Vegetable Oil Adulteration Research Articles

A portable LED-induced fluorescence system for quantitative detection of different kinds of vegetable oil adulteration

This study developed a portable LED-induced fluorescence detection system for quantitative detection of various vegetable oil adulteration. Eight common vegetable oils and 14 different adulterated samples with adulteration concentrations ranging from 0 to 50% were prepared. Before quantitative analysis, different classification models were established for the determination of types of adulteration oil. The overall recognition accuracy was greater than 98%. Furthermore, with a simple calculation, the proposed normalized spectral ratio (NSR) preprocessing method was used to eliminate the light scattering effects in the raw fluorescence spectra. In addition, the competitive adaptive reweighted sampling (CARS) method was used to select characteristic wavelengths. The final oil adulteration quantitative analysis model was NSR_CARS+PLS. The range of correlation coefficient (Rp) and root-mean-square-error (RMSEP) for the prediction datasets were 0.9548-0.9974 and 1.0265%-5.0236%, respectively.

Spectroscopic and chemometric analysis and oil stability index characterization of thermo‐oxidized edible vegetable oils

AbstractEdible vegetable oils are sources of polyunsaturated fatty acids, necessary for a balanced diet capable of providing elements that act on the energetic, structural, and hormonal composition of humans. The growing consumption of these foods has encouraged the search for techniques capable of characterizing their compositions and transformations when subjected to industrial processes or during domestic use. We propose to analyze the transformations undergone by edible vegetable oils originating from different plants due to thermal oxidation. For this, dynamic viscosity, oxidative stability index, fatty acid profile, and infrared spectra determined before and after being subjected to thermal oxidation. The results from infrared spectroscopy were improved through Principal Component Analysis (PCA). Among other results, it was possible to establish correlations between the FTIR spectra, dynamic viscosity, and the profile of fatty acids, allowing the prediction of the concentration of polyunsaturated fatty acids (PUFA) after thermal oxidation by measuring the spectrum of samples before the thermal oxidation process. Furthermore, it is observed that the dynamic viscosity is strongly altered by thermal oxidation, which is directly related to the decrease in PUFA content. The results obtained can be used to predict quality factors of edible vegetable oils, helping to choose the right type of oil for each industrial or domestic process.Practical Applications: This research holds significant practical implications, particularly in detecting adulteration and fraud of edible vegetable oils. The developed method uses physicochemical properties and infrared spectroscopy with principal component analysis to characterize oils and to determine the oil stability index.

Pearson Correlation and Multiple Correlation Analyses of the Animal Fat S-Parameter

In specialised food products, the detection and identification of various sources of oils and fats are essential. Consumers are concerned about the adulteration of vegetable oils to avoid health problems and the risk of developing heart disease. There were a few cases where less expensive animal fat, in particular pork fat, was found in halal meals. The limitations of conventional methods for detecting pork in other meats can be overcome with the use of non-destructive methods. This study reveals the feasibility of using the microwave non-destructive testing method to identify animal fats. In the frequency between 8 GHz and 12 GHz, the microwave non-destructive testing method was used to obtain the S-parameters of the chicken, beef, and pork fats in the form of raw, baked, and oil. Single and multiple correlation analyses were then performed to identify the relationship between the animal fats and to differentiate them. The results of the Pearson correlation analysis show that for all animal fats, both raw and baked fat had a higher correlation value, which indicated they have the same or similar features. The different correlation values obtained from the multiple correlation analysis demonstrated that animal fats have distinct features, regardless of whether they are raw, baked, or liquid forms. The multiple correlation analysis is a more suitable technique to detect animal fats compared to the Pearson correlation analysis.

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.

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.

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 walnut oil adulterated with high-linoleic acid vegetable oils using triacylglycerol pseudotargeted method based on SFC-QTOF-MS

Authentication of walnut oil (WO) is challenging due to the adulteration of high-linoleic acid vegetable oils (HLOs) with similar fatty acid composition. To allow the discrimination of WO adulteration, a rapid, sensitive and stable scanning method based on supercritical fluid chromatography quadrupole time-of-flight mass spectrometry (SFC-QTOF-MS) was established to profile 59 potential triacylglycerol (TAGs) in HLOs samples within 10min. Limit of quantitation of the proposed method is 0.002µgmL-1 and the relative standard deviations range from 0.7% to 12.0%. TAGs profiles of WO samples from various varieties, geography origins, ripeness, and processing methods were used to construct orthogonal partial least squares-discriminant analysis (OPLS-DA) and OPLS models that were highly accurate in both qualitative and quantitative prediction at adulteration levels as low as 5% (w/w). This study advances the TAGs analysis to characterize vegetable oils and holds promise as an efficient method for oil authentication.

Rapid identification and quantification of vegetable oil adulteration in raw milk using a flash gas chromatography electronic nose combined with machine learning

Rapid identification and quantification of vegetable oil adulteration in raw milk using a flash gas chromatography electronic nose combined with machine learning

Detection of Vegetable Oil Adulteration in Pre-Grated Bovine Hard Cheeses via 1H NMR Spectroscopy.

Adulteration of food products is a widespread problem of great concern to society and dairy products are no exception to this. Due to new methods of adulteration being devised in order to circumvent existing detection methods, new detection methods must be developed to counter fraud. Bovine hard cheeses such as Asiago, Parmesan, and Romano are widely sold and consumed in pre-grated form for convenience. Due to being processed products, there is ample opportunity for the introduction of inexpensive adulterants and as such, there is concern regarding the authenticity of these products. An analytical method was developed using a simple organic extraction to verify the authenticity of bovine hard cheese products by examining the lipid profile of these cheeses via proton Nuclear Magnetic Resonance (NMR) spectroscopy. In this study, 52 samples of pre-grated hard cheese were analyzed as a market survey and a significant number of these samples were found to be adulterated with vegetable oils. This method is well suited to high throughput analysis of these products and relies on ratiometrics of the lipids in the samples themselves. Genuine cheeses were found to have a very consistent lipid profile from sample to sample, improving the power of this approach to detect vegetable oil adulteration. The method is purely ratiometric with no need for internal or external references, reducing sample preparation time and reducing the potential for the introduction of error.

Use of Fourier transform infrared spectroscopy and multi-variant analysis for detection of butter adulteration with vegetable oil

ABSTRACT During butter manufacturing, adulteration of animal and plant fats is one of the major issues in dairy industries. In dairy products, there is a huge potential in spectroscopic techniques such as Fourier transform infrared spectroscopy (FTIR) for the rapid determination of various adulterants. These spectroscopic techniques are reliable, rapid, and accurate as compared to traditional methods. Therefore, the aim of this study was to measure the potential of FTIR spectroscopy along with mathematical modeling for the detection of vegetable oil in butter samples. In this study, different levels of vegetable oil were added to the butter. FTIR spectra of different samples were collected and processed using principal component analysis (PCA) as well as partial least square (PLS) regression. PCA results postulated that 98% of the total variance was accounted by the first two principal components (PC) with a predominance of PC 1 (85%). PLS regression analysis showed values of R2 for calibration as 0.95 and R2 for validation as 0.90 which described good prediction efficiency of vegetable oil adulteration through FTIR data. The present work summarized that Fourier transform infrared spectroscopy along with multivariate analysis can be used to measure the adulteration in butter.

Authenticity Identification of Copaiba Oil Using a Handheld NIR Spectrometer and DD-SIMCA

Copaiba oil (CO) is a non-timber forest product, obtained from the Copaifera genus, used in popular medicine, pharmaceutical, and cosmetic industries. The increasing demand and price of this oil also increased the occurrence of adulterations in commercial products and small providers in cooperatives. Nowadays, the GC-MS is the most suitable method used for quality control. However, the high cost of this technique is unfeasible for small traders and cooperatives. Considering the lack of alternative methods for CO analysis, this work presents an easy, direct, fast, and inexpensive method based on a handheld NIR spectrometer and a DD-SIMCA model for purity authentication. The training set was composed of 65 pure CO samples originated by 12 distinct sources of Copaifera sp. oil, 9 distinct commercial samples, binary and ternary mixtures among them. The validation was accomplished using 213 samples constituted by independent authentic samples, samples submitted to thermal stress, commercial samples with and without edible vegetable oil adulteration, and samples purposefully adulterated with edible oils (palm, olive, sunflower, coconut, and soybean). The optimized DD-SIMCA model showed 95% of sensitivity regarding the target class, where 5% the false-negative rate revealed the necessity of increasing the number of commercial samples in the training set. The specificity, related to samples not belonging to the target class, was 97.3%, showing that the model is highly effective to detect adulterated samples. The average efficiency of the method was 92.3%, revealing to be very promising for quality control and authenticity testing applications in small cooperatives.

A simple and quick method to detect adulterated sesame oil using 3D fluorescence spectra.

Adulterated sesame oil seriously damages the interests of consumers and the health of market. In this paper, a simple, fast and real-time model for identifying adulterated sesame oil (ASO) was proposed by combining 3D fluorescence spectra with wavelet moments (WMs). First, noise and data volume of the experimental data were reduced by wavelet multiresolution decomposition (WMRSD), which improved the stability and real-time of the model. Next, WMs were used to extract the features of the 3D fluorescence spectra and proved to be effective by hierarchical clustering results. Then, the qualitative quality of WMs of the same orders, different orders and the combinations were evaluated by Dunn's validity index (DVI), and the rules were given, respectively. Finally, the target WMs for identifying ASO were determined. This model is simple and fast, and expandable to online measurement, providing a reference for identification and adulteration of vegetable oils.

Rapid identification and quantification of cheaper vegetable oil adulteration in camellia oil by using excitation-emission matrix fluorescence spectroscopy combined with chemometrics

Camellia oil is a high quality oil mainly produced in southern China. It is common that unscrupulous merchants attempt to make huge profits by adulterating camellia oil with other cheaper or lower-quality vegetable oils. Therefore, this paper proposed excitation-emission matrix fluorescence spectroscopy combined with chemometric methods for the rapid identification and quantification of camellia oil adulteration with other cheaper vegetable oils. A five-component parallel factor analysis (PARAFAC) model roughly completed spectral characterization of oil samples, and obtained chemically meaningful information. Four advanced chemometrics methods were used for the classification of camellia oil and other vegetable oils (model 1) and the classification of camellia oil and adulterated camellia oil (models 2 and 3), respectively. Two-directional two-dimensional linear discriminant analysis ((2D)2LDA) was used for chemical data for the first time and showed huge potential. Furthermore, the developed N-PLS regression model used for the prediction of adulteration level in camellia oil showed satisfactory accuracy.

Identification and quantification of vegetable oil adulteration with waste frying oil by laser-induced fluorescence spectroscopy

Vegetable oils provide some important components and health benefits for human nutrition and In this work, we have investigated the potential of laser-induced fluorescence (LIF) spectroscopy combined with the principal component analysis (PCA) method and partial least squares (PLS) model as a tool for the identification and quantification of vegetable oils adulterated with waste frying oil. Four types of vegetable oils (rapeseed, olive, peanut, and corn) were selected as the original oil samples, and a total of 210 sets samples were measured. By employing a PLS model, the values of prediction linearity greater than 0.995 were obtained when four types of vegetable oils were adulterated with waste frying oil with a mean square error less than 2%. The results indicate that the method proposed in this work is feasible for the detection and quantification of vegetable oil adulteration with waste frying oil.

Atmospheric pressure chemical ionisation (APCI) and photoionisation (APPI) mass spectrometry for detection of unsaturated fatty acids: potential for rapid detection of adulteration of vegetable oils

Unsaturated fatty acids of vegetable oils can be rapidly determined with APCI and APPI mass spectrometry by comparison to standards. The difference in the ionization efficiencies of FA can be addressed by calculation of correction factors.

基于激光诱导荧光的植物油掺假检测与量化分析

基于激光诱导荧光的植物油掺假检测与量化分析

Fast simultaneous determination of capsaicin, dihydrocapsaicin and nonivamide for detecting adulteration in edible and crude vegetable oils by UPLC-MS/MS

ABSTRACTCapsaicinoids are pungent components in hot peppers, which have been detected in waste cooking oil. However, trace analysis of capsaicinoids in edible and crude vegetable oils is a challenging task due to the complex matrix. In this study, a simple liquid-liquid extraction and solid phase extraction (SPE) coupled with RP-UPLC-ESI-MS/MS method was developed for the quantification of capsaicinoids in edible and crude vegetable oils to screen the adulteration with waste cooking oil. This method was used to simultaneously determine 3 capsaicinoids (capsaicin, dihydrocapsaicin, and nordihydrocapsaicin) with capsaicin-d3, and dihydrocapsaicin-d3 as internal standards. This method allows the complete analysis of a sample in only an hour, even including sample preparation and chromatographic separation. The linear range of 3 capsaicinoids ranged between 0.5 and 40 µg/kg. The limit of detection (LOD) and limit of quantification (LOQ) for capsaicinoids were calculated as 0.15 and 0.5 µg/kg, respectively. Quantitative recoveries ranging from 92.9% to 105% were obtained by the analysis of spiked oil. The relative standard deviations were less than 5% (n = 6). The established method can potentially overcome the interference of triacylglycerols and fatty acids in edible and crude vegetable oils, and have been successfully applied to analyse real oil samples. This method provided a rapid and reliable method for the detection of adulteration of vegetable oils with waste cooking oils.

MCR-ALS and PLS coupled to NIR/MIR spectroscopies for quantification and identification of adulterant in biodiesel-diesel blends

Fourier transforming near infrared (FT-NIR) and mid infrared (FT-MIR) spectroscopies combined with multivariate curve resolution alternating least squares (MCR-ALS) and partial least squares (PLS) regression were utilized to identify and quantify residual vegetable oil adulteration in commercial diesel. In addition, physiochemical methods following ASTM standards were performed for comparison in identifying diesel adulteration. In total, 16 commercial diesel samples containing 8% biodiesel and 10mg sulfur/kg (named S10B8) were prepared with the addition of residual vegetable oil (1–60%) and were analyzed by FT-NIR and FT-MIR spectroscopies. The physical–chemical properties analyzed were atmospheric distillation, flash point, kinematic viscosity and specific mass tests. Only the distillation curve was able to identify the adulteration. A paired t-test and an F-test were performed for the MCR-NIR, MCR-MIR, PLS-NIR and PLS-MIR models. Through the results, it can be stated that all the models used in this work are valid for a 95% confidence level. Furthermore, no statistical difference was observed between the estimated concentrations of adulterant and the reference values. In this work, MCR-ALS was applied as a spectral decomposition tool as a new approach to identify the adulterant profile on biodiesel/diesel blends, followed by its quantification. MCR-ALS was able to recover the related pure spectral profile of the fuels and adulterant, and to accurately predict the concentration of frying oil in biodiesel/diesel samples.

High sensitive and efficient detection of edible oils adulterated with used frying oil by electron spin resonance

Edible oil adulteration exposes a high risk to consumer's health and is strictly regulated for food industry. However, efficient and reliable detection of food oil adulteration, particularly for detection of adulteration of commonly vegetable oils (rapeseed oil, peanut oil, sunflower oil, corn oil and olive oil), is not an easy task. In this paper, a high sensitive and reliable method of electron spin resonance (ESR) spin-trapping technique was introduced. α-phenyl-N-tert-butylnitrone (PBN) was used as spin trap to discriminate the adulteration of qualified vegetable oils, very intense spectra revealing a hyperfine splitting made up of a triplet of doublets (aN = 14.88G and aH = 1.81G) were applied. The initial signal intensities and increasing rate of the UFO were both higher than the qualified oils. All five qualified vegetable oils with higher adulteration proportions had higher initial signal intensities detected at 2 min, which could be assigned to thermal oxidation products that were present in adulterated oils. Initial signal intensities and adulteration proportions (<20%) were treated for quantitative analysis by linear regression. The simple linear equations were built and the coefficients of determination (R2) were all above 0.94 except PO (0.8920). Therefore, the proposed ESR spin-trapping technique could be applied to discriminate adulteration of qualified vegetable oils.

Simultaneous determination of capsaicin and dihydrocapsaicin for vegetable oil adulteration by immunoaffinity chromatography cleanup coupled with LC-MS/MS.

Capsaicin and dihydrocapsaicin were selected as adulteration markers to authenticate vegetable oils. In this study, a method of immunoaffinity chromatography (IAC) combined with liquid chromatography-tandem mass spectrometry was established for the determination of capsaicin and dihydrocapsaicin in vegetable oils. In this method, immunosorbents were obtained by covalently coupling highly specific capsaicinoid polyclonal antibodieswith CNBr-activated Sepharose 4B, and then packed into a polyethylene column. In this paper, the major parameters affecting IAC extraction efficiency, including loading, washing and eluting conditions, were also investigated. The IAC column displayed high selectivity for capsaicin and dihydrocapsaicin with the maximum capacity of 240ng. The limit of detection (LOD) and limit of quantification (LOQ) for capsaicin were calculated as 0.02 and 0.08μgkg(-1), and for dihydrocapsaicin were 0.03 and 0.10μgkg(-1). The recoveries of capsaicin and dihydrocapsaicin in oil samples were in the range of 87.3-95.2% with the relative standard deviation (RSD) of less than 6.1%. The results indicated that capsaicinoid compounds could not be found in edible vegetable oils. Therefore, the proposed method is simple, reliable and adequate for routine monitoring of capsaicinoid compounds in vegetable oils and has an excellent potential for detection of adulteration with inedible waste oil.