Detection of adulteration of sesame and peanut oils via volatiles by GC × GC–TOF/MS coupled with principal components analysis and cluster analysis

The method of headspace coupled with comprehensive two‐dimensional GC–time‐of‐flight MS (HS‐GC × GC–TOF/MS) was applied to differentiate the volatile flavor compounds of three types of pure vegetable oils (sesame oils, peanut oils, and soybean oils) and two types of adulterated oils (sesame oils and peanut oils adulterated with soybean oils). Thirty common volatiles, 14 particular flavors and two particular flavors were identified from the three types of pure oils, from the sesame oils, and from the soybean oils, respectively. Thirty‐one potential markers (variables), which are crucial to the forming of different vegetable oil flavors, were selected from volatiles in different pure and adulterated oils, and they were analyzed using the principal component analysis (PCA) and cluster analysis (CA) approaches. The samples of three types of pure vegetable oil were completely classified using the PCA and CA. In addition, minimum adulteration levels of 5 and 10% can be differentiated in the adulteration of peanut oils and sesame oils with soybean oils, respectively. Practical applications: The objective was to develop one kind of potential differentiated method to distinguish high cost vegetable oils from lower grade and cheaper oils of poorer quality such as soybean oils. The test result in this article is satisfactory in discriminating adulterated oils from pure vegetable oils, and the test method is proved to be effective in analyzing different compounds. Furthermore, the method can also be used to detect other adulterants such as hazelnut oil and rapeseed oil. The method is an important technical support for public health against profit‐driven illegal activities.     

Detection of adulteration of olive oil with seed oils by a combination of column and gas liquid chromatography

Samples of virgin olive oil and refined seed oils, as well as mixtures of olive oil with 10 and 5% seed oils were fractionated by column chromatography on silicic acid impregnated with ammoniacal silver nitrate. It was possible to isolate a characteristic fraction enriched in polyunsaturated triglycerides. Its linoleic acid content in pure olive oil never exceeds 9.3%, whereas in pure seed oils, it varies between 38.1 and 70.1%; in mixtures of olive oil with 10 and 5% of seed oils, the respective values are 22.3–38.2% and 15.6–32.1%. The oleic-to-linoleic acid ratios of the same fraction are more than 7.6 (olive oil), 0.2–0.8 (seed oils), 1.1–2.0 (olive oil with 10% seed oils) and 1.4–3.6 (olive oil with 5% seed oils). These analytical values may be used as a safe criterion for the eventual adulteration of olive oil with seed oils. This work was taken in part from the doctoral dissertation of S. Passaloglou-Emmanouilidou.     

Detection of adulteration in camellia seed oil and sesame oil using an electronic nose

An electronic nose was used for the detection of maize oil adulteration in camellia seed oil and sesame oil. The results of multivariate analysis of variance showed that the sensor signals of different kinds of oil are significantly different from each other. Principal component analysis (PCA) cannot be used to discriminate the adulteration of camellia seed oil, but can be used in the discrimination of adulteration in sesame oil. Linear discriminant analysis (LDA) is more effective than PCA and can be used in adulteration discrimination for both camellia seed oil and sesame oil. In order to check the discriminative power of LDA, canonical discriminant analysis was performed as well. Acceptable results were also obtained: The accuracy of prediction was 83.6% for camellia seed oil and 94.5% for sesame oil. The artificial neural network (ANN) model was used to detect the percentage of adulteration in camellia seed oil and sesame oil. The results showed that, based on ANN as its pattern recognition technique, the electronic nose cannot predict the percentage of adulteration in camellia seed oil, but can be used in the quantitative determination of adulteration in sesame oil.     

Measurement of adulteration of olive oils by near-infrared spectroscopy

Authentication of olive oils is of great importance, not only because they command a high price but also because of the health implications of adulteration with seed oils. A method for predicting the level of adulteration in a set of virgin and extra-virgin olive oils adulterated with corn oil, sunflower oil, and raw olive residue oil by near-infrared spectroscopy is presented. The best result was a correct prediction for 98% of the samples. Principal component analysis was used to predict the type of adulterant. The best result was a 75% prediction rate. From these results, it is concluded that it is possible to design a quality control system, which uses near-infrared technology to measure the level of adulteration. In the case where the only test is whether the sample is adulterated or not, a simple calibration for adulteration can be used. The results suggest that principal component analysis may offer a means of identifying the adulterant, although more work is required to give an acceptable level of accuracy.     

Detection of adulteration of cottonseed oil by gas chromatography

To detect adulterant vegetable oils in cottonseed oil, soybean, rapeseed, and ricebran oils were mixed into cottonseed oil extracted experimentally from seeds. These adulterated oils and the component oils were analyzed for sterols, fatty acids, and triglycerides by gas chromatography. In sterol analysis, stigmasterol was determined for adulteration with soybean and ricebran oils. Brassicasterol content seemed to be reliable as the indicator of adulteration for rapeseed oil. In fatty acid analysis, erucic acid for rapeseed oil and linolenic acid for soybean and ricebran oils were proof of adulteration. Triglyceride analysis was not so reliable as sterol analysis for detecting contamination, except that triglycerides with carbon-58, 60, and 62 indicate adulteration with rapeseed oil. Rapeseed oil (5%) and soybean and ricebran oils (10%) were the limits of detection for adulteration in cottonseed oil. Analysis of cottonseed oil from six refineries did not show positive indications of adulteration.     

Identification of adulterants in olive oils

The application of discriminant analysis for identifying and quantifying adulterants in extra virgin olive oils is presented. Three adulterants were used (sunflower oil, rapessed oil, and soybean oil) and were present in the range 5–95%. Near-infrared spectroscopy and principal components analysis were used to develop a discriminant analysis equation that could identify correctly the type of seed oil present in extra virgin olive oil in 90% of cases. Partial least squares analysis was used to develop a calibration equation that could predict the level of adulteration. Cross validation suggested that it was possible to measure the level of adulteration to an accuracy of ±0.9%. External validation of the derived calibation equation gave a standard error of performance of ±2.77%.     

Multivariate statistical analysis of diamondoid and biomarker data from Brazilian basin oil samples

Diamondoids are compounds commonly found in oils. Highly stable, they are more resistant to thermal and biological destruction than other hydrocarbons. Diamondoid-derived parameters have been used to assess the degree of thermal evolution and the extent of secondary cracking of light oils and condensates and in the identification of mixtures of oils from distinct migration pulses. Twenty-one samples of oils from five sedimentary basins of Brazilian continental margin were selected and studied on diamondoids and biomarkers. The oil samples were analyzed by gas chromatography coupled to mass spectrometry and principal components analysis (PCA) aiming to correlate biomarker and diamondoid parameters. Diamondoid and biomarker ratios analyzed by PCA led to a more accurate assessment of the maturity of the oils. The combination of diamondoid data with biomarker parameters also allowed to rank the oils from one of the five basins (JX basin) as the most mature oils, while oils from another basin (JE basin) were classified as mixtures formed from different pulses of migration.     

Discrimination of base oils and semi-products using principal component analysis and self organizing maps

¹H NMR spectroscopy, combined with pattern recognition techniques (PCA and SOM) was used in discriminating base oils and refinery-intermediate products. Both PCA and SOM enabled correct oil discrimination into groups in accordance with API 1509. Moreover, information about the structural compositions of the samples, correlated with their physical and chemical properties, was provided. The PCA score plot enabled component identification and semiquantitative analysis of binary mixtures of base oils, including semisynthetic oils.     

Detection of olive oil adulteration with canola oil from triacylglycerol analysis by reversed-phase high-performance liquid chromatography

The objective of this study was to explore the use of reversed-phase high-performance liquid chromatography (RP-HPLC) as a means to detect adulteration of olive oil with less expensive canola oil. Previously this method has been shown to be useful in the detection of some other added seed oils; however, the detection of adulteration with canola oil might be more difficult due to similarities in fatty acid composition between canola oil and olive oil. Various mixtures of canola oil with olive oils were prepared, and RP-HPLC profiles were obtained. Adulteration of olive oil samples with less than 7.5% (w/w) canola oil could not be detected.     

Characterization of paraffin oils and petrolatum using LDI‐TOF MS and principle component analysis

Laser desorption/ionization time‐of‐flight mass spectrometry (LDI‐TOF MS) followed by evaluation of the mass spectra with principal component analysis (PCA) was used for the in‐depth characterization of paraffin oils (mineral oils) and petrolatum (paraffin jelly) samples. These raw materials are liquid and semisolid mixtures of hydrocarbons obtained from petroleum. Mass spectrometric analysis was done using a solvent‐free sample preparation with silver trifluoroacetate. The analysis was carried out on a commercially available matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometer. Mass spectra were evaluated using parameters calculated from the areas of different alkane and cycloalkane species and by PCA. The ratios between specific peak areas were chosen as PCA‐input data instead of the less reproducible absolute peak areas. The principal components enable comparison of a large number of samples and can also be used for visualization of data. In this work, it is clearly demonstrated that combining LDI‐TOF MS and PCA provides a fast and efficient tool for the characterization of paraffin oils and petrolatum. Petrolatum and four different kinds of paraffin oil were analyzed and the results compared with other analytical methods.     

Evaluation of MALDI-TOF/MS Technology in Olive Oil Adulteration

Adulteration of extra virgin olive oil (EVOO) by addition of other vegetable oils or lower-grade olive oils is a common problem of the oil market worldwide. Therefore, we developed a fast protocol for detection of EVOO adulteration by mass spectrometry fingerprinting of triacylglycerol (TAG) profiles based on MALDI-TOF/MS. For that purpose, EVOO TAG profiles were compared with those of edible sunflower oil and olive oil composed of refined olive oil and virgin olive oils. Adulteration of EVOO was simulated by addition of sunflower and mixture of refined olive oil and virgin olive oils at 1, 10 and 20% w/w. Results of mass spectrometry TAG profiling were compared with routinely assessed K values for identification of adulteration. MALDI-TOF/MS technology coupled with statistical analysis was proven as useful for detection of adulteration in EVOO at a rate down to 1%. In contrast, standard spectrophotometric methods failed to identify minor adulterations. In addition, the ability of MALDI-TOF/MS in detection of adulteration was tested on EVOO samples from different geographical regions. Results demonstrated that MALDI-TOF/MS technology coupled with statistical analysis is able to distinguish adulterated oils from other EVOO.     

Use of the SAW Sensor Electronic Nose for Detecting the Adulteration of Virgin Coconut Oil with RBD Palm Kernel Olein

An electronic nose (zNose™) was applied to the detection of adulteration of virgin coconut oil. The system, which is based on a surface acoustic wave sensor was used to generate a pattern of volatile compounds present in the samples. Virgin coconut oil was mixed with refined, bleached and deodorized palm kernel olein at a level of adulteration from 1 to 20% (wt/wt). Adulterant peaks were identified from the chromatogram profile and fitted to a curve using linear regression. The best relationship (R ² = 0.91) was obtained between the peak tentatively identified as methyl dodecanoate and the percentage of palm kernel olein added. Pearson’s correlation coefficients (r) of 0.92 and 0.89 were obtained between adulterant peak methyl dodecanoate and of the iodine and peroxide values, respectively. Principal component analysis (PCA) was used to differentiate between pure and adulterated samples. The PCA provided good differentiation of samples with 74% of the variation accounted for by PC 1 and 17% accounted for by PC 2. Pure samples formed a separate cluster from all of the adulterated samples.     

Detection of adulteration of olive oil by argentation thin layer chromatography

Minute amounts of adulterant seed oils in olive oil can be detected by GLC analysis of fatty acids of the polyunsaturated triglyceride fraction, obtained by TLC on silver nitrate impregnated silica gel. Every possible effort was made to avoid any critical or time consuming manipulation in this method in order to develop it as a routine testing procedure. A complete analysis is possible in less than 2 hr and the detection of as low as 2% adulteration by other seed oils is accurate and reliable.     

A New Strategy for Quantitative Proportions in Complex Systems of Blended Oils by Triacyglycerols and Chemometrics Tools

Many oils sold in China and India are a blend of various oils to improve performance, stability, and nutritional characteristics, which are required in their respective markets. Quantitative analysis of the proportions of constitutive components is fundamental to the conformity and adulteration checking of edible blended oil products. A multi linear regression model with constrained linear least squares and exhaustion calculation was applied in this study. The source of the varieties in the model is a database (614 pure oils) of triacylglycerols (TAGs) collected by GC–FID and HPLC–RID. There were 20 groups of binary and ternary blended oils consisting of two or three oils out of five kinds, namely soybean, corn, peanut, rapeseed, and sunflower, which were analyzed and processed separately. Results showed that the method was able to predict the proportions of constitutive components in the edible blended oils, given that relative errors required less than 20%, the accuracy was 98.2% for the binary system if the proportion of each oil in blended oils was more than 20%, while the accuracy was 84.7% for the ternary system if the proportion of each oil in blended oils was more than 10%. The quantitative method is based on a simple analysis to determine the TAGs composition and thus it is useful for quick segregation and quality control of blended oils in routine analysis.     

Authentication of Edible Vegetable Oil and Refined Recycled Cooking Oil Using a Micro-UV Spectrophotometer Based on Chemometrics

We have developed a new method to make a clear distinction between edible oil and refined recycled cooking oil by using a micro-ultraviolet spectrophotometer and analysis of spectra full data combined with principal component analysis (PCA) and cluster discriminant analysis (CDA). This method shows an excellent capability of distinguishing edible oil from refined recycled cooking oil, which is difficult to accomplish by previous methods using physical and chemical properties. Edible oils and refined recycled cooking oils have the different positions on the resulting plot of PCA and CDA. The oil samples are respectively concentrated relatively distribution and distinct from other kinds, with a certain amount of edge intersection between samples. But the edible oil and refined recycled cooking oil samples have a clear divisional interface. By the increase in sampling and the improvement of modeling, the edge intersection or overlap of commonality between samples can be reduced. Using this method, it is possible to determine qualitatively the identity of unknown samples.     

Discrimination of n-3 Rich Oils by Gas Chromatography

Exploring the capabilities of instrumental techniques for discriminating n-3 rich oils derived from animals is a very important though much neglected area that was emphasized more than 100 years ago. In this study the potential of gas chromatography (GC) for discriminating full fatty acid methyl ester (FAME) profiles from fish (cod liver and salmon) and marine mammal (seal and whale) oils is evaluated by means of principal component analysis (PCA). The FAME profiles from plant oils such as rapeseed, linseed and soy oils and seven different brands of n-3 supplements are also used in the discrimination process. The results from the PCA plots can reliably distinguish between plant, n-3 supplements, fish and marine mammal oils. By removing the contribution of the n-3 supplements and plant oils it is possible to discriminate between types of fish and marine animal oils. GC offers a rapid, simple and convenient means of discriminating oils from different species, brands and grades.     

Use of high-resolution 13C nuclear magnetic resonance spectroscopy for the screening of virgin olive oils

¹³C Nuclear magnetic resonance (NMR) spectra of 104 oil samples were obtained and analyzed in order to study the use of this technique for routine screening of virgin olive oils. The oils studied included the following: virgin olive oils from different cultivars and regions of Europe and north Africa, and refined olive, “lampante” olive, refined olive pomace, high-oleic sunflower, hazelnut, sunflower, corn, soybean, rapeseed, grapeseed, and peanut oils, as well as mixtures of virgin olive oils from different geographical origins and mixtures of 5–50% hazelnut oil in virgin olive oil. The analysis of the spectra allowed us to distinguish among virgin olive oils, oils with a high content of oleic acid, and oils with a high content of linoleic acid, by using stepwise discriminant analysis. This parametric method gave 97.1% correct validated classifications for the oils. In addition, it classified correctly all the hazelnut oil samples and the mixtures of hazelnut oil in virgin olive oil assayed. All of these results suggested that ¹³C NMR may be used satisfactorily for discriminating some specific groups of oils, but to obtain 100% correct classifications for the different oils and mixtures, more information than that obtained from the direct spectra of the oils is needed.     

Triacylglycerol Composition Profiling and Comparison of High‐Oleic and Normal Peanut Oils

Oilseed plants produce huge amounts of fatty acids (FA) stored as triacylglycerols (TAG) in seeds that give a great variation in their composition. The variety and content of TAG directly affect the nutrition and function of lipids. TAG composition of 12 high‐oleic and normal peanut oil samples were profiled by two‐dimensional liquid chromatography (2D LC) coupled with atmospheric pressure chemical ionization mass spectrometry (APCI‐MS). The statistical evaluation of the TAG profiles determined was conducted on the basis of multidimensional data matrix using Principal Component Analysis (PCA). The technique enabled the differentiation of high‐oleic oils from normal peanut oils—as results illustrated TAG of high‐oleic peanut oil were clearly different from those of normal peanut oils. High‐oleic and normal peanut oils had different profiles mainly in the contents of OOO, OPO and POL. This finding provided theoretical foundation for detecting the adulteration of edible oils and analyzing the nutrition and function of high‐oleic peanut oils.     

Rapid Detection and Quantification by GC–MS of Camellia Seed Oil Adulterated with Soybean Oil

Camellia seed oil with high nutritional value is widely used in southern China and southeastern Asia for cooking. Due to the high price of camellia seed oil, fraudulent traders blended the oil with inexpensive oils to increase profits. In this paper, a new method was introduced to detect the adulteration of camellia seed oil with soybean oil by GC–MS with consideration of a parameter which was defined by the total content of oleic and linoleic acid, the oleic to linoleic acid ratio and the content of linolenic acid. Oils samples were prepared by blending pure camellia seed oil with pure soybean oil at levels from 1 to 50 %. Fatty acids esterified by TMSH and TBME in seconds were separated and identified by GC–MS. The detection limit of adulteration was as low as 5 %, and even much lower than 5 % for most kinds of camellia seed oil, which was lower than those measured by other methods. All the results indicated that this simple, accurate and rapid method can also be recommended for the authentication of olive oil with some modification.     

Discrimination of Cod Liver Oil According to Wild/Farmed and Geographical Origins by GC and <Superscript>13</Superscript>C NMR

The objective of this study was to test the possibility of using lipid profiles obtained by gas chromatography (GC) and ¹³C nuclear magnetic resonance (NMR) in authentication of cod liver oils according to wild/farmed and geographical origin. GC and ¹³C-NMR data of cod liver oil from wild and farmed fish from different locations in Norway and Scotland were obtained, and analyzed by principal component analysis (PCA) and linear discriminant analysis (LDA) to test if it was possible to differentiate oil from wild and cultured cod (Gadus morhua L.), and to further elucidate differences between fish from the different farms/catch area. Cod liver oils of wild and farmed origin were clearly separated in the PCA score plot both from GC and NMR data. From NMR data it was also possible to observe groupings based on geographical origin (farm/catch area) of the different samples. Using LDA with cross validation the wild/farmed classification rates were 97% for GC data and 100% for NMR data. In the classification of cod liver oils according to geographical origin (38 samples from six different farms/catch area), the correct classification rate was 63% for GC data and 95% for NMR data.