Adulterated Sesame Oil Research Articles

Discrimination of pressed sesame oil: A comparison study of non-targeted UV spectral fingerprints combined with different chemometric methods

This study explores the utilization of various chemometric analytical methods for determining the quality of pressed sesame oil with different adulteration levels of refined sesame oil using UV spectral fingerprints. The goal of this study was to provide a reliable tool for assessing the quality of sesame oil. The UV spectra of 51 samples of pressed sesame oil and 420 adulterated samples with refined sesame oil were measured in the range of 200–330 nm. Various classification and prediction methods, including linear discrimination analysis (LDA), support vector machines (SVM), soft independent modeling of class analogy (SIMCA), partial least squares regression (PLSR), support vector machine regression (SVR), and back-propagation neural network (BPNN), were employed to analyze the UV spectral data of pressed sesame oil and adulterated sesame oil. The results indicated that SVM outperformed the other classification methods in qualitatively identifying adulterated sesame oil, achieving an accuracy of 96.15%, a sensitivity of 97.87%, and a specificity of 80%. For quantitative analysis, BPNN yielded the best prediction results, with an R2 value of 0.99, RMSEP of 2.34%, and RPD value of 10.60 (LOD of 8.60% and LOQ of 28.67%). Overall, the developed models exhibited significant potential for rapidly identifying and predicting the quality of sesame oil.

Compact three-dimensional fluorescence spectroscopy and its application in food safety

Three-Dimensional fluorescence spectroscopy is an indispensable tool for identification of substances by virtue of its ability to supply abundant characteristic information and high spectral resolution. In this work, we present a compact three-dimensional fluorescence spectrometer utilizing 18 different wavelength LEDs as excitation light source, instead of the conventional bulky multi-wavelength illumination strategy. This spectrometer can in situ obtain the spectral information accurately and fast, while reducing the weight and volume of system. To show its broad biochemical utility, we utilize the compact spectrometer to measure four types of vegetable oil samples from different origins, and analyzed their spectral characteristics. Moreover, we construct a deep learning model based on the 2D-CNN network to detect sesame oil adulteration qualitatively and quantitively. The Compact fluorescence spectrometer proposed has the potential to classify and grade food oils in situ, addressing the problem of sesame oil adulteration on the market.

Two flavors in adulterated sesame oil: discovery, confirmation, and content regularity study.

Exploring and accurately detecting new adulteration markers in sesame oil is an important measure for sesame oil adulteration monitoring. In this study, two endogenous flavors sulfurol and γ-nonalactone which can be used as potential adulteration markers were first discovered in sesame oil and accurately quantified. First, the two endogenous flavors were discovered using gas chromatography-mass spectrometry (GC-MS), and their structures were confirmed by comparing the mass spectrograms with the NIST spectral library. Then the liquid chromatography-tandem mass spectrometry (LC-MS/MS) method using direct methanol extraction pretreatment and vanillin-D3 as an internal standard was developed for rapid quantitation and application. The method was successfully validated with recoveries ranging from 88.5% to 102.2% and relative standard deviations between 2.6% and 10.5% (n = 6). The combined method of GC-MS and LC-MS/MS was indicated to be efficient and highly sensitive for detection of sulfurol and γ-nonalactone in edible oil. Subsequently, 31 sesame oils from the market were detected, revealing that 31 samples contained the identified flavors within a relatively consistent range. However, the concentration of these flavor substances in one sample was abnormally high, indicating that there was a potential risk of adulteration. Therefore, the developed method shows good potential for quality evaluation and adulteration screening of sesame oil.

Monitoring thermal stability of pure and adulterated sesame oil using Fourier transform infrared spectroscopy and chemometric analysis

This work investigates the application of Fourier Transform Infrared spectroscopy (FTIR) combined with chemometrics as a rapid and non-destructive tool for monitoring the thermal stability of pure sesame oil (SeO) as well as SeO blends with adulterants, including corn, soybean, and sunflower oils in different proportions (99 + 1, 95 + 5, 90 + 10, and 80 + 20 in volume). The oil samples were subjected to thermal treatment at five temperatures (25, 60, 100, 150, and 180 °C) as a function of time up to 96 h. FTIR provided sensitive approach for analysing thermal degradation and compositional changes of pure and adulterated SeO under controlled temperature conditions during varied heating times. Principal component analysis (PCA) was used to discriminate pure SeO at different treatment conditions. In addition, soft independent modelling class analogy (SIMCA) was useful to differentiate between pure and adulterated samples, and the results showed high precision and accuracy, enabling successful discrimination, with a maximum error rate of 6.00%. Finally, partial least squares regression (PLSR) models were developed and showed a good linear correlation between the FTIR signals at different conditions and the proportion of pure SeO. The root mean square of prediction (RMSEP) ranges between 0.8802 and 2.3827 with a coefficient of determination (R2) between 0.9841 and 0.8834, respectively. These findings demonstrate the efficacy of FTIR combined with chemometrics in detecting adulteration in SeO, thus offering a solution for quality control and food safety assessments in the food industry.

A methodological approach to preprocessing FTIR spectra of adulterated sesame oil.

Fourier transform infrared (FTIR) spectroscopy is established as an effective and fast method for the confirmation of the authenticity of food and among other, edible oils. However, no standard procedure is available for applying preprocessing as a vital step in obtaining accurate results from spectra. This study proposes a methodological approach to preprocessing FTIR spectra of sesame oil adulterated with vegetable oils (canola oil, corn oil, and sunflower oil). The primary preprocessing methods investigated are orthogonal signal correction (OSC), standard normal variate transformation (SNV), and extended multiplicative scatter correction (EMSC). Other preprocessing methods are used both as standalone methods and in combination with the primary preprocessing methods. The preprocessing results are compared using partial least squares regression (PLSR). OSC alone or with detrending were the most accurate in predicting the adulteration level of sesame oil, with a maximum coefficient of prediction (R2p) range of 0.910 to 0.971 for different adulterants.

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.

Aromatic Fingerprints: VOC Analysis with E-Nose and GC-MS for Rapid Detection of Adulteration in Sesame Oil.

Food quality assurance is an important field that directly affects public health. The organoleptic aroma of food is of crucial significance to evaluate and confirm food quality and origin. The volatile organic compound (VOC) emissions (detectable aroma) from foods are unique and provide a basis to predict and evaluate food quality. Soybean and corn oils were added to sesame oil (to simulate adulteration) at four different mixture percentages (25-100%) and then chemically analyzed using an experimental 9-sensor metal oxide semiconducting (MOS) electronic nose (e-nose) and gas chromatography-mass spectroscopy (GC-MS) for comparisons in detecting unadulterated sesame oil controls. GC-MS analysis revealed eleven major VOC components identified within 82-91% of oil samples. Principle component analysis (PCA) and linear detection analysis (LDA) were employed to visualize different levels of adulteration detected by the e-nose. Artificial neural networks (ANNs) and support vector machines (SVMs) were also used for statistical modeling. The sensitivity and specificity obtained for SVM were 0.987 and 0.977, respectively, while these values for the ANN method were 0.949 and 0.953, respectively. E-nose-based technology is a quick and effective method for the detection of sesame oil adulteration due to its simplicity (ease of application), rapid analysis, and accuracy. GC-MS data provided corroborative chemical evidence to show differences in volatile emissions from virgin and adulterated sesame oil samples and the precise VOCs explaining differences in e-nose signature patterns derived from each sample type.

Comprehensive adulteration detection of sesame oil based on characteristic markers.

Sesame oil has a unique flavor and is very popular in Asian countries, and this leads to frequent adulteration. In this study, comprehensive adulteration detection of sesame oil based on characteristic markers was developed. Initially, sixteen fatty acids, eight phytosterols, and four tocopherols were utilized to construct an adulteration detection model, which screened seven potentially adulterated samples. Subsequently, confirmatory conclusions were drawn based on the characteristic markers. Adulteration with rapeseed oil in 4 samples was confirmed using the characteristic marker of brassicasterol. The adulteration of soybean oil in 1 sample was confirmed using the isoflavone. The adulteration of 2 samples with cottonseed oil was demonstrated by sterculic acid and malvalic acid. The results showed that sesame oil adulteration could be detected by screening positive samples using chemometrics and verifying with characteristic markers. The comprehensive adulteration detection method could provide a system approach for market supervision of edible oils.

Rapid detection of sesame oil multiple adulteration using a portable Raman spectrometer

Multiple adulteration is a commonly used fraud of illegal traders to mask the traditional adulteration detection methods. In this study, rapid detection of multiple adulteration of sesame oil was proposed using a portable Raman spectrometer. Two strategies including simplex theory of mixtures and D-optimal mixture design were used to conduct variable selection and model evaluation, respectively. Based on simplex theory of mixtures, the important variables were selected by orthogonal partial least squares discriminant analysis of preprocessed Raman spectra of sesame oils and four adulterant oils. Moreover, multiple adulteration identification model was built by one-class partial least squares and validated by representative adulterated samples prepared by D-Optimal mixture design. The validation results show that 40 sesame oils adulterated with four types of adulterant oils can be correctly identified, indicating Raman spectroscopy is an effective tool for the detection of multiple adulteration of sesame oil, especially for on-site applications.

Authenticity Assessment from Sesame Seeds to Oil and Sesame Products of Various Origin by Differential Scanning Calorimetry.

The aim of this study was to conduct thermal characterization of sesame seeds and oils from various geographical origins (Ethiopia, India, Nigeria, Sudan, Turkey), different method of extraction (hexane and cold-pressing), and different types of derived products (halva and tahini). Thermal characterization was investigated using differential scanning calorimetry (DSC), which showed that origin of the seeds has no influence on the melting profile of sesame oil (peak temperature and enthalpy). Method of extraction (hexane and cold-pressing) influenced the peak temperatures of the resulting oils (p ≤ 0.05). The addition of 20% of palm olein to pure sesame oil influenced the significant changes in thermodynamic parameters such as peak temperature (Tm2), which was lowered from -5.89 °C to -4.99 °C, peak half width (T1/2), elevated from 3.01 °C to 4.52 °C, and the percentage of first peak area (% peak 1) lowered from 87.9 to 73.2% (p ≤ 0.05). The PCA method enabled to distinguish authentic and adulterated sesame oils of various origins. There were no significant differences in thermal properties among the products (halva, tahini) and the authentic sesame oil (p > 0.05). The obtained results showed DSC feasibility to characterize sesame oil and sesame products in terms of authenticity.

Vision transformer for quality identification of sesame oil with stereoscopic fluorescence spectrum image

Sesame oil (SO), as a high-priced edible oil, is often counterfeited and adulterated. A new method for SO quality identification using Vision Transformer (ViT) network based on stereoscopic images of Excitation-emission matrix fluorescence (EEMF) and Total synchronous fluorescence (TSyF) spectroscopy was proposed. The basic samples including pure, counterfeit and adulterated SOs were characterized by fluorescence spectroscopy. A data augmentation strategy including linear interpolation, shift and noise injection was selected for few sample learning. All fluorescence spectral data were visualized as stereoscopic images with rich spectral characteristics. The ViT network architecture based on attention mechanism was designed and trained to establish four SO quality identification models. The macro averages of precision, recall and F1-score on the validation set were greater than 0.99. The values of these indicators on the test samples were equal to one. In conclusion, deep learning based on ViT using stereoscopic fluorescence spectrum image provided a new method for sesame oil identification.

Visual classification for sesame oil adulteration detection and quantification of compounds used as adulterants using flavor compounds targeted array sensor in combination with DD-SIMCA and PLS

Sesame oil is a kind of vegetable oil, which is loved by people all over the world due to its unique aroma, edible and medicinal value. Driven by interests, counterfeit and shoddy sesame oil products often appear on the market. This study is based on the color change caused by the competitive coordination of Zn2 + at the phase interface between four polyphenol dyes (Alizarin Red S, bromocatechol red, pyrogallol red and catechol violet) and volatile authenticity markers (VAM) in sesame oil. The DD-SIMCA model was constructed based on the RGB values of each sensing point after the reaction of real sesame oil and adulterated sesame oil with four polyphenol dyes. The results of the DD-SIMCA classification model show that the accuracy of sesame oil classification can reach 100%, and the result of the array sensor is much better than that of the single dye. Furthermore, the PLSR quantitative analysis model is used to verify that the RGB value obtained by the sensor is linearly related to the adulteration concentration. Therefore, this study established a visual array sensor for rapid authenticate of sesame oil adulteration based on the flavor compounds.

Non-destructive Detection of Sesame Oil Adulteration by Portable FT-NIR, FT-MIR, and Raman Spectrometers Combined with Chemometrics

Edible oils are often adulterated with fixed oils because of their high quality and price. Sesame oil is prone to adulteration due to its high commodity value and popularity. Therefore, a rapid, simple, and non-invasive method to detect adulteration in sesame oil is necessary for quality control purposes. Handheld and portable FT-NIR, FT-MIR, and Raman spectrometers are easy to operate, non-destructive, rapid, and easy to transport for in-situ assessments as well as being cheaper alternatives to traditional instruments. This study aimed to evaluate three different vibrational spectroscopic techniques in detecting sesame oil adulteration with sunflower and canola oil. Sesame oils were adulterated with fixed oils at different concentrations (0 – 25%) (w/w). Spectra were collected with portable devices and analyzed using Soft Independent Modelling of Class Analogy (SIMCA) to generate a classification model to authenticate pure sesame oil and Partial Least Squares Regression (PLSR) to predict the levels of the adulterant. For confirmation, the fatty acid profile of the oils was determined by gas chromatography (GC). In all three instruments, SIMCA provided distinct clusters for pure sesame oils and adulterated samples with interclass distance (ICD) over 3. Furthermore, FT-NIR and FT-MIR showed excellent performance in predicting adulterant levels with rval>0.96. Specifically, the FT-MIR unit provided more precise classification and PLSR prediction models over FT-NIR and Raman units. Still, all the units can be used as an alternative method to traditional methods such as GC, GC-MS, etc. These units showed great potential for in-situ surveillance to detect sesame oil adulterations.

Maillard reaction products and guaiacol as production process and raw material markers for the authentication of sesame oil

Sesame oil has an excellent flavor and is widely appreciated. It has a higher price than other vegetable oils because of the high price of its raw materials, and different processing techniques also result in products of different quality levels, which can command different prices. In the market, there is a persistent problem of adulteration of sesame oil, driven by economic interests. The screening of volatile markers used to distinguish the authenticity of sesame oil raw materials and production processes is therefore very important. In this work, six markers related to the production processes and raw materials of sesame oil were screened by gas chromatography-tandem mass spectrometry (GC-MS/MS) combined with chemometric analysis. They were 3-methyl-2-butanone, 2-ethyl-5-methyl-pyrazine, guaiacol, 2,6-dimethyl-pyrazine, 5-methyl furfural, and ethyl-pyrazine. The concentration of these markers in sesame oil is between 10 and1000 times that found in other vegetable oils. However, only 3-methyl-2-butanone and 2-ethyl-5-methyl-pyrazine differed significantly as the result of the use of different production processes. Except for guaiacol, which was mainly derived from raw materials, the other five compounds mentioned above all result from the Maillard reaction during thermal processing. The six compounds mentioned above are sufficient to distinguish fraud involving sesame oil raw materials and production processes, and can identify accurately adulteration levels of 30% concentration. In this study, the classification markers can identify the adulteration of sesame oil accurately. These six compounds are therefore important for the authenticity of sesame oil and provide a theoretical basis for the rapid and accurate identification of the authenticity of sesame oil. © 2021 Society of Chemical Industry.

Dielectric spectroscopy coupled with artificial neural network for classification and quantification of sesame oil adulteration

Adulteration using cheap vegetable oils into expensive oils such as sesame oil is a considerable challenge in the edible oil market. To discriminate pure and adulterated sesame oil with sunflower and canola oils (commonly used as an adulterant to the high-price oils), dielectric spectroscopy was applied in the range of 40 kHz–20 MHz. The principal component analysis (PCA) plots were able to distinguish the pure sesame oil, while it was impossible to separate the adulterated oils based on the kind of adulteration. The correlation-based feature selection (CFS) method was used to select the more relevant dielectric data within the spectrum and to reduce the dimensionality of the input vector belongs to the artificial neural network (ANN). The ANN classifier with topology of 19-5-4 structure showed a perfect accuracy of 100% in detecting the authentic and the adulterated sesame oil. The regression ANN with the topology of 15-5-1, 21-8-1 and 10-11-1 were the most robust models in quantifying the amount of adulteration in sesame oil generated by sunflower oil, canola oil and sunflower + canola oils, with R2Test of 1, 1 and 0.999 9, respectively. The proposed technique is a powerful and simple method to detect and quantify adulteration of sesame oil. The novelty of this research is capability of used system for authentication of adulterated sesame oil using low frequency. Furthermore, the developed system has a good capability for other types of sesame oil adulterations as well as to detect adulteration in other expensive edible oils.

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.

Total synchronous fluorescence spectroscopy coupled with deep learning to rapidly identify the authenticity of sesame oil.

The quality of sesame oil (SO) has been paid more and more attention. In this study, total synchronous fluorescence (TSyF) spectroscopy and deep neural networks were utilized to identify counterfeit and adulterated sesame oils. Firstly, typical samples including pure SO, counterfeit sesame oil (CSO) and adulterated sesame oil (ASO) were characterized by TSyF spectra. Secondly, three data augmentation methods were selected to increase the number of spectral data and enhance the robustness of the identification model. Then, five deep network architectures, including Simple Recurrent Neural Network (Simple RNN), Long Short-Term Memory (LSTM) network, Gated Recurrent Unit (GRU) network, Bidirectional LSTM (BLSTM) network and LSTM fortified with Convolutional Neural Network (LSTMC), were designed to identify the CSO and trace the source with 100% accuracy. Finally, ASO samples were also 100% correctly identified by training these network architectures. These results supported the feasibility of the novel method.

Exploration of total synchronous fluorescence spectroscopy combined with pre-trained convolutional neural network in the identification and quantification of vegetable oil

In order to distinguish different vegetable oils, adulterated vegetable oils, and to identify and quantify counterfeit vegetable oils, a method based on a small sample size of total synchronous fluorescence (TSyF) spectra combined with convolutional neural network (CNN) was proposed. Four typical vegetable oils were classified by three ways of fine-tuning the pre-trained CNN, the pre-trained CNN as a feature extractor, and traditional chemometrics. The pre-trained CNN was combined with support vector machines to distinguish adulterated sesame oil and counterfeit sesame oil separately with 100% correct classification rates. The pre-trained CNN combined with partial least square regression was used to predict the level of counterfeit sesame oil. The coefficient of determination for calibration (Rc2) values were all greater than 0.99, and the root mean square errors of validation were 0.81% and 1.72%, respectively. These results show that it is feasible to combine TSyF spectra with CNN for vegetable oil identification.

Rapid authentication of sesame oil using ion mobility spectrometry and chemometrics

To ensure authenticity of sesame oil, an authentication technology was proposed using ion mobility spectrometry (IMS) and chemometrics. One-class classification (OCC) methods including one-class partial least squares (OCPLS) and one-class support vector machine (OCSVM) were employed to build authentication models for sesame oil. Subsequently, an independent test set was used to validate the constructed models. Validation set of 45 adulterated oils indicated that prediction correction rate of OCPLS model reached 95.6% (43 out of 45). Moreover, the complete set of sesame oils adulterated by sesame oil essence could be identified as counterfeit. Compared with previous studies, OCPLS model could work to identify untargeted adulteration. In conclusion, OCC method could effectively detect adulterated sesame oils containing as little as 10% other vegetable oils. This study provided a rapid screening method for adulterated sesame oil in market surveillance and a reference for developing authentication methods of other edible oils.

Rapid detection of the authenticity and adulteration of sesame oil using excitation-emission matrix fluorescence and chemometric methods

Sesame oil (SO) is a high-quality oil that is more expensive than other edible oils, and therefore becomes a target of economically motivated adulteration. An approach based on excitation-emission matrix (EEM) fluorescence and chemometric methods was applied for the rapid classification and determination of the authenticity of SO. First, a five-factor alternating trilinear decomposition (ATLD) model roughly completed the characterization of the fluorescent components in the edible oil samples, providing meaningful chemical information. Then, four chemometric methods, including linear discriminant analysis (LDA), partial least squares–discriminant analysis (PLS-DA), support vector machine (SVM) and unfolded partial least-squares discriminant analysis (UPLS-DA), were used to establish the models for the classification of SO and other edible oils (Model 1), and determine the authenticity of SO and adulterated SOs (Model 2). All models achieved good classification results. The combination of the second-order calibration algorithm (ATLD) and the pattern recognition algorithm (LDA, PLS-DA, or SVM) not only achieved the characterization of the components in edible oils but also realized the rapid detection of adulterated SOs. The proposed method is rapid, accurate, requires a simple sample pre-treatment and can be used to determine the authenticity and adulteration of high-quality edible oils.