Fast quantitative detection of sesame oil adulteration by near-infrared spectroscopy and chemometric models

Adulteration of foods has been known to exist for a long time and various analytical tests have been reported to address this problem. Among them, authenticity of sesame oil has attracted much attention. Near-infrared (NIR) spectral quantitative detection models of sesame oil adulterated with other oils are constructed by chemometric methods, i.e., competitive adaptive reweighted sampling (CARS), elastic component regression (ECR) and partial least squares (PLS). Sixty samples adulterated with different proportions of five kinds of other oils of lower price were scanned by a Fourier-transform-NIR spectrometer and the NIR spectra were collected in 4500–10000 cm⁻¹ region by transmission mode. All samples were divided into the training set and an independent test set. Model population analysis has also been carried out and confirms the importance of selecting representative samples. The experimental results indicate that the PLS model using only 10 variables from CARS and the ECR model show similar performance and both are superior to the full-spectrum PLS model. CARS focuses on selecting variables and ECR focuses on optimizing the parameters, implying that both roads lead to the same destination. It seems that NIR technique combined with CARS or ECR is feasible for rapidly detecting sesame oil adulterated with other vegetable oils.     

Screening of adulteration in packaging biocomposites by infrared spectroscopy and chemometrics

Fourier transform infrared spectroscopy coupled with chemometrics was employed to detect packaging polylactic acid-based biocomposite samples adulterated with polypropylene (PP) 30–45% and linear low-density polyethylene 2–10%. Principal component analysis, soft independent modeling of class analogy (SIMCA) and partial least square discriminate analysis (PLS-DA) chemometric techniques were utilized to classify samples in different classes. Totally, 362 samples were modeled in three different classes (two adulterated and one non-adulterated). The obtained results revealed that PLS-DA is the most suitable chemometric approach for prediction of probable adulteration in biocomposite samples with reliable specificity and selectivity. It could provide 99% correct class prediction rate between non-adulterated biocomposite samples and adulterated ones, while SIMCA methods provided 73.33% prediction accuracy in classification.     

Synchronous fluorescence spectroscopy for quantitative determination of virgin olive oil adulteration with sunflower oil

Adulteration of extra virgin olive oil with sunflower oil is a major issue for the olive oil industry. In this paper, the potential of total synchronous fluorescence (TSyF) spectra to differentiate virgin olive oil from sunflower oil and synchronous fluorescence (SyF) spectra combined with multivariate analysis to assess the adulteration of virgin olive oil are demonstrated. TSyF spectra were acquired by varying the excitation wavelength in the region 270–720 nm and the wavelength interval (Δλ) in the region from 20 to 120 nm. TSyF contour plots for sunflower, in contrast to virgin olive oil, show a fluorescence region in the excitation wavelength range 325–385 nm. Fifteen different virgin olive oil samples were adulterated with sunflower oil at varying levels (0.5–95%) resulting in one hundred and thirty six mixtures. The partial least-squares regression model was used for quantification of the adulteration using wavelength intervals of 20 and 80 nm. This technique is useful for detection of sunflower oil in virgin olive oil at levels down to 3.4% (w/v) in just two and a half minutes using an 80-nm wavelength interval.     

Detection of refined olive oil adulteration with refined hazelnut oil by employing NMR spectroscopy and multivariate statistical analysis

NMR spectroscopy was employed for the detection of adulteration of refined olive oil with refined hazelnut oil. Fatty acids and iodine number were determined by ¹H NMR, whereas ³¹P NMR was used for the quantification of minor compounds including phenolic compounds, diacylglycerols, sterols, and free fatty acids (free acidity). Classification of the refined oils based on their fatty acids content and the concentration of their minor compounds was achieved by using the forward stepwise canonical discriminant analysis (CDA) and the classification binary trees (CBTs). Both methods provided good discrimination between the refined hazelnut and olive oils. Different admixtures of refined olive oils with refined hazelnut oils were prepared and analyzed by ¹H NMR and ³¹P NMR spectroscopy. Subsequent application of CDA to the NMR data allowed the detection of the presence of refined hazelnut oils in refined olive oils at percentages higher than 5%. Application of the non-linear classification method of the binary trees offered better possibilities of measuring adulteration of the refined olive oils at a lower limit of detection than that obtained by the CDA method.     

Visible and near-infrared absorption spectroscopy by an integrating sphere and optical fibers for quantifying and discriminating the adulteration of extra virgin olive oil from Tuscany

Because of its high price, extra virgin olive oil is frequently targeted for adulteration with lower quality oils. This paper presents an innovative optical technique capable of quantifying and discriminating the adulteration of extra virgin olive oil caused by lower-grade olive oils. An original set-up for diffuse-light absorption spectroscopy in the wide 400–1,700 nm spectral range was experimented. It made use of an integrating sphere containing the oil sample and of optical fibers for illumination and detection; it provided intrinsically scattering-free absorption spectroscopy measurements. This set-up was used to collect spectroscopic fingerprints of authentic extra virgin olive oils from the Italian Tuscany region, adulterated by different concentrations of olive-pomace oil, refined olive oil, deodorized olive oil, and refined olive-pomace oil. Then, a straightforward multivariate processing of spectroscopic data based on principal component analysis and linear discriminant analysis was applied which was successfully capable of predicting the fraction of adulterant in the mixture, and of discriminating its type. The results achieved by means of optical spectroscopy were compared with the analysis of fatty acids, which was carried out by standard gas chromatography.     

Analysis of olive oil and seed oil triglycerides by capillary gas chromatography as a tool for the detection of the adulteration of olive oil

Individual triglyceride (TG) species of olive oil and several seed oils (corn, cottonseed, palm, peanut, soybean, and sunflower) are baseline separated on a WCOT TAP CB fused-silica capillary column by capillary gas chromatography (CGC) with a flame-ionization detector (FID) and either cold on-column or split injection. An adulteration of olive oil with a low content (< 5%) of these seed oils (except peanut oil) can be verified by the detection of the increasing levels of trilinolein or tripalmitin in olive oil in which these TG species are normally absent or present at very low levels (< 0.5%). An adulteration with over 20% peanut oil can be detected by the increasing levels of palmitodilinolein. TG species that can be coeluted with trilinolein in the reversed-phase high-performance liquid chromatographic (RP-HPLC) mode are baseline separated by the CGC technique, and their structures are identified by selective ion monitoring mass spectrometry. The following comparisons--the CGC-FID and RP-HPLC methods for detection of adulteration, cold on-column and split-injection modes for CGC-FID, and silylation or thin-layer chromatography pretreatment and simple dilution of one or more of the oil samples--are also presented. The normalized percentage area of the TG species is sufficient for the method limits used in this study. Mixtures of virgin olive oil with refined or residue olive oil could not be distinguished from the virgin type by the method used in this study.     

Direct olive oil authentication: detection of adulteration of olive oil with hazelnut oil by direct coupling of headspace and mass spectrometry, and multivariate regression techniques

Control of adulteration of olive oil, together with authentication and contamination, is one of the main aspects in the quality control of olive oil. Adulteration with hazelnut oil is one of the most difficult to detect due to the similar composition of hazelnut and olive oils; both virgin olive oil and olive oil are subjected to that kind of adulteration. The main objective of this work was to develop an analytical method able to detect adulteration of virgin olive oils and olive oils with hazelnut oil by means of its analysis by a headspace autosampler directly coupled to a mass spectrometer used as detector (ChemSensor). As no chromatographic separation of the individual components of the samples exists, a global signal of the sample is obtained and employed for its characterization by means of chemometric techniques. Four different crude hazelnut oils from Turkey were employed for the development of the method. Multivariate regression techniques (partial least squares and principal components analysis) were applied to generate adequate regression models. Good values were obtained in both techniques for the parameters employed (standard errors of prediction (SEP) and prediction residual error sum of squares (PRESS)) to evaluate its goodness. With the proposed method, minimum adulteration levels of 7 and 15% can be detected in refined and virgin olive oils, respectively. Once validated, the method was applied to the detection of such adulteration in commercial olive oil and virgin olive oil samples.     

Assessment of dentifrice adulteration with diethylene glycol by means of ATR-FTIR spectroscopy and chemometrics

A direct and fast method for determination of the adulterant diethylene glycol (DEG) in toothpaste and gel dentifrices combining attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy with partial least squares (PLS) regression has been proposed. Considering the high heterogeneity of dentifrices available in the market, the possibility of reducing the number of calibration samples for PLS was evaluated. Similar prediction performance was achieved by both employing a large calibration set of 20 dentifrices spiked with different amounts of DEG and a reduced calibration set of seven ones selected by means of hierarchical cluster analysis (HCA). The feasibility of using the simple calibration model to predict DEG adulteration in a wide variety of unknown dentifrice samples increases the applicability of the proposed method. With this approach, DEG was quantified with a root mean squared error of prediction value of 1.1% for a validation set of 40 different dentifrices containing DEG in the range 0–16% (w:w).     

Detection and quantification of phenolic compounds in olive oil by high resolution H nuclear magnetic resonance spectroscopy

High resolution ¹H NMR spectroscopy has been employed as a versatile and rapid method to analyze the polar fraction of extra virgin olive oils containing various classes of phenolic compounds. The strategy for identification of phenolic compounds is based on the NMR chemical shifts of a large number of model compounds assigned by using two-dimensional (2D) NMR spectroscopy. Furthermore, 2D NMR was applied to phenolic extracts in an attempt to discover additional phenolic compounds. The ¹H NMR methodology was successful in detecting simple phenols, such as p-coumaric acid, vanillic acid, homovanillyl alcohol, vanillin, free tyrosol, and free hydroxytyrosol, the flavonols apigenin and luteolin, the lignans (+) pinoresinol, (+) 1-acetoxypinoresinol and syringaresinol, two isomers of the aldehydic form of oleuropein and ligstroside, the dialdehydic form of oleuropein and ligstroside lacking a carboxymethyl group, and finally total hydroxytyrosol and total tyrosol reflecting the total amounts of free and esterified hydroxytyrol and tyrosol, respectively. The absolute amount of each phenolic constituent was determined in the polar fraction by using anhydrous 1,3,5-triazine as an internal standard.     

Simultaneous prediction of ethylene content and melt temperature in melt-state polypropylene by near-infrared spectroscopy and chemometrics.

This paper reports on a simultaneous prediction method for ethylene (C(2)) content and the melt temperature in melt state random polypropylene (RPP) and block polypropylene (BPP) by near-infrared (NIR) spectroscopy and chemometrics. The NIR spectra of RPP and BPP in melt states were measured by a FT-NIR on-line monitoring system. The predicted values of the C(2) content from the RPP or BPP spectra measured at 190 and 250 degrees C were investigated using a calibration model for the C(2) content developed by using each RPP or BPP spectra set measured at 230 degrees C. The errors in the predicted values of the C(2) content depended on the pretreatment methods for each calibration model. It was found that a multiplicative signal correction (MSC) is very effective to compensate for the influence of the change of the RPP or BPP sample temperature on the predicted C(2) content. By using modified MSC, it was demonstrated that simultaneous predictions of the C(2) contents and the relative temperature of the melt RPP and BPP from the NIR spectra could be realized.     

Durum wheat adulteration detection by NIR spectroscopy multivariate calibration

In the present work, we explored the possibility of using near-infrared spectroscopy in order to quantify the degree of adulteration of durum wheat flour with common bread wheat flour. The multivariate calibration techniques adopted to this aim were PLS and a wavelet-based calibration algorithm, recently developed by some of us, called WILMA. Both techniques provided satisfactory results, the percentage of adulterant present in the samples being quantified with an uncertainty lower than that associated to the Italian official method. In particular the WILMA algorithm, by performing feature selection, allowed the signal pretreatment to be avoided and obtaining more parsimonious models.     

The application of NMR and MS methods for detection of adulteration of wine,fruit juices,and olive oil. A review

This review covers two important techniques, high resolution nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), used to characterize food products and detect possible adulteration of wine, fruit juices, and olive oil, all important products of the Mediterranean Basin. Emphasis is placed on the complementary use of SNIF-NMR (site-specific natural isotopic fractionation nuclear magnetic resonance) and IRMS (isotope-ratio mass spectrometry) in association with chemometric methods for detecting the adulteration.     

Sequential (step-by-step) detection,identification and quantitation of extra virgin olive oil adulteration by chemometric treatment of chromatographic profiles

An analytical method for the sequential detection, identification and quantitation of extra virgin olive oil adulteration with four edible vegetable oils--sunflower, corn, peanut and coconut oils--is proposed. The only data required for this method are the results obtained from an analysis of the lipid fraction by gas chromatography-mass spectrometry. A total number of 566 samples (pure oils and samples of adulterated olive oil) were used to develop the chemometric models, which were designed to accomplish, step-by-step, the three aims of the method: to detect whether an olive oil sample is adulterated, to identify the type of adulterant used in the fraud, and to determine how much aldulterant is in the sample. Qualitative analysis was carried out via two chemometric approaches--soft independent modelling of class analogy (SIMCA) and K nearest neighbours (KNN)--both approaches exhibited prediction abilities that were always higher than 91% for adulterant detection and 88% for type of adulterant identification. Quantitative analysis was based on partial least squares regression (PLSR), which yielded R2 values of >0.90 for calibration and validation sets and thus made it possible to determine adulteration with excellent precision according to the Shenk criteria.     

Optimization and application of methods of triacylglycerol evaluation for characterization of olive oil adulteration by soybean oil with HPLC-APCI-MS-MS

Triacylglycerols (TAGs) are the main constituents of vegetable oils where they occur in complex mixtures with characteristic distributions. Mass spectrometry using an atmospheric pressure chemical ionization interface (APCI-MS) run in positive mode and an Ion Trap mass analyser were applied in the study of olive and soybean oils and their mixtures. Direct injections of soybean and olive oil solutions allowed the identification of ions derived from the main TAGs of both oils. This procedure showed to be a simple and powerful tool to evaluate mixtures or addition of soybean to olive oil. TAG separation was optimized by high performance liquid chromatography (HPLC) using an octadecylsilica LiChrospher column (250 mm × 3 mm; 5 μm) and a gradient composed of acetonitrile and 2-propanol allowed the separation of the main TAGs of the studied oils. APCI vaporization temperature was optimized and best signals were obtained at 370 °C. Multiple reaction monitoring (MRM) employing the transition of the protonated TAG molecules ([M+H]⁺) to the protonated diacylglycerol fragments ([M+H−R]⁺) improved the selectivity of TAG detection and was used in quantitative studies. Different strategies were developed to evaluate oil composition following TAG analysis by MRM. The external standard calibration and standard additions methods were compared for triolein quantification but the former showed to be biased. Further quantitative studies were based on the estimates of soybean and olive oil proportions in mixtures by comparison of TAG areas found in mixtures of known and unknown composition of both oils. Good agreement with expected or labeled values was found for a commercial blend containing 15% (w/w) of olive oil in soybean oil and to a 1:1 mixture of both oils, showing the potential of this method in characterizing oil mixtures and estimating oil proportions. Olive oils of different origins were also evaluated by mass spectra data obtained after direct injections of oil solutions and principal component analysis (PCA). Argentinean olive oils were clustered in a different area of the principal components plot (PC2 × PC1) in comparison with European olive oils. The commercial blend containing 15% (w/w) of olive oil in soybean oil appeared in a completely different area of the graphic, showing the potential of this method to screen out for olive oil adulterations.     

Determination of betaines in vegetable oils by capillary electrophoresis tandem mass spectrometry--application to the detection of olive oil adulteration with seed oils

A CE–tandem mass spectrometry (MS²) methodology enabling the simultaneous determination of betaines (glycine betaine, trigonelline, proline betaine and total content of carnitines) in vegetable oils was developed. Betaines were derivatized with butanol previous to their baseline separation in 10 min using a 0.1 M formic acid buffer at pH 2.0. Ion trap conditions were optimized in order to maximize the selectivity and sensitivity. Analytical characteristics of the proposed method were established by evaluating its selectivity, linearity, precision (RSDs ranged from 4.8 to 10.7% for corrected peak areas) and accuracy by means of recovery studies (from 80 to 99%) and LODs and LOQs at 0.1 ppb level. The method was applied for the determination of the selected betaines in seed oils and extra virgin olive oils. MS² experiments provided the fingerprint fragmentation for the betaines identified in vegetable oils. In extra virgin olive oils, carnitines were not detected, making it possible to propose them as a feasible novel marker for the detection of adulterations of olive oils. Application of the developed method for the analysis of different mixtures of extra virgin olive oil with seed oil (between 2 and 10%) enabled the detection and quantitation of the total content of carnitines. The results obtained show the high potential of the developed method for the authentication and quality control of olive oils.     

Screening adulteration of polypropylene bottles with postconsumer recycled plastics for oral drug package by near-infrared spectroscopy

Adulteration of pharmaceutical packaging containers with postconsumer recycled plastic materials was considerably difficult to identify due to the similar chemical compositions of virgin and recycled plastics. In the present study, near-infrared (NIR) spectroscopy coupled with conformity test was proposed to screen the adulteration of pharmaceutical packaging containers. Two kinds of representative screening models were investigated on polypropylene (PP) bottles for oral drug package. The reliability of the screening models was validated through studying the identification reliability, specificity, and robustness of the methods. The minimum spiking level of two modeled adulterants at the proportion of 20% could be detected, and the unqualified sample from a domestic manufacturer was rejected by this developed method. This strategy represents a rapid and promising analytical method for screening the adulteration of pharmaceutical plastic packaging containers with postconsumer recycled plastics.     

Rapid detection of Rosa laevigata polysaccharide content by near-infrared spectroscopy

We have investigated surface-enhanced Raman spectroscopy (SERS) spectrum of Omethoate (O,O-dimethyl-S-methylcarbamoylmethylthiophosphate). It is found significant signals in the ordinary Raman spectrum for solid-state Omethoate as well as strong vibrational signals absorbed on the silver sol surface which is prepared by γ-irradiation technique at a very low concentration. Effects of pH and anions (Cl-, Br-, I-) on the adsorption orientation are investigated as well. Two different adsorption mechanisms are deduced, depending on the experimental conditions. The sulfur atom or the sulfur and two oxygen atoms are adsorbed onto the silver sol surface. Among halide ions, Br- and I- are more strongly adsorbed onto the silver sol surface. As a result, the adsorption of Omethoate is less effective due to their steric hindrance.     

Tracing the origin of extra virgin olive oils by infrared spectroscopy and chemometrics: A case study

In this paper, the potential of coupling mid- and near-infrared spectroscopic fingerprinting techniques and chemometric classification methods for the traceability of extra virgin olive oil samples from the PDO Sabina was investigated. To this purpose, two different pattern recognition algorithm representative of the discriminant (PLS-DA) and modeling (SIMCA) approach to classification were employed. Results obtained after processing the spectroscopic data by PLS-DA evidenced a rather high classification accuracy, NIR providing better predictions than MIR (as evaluated both in cross-validation and on an external test set). SIMCA confirmed these results and showed how the category models for the class Sabina can be rather sensitive and highly specific. Lastly, as samples from two harvesting years (2009 and 2010) were investigated, it was possible to evidence that the different production year can have a relevant effect on the spectroscopic fingerprint. Notwithstanding this, it was still possible to build models that are transferable from one year to another with good accuracy.     

Enhanced Raman spectroscopy coupled to chemometrics for identification and quantification of acetylcholinesterase inhibitors

In this work, we present a new complete method using Surface Enhanced Raman Spectroscopy (SERS) and chemometrics for the qualitative and quantitative detection of pesticides by measuring the acetylcholinesterase (ACHE) activity. The Raman SERS is not only used for measuring the ACHE activity, but also for the direct detection of pesticides individually and for their identification. Gold nanoparticles (AuNPs) were used as dynamic SERS substrates for sensitive monitoring of ACHE activity in the presence of very low levels of organophosphate and carbamate pesticides, chemical warfare agents that are known to be ACHE inhibitors. The lowest detectable level for paraoxon was determined at 4.0 × 10⁻¹⁴ M and 1.9 × 10⁻⁹ M for carbaryl. The use of the enzyme allowed limits of detection for both pesticides that were much lower than the limits obtained by direct SERS analysis of the pesticides. The system shows a linear relationship between the intensity band at 639 cm⁻¹ and pesticide concentration. These results suggest that this biosensor could be used in the future for the non-selective detection of all ACHE inhibitors at very low concentrations with possible identification of the inhibitor.     

Feasibility of near-infrared spectroscopy and chemometrics analysis for discrimination of Cymbopogon nardus from Cymbopogon citratus

The authenticity of essential oils has become an important issue in supplying essential oil raw materials for the pharmaceutical, perfume, and cosmetic industries. Citronella oil is one of the essential oils used in those industries. Cymbopogon nardus is one of the lemongrass species that can produce citronella oil. However, with the high price of citronella oil from C. nardus, there is a possibility of being substituted or adulterated with closely related plants, namely Cymbopogon citratus. This paper described the feasibility of near-infrared (NIR) spectroscopy combined with chemometrics analysis for rapid identification and authentication of C. nardus from C. citratus essential oil. NIR spectra of both essential oils and their mixture (10 % and 25 % v/v of C. citratus in C. nardus) showed a similar spectral profile, so we cannot easily discriminate them and need help from chemometrics analysis. For chemometrics analysis, we used absorbance data from the preprocessed NIR spectra at wavenumbers 4000–6500 cm^?1. Using PCA, we could separate each essential oil from C. nardus and C. citratus but cannot discriminate between 10 % and 25 % of CC in CN. While using OPLS-DA with R2X(cum) = 0.88, R2Y(cum) = 0.859 and Q2(cum) = 0.723, we could group each sample. The OPLS-DA score plot clearly shows the difference between C. nardus and C. citratus essential oils and their mixtures. The combination of NIR and OPLS-DA could provide a suitable method for identifying and authenticating C. nardus from C. citratus essential oil.