of focusing lens and target surface was obtained, and the identification accuracy of SVM and KNN in the filament was 100% and 90%, respectively. The source data set of the filament was obtained by PCA for the first three PCs, which were randomly selected as the training set and test set of SVM and KNN in 3:2. The identification accuracy of SVM and KNN was 97.5% and 92.5%, respectively. The research results can provide a reference for the identification of aluminum alloys by FIBS.     

Fourier‐transform Raman analysis of milk powder: a potential method for rapid quality screening

In this work, FT‐Raman spectroscopy was explored as a fast and reliable screening method for the assessment of milk powder quality and the identification of samples adulterated with whey (1–40% w/w). Raman measurements can easily differentiate milk powders without the need of sample preparation, whereas the traditional methods of quality control, including high‐performance liquid chromatography, are laborious and slow. The FT‐Raman spectra of whole, low‐fat, and skimmed milk powder samples were obtained and distinguished from commercial milk powder samples. In addition, the exploratory analysis employing data from Raman spectroscopy and principal component analysis (PCA)allowed the separation of milk powder samples according to type,identifying differences between samples in the same group. Multivariate analysis was also developed to classify the adulterated milk powder samples using PCA and partial least squares discriminate analysis (PLS‐DA). The resulting PLS‐DA model correctly classified 100% of the adulterated samples. These results clearly demonstrate the utility of FT‐Raman spectroscopy combined with chemometrics as a rapid method for screening milk powder. Copyright © 2011 John Wiley & Sons, Ltd.     

Application of portable Raman spectroscopy and benchtop spatially offset Raman spectroscopy to interrogate concealed biomaterials

In this paper, we demonstrate the ability of portable Raman spectroscopy and benchtop spatially offset Raman spectroscopy (SORS) techniques to rapidly identify real and fake ivory samples. Both techniques were able to identify exposed genuine from fake ivory samples. In contrast to conventional Raman spectroscopy, SORS was, in addition, able to identify ivory concealed by plastics, paints, varnishes and cloth. Application of the SORS technique allows the interrogation of biomaterial samples through materials in which conventional Raman spectroscopic instrumentation cannot penetrate. Copyright © 2009 John Wiley & Sons, Ltd.     

Laser-induced breakdown spectroscopy applied to the characterization of rock by support vector machine combined with principal component analysis

Laser-induced breakdown spectroscopy(LIBS) is a versatile tool for both qualitative and quantitative analysis.In this paper,LIBS combined with principal component analysis(PCA) and support vector machine(SVM) is applied to rock analysis.Fourteen emission lines including Fe,Mg,Ca,Al,Si,and Ti are selected as analysis lines.A good accuracy(91.38% for the real rock) is achieved by using SVM to analyze the spectroscopic peak area data which are processed by PCA.It can not only reduce the noise and dimensionality which contributes to improving the efficiency of the program,but also solve the problem of linear inseparability by combining PCA and SVM.By this method,the ability of LIBS to classify rock is validated.     

蔬菜表面农药残留可见-近红外光谱探测与分类识别研究

利用在600～1 100nm波段范围内可见-近红外反射光谱分析技术,对常见的高残留农药在绿色植物活体上的无损检测进行了研究。首先将采集到的漫反射光谱数据进行小波变换提取光谱特征,然后再利用主成分分析方法进一步对光谱特征进行分析,最后把这些光谱的前两个主成分得分作为神经网络的输入信息,建立了多神经元的神经网络感知器。对农药残留检测的结果表明,该方法可有效甄别农药残留和种类,识别得到较好的分类效果。总之,该研究为蔬菜和瓜果表面的农药残留快速无损检测和识别提供了一条新途径。     

Strategy to improve the reproducibility of colloidal SERS

There is a pressing need to improve the reproducibility of surface enhanced Raman spectroscopy (SERS) measurements, if the technique is to be used routinely for trace analysis. This is particularly true for colloidal SERS, in which data reproducibility is dominated by the final shape and size of metal clusters produced during colloid aggregation. This study presents general guidelines for designing appropriate measurement strategies that can be used to identify and optimise crucial steps in a protocol that leads to better reproducibility of the results. We show that the data reproducibility can be improved by optimising vortexing time during colloid aggregation, which we attribute to the formation of more reproducible metal clusters under conditions of ‘forced convection’. The study also investigated the effects of different storage conditions on the data reproducibility of SERS during a 6‐month study period. Storage conditions did not significantly influence the SERS reproducibility. However, at the end of 6 months, colloids that were stored (in plastic containers) at room temperature showed a difference in their quality, as mirrored by their different opto‐physical properties. This was made apparent through the analysis of UV‐vis spectroscopy measurements by principal component analysis. Copyright © 2007 John Wiley & Sons, Ltd.     

Automated detection and characterization of graphene and few‐layer graphite via Raman spectroscopy

Several processes have to be automated in order to use graphene in future industrial applications. One of these is the detection and characterization of graphene and few‐layer graphite (FLG) flakes on a substrate. Raman spectroscopy is an ideal tool for this purpose, as it allows not only the identification of these graphitic materials on arbitrary substrates but also monitoring the quality of flakes within the sample. In this paper, we report how graphene and FLG crystallites can be automatically detected and characterized by monitoring the evolution of Raman bands. We present an algorithm that achieves this purpose and thus has special potential in industrial applications of graphene. Copyright © 2010 John Wiley & Sons, Ltd.     

Preparation and characterization of poly(lactic acid)/poly(methyl methacrylate) blend tablets for application in quantitative analysis by micro Raman spectroscopy

Poly(lactic acid) (PLA) is a biodegradable polymer that has a variety of applications, one of which is as biomaterial in surgery or as functional layers on implants, due to its compatibility with living tissue. This paper reports the possibilities of quantification of poly(lactic acid) (PLA) in a polymer matrix such as poly(methyl methacrylate) (PMMA) by micro Raman spectroscopy (MRS). Blends of amorphous poly(DL‐lactic acid) with poly(methyl methacrylate) were prepared by the procedure of dissolution/precipitation. Thermal properties of the blends such as the glass transition temperature (Tg) were characterized by differential scanning calorimetry (DSC). The PLA/PMMA blends exhibited only a single glass transition region, indicating that this system is miscible. The PLA/PMMA system obeys the Gordon–Taylor equation (Tg versus PLA content). Various concentration ratios of PLA blends were prepared to use as a basis for quantitative analysis by MRS. Intensities of the characteristic bands at 813 cm⁻¹ (νCOC of PMMA) and 873 cm⁻¹ (νC―COO of PLA) were used for the calculation. The calibration graph showed a good linear correlation with an R² value of 0.9985. On the basis of the calibration curve obtained, the determined content of several PLA/PMMA blends was in good agreement when compared with nominal contents. The limit of detection (LOD) and quantification (LOQ) were calculated by the calibration data set as signal‐to‐noise method. The relative standard deviation of this method was lower than 10% and the accuracy better than 4%. This study demonstrated that Raman spectroscopy provides an alternative non destructive method for quantitative analysis of PLA in a PMMA matrix. Copyright © 2015 John Wiley & Sons, Ltd.     

Trace level detection and identification of nitro‐based explosives by surface‐enhanced Raman spectroscopy

Methods for rapid identification of explosives and their associated compounds at trace level quantities are needed for security screening applications. In this paper, we apply the surface‐enhanced Raman spectroscopy (SERS) to detect and identify traces (as low as tens of pg) of pentaerythritol tetranitrate (PETN), ethylene glycol dinitrate (EGDN), cyclotrimethylene‐trinitramine (RDX) and trinitrotoluene (TNT) using commercially available substrates (Klarite®, Renishaw diagnostics). High quality spectra were achieved within 10 s with a compact Raman spectrometer. Principal component analysis (PCA) of the data was performed to understand what factors affected the spectral variation across the samples. It was found that 76% of the spectral variation was explained by the first three PCs. Score plots for these components showed that the energetic materials can be clearly classified on the basis of SERS spectra also at trace level quantity. Our measurements further demonstrate the potential for using SERS as fast, in situ analytical tool for safety devices, with a sensitivity which competes and, in some cases, overcomes other techniques. Copyright © 2013 John Wiley & Sons, Ltd.     

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.