A novel method for quantification of ethanol and methanol in distilled alcoholic beverages using Raman spectroscopy

In this study, direct quantification of ethanol and methanol in distilled alcoholic beverages using Raman spectroscopy was performed. Raman spectra of varying ethanol–methanol mixtures were obtained, baseline corrections were made, and the data were normalized using Raman scattering intensity of an internal standard (acetonitrile, 921 cm^–1). Then, calibration graphs were produced for ethanol and methanol concentrations in the ranges of 0–7 M and 0–10 M, respectively. Accurate R² values of the calibration graphs proved the notable linear correlations (0.998 for ethanol and 0.998 for methanol). The method was validated based on linearity, sensitivity, intraday and interday repeatability, and recovery tests. The limit of detection and limit of quantification values of the validated method were determined for ethanol concentration as 1.2 and 3.7 mM, and for methanol concentration as 3.4 and 10.3 mM, respectively. The ability of the developed method to detect ethanol and methanol concentrations in real samples was also investigated. The results of the developed method were compared with the experimental results from traditional method and high correlation value (R² = 0.926) was obtained. Besides being sensitive and cheap, the developed method is rapid with the analysis time of less than 30 s. Furthermore, it eliminates labor‐consuming operations, chromatographic separation, and measurement error due to the high number of experiment steps in the standard method. Copyright © 2012 John Wiley & Sons, Ltd.     

Quantitative detection of adulterated olive oil by Raman spectroscopy and chemometrics

Commercially available extra virgin olive oils are often adulterated with some other cheaper edible oils with similar chemical compositions. A set of extra virgin olive oil samples adulterated with soybean oil, corn oil and sunflower seed oil were characterized by Raman spectra in the region 1000–1800 cm⁻¹. Based on the intensity of the Raman spectra with vibrational bands normalized by the band at 1441 cm⁻¹ (CH₂), external standard method (ESM) was employed for the quantitative analysis, which was compared with the results achieved by support vector machine (SVM) methods. By plotting the adulterant content of extra virgin olive oil versus its corresponding band intensity in the Raman spectrum at 1265 cm⁻¹, the calibration curve was obtained. Coefficient of determination (R²) of each curve was 0.9956, 0.9915 and 0.9905 for extra virgin olive oil samples adulterated with soybean oil, corn oil and sunflower seed oil, respectively. The mean absolute relative errors were calculated as 7.41, 7.78 and 9.45%, respectively, with ESM, while they were 5.10, 6.96 and 4.55, in the SVM model, respectively. The prediction accuracy shows that the ESM based on Raman spectroscopy is a promising technique for the authentication of extra virgin olive oil. The method also has the advantages of simplicity, time savings and non‐requirement of sample preprocessing; especially, a portable Raman system is suitable for on‐site testing and quality control in field applications. Copyright © 2011 John Wiley & Sons, Ltd.     

Visible Raman spectroscopy for the discrimination of olive oils from different vegetable oils and the detection of adulteration

We have investigated the potential of Raman spectroscopy with excitation in the visible spectral range (VIS Raman) as a tool for the classification of different vegetable oils and the quantification of adulteration of virgin olive oil as an example. For the classification, principal component analysis (PCA) was applied, where 96% of the spectral variation was characterized by the first two components. A significant similarity between sunflower oil and extra‐virgin olive oil was found using this approach. Therefore, sunflower oil is a potential candidate for adulteration in most commercially available olive oils. Beside the classification of the different vegetable oils, we have successfully applied Raman spectroscopy in combination with partial least‐squares (PLS) regression analysis for very fast monitoring of adulteration of extra‐virgin olive oil with sunflower oil. Different mixtures of extra‐virgin olive oil with three different sunflower oil types were prepared between 5 and 100% (v/v) in 5% increments of sunflower oil. While in the present context the adulteration usually refers to the addition of reasonable amounts of the adulterant (given the similarity with the basic product), we show that the technique proposed can also be used for trace analysis of the adulterant. Without using techniques like surface‐enhanced Raman scattering (SERS), a quantitative detection limit down to 500 ppm (0.05%) could be achieved, a limit irrelevant for adulteration in commercial terms but significant for trace analysis. The qualitative detection limit even was at considerably lower concentration values. Based on PCA, a clear discrimination between pure extra‐virgin olive oil and olive oil adulterated with sunflower oil was achieved. The adulterant content was successfully determined using PLS regression with a high correlation coefficient and small root mean‐square error for both prediction and validation. Copyright © 2009 John Wiley & Sons, Ltd.     

Identification and classification of organic and inorganic components of particulate matter via Raman spectroscopy and chemometric approaches

In this paper, a novel method for developing a tree‐like classifier which differentiates between organic and inorganic particulate matter by means of Raman spectroscopy is introduced. The algorithm is fully automatic and optimises itself without any human interaction. This method uses a tree‐like structure to classify Raman spectra as a decision tree. On every knot of this tree, the optimal classifier is automatically obtained, tested and trained. The optimal classifier is an artificial neural network, linear discriminant analysis or a support vector machine, where different kernels are possible. The support vector machine is optimised by the simulated annealing method to achieve the best possible classifier. After the training, a hold‐out experiment with two completely independent sets of Raman spectra was tried to show the abilities of this method for real‐world application. Copyright © 2010 John Wiley & Sons, Ltd.     

Applications of Raman spectroscopy in detection of water quality

Water pollution is hazardous to the health of humans and other organisms, and detection of pollutants in aquatic environments is of primary importance for water quality monitoring. Raman spectroscopy offers an effective tool for qualitative analysis and quantitative detection of contaminants in a water environment. This article focuses on applications of Raman spectroscopy for detection of water quality. In this article, various Raman spectroscopy techniques employed for water quality detection are presented based on the types of pollutants: organics, inorganics, and biological contaminants. Additionally, the relevant detection parameters are reviewed, such as detection materials, limit of detection, detection range, peak positions, and selectivity. Furthermore, the advantages and limitations of various Raman spectroscopy techniques are summarized. Finally, the future development of Raman spectroscopy for detection of water quality is discussed.     

Screening of textiles for contraband drugs using portable Raman spectroscopy and chemometrics

The impregnation of items of clothing with drugs of abuse that are then smuggled through airports and ports of entry is a growing problem for law enforcement. This work describes the application of portable Raman spectroscopic techniques for the analysis of a range of natural and artificial fibre items of clothing impregnated with drugs of abuse. Textile pieces were soaked with the solutions of the drugs then left overnight to dry prior to spectroscopic examination. The feasibility of detection of the characteristic Raman spectral bands in the presence of background matrix signals is demonstrated, even for dyed clothing. Definitive evidence for contamination of the items of clothing concerned can be acquired within 20–25 s, without any form of sample pre‐treatment or extraction being necessary. The feasibility of automatic spectral recognition of such illicit materials by Raman spectroscopy has been investigated by searching a database stored on the spectrometer computer and the use of principal component analysis. Copyright © 2014 John Wiley & Sons, Ltd.     

Experimental and statistical analysis methods for peptide detection using surface‐enhanced Raman spectroscopy

Surface‐enhanced Raman spectroscopy (SERS) has the potential to make a significant impact in biology research due to its ability to provide information orthogonal to that obtained by traditional techniques such as mass spectrometry (MS). While SERS has been well studied for its use in chemical applications, detailed investigations with biological molecules are less common. In addition, a clear understanding of how methodology and molecular characteristics impact the intensity, the number of peaks, and the signal‐to‐noise of SERS spectra is largely missing. By varying the concentration and order of addition of the SERS‐enhancer salt (LiCl) with colloidal silver, we were able to evaluate the impact of these variables on peptide spectra using a quantitative measure of spectra quality based on the number of peaks and peak intensity. The LiCl concentration and order of addition that produced the best SERS spectra were applied to a panel of synthetic peptides with a range of charges and isoelectric points (pIs) where the pI was directly correlated with higher spectral quality. Those peptides with moderate to high pIs and spectra quality scores were differentiated from each other using the improved method and a hierarchical clustering algorithm. In addition, the same method and algorithm was applied to a set of highly similar phosphorylated peptides, and it was possible to successfully classify the majority of peptides on the basis of species‐specific peak differences. Copyright © 2008 John Wiley & Sons, Ltd.     

用于Raman光谱与微纳米结构同步检测的Raman-AFM系统研究

研究和发展了一种将微区拉曼(Raman)光谱检测与原子力显微镜(AFM)微纳米扫描成像相结合的新型Raman-AFM技术。设计了Raman光谱与AFM扫描成像的原位检测探头;研制出相应的Raman-AFM系统;利用该系统,对ZnO纳米颗粒和TiO2纳米薄膜开展了微区Raman光谱与微纳米结构的检测实验。研究表明,所获得的Raman光谱检测结果与理论值良好吻合,同时,AFM扫描检测得到的图像很好地表征了样品的微纳米结构,从而实现了微区Raman光谱与AFM图像的原位及同步检测,验证了这一技术的可行性,为Raman光谱技术与微纳米技术领域的实际应用提供了技术基础。     

Using Raman spectroscopy as a tool for the detection of iron in glass

Raman spectroscopy has been used to identify iron‐containing glasses. This nondestructive technique offers a fast method to obtain qualitative information about the presence of iron oxides in glass. The effect of the iron content in glass samples is reflected on the topology of the Raman spectra: A strong link between the ratio of the Q₂/Q₃ vibration units of the silica tetrahedral structure is seen. If matrix effects are taken into account, also (semi)quantitative results can be obtained from the calibration lines. The linear calibration is based on the normalized band intensity at 980 cm⁻¹ (I₉₈₀/I₁₀₉₀) and the iron oxide concentration for similar glasses. In amber and dark colored glasses, an extra peak in the spectrum indicates the presence of a FeS chromophore. Different series of glasses of various origins (ancient and modern/industrial glass) have been considered. Copyright © 2011 John Wiley & Sons, Ltd.     

激光拉曼光谱对苯的低浓度探测研究

激光拉曼光谱技术是基于拉曼散射理论的检测技术,具有快速、无损、样品无需预处理等优点。运用激光拉曼光谱技术对苯的25种不同浓度的样品进行了研究,结果表明,在184.8g/L~0.264g/L浓度范围内,苯的振动拉曼光谱强度与其浓度呈线性关系,利用最小二乘法拟合得到线性相关系数R=0.99626,检出限为0.223g/L。     

Experimental study of the Raman strain rosette based on the carbon nanotube strain sensor

This work presents a new technique named Raman strain rosette for the micro‐strain measurement of both Raman active and Raman inactive materials. The technique is based on the theoretical model of the carbon nanotube (CNT) strain sensor that applies the resonance and polarization Raman properties of CNTs and calculates the synthetic contributions of uniformly dispersed CNTs to the entire Raman spectrum. In our work, the proposed technique is applied in different experiments on the Raman inactive materials, such as step‐by‐step uniaxial tensile and Raman mapping around a circular hole. The experimental results reached by the Raman strain rosette are consistent with the actual values as a whole. This study verifies that the Raman strain rosette is applicable to quantitative measurement of all the in‐plane components of the strain tensor (including both normal and shear strains) by three polarized Raman detections for each sampling spot on a microscale. The technique is further applicable to achieving the strain fields through Raman mapping. Copyright © 2010 John Wiley & Sons, Ltd.     

Depth profiling of porcine adipose tissue by Raman spectroscopy

Raman spectroscopy was applied on a depth profile of porcine adipose tissue (from skin to meat) with the purpose of (1) discriminating between fat layers and (2) estimating the variation in fatty acid composition as a function of fat depth and fat layer: total degree of unsaturation (iodine value), fractions of saturated, and monounsaturated and polyunsaturated fatty acids. The thickness and composition of the outer layer of porcine adipose tissue influences the final quality of backfat. A too‐thick outer layer is associated with problems such as oily appearance, rancidity development, and difficulties in separating muscle and adipose tissue when cutting. From principal component analysis on standard normal variate preprocessed Raman spectra (1800–800 cm^–1), it was possible to discriminate between the outer and the inner backfat layer. Principal component analysis loadings showed that the separation of layer was mainly explained by variation in the bands originating from vibration of double bond C = C stretching plus = C–H twisting and rocking. In the prediction of iodine value a three‐component partial least squares regression model based on full range Raman spectra showed a root mean square error of cross validation of 2.00 and R² = 0.69. Applying Cauchy–Lorentz band fitting proved that information regarding fat unsaturation was found not only in band intensity, but also in band parameters such as location and width. The results suggest Raman spectroscopy as a potential measurement technique for rapid grading of pork carcasses. Copyright © 2011 John Wiley & Sons, Ltd.     

微过氧化酶的共振拉曼和表面增强拉曼光谱研究

采用电化学、共振拉曼和表面增强共振拉曼光谱研究了溶液态和吸附态微过氧化酶－１１（ＭＰ－１１）的电子传递及溶液和界面结构特征。结果表明,ＭＰ－１１在溶液中中心血红素铁以六配位状态存在,吸附于粗糙银电极表面时,则因ＭＰ－１１分子的重新取向而部分转化为五配位状态,氧化还原反应进一步促进这种配位状态的转化,并且配位状态也直接影响ＭＰ－１１的氧化还原性质。     

Rapid detection of gasoline by a portable Raman spectrometer and chemometrics

Identification of the gasoline purity is important for quality control and detection of gasoline adulteration. Principal component analysis and Raman spectroscopy were used to authenticate gasoline adulterated with methyl tert‐butyl ether (MTBE) and benzene. Gasoline could be clearly distinguished from gasoline adulterated with MTBE and benzene by a plot of the first principal component (x‐axis) against the second principal component (y‐axis). And the radial basis function neural network was used for quantitative prediction of the volume percentages of MTBE and benzene in gasoline based on Raman Spectra. The correlation coefficient (r) and mean absolute percentage error between predictive values and spiked values were 0.9907 and 0.9934 and 15.73 and 8.19%, respectively. Moreover, the Raman spectra of the samples were obtained with a portable Raman spectrometer. Therefore, the method is simple, effective, fast, does not require sample pre‐processing, and is promising for rapid gasoline detection. Copyright © 2012 John Wiley & Sons, Ltd.     

An IH-QCL based gas sensor for simultaneous detection of methane and acetylene

Extended wavelength tuning of an IH-QCL (integrated heater quantum cascade laser) is exploited for simultaneous detection of methane and acetylene using direct absorption spectroscopy. The integrated heater, placed within few microns of the laser active region, enables wider wavelength tuning than would be possible with a conventional DFB (distributed feedback) QCL. In this work, the laser current and heater resistor current are modulated simultaneously at 25?kHz to tune the laser over 1279.6–1280.1 cm^?1, covering absorption transitions of methane and acetylene. The laser is characterized extensively to understand the dependence of wavelength tuning on modulation frequency, modulation amplitude and phase difference between laser/heater modulation. Thereafter, the designed sensor is validated in both room-temperature static cell experiments and non-reactive high-temperature-measurements in methane-acetylene-argon gas mixtures in the shock tube. Finally, the sensor is applied for simultaneous detection of methane and acetylene during the high-temperature pyrolysis of iso-octane behind reflected shock waves.     

Surface‐enhanced Raman spectroscopy for trace‐level detection of explosives

The detection of explosives and their associated compounds for security screening is an active area of research and a wide variety of detection methods are involved in this very challenging area. Surface‐enhanced Raman scattering (SERS) spectroscopy is one of the most sensitive tools for the detection of molecules adsorbed on nano‐scale roughened metal surface. Moreover, SERS combines high sensitivity with the observation of vibrational spectra of species, giving complete information on the molecular structure of material under study. In this paper, SERS was applied to the detection of very small quantities of explosives adsorbed on industrially made substrates. The spectra were acquired with a compact Raman spectrometer. Usually, a high signal‐to‐noise (S/N) spectrum, suitable for identification of explosive molecules down to few hundreds of picograms, was achieved within 30 s. Our measurements suggest that it is possible to exploit SERS using a practical detection instrument for routine analysis. Copyright © 2010 John Wiley & Sons, Ltd.     

Raman and surface-enhanced Raman spectroscopy evidence for oxidation-induced decomposition of graphite

It has been proposed that reduction of exfoliated graphite oxide could be a potential method for producing large quantities of graphene. Raman and surface-enhanced Raman spectroscopy are used to show that oxidation of graphite and exfoliated graphite significantly increases the defect structure of both materials. This would likely lead to a heavily defected graphene structure when oxygen is removed. To insure the observed decomposition is not due to the laser light, the effect of laser intensity on the materials was investigated. It was found that at the highest laser intensity (1.4 × 10⁸ W/M²) there was a significant increase in defects. However, lower laser intensity was found which did not produce defects and was used in the studies of the effect of oxidation on the spectra.     

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.     

Quantitative Analysis of Simulated Illicit Street-Drug Samples Using Raman Spectroscopy and Partial Least Squares Regression

Modern drug laws require that a seized sample be characterized for both the illegal substances present and the quantity of each of those substances. The goal of this work was to develop a common approach to model development based on Raman spectroscopic analysis followed by partial least squares (PLS) regression that would allow us to obtain quantitative information from simulated street-drug samples. Each drug sample contained one drug surrogate—either isoxsuprine, norephedrine, benzocaine, or lidocaine—and up to 3 different cutting agents. All spectra were acquired on a homebuilt Raman instrument equipped with a rotating sample holder. The same steps were employed for developing separate models for each drug surrogate, including spectral preprocessing by Savitsky-Golay smoothing, differentiation, mean-centering, and autoscaling. PLS models were developed using 2 latent variables that yielded root mean square errors of calibration (RMSEC) values in the 3% range and root mean square error of prediction (RMSEP) values in the 4% range.     

Information‐theoretic chemometric analyses of Raman data for chemical reaction studies

A methodology of multivariate chemometric techniques based on the information‐theoretic approach was applied for elucidating chemical reaction information from a Raman data array R _m×ν that arises from in situ reaction monitoring. This reaction‐induced dynamic dataset R _m×ν can be contaminated by random cosmic ray spikes found in the midst of characteristic spectral variations associated with the disappearance or emergence of Raman active reactants, intermediates and products. Such spurious cosmic spikes were identified and removed using a novel and fast numerical approach based on maximum and minimum spectral entropy principles while preserving the genuine reaction‐induced spectral variations. Subsequently, the band‐target entropy minimization (BTEM) algorithm, a minimum spectral entropy based self‐modeling curve resolution technique, was applied to recover the pure component spectra of Raman active chemical species. Information gain through the chemometric analyses was calculated using information entropies with base 2 logarithm. This sequence of information‐theoretic chemometric analyses (or transinformations) was successfully tested on the reaction spectral data obtained from alcoholysis of acetic anhydride, which contains four Raman active chemical species. It is envisioned that this series of multivariate statistical analyses will be useful in chemical reaction studies and process analytical technology (PAT) applications that utilize in situ Raman spectroscopy to monitor transient dynamic changes in chemical concentrations, and also in Raman microscopy/imaging data containing spatial variations. Copyright © 2010 John Wiley & Sons, Ltd.