Oil Adulteration Research Articles

Rapid Authentication and Detection of Olive Oil Adulteration Using Laser-Induced Breakdown Spectroscopy.

The adulteration of olive oil is a crucial matter for food safety authorities, global organizations, and consumers. To guarantee olive oil authenticity, the European Union (EU) has promoted the labeling of olive oils with the indices of Protected Designation of Origin (PDO) and Protected Geographical Identification (PGI), while food security agencies are also interested in newly emerging technologies capable of operating reliably, fast, and in real-time, either in situ or remotely, for quality control. Among the proposed methods, photonic technologies appear to be suitable and promising for dealing with this issue. In this regard, a laser-based technique, namely, Laser-Induced Breakdown Spectroscopy (LIBS), assisted via machine learning tools, is proposed for the real-time detection of olive oil adulteration with lower-quality oils (i.e., pomace, soybean, sunflower, and corn oils). The results of the present work demonstrate the high efficiency and potential of the LIBS technique for the rapid detection of olive oil adulteration and the detection of adulterants.

Predictive techniques for authenticating and quantifying crude palm oil adulterated with leaf extract and food color − An ultra-violet visible spectrophotometric approach

Analytical techniques such as GC-MS and HPLC are benchmarks for determining vegetable oil adulteration but novel methods of oil manipulation have presented new challenges. In addition to the complexities of sorghum leaf and food color adulteration in crude palm oil (CPO), the sophistication, cost and analytical time of these benchmark methods have elicited the search for alternative approaches. Spectroscopy is one of such approaches that have been lauded for non-invasive and rapid analysis but has never been applied to authenticate CPO containing sorghum leaf extract and food color. This study tested the feasibility of using conventional techniques and UV-Vis spectrophotometry and chemometrics to detect CPO containing different concentrations of food colour and sorghum leaf extract. Linear discriminant analysis models were able to classify different concentrations of sorghum leaf dye and food color in crude palm oil with good accuracy. Testing the developed models with samples from 5 different markets revealed a possible adulteration of some market samples after cross-validation. The purity of crude palm oil could be predicted with an R2CV of 0.937, RMSECV of 0.161 g/100 g and RPD of 3.362 after cross-validation using partial least squares regression and testing 18 different mathematical spectral pretreatment methods. The results suggest a potential for rapid detection of concentrations as low as 0.2 g/mL of food color in CPO and color extract from sorghum leaf size 10 cm by 1 cm.

Authentication of pure and adulterated edible oils using non-destructive ultrasound.

At present, the quality of edible oil is evaluated using traditional analysis techniques that are generally destructive. Therefore, efforts are being made to find alternative methods with non-destructive techniques such as Ultrasound. This work aims to confirm the feasibility of non-destructive ultrasonic inspection to characterise and detect fraudulent practices in olive oil due to adulteration with two other edible vegetable oils (sunflower and corn). For this purpose, pulsed ultrasonic signals with a frequency of 2.25MHz have been used. The samples of pure olive oil were adulterated with the other two in variable percentages between 20% and 80%. Moreover, the viscosity and density values were measured. Both these physicochemical and acoustic parameters were obtained at 24°C and 30°C and linearly correlated with each other. The results indicate the sensitivity of the method at all levels of adulteration studied. The responses obtained through the parameters related to the components of velocity, attenuation, and frequency of the ultrasonic waves are complementary to each other. This allows concluding that the classification of pure and adulterated oil samples is possible through non-destructive ultrasonic inspection.

Discrimination of baru oil adulteration with soy oil using portable near infrared spectroscopy, Fourier-transform infrared spectroscopy and Partial Least-Squares Discriminant

Considered an important source of bioactive compounds and unsaturated fatty acids, Baru oil comes from the Brazilian Cerrado ecoregion and is sold with high value. Methods to detect tampering to certify the authenticity of this type of product are in great demand for quality control. Herein, this study used partial least squares for discriminant analysis (PLS-DA) combined with Fourier-transform infrared spectroscopy (FTIR) and portable near-infrared spectroscopy (NIR) for the detection of adulteration in Baru oil, using soybean oil as an adulterant. Adulteration concentrations were 10, 30, 50, 60, and 80% for analysis by NIR and 10, 30, 50, 60, 70, and 90% for FTIR. After the spectral range selection and pre-processing steps, discrimination models showed good discrimination, and the efficiency was confirmed from the figures of merit, which highlighted percentages above 99% with both techniques used. Anomalous samples were identified in discrimination models obtained with a portable NIR spectrometer in the outlier detection step. The methodology evaluated is direct, fast, and non-destructive, in which the PLS-DA models obtained from the sets of spectra by FTIR presented superior results than those evaluated with the portable NIR spectrometer; however, the latter was also efficient in discriminating as adulterated, reinforcing the feasibility of using spectroscopic techniques and chemometric tools.

Mineral oil detection in ghee using attenuated total reflectance-fourier transform infrared spectroscopy (ATR-FTIR) in conjunction with chemometrics

Ghee (anhydrous milk fat), being a costly product, is susceptible to adulteration by unscrupulous personnel in the market. The presence of mineral oil in ghee was examined using Attenuated Total Reflectance-Fourier Transform Infrared spectroscopy (ATR-FTIR) spectroscopy in conjunction with chemometrics. Spectral data in the wavenumber range 4000–500 cm−1 were collected for pure ghee, mineral oil, and pure ghee samples spiked with mineral oil at different concentrations (2 %, 4 %, 6 %, 10 %, and 15 %). Principal Component Analysis (PCA) revealed distinct clustering patterns in specific wavenumber regions (1350–950 and 1800–1600 cm−1) at 5% level of significance, effectively distinguishing pure ghee from adulterated samples. Soft Independent Modeling of Class Analogy (SIMCA) showed classification efficiency of 100 % for the samples of pure ghee and mineral oil. Developed Partial Least Square (PLS) and Principal Component Regression (PCR) models showed high R2 values (above 0.99) indicating strong predictive accuracy for mineral oil adulteration levels in ghee. Thus, using ATR-FTIR and chemometrics, detection of as low as 2 % of mineral oil in ghee is possible.

Quality Standards and Pharmacological Interventions of Natural Oils: Current Scenario and Future Perspectives.

Medicinal plants are rich sources of natural oils such as essential and fixed oils used traditionally for nutritive as well as medicinal purposes. Most of the traditional formulations or phytopharmaceutical formulations contain oil as the main ingredient due to their own therapeutic applications and thus mitigating several pathogeneses such as fungal/bacterial/viral infection, gout, psoriasis, analgesic, antioxidant, skin infection, etc. Due to the lack of quality standards and progressive adulteration in the natural oils, their therapeutic efficacy is continuously deteriorated. To develop quality standards and validate scientific aspects on essential oils, several chromatographic and spectroscopic techniques such as HPTLC, HPLC, NMR, LC-MS, and GC-MS have been termed as the choices of techniques for better exploration of metabolites, hence sustaining the authenticity of the essential oils. In this review, chemical profiling and quality control aspects of essential or fixed oils have been explored from previously reported literature in reputed journals. Methods of chemical profiling, possible identified metabolites in essential oils, and their therapeutic applications have been described. The outcome of the review reveals that GC-MS/MS, LC-MS/MS, and NMR-based chromatographic and spectroscopic techniques are the most liable, economic, precise, and accurate techniques for determining the spuriousness or adulteration of oils based on their qualitative and quantitative chemical profiling studies. This review occupies the extensive information about the quality standards of several oils obtained from natural sources for their regulatory aspects via providing the detailed methods used in chemoprofiling techniques. Hence, this review helps researchers in further therapeutic exploration as well as quality-based standardization for their regulatory purpose.

Multicomponent prediction of Sudan dye adulteration in crude palm oil using SERS – Based bimetallic nanoflower combined with genetic algorithm

Crude palm oil (CPO) is an important edible vegetable oil used globally, recently subjected to Sudan dye adulteration. This study explored the feasibility of a novel SERS-based Au@Ag substrate to detect four Sudan dyes (I - IV) in CPO. When mixed with spiked CPO, the SERS substrate produced strong signals that increased with increasing concentrations from 0.001 to 4.0 ppm. The genetic algorithm partial least square (GA-PLS) model outperformed the partial least square (PLS) and the ant colony optimization – PLS (ACO-PLS) models with Rc values of 0.9844, 0.9865, 0.9884, and 0.9888 0.9846. The calculated LOD were 0.00088 ppm, 0.00092 ppm, 0.00095 ppm, and 0.00097 and real sample recovery rates of 88.0–113.0%, 92.0–103.0%, 91.2–98.5% and 97.0–109.5% for Sudan I, II, III, and IV, respectively. The findings affirmed the SERS sensor’s considerable potential for rapid and selective detection of Sudan dyes in CPO when combined with chemometrics.

Rapid detection of argan oil adulteration by frying oils using laser induced fluorescence spectroscopy combined with chemometrics tools

There is a contentious need for robust and rapid methodologies for maintaining the authenticity of foods. The aim of this study was to detect and quantify argan oil adulteration using Laser Induced Fluorescence (LIF) spectroscopy coupled with chemometric methods. Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR) were used to assess argan oil authenticity; PCA was used to classify samples according to their quality and the PLS model to determine the amount of adulterants in pure argan oil. The correlation coefficient of the obtained model was about 0.99, with Root Mean Square Error of Prediction (RMSEP) and Standard Error of Prediction (SEP) of 2%. This study demonstrated the feasibility of LIF spectroscopy combined with chemometric tools to identify adulterants in pure argan oil from a percentage of adulteration, of 0.35 % without the need to destruct samples.

Quality Characterisation of Mustard (Brassica Nigra) Oil and Its Blends with Argemone (Argemone Mexicana) Oil under Varying Dielectric Frequency and Temperature

Physical properties of edible oil are a decisive factor for the characterisation of its quality. Adulteration of pure mustard oil (MO) with argemone oil (AO) is common and rampant. In this study, the effect of dielectric frequency (200, 1000 Hz) on the physical properties such as specific gravity, viscosity, refractive index, and colour of MO blended (0, 25, 50, 75, 100%) with AO was investigated at five (10 - 50 °C) levels of temperature is reported. The individual and interaction effects of frequency, blend ratio, and temperature indicated that there was no significant effect (p≤0.05) on the physical properties of oil samples due to the dielectric frequency. However, the effects of blend ratio on all measured parameters were highly significant (p≤0.01). The effect of temperature was significant (p≤0.05) for all parameters, except refractive index. Viscosity and specific gravity correlated with temperature and blend ratio in third-degree polynomial regression model (R2>0.9) for MO and AO blends. The findings of this study established that dielectric spectroscopy can be used for detection of adulteration in MO without affecting its physico-chemical properties.

A rapid and low-cost method for detection of nine kinds of vegetable oil adulteration based on 3-D fluorescence spectroscopy

The integrity of vegetable oil has become a focal concern within the global food industry, particularly in specific regions where economic interests drive authenticity issues. In this study, we used three-dimensional (3-D) fluorescence spectroscopy for the qualitative and quantitative analysis of adulteration in different vegetable oils, offering the advantages of rapid and low-cost detection. Nine types of adulterated vegetable oils were modeled using five machine learning methods: K-nearest neighbor (KNN), Random Forest (RF), Support Vector Machine (SVM), Partial Least Squares (PLS), and Convolutional Neural Network (CNN). In the qualitative analysis, the Rp2 and RMSEP values ranged from 0.89 to 0.99 and 0.01 to 0.05, respectively. For qualitatively analysis, a threshold of 5 % was established for detecting adulteration in soybean oil with palm oil and sesame oil with palm oil, achieving an accuracy (ACC) greater than 90 %. Meanwhile, a threshold of 10 % was effective for remaining seven adulterated oils, with an ACC exceeding 90 %. Moreover, an ACC rate of over 95 % was attained when the threshold was set at 15 %. The findings demonstrate that 3-D fluorescence spectroscopy presents a robust tool for detecting various adulterated oils, heralding a valuable contribution to food safety and quality control.

Raman identification of adulteration in poly-alpha-olefin synthetic lubricant using principal component analysis and two-dimensional correlation spectroscopy

Poly-alpha-olefin (PAO) synthetic lubricating oil provides significant advantages over mineral oil. Highly discerning methods of PAO adulteration with inexpensive mineral oil is crucial to promote energy conservation and emission reduction and safeguard the rights of consumers. In this research, conventional principal component analysis (PCA), modified principal component analysis (MPCA) and two-dimensional correlation spectroscopy (2D-COS) with temperature as perturbation factor were used for Raman identification of PAO adulteration with 5 %, 20 % and 50 % mineral oil (150 N). The influence of spectrum normalization on identification was also studied. When using maximum intensity normalization, the modified principal component analysis is more efficient than the conventional PCA and can distinguish the adulterated oil from the authentic PAO even when the adulteration ratio decreases to 5 %. Comparably, the conventional PCA can distinguish the adulteration with 5 % mineral oil by using peak area normalization. These results suggest that the area normalization is prior to the maximum intensity normalization for PCA identification, as attributed to that the maximum intensity normalization might weaken the intensity difference of strongest peak among different samples. By either peak area or maximum intensity normalization, the difference between the authentic and the adulterated PAO can be detected by synchronous 2D-COS, in which both the autocorrelation peak at 2843 cm−1 and the negative cross correlation peak at (2843, 2935 cm−1) become weakened along with the increase of adulteration ratio. This work can provide important technical support to ensure the safe operation of equipment and crack down on the illegal trade of counterfeit synthetic lubricants.

Untargeted 4D-metabolomics using Trapped Ion Mobility combined with LC-HRMS in extra virgin olive oil adulteration study with lower-quality olive oils

Metabolomics is widely established in the field of food authenticity to address demanding issues, such as adulteration cases. Trapped ion mobility spectrometry (TIMS) coupled to liquid chromatography (LC) and high-resolution mass spectrometry (HRMS) provides an additional analytical dimension, introducing mobility-enhanced metabolomics in four dimensions (4D). In the present work, the potential of LC-TIMS-HRMS as a reliable analytical platform for authenticity studies is being explored, applied in extra virgin olive oil (EVOO) adulteration study. An integrated untargeted 4D-metabolomics approach is being implemented to investigate adulteration, with refined olive oils (ROOs) and olive pomace oils (OPOs) set as adulterants. Robust prediction models are built, successfully discriminating authentic EVOOs from adulterated ones and highlighting markers in each group. Noteworthy outcomes are retrieved regarding TIMS added value in LC-HRMS workflows, resulting in a significant increase of metabolic coverage, while, thanks to platform’s enhanced sensitivity, detection of adulteration is being achieved down to 1%.

Molecular Detection for Identification and Seed Adulteration of Camellia Oils Based on Chloroplast Simple Sequence Repeat Markers

Molecular Detection for Identification and Seed Adulteration of Camellia Oils Based on Chloroplast Simple Sequence Repeat Markers

Comparing Different Chemometric Approaches to Detect Adulteration of Cold-Pressed Flaxseed Oil with Refined Rapeseed Oil Using Differential Scanning Calorimetry.

Flaxseed oil is one of the best sources of n-3 fatty acids, thus its adulteration with refined oils can lead to a reduction in its nutritional value and overall quality. The purpose of this study was to compare different chemometric models to detect adulteration of flaxseed oil with refined rapeseed oil (RP) using differential scanning calorimetry (DSC). Based on the melting phase transition curve, parameters such as peak temperature (T), peak height (h), and percentage of area (P) were determined for pure and adulterated flaxseed oils with an RP concentration of 5, 10, 20, 30, and 50% (w/w). Significant linear correlations (p ≤ 0.05) between the RP concentration and all DSC parameters were observed, except for parameter h1 for the first peak. In order to assess the usefulness of the DSC technique for detecting adulterations, three chemometric approaches were compared: (1) classification models (linear discriminant analysis-LDA, adaptive regression splines-MARS, support vector machine-SVM, and artificial neural networks-ANNs); (2) regression models (multiple linear regression-MLR, MARS, SVM, ANNs, and PLS); and (3) a combined model of orthogonal partial least squares discriminant analysis (OPLS-DA). With the LDA model, the highest accuracy of 99.5% in classifying the samples, followed by ANN > SVM > MARS, was achieved. Among the regression models, the ANN model showed the highest correlation between observed and predicted values (R = 0.996), while other models showed goodness of fit as following MARS > SVM > MLR. Comparing OPLS-DA and PLS methods, higher values of R2X(cum) = 0.986 and Q2 = 0.973 were observed with the PLS model than OPLS-DA. This study demonstrates the usefulness of the DSC technique and importance of an appropriate chemometric model for predicting the adulteration of cold-pressed flaxseed oil with refined rapeseed oil.

A high performance thin layer chromatography (HPTLC) method for the quality assessment of citronella oil: application in commercial sample analysis

Citronella oil, extracted from Cymbopogon species (winterianus and nardus) is a commercially valuable essential oil used in personal-care products and insect repellents. Routine analysis in gas chromatography is incapable of detecting high-boiling adulterants therein. In this study, an HPTLC technique was developed for the absolute quantification of citronellal (characteristic chemical marker) and triglyceride (main constituent of vegetable oil adulterant) in citronella oil for its quality assessment. It was validated in terms of specificity, linearity, sensitivity, accuracy and precision. Further, the developed method was employed to quantify citronellal and triglyceride in twenty commercial samples. The results showed a wide variation in citronellal content (trace to 30.65% w/w) and could differentiate its two chemotypes. Also, it revealed the possibility of vegetable oil adulteration through the detection and quantification of triglyceride in selected samples. It can be a simple and rapid technique for the quality control of citronella oil.

Detection of common adulterants in olive oils by bench top 60 MHz 1H NMR with partial least squares regression

Olive oil is the oil obtained from the fruit of the olive tree, Olea europaea L., and is commonly used in cooking, cosmetics, and soaps. The high value and demand for olive oil products has led to some producers adding cheaper edible non-olive oils to olive oil to increase their profitability. Many of these adulterant oils have similar taste profiles to extra virgin olive oil; however, these can cause allergic reactions in individuals with sensitivities to the non-olive oil. Given the implications of olive oil adulteration, there is a demand for methods to identify and quantify adulterant oils in olive oil samples. This work explores the utility of low-field benchtop nuclear magnetic resonance (NMR) with a novel chemometrics workflow for the quantification of non-olive oils in authentic extra virgin olive oil samples. The measurements were obtained using a 60 MHz 1H NMR. The icoshift algorithm was utilized for alignment, and partial least squares regression models of the entire NMR spectrum were used for quantification. Here, 17 different varieties of edible non-olive oils were investigated. The models were highly linear, accurate, and precise. Most of these adulterant oils were detectable below 10% v/v in extra virgin olive oil samples.

Impact of the Consumption of Adulterated Oils and Alcoholic Beverages.

Impact of the Consumption of Adulterated Oils and Alcoholic Beverages.

Rapid and Low-Cost Quantification of Adulteration Content in Camellia Oil Utilizing UV-Vis-NIR Spectroscopy Combined with Feature Selection Methods.

This study aims to explore the potential use of low-cost ultraviolet-visible-near infrared (UV-Vis-NIR) spectroscopy to quantify adulteration content of soybean, rapeseed, corn and peanut oils in Camellia oil. To attain this aim, test oil samples were firstly prepared with different adulterant ratios ranging from 1% to 90% at varying intervals, and their spectra were collected by an in-house built experimental platform. Next, the spectra were preprocessed using Savitzky-Golay (SG)-Continuous Wavelet Transform (CWT) and the feature wavelengths were extracted using four different algorithms. Finally, Support Vector Regression (SVR) and Random Forest (RF) models were developed to rapidly predict adulteration content. The results indicated that SG-CWT with decomposition scale of 25 and the Iterative Variable Subset Optimization (IVSO) algorithm can effectively improve the accuracy of the models. Furthermore, the SVR model performed best for predicting adulteration of camellia oil with soybean oil, while the RF models were optimal for camellia oil adulterated with rapeseed, corn, or peanut oil. Additionally, we verified the models' robustness by examining the correlation between the absorbance and adulteration content at certain feature wavelengths screened by IVSO. This study demonstrates the feasibility of using low-cost UV-Vis-NIR spectroscopy for the authentication of Camellia oil.

A dedicated electronic nosecombined with chemometric methods for detection of adulteration in sesame oil.

Sesame oil (SO), one of the most popular and expensive edible oils, is prone to adulteration. In this study, the fatty acid profiles of pure sesame seed oil and samples adulterated with two less expensive edible oils (canola and sunflower) were analyzed using Gas Chromatography. A dedicated e-nose system was developed and tested on 15 mixtures of sesame-canola and sesame-sunflower samples. Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Multi-Layered Perceptron (MLP) methods were utilized to identify adulteration through the evaluation of Volatile Organic Compound. Result of chromatography showed that most samples of sesame oil containing impurities at levels less than 30% were recognized incorrectly in the standard range of SO fatty acids. This is while the developed e-nose system was able to detect adulteration at much lower levels. According to the results, PCA and LDA methods can describe the data set variance with precision of 95.6% and 97%, respectively. The MLP model had better results compared to PCA and LDA, with high determination coefficient (R2 = 0.981) and low RMSE (0.0178). Results indicate that the e-nose system provided an effective non-destructive method to detect SO adulteration at levels as low as 5%, which GC was unable to detect.

A Simple and Precise Detection of the Adulteration of Olive Oil with Hazelnut Oil Using Lawesson’s Reagent

A Simple and Precise Detection of the Adulteration of Olive Oil with Hazelnut Oil Using Lawesson’s Reagent