Use of Raman spectroscopy for analyzing edible vegetable oils

Vegetable oils provide high nutritional value in the human diet. Specifically, extra virgin olive oil is one of the main ingredients of the Mediterranean diet, which is among the healthiest of eating practices. This article reviews the use of Raman spectroscopy for analyzing edible vegetable oils including olive oil. Although the spectra for edible vegetable oils are similar, they exhibit some differences which, however small, enable their discrimination. Thus, Raman spectra allow one to determine the degree of unsaturation of oils. This property is correlated with the iodine value but much faster and simpler to obtain. The degree of unsaturation can be used to classify and authenticate oils, which is especially useful with high-quality oils. In fact, adulteration with mixtures of more inexpensive oils can be easily detected by Raman spectroscopy. This technique additionally allows some minor components present in unsaponifiable matter to be identified. Fats in general and vegetable oils in particular, are prone to oxidation. Thus, double bonds in them are oxidized to form triglycerides. Vegetable oils are widely used for frying and Raman spectroscopy allows for their oxidative stability against heating at the usual frying temperatures to be assessed.     

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.     

Quantitative analysis of essential oils from rosemary in virgin olive oil using Raman spectroscopy and chemometrics

In this work, virgin olive oil mixed with essential oils from rosemary has been analyzed by means of Raman spectroscopy. First of all, experimental design has been employed in order to define the Raman spectroscopy's parameters, final measuring conditions were: acquisition time of 30 s, five accumulations, and the intensity of the laser power at 75 mW. The Raman spectra were initially measured at full range (150–3000 cm⁻¹), but a narrower window assured faster accumulations and more accurate predictions. The calibration solutions of eucalyptol and camphor in olive oil were prepared following a central composite design and different spectra pre‐processing algorithms were evaluated. To conclude, essential oils obtained by means of Supercritical Fluid Extraction, Ultrasounds, and hydrodistillation were mixed with virgin olive oil and quantified with Raman spectroscopy. Predicted concentrations of the olive oil mixtures were compared with concentrations obtained for the same samples by a Comprehensive Two‐Dimensional Gas Chromatographic (GC × GC) method. Copyright © 2011 John Wiley & Sons, Ltd.     

Composition and authentication of virgin olive oil from French PDO regions by chemometric treatment of Raman spectra

The search for the origin and the authentication of virgin olive oils (VOOs) is a problem of topical interest. VOOs with the protected designation of origin (PDO) label can be subject to fraudulent practices owing to high price commanded by these oils. This work evaluates the capability of confocal Raman spectroscopy combined with chemometric treatments for determining the fatty acid and triacylglycerol (TAG) compositions of VOOs and for authenticating the PDO labels of six French VOOs. Fatty acid and TAG compositions were determined by chemometric analysis of the Raman spectra using the partial least square (PLS) algorithms. Predictions have provided good results on the high percentage compounds, but for the fatty acids at very low percentages the predicted results were very poor. The PLS‐discriminant analysis model on Raman spectra was able to correctly classify 92.3% of French PDOs and 100% of PDO samples made with only one principal cultivar. However, it was noticeable that the first regression coefficient (which explains the greatest amount of variation of the data) obtained for each PDO could be compared one by one with the first regression coefficient obtained for the quantitative analysis of fatty acids and TAGs. The first regression coefficients obtained for PDOs differed widely from each other. Their study allowed interpreting the PDOs according to their chemical composition. This work confirms that fatty acid and TAG percentages may be used to authenticate French VOOs into PDOs. Copyright © 2011 John Wiley & Sons, Ltd.     

基于拉曼光谱和最小二乘支持向量机的橄榄油掺伪检测方法研究

为实现橄榄油中掺伪油类型的识别和掺伪量预测,对掺入葵花籽油、大豆油、玉米油的橄榄油共117个样品进行拉曼光谱检测,并用基于多重迭代优化的最小二乘支持向量机模型对掺入油的类型进行识别,综合识别率为97%。同时分别采用最小二乘支持向量机、人工神经网络模型、偏最小二乘回归建立橄榄油中葵花籽油、大豆油、玉米油含量的拉曼光谱定标模型,结果显示最小二乘支持向量机具有最优的预测效果,其预测均方根误差(RMSEP)在0.007 4～0.014 2之间。拉曼光谱结合最小二乘支持向量机可为橄榄油掺伪检测提供一种精确、快速、简便、无损的方法。     

Optofluidic Raman sensor for simultaneous detection of the toxicity and quality of alcoholic beverages

We report a chemometric prediction of the toxicity and quality of liquor using an optofluidic sensor based upon Waveguide Confined Raman Spectroscopy (WCRS). The WCRS sensor was used to record the Raman spectra, each obtained from a 20 µl sample of a given alcoholic beverage with and acquisition time of 20 s. This was used to predict, simultaneously, both the methanol concentration (toxicity) and ethanol concentration (quality), with an accuracy of 0.1% and 0.7% by volume, respectively, using a Partial Least Squares‐based chemometric model. The model sensor is shown to be capable of identifying toxic liquors, based on the test performed on different types of liquor samples. Copyright © 2013 John Wiley & Sons, Ltd.     

Raman spectroscopy for identification and quantification analysis of essential oil varieties: a multivariate approach applied to lavender and lavandin essential oils

Lavender (Lavandula angustifolia) is used for cosmetics, perfumes and medicine (antimicrobial activity and relaxant properties) while lavandin (sterile hybrid of L. angustifolia P. Mill. × Lavandula latifolia (L.f.) Medikus) is used for air fresheners, deodorants and soaps. These plants are widely cultivated for essential oil production. In this study, 104 samples were analyzed including 62 lavandin and 42 lavender oil samples from several varieties. The Raman spectra are similar but can be differentiated by chemometrics treatment. Data structure may be studied by PCA. A PLS regression model was used for quantitative analysis of the main compounds such as linalyl acetate, linalool and eucalyptol. The reference data were obtained by gas chromatography. The performance of the method was also tested to discriminate between the two species and the seven varieties (Abrial, Fine, Grosso, Maillette, Matherone, Sumian and Super) by PLS‐DA regression. The examination of PLS and PLS‐DA regression coefficients allowed for the identification of species and of the varieties' metabolomic markers. Copyright © 2015 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。     

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.     

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.     

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.     

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.     

Multiclass discrimination of cervical precancers using Raman spectroscopy

Raman spectroscopy has the potential to differentiate among the various stages leading to high‐grade cervical cancer such as normal, squamous metaplasia, and low‐grade cancer. For Raman spectroscopy to successfully differentiate among the stages, an applicable statistical method must be developed. Algorithms like linear discriminant analysis (LDA) are incapable of differentiating among three or more types of tissues. We developed a novel statistical method combining the method of maximum representation and discrimination feature (MRDF) to extract diagnostic information with sparse multinomial logistic regression (SMLR) to classify spectra based on nonlinear features for multiclass analysis of Raman spectra. We found that high‐grade spectra classified correctly 95% of the time; low‐grade data classified correctly 74% of the time, improving sensitivity from 92 to 98% and specificity from 81 to 96% suggesting that MRDF with SMLR is a more appropriate technique for categorizing Raman spectra. SMLR also outputs a posterior probability to evaluate the algorithm's accuracy. This combined method holds promise to diagnose subtle changes leading to cervical cancer. Copyright © 2008 John Wiley & Sons, Ltd.     

Rapid prediction of fatty acid composition of vegetable oil by Raman spectroscopy coupled with least squares support vector machines

The fatty acid composition of vegetable oil plays a significant role in a nutrition‐balanced diet, which makes this industry more quality conscious. A set of store‐purchased vegetable oils and their binary mixtures were characterized by Raman spectra in a region of 800–2000 cm⁻¹. The obtained Raman spectral data were pretreated, and intensities of eight characteristic peaks were extracted as the eigenvalues of an entire spectrum. A prediction model of fatty acid content based on least squares support vector machines (LS‐SVM) were established for multivariate analysis between the Raman spectral eigenvalues and the fatty acid composition measured by gas chromatography (GC) method. The performance of the model was evaluated by comparing the predicted values to the reference values from GC analysis. The correlation coefficient for the prediction of oleic acid, linoleic acid and α‐linolenic acid was 0.9972, 0.9982 and 0.9854, respectively. Raman spectroscopy based on LS‐SVM can be a promising technique for predicting the fatty acid composition of vegetable oil with the advantages of being simple and time‐effective while not requiring any sample preprocessing. In particular, a portable Raman system is suitable for on‐site detection in practical applications. Copyright © 2013 John Wiley & Sons, Ltd.     

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.     

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.     

Vibrational spectroscopic approaches for the quality evaluation and authentication of virgin olive oil

Vibrational spectroscopy as an important branch of molecular spectroscopy has been extensively employed for the analysis of olive oil. To date, plenty of research articles have been published with regards to the analysis of olive oil, which is really a complex matrix, demonstrating the great potential of vibrational spectroscopic approaches in these studies. In this critical review, we cover latest progresses and applications of the three vibrational spectroscopies (mid-infrared, near-infrared, and Raman spectroscopy) used for the analysis of olive oil. In the first part of this review, we focus on the basic theoretical aspects related to vibrational spectroscopic techniques and chemometrics, while in the second part, we discuss their recent applications in qualitative evaluation and authentication of virgin olive oil.     

The use of infrared spectroscopy in combination with chemometrics for quality control and authentication of edible fats and oils: A review

Edible fats and oils provide a significant contribution in our diet and daily life, as cooking or frying oil, or as components used in food, pharmaceutical, and cosmetics products. Fats and oils are characterized by specific values, including acid value, saponification value, iodine value, and peroxide value, as well as the oxidation products which occur during storage due to oxidative and hydrolytic deterioration. Currently, due to the high price of edible fats and oils, some unethical producers adulterate high-value edible oils like olive oil with low-priced oils like palm and corn oils; therefore the authentication analysis of edible fats and oils must be assured by introducing reliable and fast methods like infrared spectroscopy. Fourier transform infrared (FTIR) spectroscopy is an ideal technique for monitoring the quality control of fats and oils due to its property as a “fingerprint spectra technique,” which allows analysts to differentiate among fats and oils. FTIR spectra signals of fats and oils are very complex. Fortunately, a statistical technique called chemometrics can be used to handle the complex FTIR spectral data. Chemometrics in combination with FTIR spectroscopy has been widely used in many aspects of monitoring quality control of edible fats and oils including their authenticity.     

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.     

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.