Method For Detection Of Adulteration Research Articles

A Dual and Rapid RPA-CRISPR/Cas12a Method for Simultaneous Detection of Cattle and Soybean-Derived Adulteration in Goat Milk Powder.

The adulteration of goat milk powder occurs frequently; cattle-derived and soybean-derived ingredients are common adulterants in goat milk powder. However, simultaneously and rapidly detecting cattle-derived and soybean-derived components is still a challenge. An efficient, high-throughput screening method for adulteration detection is needed. In this study, a rapid method was developed to detect the adulteration of common cattle-derived and soybean-derived components simultaneously in goat milk powder by combining the CRISPR/Cas12a system with recombinant polymerase amplification (RPA). A dual DNA extraction method was employed. Primers and crRNA for dual detection were designed and screened, and a series of condition optimizations were carried out in this experiment. The optimized assay rapidly detected cattle-derived and soybean-derived components in 40 min. The detection limits of both cattle-derived and soybean-derived components were 1% (w/w) for the mixed adulteration models. The established method was applied to a blind survey of 55 commercially available goat milk powder products. The results revealed that 36.36% of the samples contained cattle-derived or soybean-derived ingredients, which revealed the noticeable adulteration situation in the goat milk powder market. This study realized a fast flow of dual extraction, dual amplification, and dual detection of cattle-derived and soybean-derived components in goat milk powder for the first time. The method developed can be used for high-throughput and high-efficiency on-site primary screening of goat milk powder adulterants, and provides a technical reference for combating food adulteration.

Multivariate optimization of hyperspectral imaging for adulteration detection of ground beef: Towards the development of generic algorithms to predict adulterated ground beef and for digital sorting

Due to its nutritional value, and taste, minced beef meat (MBM) is highly valuable therefore is susceptible to economically motivated adulteration. High-throughput adulteration detection methods are required to guarantee the authenticity and safety of the MBM. Hyperspectral imaging (HSI) in the 400–1000 nm spectral range, in tandem with multivariate analysis, was evaluated to demonstrate the feasibility of developing generic models to detect minced beef meat adulterated with other types of meats. Partial least squares regression (PLSR) method, the ensemble Monte Carlo variable selection method (EMCVS), a range of spectral pre-treatments, and combinations of any two of them were evaluated to predict the amount of MBM in each prepared sample scanned. The beef prediction model was developed using data from MBM adulterated with minced chicken and turkey meats and validated with data from MBM adulterated with pork meat at adulteration levels ranging from 0 to 51% at approximately 3% increments. Good prediction results were obtained using the EMCVS, on the asymmetric least squares (AsLs) + Standard Normal variate (SNV) pre-treated reflectance spectra (Rp2 0.96, RMSEP 2.9% and RPD 5.4) with 23 selected wavelengths. An independent set of MBM samples adulterated with lamb and duck meat were then prepared similarly at concentrations ranging from 3 to 21% to test the developed method. Good results were obtained in the full range (R2p 0.94, RMSEp 3.92%, RPDp 3.83) and excellent prediction results (R2p 0.95, RMSEp 2.78%, RPDp 5.29) were obtained with 9 optimum wavelengths (484, 508, 593, 596, 602, 736, 739, 924 and 960 nm). PLS discriminant analysis (PLS-DA), the EMCVS and spectral pre-treatments were used to discriminate MBM from other adulterants and achieved almost perfect classification for calibration, cross-validation, and prediction using 12 selected spectral bands. This study developed a high-throughput generic minced meat adulteration prediction and discrimination method based on selected spectral bands that can be used to further develop low-cost portable sensors for the digital sorting of adulterated MBM, and demonstrates the feasibility to develop generic models to detect minced beef meat adulterated with other types of meat.

Current progress on meat food authenticity detection methods

Meat adulteration could be intentional or unintentional, which may infringe consumers’ rights and interests, endanger public health, disrupt the market, and hinder the development of the meat industry. Determining fast and efficient meat adulteration detection methods is therefore critical for the development of the food industry and the safety of consumers. Based on the classification of target substances, adulteration detection technologies are classified in protein-, metabolite-, or nucleic acids-based. Although these detection techniques have yielded good identification results when applied to meat adulteration detection, certain shortcomings are still observed. This review aims to summarize the latest progress on meat adulteration detection technologies, thus providing a reference basis for food safety and surveillance departments and researchers within this field.

Development of a sensitive and user-friendly allele-specific PCR assay for adulteration detection of spices

Spice adulteration not only seriously interferes with their flavoring functions but also leads to life-threatening poisoning for consumers. To overcome the limitations of traditional methods in spice adulteration detection, a multiplex allele-specific PCR system was developed for molecular discrimination of four commonly used spices, Foeniculum vulgare Mill., Zanthoxylum bungeanum Maxim., Illicium verum Hook.f., and Syzygium aromaticum (L.) Merr. & L.M.Perry, from their corresponding adulterants based on chloroplast SNP markers. The developed assay, eliminating the obstacles of DNA sequencing and false negative results, can detect 0.1% of spice adulteration down to 0.01 ng level of genomic DNA with absolute allelic specificity and favorable efficiency. Based on the results, a standard operating procedure for using multiplex allele-specific PCR for spice adulteration detection was established. Therefore, the present study provided a simple, reliable, and sensitive molecular method for adulteration detection of spices.

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 Low-Cost Closed-Tube Method for Detection of Adulteration in Ground Meat

ABSTRACT The increased public concern about adulteration and mislabelling of meat products has fueled a need for the development of fast, reliable, and cost-effective species detection methods. Hence, we developed a high resolution melt analysis (HRMA) for the discrimination of meat species commonly used in the meat industry and their possible adulterants. A universal primer pair spanning a ⁓238 bp fragment of mitochondrial 16S rRNA gene was designed to perform HRMA. The developed workflow was validated on standard meat samples, ternary meat mixtures of eight different species, and 56 commercial meat samples. Distinct melting profiles were generated for each species, several ternary meat mixtures, and commercial food samples. The assay was sufficiently sensitive to detect 0.003 ng/µl of DNA of every targeted species. In short, the developed HRM analysis is a rapid, cost-effective, and efficient species discrimination system for confirmation of meat origin.

Modified, innovative method for detection of adulteration of meat pro­ducts with meat of other species

The aim of this study was to detect illegal adulteration of beef meat products with meat from other species. Samples (n=120) of industrial and handmade beef products were randomly collected from retail outlets in Assiut city, Egypt: raw beef burger, oriental beef sausage, beef kofta, and beef luncheon (30 samples each). Samples were analysed using agar gel immunodiffusion (AGID) and modified AGID (MAGID) assays. Results showed that 17.5% of examined products were adulterated with chicken meat. MAGID detected that 14.1% of samples were adulterated with donkey meat, whereas all AGID results were negative. Human tissue was detected in 8.3% (AGID) and 10% (MAGID) of examined samples. Histological examination was then used to detect foreign tissue, and all categories of products were found to be adulterated, and some of them - contaminated with human blood cells. Polymerase chain reaction analysis confirmed that MAGID was more accurate and sensitive than AGID, especially for false negative AGID results. Consumers are advised not to consume too much of the studied meat products to avoid exposure to adulterated or contaminated products that might constitute a health hazard.

Development of a screening method for adulteration detection in coconut milk via mid-infrared spectroscopy: A study of linear and nonlinear regression method

In the present study, we developed a screening method for detection of adulteration in coconut milk via mid-infrared spectroscopy. Linear and nonlinear regression methods (principal component regression (PCR), partial least squares regression (PLSR), and support vector machine regression (SVMR)) were employed and compared to achieve an optimal screening method. Spectral data were scanned using the FTIR benchtop with a wavelength range of 4000–16702 nm. The calibration models of the linear and nonlinear regression methods were developed using the leave-one-out cross-validation method before testing using predictive data that had been prepared. Furthermore, five spectral data treatment techniques were employed to improve the accuracy of the proposed calibration model. The results obtained show that the SVMR method is better than PCR and PLSR for the detection of adulteration in coconut milk by mid-infrared data spectroscopy. The coefficient of determination for calibration (R2c) and prediction (R2p), the root mean square error of calibration (RMSEC) and prediction (RMSEP) and the ratio of prediction to deviation (RPD) using the SVMR method were 100%, 0.81, 98.40%, 0.87 and 7.86, respectively. Furthermore, based on RPD analysis, it is known that the SVMR model can be used to perform excellent quality control of water-adulterated coconut milk.

Establishment and evaluation of multiple adulteration detection of camellia oil by mixture design

Multiple adulteration is a common trick to mask adulteration detection methods. In this study, the representative multiple adulterated camellia oils were prepared according to the mixture design. Then, these representative oils were employed to build two-class classification models and validate one-class classification model combined with fatty acid profiles. The cross-validation results indicated that the recursive SVM model possessed higher classification accuracy (97.9%) than PLS-DA. In OCPLS model, the optimal percentage of RO, SO, CO and SUO was 2.8%, 0%, 7.2%, 0% respectively in adulterated camellia oil, which is the most similar to the authentic camellia oils. Further validation showed that five adulterated oils with the optimal percentage could be correctly identified, indicating that the OCPLS model could identify multiple adulterated oils with these four cheaper oils. Moreover, this study serves as a reference for one class classification model evaluation and a solution for multiple adulteration detection of other foods.

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.

Qualitative and Quantitative Detection of Acacia Honey Adulteration with Glucose Syrup Using Near-Infrared Spectroscopy

Honey adulteration with cheap sweeteners such as corn syrup or invert syrup results in honey of lesser quality that can harm the objectives of both manufacturers and consumers. Therefore, there is a growing interest for the development of a fast and simple method for adulteration detection. In this work, near-infrared spectroscopy (NIR) was used for the detection of honey adulteration and changes in the physical and chemical properties of the prepared adulterations. Fifteen (15) acacia honey samples were adulterated with glucose syrup in a range from 10% to 90%. Raw and pre-processed NIR spectra of pure honey samples and prepared adulterations were subjected to Principal Component Analysis (PCA), Partial Least Squares (PLS) regression, and Artificial Neural Network (ANN) modeling. The results showed that PCA ensures distinct grouping of samples in pure honey samples, honey adulterations, and pure adulteration using NIR spectra after the Multiplicative Scatter Correction (MSC) method. Furthermore, PLS models developed for the prediction of the added adulterant amount, moisture content, and conductivity can be considered sufficient for screening based on RPD and RER values (1.7401 < RPD < 2.7601; 7.7128 < RER < 8.7157) (RPD of 2.7601; RER of 8.7157) and can be moderately used in practice. The R2validation of the developed ANN models was greater than 0.86 for all outputs examined. Based on the obtained results, it can be concluded that NIR coupled with ANN modeling can be considered an efficient tool for honey adulteration quantification.

Non-destructive determination of grass pea and pea flour adulteration in chickpea flour using near-infrared reflectance spectroscopy and chemometrics.

In order to obtain more economic gains, some food products are adulterated with low-cost substances, if they are toxic, they may pose public health risks. This has called forth the development of quick and non-destructive methods for detection of adulterants in food. Near-infrared reflectance spectroscopy (NIRS) has become a promising tool to detect adulteration in various commodities. We have developed rapid NIRS based analytical methods for quantification of two cheap adulterants (grass pea and pea flour) in a popular Indian food material, chickpea flour. The NIRS spectra of pure chickpea, pure grass pea, pure pea flour and adulterated samples of chickpea flour with grass pea and pea flour (1-90%) (w/w) were acquired and preprocessed. Calibration models were built based on modified partial least squares regression (MPLSR), partial least squares (PLS), principal component regression (PCR) methods. Based on lowest values of standard error of calibration (SEC) and standard error of cross-validation (SECV), MPLSR-NIRS models were selected. These models exhibited coefficient of determination (R2 ) of 0.999, 0.999, SEC of 0.905, 0.827 and SECV of 1.473, 1.491 for grass pea and pea, respectively. External validation revealed R2 and standard error of prediction (SEP) of 0.999 and 1.184, 0.997 and 1.893 for grass pea and pea flour, respectively. The statistics confirmed that our MPLSR-NIRS based methods are quite robust and applicable to detect grass pea and pea flour adulterants in chickpea flour samples and have potential for use in detecting food fraud. © 2022 Society of Chemical Industry.

Rapid, accurate and simply-operated determination of laboratory-made adulteration of quinoa flour with rice flour and wheat flour by headspace gas chromatography-ion mobility spectrometry

The growing consumption of quinoa flour has triggered an elevating concern about its adulteration with similar but cheaper cereals. Herein, a headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) method was applied for the authentication of laboratory-made quinoa flour adulteration with wheat and rice flours. A total of 81 volatile compounds were detected from the 28 samples. Principal component analysis (PCA), cluster analysis (CA), and orthogonal partial least square discriminant analysis (OPLS-DA) were used to distinguish pure from adulterated samples. Authentic and adulterated quinoa flours using different proportions (5, 10, 20, 40, and 80%, w/w) were easily classified based on their volatile profiles. More importantly, n-hexanol, 3-methyl-1-butanol, (E)-2-hexenal, butyrolactone, 1,8-cineole, alpha-phellandrene, 2-propanone, and 2-hexanone were proposed to be volatile markers for quinoa flour authentication from the adulterants. The HS-GC-IMS based determination of volatiles was confirmed to be an effective method for adulteration detection of quinoa flour.

Nondestructive detection for adulteration of panax notoginseng powder based on hyperspectral imaging combined with arithmetic optimization algorithm‐support vector regression

AbstractIllegal merchants adulterate the powder of panax notoginseng rhizome and main root with the powder of panax notoginseng fibrous root, which reduces the efficacy and hygiene of panax notoginseng powder. To detect the adulteration of panax notoginseng powder nondestructively and rapidly, in this study, the hyperspectral images of 80 samples of adulterated rhizome powder and adulterated main root powder (AR & AM) were collected by using a hyperspectral image acquisition system (400–1000 nm). Then, Savitzky–Golay and standard normalized variable (SG‐SNV) were used to preprocess the spectrum and improve the signal‐to‐noise ratio of the data. Next, competitive adaptive reweighed sampling (CARS) and iteratively retains informative variables (IRIV) methods were respectively adopted to select feature wavelengths from the spectra data after pretreatment. Subsequently, support vector regression (SVR) was used to establish the prediction models based on the selected variables and the full spectra data. Also, the arithmetic optimization algorithm (AOA) was used to optimize the parameters of the SVR model. The results showed that CARS‐AOA‐SVR was the optimal model for AR & AM, achieving of .9693 and .9667, RMSEP of .0266 and .0264, respectively. Therefore, the hyperspectral imaging combined with the CARS‐AOA‐SVR is a feasible method to detect the adulteration concentration of panax notoginseng powder.Practical ApplicationsThe traditional detection methods for adulteration of panax notoginseng powder depended on manual detection and chemical analysis. In order to detect the adulterated concentration of panax notoginseng powder rapidly and accurately, the adulterated panax notoginseng powder was studied based on hyperspectral imaging. The results showed that hyperspectral imaging combined with CARS‐AOA‐SVR model could be used to quantitatively detect the adulteration concentration of panax notoginseng powder, which could be a fast, accurate, and nondestructive method for adulteration detection of panax notoginseng powder and other powdery materials.

Mechanism of bluish pigment formation in lotus rhizome starch with ferrous sulfate and its application in rapid detection of adulteration

ABSTRACT Lotus rhizome starch (LRS) is an important traditional processed product of lotus rhizome in China due to its good taste and high nutritional value. However, adulterated lotus rhizome starches are prevalent on the market. In this study, a novel method for the rapid detection of adulteration in LRS based on color reaction between ferrous sulfate (FS) and (-)-gallocatechin (GC) in LRS was studied. The polyphenols in LRS were analyzed by liquid chromatography-mass spectrometry (LC-MS/MS) as GC and (+)-catechin, and GC could show a bluish color with FS solution. LRS showed a bluish color when FS solution (0.01 mol/L) was used as the chromogenic agent, which was not observed for other ten kinds of starches tested in this work. There was an obvious color gradient when FS solution was used to react with adulterated LRS at different proportions, and the change in blue color was obvious when maize starch or cassava starch was adulterated in LRS at the ratio of 6:4. Then, a semi-quantitative method was used to make a color card according to the color gradient, which was then successfully used to detect the adulteration of four kinds of commercial LRS. The results indicate that the proposed method may facilitate the simple, effective and efficient detection of adulteration in LRS.

Evaluation of fresh palm oil adulteration with recycled cooking oil using GC-MS and ATR-FTIR spectroscopy: A review

Palm oil (PO) is an edible vegetable oil that is extracted from the mesocarp of oil palm fruit (Elaeis guineensis), which is known to contain an almost equal proportion of saturated fatty acids (SFAs) and unsaturated fatty acids (USFAs). PO is used globally, because of its wide application as a frying medium. Extracted from the mesocarp of the oil palm fruit, PO needs to be processed to make it of edible quality. However, to meet growing global demand, it is often adulterated with recycled cooking oil (RCO), which is of inedible quality. As the methods of fresh palm olein (FPO) adulteration are sophisticated, it created an urgent need for commensurate analytical techniques with which to detect FPO adulteration. As such, chromatography and spectroscopy are commonly used to detect adulterations in edible oil. Therefore, this study evaluated the efficacy of utilising gas chromatography-mass spectrometry (GC-MS) and attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy to detect the adulteration of FPO with inedible RCO. Although previous studies attest to the efficacy of utilising GC-MS and ATR-FTIR spectroscopy in adulteration detection, both these techniques only provided specific qualitative and quantitative insights into the compounds present in oil samples. As such, further extensive studies on the application of a variety of adulteration detection methods are needed to provide regulatory authorities with information on the reliability of these modern adulteration detection methods.

Detection of soybean oil adulteration in cow ghee (clarified milk fat): an ultrafast study using flash gas chromatography electronic nose coupled with multivariate chemometrics

Cow ghee is one of the expensive edible fats in the dairy sector. Ghee is often adulterated with low-priced edible oils, like soybean oil, owing to its high market demand. The existing adulteration detection methods are time-consuming, requiring sample preparation and expertise in these fields. The possibility of detecting soybean oil adulteration (from 10% to 100%) in pure cow ghee was investigated in this study. The fingerprint information of volatile compounds was collected using a flash gas chromatography electronic nose (FGCEN) instrument. The classification results were studied using the pattern recognition chemometric models principal component analysis (PCA), soft independent modelling of class analogy (SIMCA), and discriminant function analysis (DFA). The most powerful fingerprint odor of all the samples identified from FGCEN analysis was acetaldehyde (Z)-4-heptenal, 2-propanol, ethyl propanoate, and pentan-2-one. The odor analysis investigation was accomplished with an average analysis time of 90 s. A clear differentiation of all the samples with an excellent classification accuracy of more than 99% was achieved with the PCA and DFA chemometric methods. However, the results of the SIMCA model showed that SIMCA could only be used to detect ghee adulteration at higher concentration levels (30% to 100%). The validation study shows good agreement between FGCEN and gas chromatography-mass spectrometry methods. The methodology demonstrated coupled with PCA and DFA methods for adulteration detection in ghee using FGCEN apparatus has been an efficient and convenient technique. This study explored the capability of the FGCEN instrument to tackle the adulteration problems in ghee. © 2022 Society of Chemical Industry.

Development of amperometric enzyme‐based biosensor to evaluate the adulteration in virgin coconut oil

Virgin Coconut oil (VCO), owing to its functional properties (important in COVID-19), is costly and, therefore, susceptible to adulteration with other cheaper oils like coconut oil. An enzyme-based biosensor confirmative test of VCO was constructed by co-immobilizing enzymes onto a glassy carbon electrode. The performance of the biosensor was optimized at a potential of +0.5 V with 45 mg gelatin, 30 mg BSA coupled with 2.5% glutaraldehyde at pH 7.0 with an incubation time of 1 hr. Adulterated samples of VCO with coconut oil (CO) were analyzed. The concentration of diglyceride (DG) was estimated from the empirical relation, which showed a linear increase with the increase in adulteration. The developed biosensor was validated using high-performance liquid chromatography (HPLC) methods using a paired sample t test at a 5% significance level. The biosensor could detect adulteration in VCO with CO above 20% within 3–5 s and can be reused for 25 days. Practical applications Adulteration of edible oils is a massive issue in today's market. Virgin coconut oil due to its functional and antiviral properties becomes important for human health. This study revealed that the developed biosensor could be an effective method for adulteration detection in virgin coconut oil with coconut oil.

Essential Oil Characterization in Clove (Syzigium aromaticum Linn.) by GC-MS and Detection of Its Adulteration by ATR-FTIR Method

Syzigium aromaticum Linn. (clove) essential oil is known for its dental analgesic property, and traditionally used as antiseptic, carminative and as important condiment in foods. Clove essential oil has high medicinal values and its adulteration is done very often with cheap edible oils. The aim of the present study was to characterize the essential oil constituents and determine adulteration in essential oils isolated from the buds of clove. The essential oil composition was determined by GC-MS method and adulteration by ATR-FTIR method. GC-MS analysis of clove essential oil yielded 43 chemical compounds with eugenol (59.16%), β-selinene (9.34%), caryophyllene (7.68%), eugenol acetate (3.35%), and α-humulene (2.16%) as major compounds. The FTIR of isolated clove essential oil showed a characteristic peak at 1514 cm-1, which is missing in market essential oil. The FTIR data of market clove essential oil matches with sesame oil. The clove essential oil was adulterated with sesame oil (0, 5, 10, 20, 30, 40, 50 and 100% v/v), and the FTIR resultant adulterated binary mixtures indicated distinct peaks at 2852.72 and 2924.09 cm-1 (for -C-H sp3 & -C-H sp2 stretching) that is absent in pure essential oil of clove. The present study provides a simple, economic, quick, reliable, and reproducible method for detection of adulteration in clove essential oil. GC-MS analysis further explored the chemical composition of clove essential oil collected from Erbil, Iraq.

Adulteration detection of essence in sesame oil based on headspace gas chromatography-ion mobility spectrometry

Sesame oil is a traditional and delicious edible oil in China and Southeast Asia with a high price. However, sesame oil essence was often illegally added to cheaper edible oils to counterfeit sesame oil. In this study, a rapid and accurate headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) method was proposed to detect the counterfeit sesame oil where the other cheap oils were adulterated with essence. Combined with chemometric methods including principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA) and random forest (RF), authentic and counterfeit sesame oils adulterated with sesame essence (0.5%, w/w) were easily separated into two groups. More importantly, 2-methylbutanoic acid, 2-furfurylthiol, methylpyrazine, methional, and 2,5-dimethylpyrazine were found to be markers of sesame essence, which were used to directly identify the sesame essence. The determination of volatile compounds based on HS-GC-IMS was proven to be an effective method for adulteration detection of essence in sesame oil.