Solution and gaseous phase sensing of formaldehyde with economical triphenylmethane based sensors: a tool to estimate formaldehyde content in stored fish samples.

The use of formalin to preserve raw food items such as fish, meat, vegetables etc. is very commonly practiced in the present day. Also, formaldehyde (FA), which is the main constituent of formalin solution, is known to cause serious health issues on exposure. Considering the ill effects of formaldehyde, herein we report synthesis of highly sensitive triphenylmethane based formaldehyde (FA) sensors from a single step reaction of inexpensive reagents namely 4-hydroxy benzaldehyde and 2,6-dimethyl phenol. The synthetic method also provides highly pure product in bulk quantity. The analytical activity of the triphenylmethane sensor 1 with a limit of detection (LOD) value of 2.31 × 10-6 M for FA was significantly enhanced through induced deprotonation and thereafter a LOD value of 1.82 × 10-8 M could be achieved. To the best of our knowledge, the LOD value of the deprotonated form (sensor 2) for FA was superior to those of all the FA optical sensors reported so far. The mechanism of sensing was demonstrated by 1H-NMR titration and recording mass spectra before and after addition of FA to a solution of sensor 2. Both sensor 1 and sensor 2 exhibit quenching in emission upon addition of FA. A fluorescence study also demonstrates enhancement in analytical activity of the sensor upon induced deprotonation. Then the sensor was effectively immobilized into a hydrophilic and biocompatible starch-PVA polymer matrix which enabled detection of FA in a 100% aqueous system reversibly. Again, quick and effective sensing of FA in real food samples (stored fish) with the help of a computational application was demonstrated. The sensors have significant practical applicability as they effectively detect FA in real food samples qualitatively and quantitatively.

Performance assessment of a new G12/A1 antibody-based rapid ELISA using commercially available and gluten-spiked food samples.

OBJECTIVE: Food products with <20 mg/kg gluten can be labeled 'gluten-free' according to international regulations. Several antibodies-based ELISAs have been develop to track gluten traces in food products. Among them, R5 and G12 antibody-based ELISAs are the frequently used methods. However, these antibodies have certain limitations. We evaluated the accuracy of G12/A1 antibody-based 'Glutentox ELISA Rapid G12' and compared the results with the current reference method i.e., R5 antibody-based 'Ridascreen R5 ELISA'. METHODS: In the first step, the performance of Glutentox ELISA Rapid G12 kit was inspected by determination of the threshold value i.e., > or <20 mg/kg gluten in different food products. In the second step, quantification accuracy was assessed by quantification of gluten in gluten-free food products spiked with gliadin reference material. RESULTS: In total 47 food products (naturally and labeled gluten-free, and food with traces of gluten) were included. Of them, 29 products were quantified with <20 mg/kg, and 18 with a low level of gluten by both the kits. Six out of 29 gluten-free products were used for the recovery test at different spike levels. Gluten concentration and mean recovery rates of individual kits showed consistency. CONCLUSION: GlutenTox Rapid G12 ELISA could be an appropriate choice for detecting gluten in food products but needs more in-house validation and collaborative tests.

The pesticide residue analysis in commodities with high content of chlorophyll based on the quick, easy, cheap, effective, rugged, and safe method: A review.

In multiresidue analysis, the QuEChERS (Quick, Easy, Cheap, Effective, Rugged, and Safe) is one of the most popular techniques routinely used by researchers during pesticide analysis of food and vegetable samples. Originally, the QuEChERS method was developed for analysis of pesticide residues from fruits and vegetables, but rapidly gained popularity in the extraction of analytes from different matrices. This analytical approach shows several advantages over traditional extraction techniques: it requires lower sample and solvent amounts while shortening the time of sample preparation. However, it presents some limitations for complex matrices such as those containing high amounts of chlorophyll. To overcome the problem of strong matrix effect and influence of interferences, different approaches are applied. Most are concerning modifications of the cleanup step, that is, sorbent type and its amount. Optimization of other parameters, such as sample size, hydration level, extraction solvent, and buffering, also has an impact on overall performance. Combining proper sample preparation with modern highly sensitive and selective detection techniques enables receiving desired limits of quantification. This article presents an overview of strategies employed by researchers for analysis of green, high chlorophyll content commodities and results obtained in their studies.

An Analytical Method Based on Electrochemical Sensor for the Assessment of Insect Infestation in Flour.

Uric acid is an important indicator of the insect infestation assessment in flour. In this work, we propose a method for uric acid detection based on voltammetry. This technique is particularly considered for the physicochemical properties of flour and contains a simple pretreatment process to rapidly achieve extraction and adsorption of uric acid in flour. To achieve specific recognition of uric acid, graphene and poly(3,4-ethylenedioxythiophene) (PEDOT) were used for the adsorption and concentration of uric acid in flour. The adsorbed mixture was immobilized on the surface of a screen-printed electrode for highly sensitive detection of the uric acid. The results showed that electrocatalytic oxidation of uric acid could be achieved after adsorption by graphene and PEDOT. This electrocatalytic reaction allows its oxidation peak to be distinguished from those of other substances that commonly possess electrochemical activity. This voltammetry-based detection method is a portable and disposable analytical method. Because it is simple to operate, requires no professional training, and is inexpensive, it is a field analysis method that can be promoted.

Rapid Assessment of Quality Changes in French Fries during Deep-frying Based on FTIR Spectroscopy Combined with Artificial Neural Network.

Fourier transform infrared (FTIR) spectroscopy combined with backpropagation artificial neural network (BP-ANN) were utilized for rapid and simultaneous assessment of the lipid oxidation indices in French fries. The conventional indexes (i.e. total polar compounds, oxidized triacylglycerol polymerized products, oxidized triacylglycerol monomers, triacylglycerol hydrolysis products, and acid value), and FTIR absorbance intensity in French fries were determined during the deep-frying process, and the results showed the French fries had better quality in palm oil, followed by sunflower oil, rapeseed oil and soybean oil. The FTIR spectra of oil extracted from French fries were correlated to the reference oxidation indexes determined by AOCS standard methods. The results of BP-ANN prediction showed that the model based on FTIR fitted well (R2 > 0.926, RMSEC < 0.481) compared with partial least-squares model (R2 > 0.876). This facile strategy with excellent performance has great potential for rapid characterization quality of French fries during frying.

Assessment of the Botanical Origin of Polish Honeys Based on Physicochemical Properties and Bioactive Components with Chemometric Analysis.

Is it possible to characterize the types of honey based on their chemical composition, their content of bioactive substances, and their physicochemical properties? The objective of this study was a comparative analysis of four types of honey from the Carpathian Foothills area, located in south-east Poland, based on the content of the main phenolic acids and proline, the mineral composition, and selected physicochemical properties. Most analyses, such as those of phenolic acids, sugars, and proline content, in honey samples were performed using chromatographic methods. These experiments demonstrated that honeydew honeys were the richest in phenolic acids, minerals, as well as oligosaccharides, compared to other honeys. Dark-colored honeys were characterized by the highest proline content. The dominant elements in all types of honey were potassium and calcium. The results of the present study show that analyses of specific phenolic acids, minerals, proline, and sugar content, in combination with chemometrics analysis, may successfully differentiate between the biological origins of honey samples and allow the preliminary verification of the samples before performing time-consuming pollen analysis.

The New Hybrid Nutrient Density Score NRFh 4:3:3 Tested in Relation to Affordable Nutrient Density and Healthy Eating Index 2015: Analyses of NHANES Data 2013-16.

Background: Hybrid nutrient density scores are based on both nutrients and selected food groups. Objective: To compare the new hybrid nutrient-rich food NRFh 4:3:3 score to other nutrient-rich food (NRF) scores, energy density, and energy cost and to model the impact on the Healthy Eating Index (HEI-2015) of partially replacing less nutrient-rich with more nutrient-rich foods. Methods: Analyses were based on 5870 foods and beverages in the Food and Nutrient Database for Dietary Studies and on 24 h dietary recalls from the National Health and Nutrition Examination Survey (NHANES 2013-16). The NRFh 4:3:3 model was based on four nutrients to encourage (protein fiber, potassium, MUFA + PUFA); three food groups to encourage (dairy, fruit, whole grains); and three nutrients to limit (saturated fat, added sugar, sodium). Ratings generated by NRFh 4:3:3 and by other NRF models were correlated with score components, energy density (kcal/100 g), and energy cost (USD/100 kcal). The impact on HEI-2015 of replacing foods in the lowest nutrient density tertile (T1) with top tertile (T3) foods at 10%, 20%, 30%, and 100% equicaloric replacement was modeled using NHANES 2013-16 dietary data by population subgroups. Results: The NRFh 4:3:3 model awarded higher scores to foods containing dairy, fruit, and whole grains and proportionately lower scores to vegetables when compared to the NRF 9.3 model. Higher NRF and NRFh nutrient density scores were linked to lower energy density and higher energy cost; however, both correlations were lower for the NRFh 4:3:3. Isocaloric replacement of bottom tertile with top tertile foods as rated by both models led to significantly higher HEI-2105 values, based on complete (100%) and on partial (10-30%) replacement. Conclusion: The new NRFh 4:3:3 model provides the basis for developing new metrics of affordable nutrient density. The model identified "best value" food categories that were both affordable and nutrient-rich. Total and partial replacement of low nutrient density with high nutrient density foods was associated with higher HEI-2015 scores, suggesting that even partial inclusion of more nutrient dense foods in the diet may have an important impact on total diet quality.

Evaluating low- mid- and high-level fusion strategies for combining Raman and infrared spectroscopy for quality assessment of red meat.

The implementation of Raman and infrared spectroscopy with three data fusion strategies to predict pH and % IMF content of red meat was investigated. Raman and FTIR systems were utilized to assess quality parameters of intact red meat. Quantitative models were built using PLS, with model performances assessed with respect to the determination coefficient (R2), root mean square error and normalized root mean square error (NRMSEP). Results obtained on validation against an independent test set show that the high-level fusion strategy had the best performance in predicting the observed pH; with RP2 and NRMSEP values of 0.73 and 12.9% respectively, whereas low-level fusion strategy showed promise in predicting % IMF (NRMSEP = 8.5%). The fusion of data from more than one technique at low and high level resulted in improvement in the model performances; highlighting the possibility of information enhancement.

Method validation for the determination of 314 pesticide residues using tandem MS systems (GC-MS/MS and LC-MS/MS) in raisins: Focus on risk exposure assessment and respective processing factors in real samples (a pilot survey).

A multi-residue method for the simultaneous analysis of a wide range of pesticides in raisins using liquid and gas chromatography-tandem mass spectrometry (LC-MS/MS and GC-MS/MS) has been validated. Pesticides are extracted from raisins with ethyl acetate, followed by centrifugation. The validation study was in accordance with DG SANTE guidelines. Validation experiments have been performed in both analytical instruments. A total number of 314 pesticides were spiked in raisins of organic farming at two spiking levels for GC-MS/MS (0.025 and 0.1 mg/kg), and at three spiking levels for LC-MS/MS (0.005, 0.05, and 0.1 mg/kg) with 6 replicates at each concentration. The scope of validation included linearity, limits of quantification (LOQ), accuracy, precision, and matrix effects (%) for each pesticide. The validated method was then applied for the analysis of 37 commercial raisin samples purchased from the market. For the evaluation of the results, processing factors (PFs) have been applied to derive the amount of residue in raisins, from the maximum residue levels (MRLs) of grapes, and which in this paper will be referred as to the MRL expressed in raisins. In all conventional samples, pesticides were detected at concentrations above the LOQ. In total, 55 different pesticides were detected. All conventional samples contained multiple pesticides ranging from 2 to 24. On the other hand, samples from organic farming were found to be free of the analysed pesticides. The 13.5% of the examined samples were considered as violations. The exposure assessment for the acute risk of the violating samples indicated that no potential risk derives from the detected and approved in the EU pesticides, while the detection of not approved pesticides in the EU, and the lack of toxicological reference values for certain pesticides raise concerns for the human health, especially for children. The results of the survey study indicate the need to include processed samples, and in particular dry fruits with a high consumption rate such as raisins, in the official controls of pesticide residues in food.

Assessment of the impact of hydrolysis on bound sulfonamide residue determination in honey using stable isotope dilution ultrahigh performance liquid chromatography tandem mass spectrometry.

In this study, an analytical method based on isotope dilution-liquid chromatography tandem mass spectrometry (ID-LC-MS/MS) was developed as a candidate reference method for the determination of sulfonamides (SAs) in honey. To guarantee the accuracy and authenticity, the impact of hydrolysis on bound SA residues was first investigated by enabling (i) identification of sugar-bound SAs, (ii) clarifying the binding reaction rule between the SAs and sugar, (iii) detection of free SAs and sugar-bound SAs, and (iv) preparation of SA-contaminated honey. Thus, the efficiency of different hydrolysis conditions was assessed by comparing the bound SA content before and after hydrolysis. In addition, optimization of the sample pretreatment procedures and LC conditions to minimize matrix effects by separation from significant matrix interferences was also performed. Satisfactory results in terms of hydrolysis efficiency (approximately 88.3%-99.2%), extraction efficiency (84.2%-105.3%), recovery (95.9%-103.1%), and limit of quantification (0.6-1.5 µg·kg-1) were obtained.

Detection of Cyclospora cayetanensis on bagged pre-cut salad mixes within their shelf-life and after sell by date by the U.S. food and drug administration validated method.

Recently, outbreaks of Cyclospora cayetanensis in the U.S. were linked to the consumption of a variety of salads containing romaine and/or iceberg lettuce, carrots and/or red cabbage. The Bacteriological Analytical Manual (BAM) Chapter 19b method was validated for the detection of C. cayetanensis in carrots, cabbage and romaine lettuce, but has not been previously evaluated in ready-to-eat (RTE) salad mixes. In addition, the only samples available for traceback investigations are sometimes leftovers in bad conditions. This study evaluated the validated BAM method for detection of C. cayetanensis in two different RTE mixed salads (mix 1: romaine and iceberg lettuces, carrots, and red cabbage and mix 2: romaine and iceberg lettuces, carrots, red cabbage, radish, and pea pods) in good condition and after their sell by date. Individual samples (25 g) were seeded with five and 200 C. cayetanensis oocysts. Unseeded produce was used as negative control. The method included washing of the produce, concentration and extraction of C. cayetanensis DNA and molecular detection of C. cayetanensis 18 S rRNA gene. As few as five oocysts were detected in both fresh and after sell by date mix salads. All unseeded samples were negative, and all samples of both salad types seeded with 200 oocysts were positive. In samples seeded with 200 oocysts, average 18 S rRNA C. cayetanensis CT values were significantly higher in fresh salad mix 1 compared to fresh salad mix 2; CT values were significantly higher in the after sell by date salads compared to their respective fresh mixes (p < 0.05). In conclusion, the BAM method was able to detect as few as five oocysts even in after sell by date RTE mix salads. However, the differences in detection observed, highlight the importance of evaluating the performance of the validated C. cayetanensis detection method in different food matrices and conditions, in advance for future outbreak investigations.

Assessment of multiple stable isotopes for tracking regional and organic authenticity of plant products in Hesse, Germany.

As demand for regional and organically produced foodstuff has increased in Europe, the need has arisen to verify the products' origin and production method. For food authenticity tracking (production method and origin), we examined 286 samples of wheat (Triticum aestivum), potatoes (Solanum tuberosum), and apples (Malus domestica) from different regions in Germany for their stable isotope compositions of oxygen, hydrogen, carbon, nitrogen and sulphur. Single-variate authentication methods were used. Suitable isotope tracers to determine wheat's regional origin were δ18O and δ34S. δ13C helped to distinguish between organic and conventional wheat samples. For the separation of the production regions of potatoes, several isotope tracers were suitable (e.g. δ18O, δ2H, δ15N, δ13C and δ34S isotopes in potato protein), but only protein δ15N was suitable to differentiate between organic and conventional potato samples. For the apple samples, 2H and 18O isotopes helped to identify production regions, but no significant statistical differences could be found between organically and conventionally farmed apples. For food authenticity tracking, our study showed the need to take the various isotopes into account. There is an urgent need for a broad reference database if isotope measurements are to become a main tool for determining product's origin.

Deep Neural Networks for Image-Based Dietary Assessment.

Due to the issues and costs associated with manual dietary assessment approaches, automated solutions are required to ease and speed up the work and increase its quality. Today, automated solutions are able to record a person's dietary intake in a much simpler way, such as by taking an image with a smartphone camera. In this article, we will focus on such image-based approaches to dietary assessment. For the food image recognition problem, deep neural networks have achieved the state of the art in recent years, and we present our work in this field. In particular, we first describe the method for food and beverage image recognition using a deep neural network architecture, called NutriNet. This method, like most research done in the early days of deep learning-based food image recognition, is limited to one output per image, and therefore unsuitable for images with multiple food or beverage items. That is why approaches that perform food image segmentation are considerably more robust, as they are able to identify any number of food or beverage items in the image. We therefore also present two methods for food image segmentation - one is based on fully convolutional networks (FCNs), and the other on deep residual networks (ResNet).

Quality Assessment of Five Mono-cultivar Virgin Olive Oils Produced in Longnan (China) from 2013 to 2017.

The aim of this work was to evaluate the quality of the five mono-cultivar (Frantoio, Leccino, Picholine, Coratina and Ezhi-8) virgin olive oils (Mc-VOOs) produced in Longnan (China) from 2013 to 2017 through analysing the organoleptic quality, physicochemical properties, phenolic contents, antioxidant activity and fatty acid composition. The leading principal components for assessing the quality of Mc-VOOs were extracted by principal component analysis (PCA). The results indicated that the five Mc-VOOs showed obvious differences (p < 0.05) in flavour and substance composition with the variation of cultivar and production year; however, the same cultivar of VOO displayed certain homogeneity in five consecutive years of assessment. The five Mc-VOOs were rich in phenolic compounds and unsaturated fatty acids such as oleic acid. The quality of VOO was mainly determined by the genetic characteristics of olive cultivar, meanwhile, fruit maturity, soil and climate factors also affected its quality. The content of phenolic compound, DPPH· scavenging rate, proportion of unsaturated fatty acids and iodine value of Coratina were the highest, on the contrary, Ezhi-8 was the lowest in general. The results of PCA showed that the five leading principal components to evaluate the quality of Mc-VOOs were oleic acid, linoleic acid, acid value, total phenol and trace components (such as C20:1 and squalene) successively. In conclusion, the five Mc-VOOs from Longnan show excellent quality and have certain uniformity in different production years.

Development and assessment of GC/MS and HPAEC/PAD methodologies for the quantification of α-galacto-oligosaccharides (GOS) in dry beans (Phaseolus vulgaris).

The quantification of α-Galacto-oligosaccharides (GOS) in beans has been increasingly approached through different methodologies. However, reported GOS contents revealed up to 8-times disparity, which cannot be only attributed to the bean cultivar and underlines the need of using validated analytical methodologies. This study aimed to optimize and validate the extraction of the most abundant GOS found in beans, namely raffinose, stachyose and verbascose, and comparatively assess their determination by High-Performance Anion Exchange Chromatography/Pulsed Amperometric Detector (HPAEC/PAD) and Gas Chromatography/Mass Spectrometry (GC/MS). Hot sonication followed by shaking with 70% ethanol resulted in excellent GOS extraction efficiencies (92.54-107.94%). GC/MS determination was more reliable than HPAEC/PAD, with limits of quantification of 4.48-224.31 mg/kg and intra/inter-day repeatabilities <10%. The analysis of six bean varieties proved the feasibility of the GC/MS methodology, displaying total GOS contents from 1453.07 ±â€¯169.31 to 2814.34 ±â€¯95.28 mg/100 g. Stachyose was significantly (p < 0.05) the main GOS in all samples.

Determination and dietary intake risk assessment of 14 pesticide residues in apples of China.

The presence of pesticide residues in apples raises serious health concerns. In this study, a novel, sensitive, high-performance method was developed to simultaneously analyze the residues of 14 pesticides in apples using modified QuEChERS sample pretreatment coupled with ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). The optimized purification procedure demonstrated satisfactory recovery and precision for all the tested pesticides. The limits of detection (LOD) and quantification (LOQ) values of 14 pesticides in the apple matrix ranged from 0.03 µg/kg to 0.3 µg/kg and 0.1 µg/kg to 1.0 µg/kg, respectively. The proposed method detected six pesticides in the apple samples collected from 20 counties in China's major apple-producing regions. Furthermore, the risk quotient (RQ, %) of the detected pesticides was evaluated by the national estimated acceptable daily intake. The RQs of six pesticides in Chinese people of different age groups were less than 100%.

Non-destructive sugar content assessment of multiple cultivars of melons by dielectric properties.

BACKGROUND: Non-destructive determination of the internal quality of fruit with a thick rind and of a large size is always difficult and challenging. To investigate the feasibility of the dielectric spectroscopy technique with respect to determining the sugar content of melons during the postharvest stage, three cultivars of melon samples (160 melons for each cultivar) were used to acquire dielectric spectra over the frequency range 20-4500 MHz. The three cultivars of melons were divided separately into a calibration set and a prediction set in a ratio of 3:1 by a joint x-y distance algorithm. Partial least squares (PLS) and extreme learning machine (ELM) methods were applied to develop individual-cultivar and multi-cultivar models based on full frequencies (FFs) and effective dielectric frequencies (EDFs) selected by the successive projection algorithm (SPA). RESULTS: The results showed that ELM models demonstrated a better performance than PLS models for the same input dielectric variables. Most of the models built based on the EDFs selected by SPA had a slightly worse performance compared to those based on FFs. For both PLS and ELM methods, the models for multi-cultivars demonstrated a worse calibration and prediction performance compared to those for individual cultivars. When individual-cultivar and multi-cultivar samples were used to build sugar content determination models, the best model was FFs-ELM (Rp  = 0.887, RMSEP = 0.986), FFs-ELM (Rp  = 0.870, RMSEP = 1.028), FFs-PLS (Rp  = 0.882, RMSEP = 1.010) and FFs-ELM (Rp  = 0.849, RMSEP = 1.085) for 'Hongyanliang', 'Xinzaomi', 'Manao' and multi-cultivar melons, respectively. CONCLUSION: The present study indicates that it is possible to develop both individual-cultivar and multi-cultivar models for determining the sugar content of melons based on the dielectric spectroscopy technique. © 2021 Society of Chemical Industry.

Ochratoxin A and 2'R-Ochratoxin A in Selected Foodstuffs and Dietary Risk Assessment.

The aim of this study was to estimate the contamination of grain coffee, roasted coffee, instant coffee, and cocoa purchased in local markets with ochratoxin A (OTA) and its isomerization product 2'R-ochratoxin A (2'R-OTA), and to assess risk of dietary exposure to the mycotoxins. OTA and 2'R-OTA content was determined using the HPLC chromatography with immunoaffinity columns dedicated to OTA. OTA levels found in all the tested samples were below the maximum limits specified in the European Commission Regulation EC 1881/2006. Average OTA concentrations calculated for positive samples of grain coffee/roasted coffee/instant coffee/cocoa were 0.94/0.79/3.00/0.95 µg/kg, with the concentration ranges: 0.57-1.97/0.44-2.29/0.40-5.15/0.48-1.97 µg/kg, respectively. Average 2'R-OTA concentrations calculated for positive samples of roasted coffee/instant coffee were 0.90/1.48 µg/kg, with concentration ranges: 0.40-1.26/1.00-2.12 µg/kg, respectively. In turn, diastereomer was not found in any of the tested cocoa samples. Daily intake of both mycotoxins with coffee/cocoa would be below the TDI value even if the consumed coffee/cocoa were contaminated with OTA/2'R-OTA at the highest levels found in this study. Up to now only a few papers on both OTA and 2'R-OTA in roasted food products are available in the literature, and this is the first study in Poland.

Evaluation of the QuEChERS method for the determination of phenolic compounds in yellow (Brassica alba), brown (Brassica juncea), and black (Brassica nigra) mustard seeds.

Studies about the phenolic composition of yellow (Brassica alba), brown (Brassica juncea), and black (Brassica nigra) mustard seeds are still scarce in the literature. Hence, this study describes, for the first time, the use of the QuEChERS extraction method followed by UHPLC-MS/MS analysis for phenolic compound determination in the seeds of these mustard species. Under the optimized extraction and analysis conditions, twenty-one phenolic compounds were evaluated. Six, eleven, and seven were found in B. alba, B. juncea, and B. nigra seeds, respectively. The most abundant phenolic compound was sinapic acid, which was found in amounts ranging from 44 to 82 times higher than the other major compounds found in the mustard seeds, ferulic, 4-hydroxybenzoic and protocatechuic acids. Overall, these results are an important contribution to the characterization of the phenolic composition of the three in natura mustard seeds species, and support future reliable phenolic compounds determination with the QuEChERS method.

Review of some adulteration detection techniques of edible oils.

Edible oils have economical and nutritional benefits. These oils offer nutrients that are essential to human health because they are the primary source of mono- and poly-unsaturated fats. Moreover, edible oils are used in home cooking and industrial food manufacturing. Therefore, edible oils have a considerable demand worldwide. However, some edible oils, such as olive oil, are more expensive than any other vegetable oils. Thus, oils such as olive oil are mixed with cheap edible oils as a result of the high price difference. Accordingly, adulteration in edible oils to obtain additional profit for the producer becomes a major issue of high concern for consumers. Moreover, adulteration in edible oils can cause several problems that affect consumer health. Therefore, the need for a sensitive, accurate and suitable method to detect the adulteration is highly considered. We provide a brief review of the different methods and techniques used to detect adulteration in edible oils, especially olive oil, with the aim of promoting consumer awareness of the authenticity of edible oils. © 2020 Society of Chemical Industry.