PCR-CE-SSCP used to authenticate edible oils

Analytical approaches for the determination of adulterated animal fats and vegetable oils in food and non-food samples

Edible oils and fats are crucial components of everyday cooking and the production of food products, but their purity has been a major issue for a long time. High-quality edible oils are contaminated with low- and cheap-quality edible oils to increase profits. The adulteration of edible oils and fats also produces many health risks. Detection of main and minor components can identify adulterations using various techniques, such as GC, HPLC, TLC, FTIR, NIR, NMR, direct mass spectrometry, PCR, E-Nose, and DSC. Each detection technique has its advantages and disadvantages. For example, chromatography offers high precision but requires extensive sample preparation, while spectroscopy is rapid and non-destructive but may lack resolution. Direct mass spectrometry is faster and simpler than chromatography-based MS, eliminating complex preparation steps. DNA-based oil authentication is effective but hindered by laborious extraction processes. E-Nose only distinguishes odours, and DSC directly studies lipid thermal properties without derivatization or solvents. Mass spectrometry-based techniques, particularly GC-MS is found to be highly effective for detecting adulteration of oils and fats in food and non-food samples. This review summarizes the benefits and drawbacks of these analytical approaches and their use in conjunction with chemometric tools to detect the adulteration of animal fats and vegetable oils. This combination provides a powerful technique with enormous chemotaxonomic potential that includes the detection of adulterations, quality assurance, assessment of geographical origin, assessment of the process, and classification of the product in complex matrices from food and non-food samples.

Novel PCR-Based Technology for the Detection of Sunflower in Edible and Used Cooking Oils

Reliable detection of sunflower (Helianthus annuus) in edible and used cooking oil (UCO) is crucial for the sustainable production of food and biodiesel. In this study, a variety of sunflower oils (crude, cold pressed, extra virgin, refined, and UCO) were examined using different methods of DNA extraction and PCR amplification to develop an efficient technology for the identification of sunflower in oils. DNA extraction kits such as NucleoSpin Food, DNeasy mericon Food, and Olive Oil DNA Isolation as well as modified CTAB method were found to be able to isolate amplifiable genomic DNA from highly processed oils. Novel uniplex, double, and nested PCR systems targeting the sunflower-specific helianthinin gene were developed for efficient identification of sunflower. New sunflower DNA markers were revealed by uniplex PCRs. The combination of modified CTAB and nested PCR was demonstrated as a reliable, rapid, and cost-effective technology for detecting traces of sunflower in 700 μL of highly processed oil, including refined and used cooking oil. The study will contribute to both the food industry and the energy sector as developed methods can be used for oil authenticity testing in food and biodiesel production.

Screening Method for the Visual Discrimination of Olive Oil from Other Vegetable Oils by a Multispecies DNA Sensor

Olive oil is a prominent agricultural product which, in addition to its nutritional value and unique organoleptic characteristics, offers a variety of health benefits protecting against cardiovascular disease, cancer, and neurodegenerative diseases. The assessment of olive oil authenticity is an extremely important and challenging process aimed at protecting consumers and producers. The most frequent adulteration involves blending with less expensive and readily available vegetable/seed oils. The methods for adulteration detection, whether based on changes in metabolite profiles or based on DNA markers, require advanced and expensive instrumentation combined with powerful chemometric and statistical tools. To this end, we present a simple, multiplex, and inexpensive screening method based on the development of a multispecies DNA sensor for sample interrogation with the naked eye. It is the first report of a DNA sensor for olive oil adulteration detection with other plant oils. The sensor meets the 2-fold challenge of adulteration detection, i.e., determining whether the olive oil sample is adulterated and identifying the added vegetable oil. We have identified unique, nucleotide variations, which enable the discrimination of seven plant species (olive, corn, sesame, soy, sunflower, almond, and hazelnut). Following a single PCR step, a 20 min multiplex plant-discrimination reaction is performed, and the products are applied directly to the sensing device. The plant species are visualized as red spots using functionalized gold nanoparticles as reporters. The spot position reveals the identity of the plant species. As low as <5-10% of adulterant was detected with particularly good reproducibility and specificity.

A comparative study on classification of edible vegetable oils by infrared, near infrared and fluorescence spectroscopy combined with chemometrics

Driven by economic benefits like any other foods, vegetable oil has long been plagued by mislabeling and adulteration. Many studies have addressed the field of classification and identification of vegetable oils by various analysis techniques, especially spectral analysis. A comparative study was performed using Fourier transform infrared spectroscopy (FTIR), visible near-infrared spectroscopy (Vis-NIR) and excitation-emission matrix fluorescence spectroscopy (EEMs) combined with chemometrics to distinguish different types of edible vegetable oils. FTIR, Vis-NIR and EEMs datasets of 147 samples of five vegetable oils from different brands were analyzed. Two types of pattern recognition methods, principal component analysis (PCA)/multi-way principal component analysis (M-PCA) and partial least squares discriminant analysis (PLS-DA)/multilinear partial least squares discriminant analysis (N-PLS-DA), were used to resolve these data and distinguish vegetable oil types, respectively. PCA/M-PCA analysis exhibited that three spectral data of five vegetable oils showed a clustering trend. The total correct recognition rate of the training set and prediction set of FTIR spectra of vegetable oil based on PLS-DA method are 100%. The total recognition rate of Vis-NIR based on PLS-DA are 100% and 97.96%. However, the total correct recognition rate of training set and prediction set of EEMs data based on N-PLS-DA method is 69.39% and 75.51%, respectively. The comparative study showed that FTIR and Vis-NIR combined with chemometrics were more suitable for vegetable oil species identification than EEMs technique. The reason may be concluded that almost all chemical components in vegetable oil can produce FTIR and NIR absorption, while only a small amount of fluorophores can produce fluorescence. That is, FTIR and NIR can provide more spectral information than EEMs. Analysis of EEMs data using self-weighted alternating trilinear decomposition (SWATLD) also showed that fluorophores were a few and irregularly distributed in vegetable oils.

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.

Development of conventional PCR and real-time PCR assays to discriminate the origins of Chinese pepper oil and herbal materials from Zanthoxylum

BACKGROUND

To ensure the safety, quality and therapeutic efficacy of processed foods and herbal medicines, it is important to identify and discriminate economically motivated adulterants. Zanthoxylum schinifolium is sold at a higher price than other Zanthoxylum species and is frequently adulterated with closely related Zanthoxylum species because of its high demand as a Korean food ingredient and medicinal material in markets. In addition, the pericarps of three Zanthoxylum species (Z. schinifolium, Z. bungeanum and Z. piperitum) are defined as herbal medicine Zanthoxyli Pericarpium in Korean pharmacopoeias, but not Z. piperitum in Chinese pharmacopoeias. Further confusion arises in the morphological similarity between Z. armatum (adulterant) and Z. bungeanum. Therefore, the aim of this study was to develop a sequence characterized amplified region (SCAR) marker for discrimination of four Zanthoxylum species.

RESULTS

With the goal of developing rapid and reliable tools for genetic discrimination of authentic Zanthoxyli Pericarpium, we designed species-specific SCAR markers, based on ITS2 sequences, that generate amplicons of less than 200 bp. Using these markers, we established both conventional and real-time PCR assay methods capable of differentiating samples at the species level. We validated the ability of SCAR markers to authenticate edible oil and herbal medicine, and confirmed that some herbal medicines contaminated with Z. armatum are being distributed as Zanthoxyli Pericarpium in Korean and Chinese markets.

CONCLUSIONS

The SCAR markers and PCR methods described represent powerful tools for protecting against adulteration and ensuring standardization of processed foods and herbal medicine. © 2018 The Authors. Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Multispecies Adulteration Detection of Camellia Oil by Chemical Markers

Adulteration of edible oils has attracted attention from more researchers and consumers in recent years. Complex multispecies adulteration is a commonly used strategy to mask the traditional adulteration detection methods. Most of the researchers were only concerned about single targeted adulterants, however, it was difficult to identify complex multispecies adulteration or untargeted adulterants. To detect adulteration of edible oil, identification of characteristic markers of adulterants was proposed to be an effective method, which could provide a solution for multispecies adulteration detection. In this study, a simple method of multispecies adulteration detection for camellia oil (adulterated with soybean oil, peanut oil, rapeseed oil) was developed by quantifying chemical markers including four isoflavones, trans-resveratrol and sinapic acid, which used liquid chromatography tandem mass spectrometry (LC-MS/MS) combined with solid phase extraction (SPE). In commercial camellia oil, only two of them were detected of daidzin with the average content of 0.06 ng/g while other markers were absent. The developed method was highly sensitive as the limits of detection (LODs) ranged from 0.02 ng/mL to 0.16 ng/mL and the mean recoveries ranged from 79.7% to 113.5%, indicating that this method was reliable to detect potential characteristic markers in edible oils. Six target compounds for pure camellia oils, soybean oils, peanut oils and rapeseed oils had been analyzed to get the results. The validation results indicated that this simple and rapid method was successfully employed to determine multispecies adulteration of camellia oil adulterated with soybean, peanut and rapeseed oils.

Rapid screening of mixed edible oils and gutter oils by matrix-assisted laser desorption/ionization mass spectrometry

Authentication of edible oils is a long-term issue in food safety, and becomes particularly important with the emergence and wide spread of gutter oils in recent years. Due to the very high analytical demand and diversity of gutter oils, a high throughput analytical method and a versatile strategy for authentication of mixed edible oils and gutter oils are highly desirable. In this study, an improved matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) method has been developed for direct analysis of edible oils. This method involved on-target sample loading, automatic data acquisition and simple data processing. MALDI-MS spectra with high quality and high reproducibility have been obtained using this method, and a preliminary spectral database of edible oils has been set up. The authenticity of an edible oil sample can be determined by comparing its MALDI-MS spectrum and principal component analysis (PCA) results with those of its labeled oil in the database. This method is simple and the whole process only takes several minutes for analysis of one oil sample. We demonstrated that the method was sensitive to change in oil compositions and can be used for measuring compositions of mixed oils. The capability of the method for determining mislabeling enables it for rapid screening of gutter oils since fraudulent mislabeling is a common feature of gutter oils.

One-class classification based authentication of peanut oils by fatty acid profiles

In this study, the authenticity identification model was built by the one-class partial least squares (OCPLS) classifier for peanut oils, which could effectively detect adulterated oils at the adulteration level of more than 4%.

Advanced detection methods for traceability of origin and authenticity of olive oils

This review focuses on the advances in the sensing and identification of adulteration of olive oil, including optical sensing, chromatography, nuclear magnetic resonance, and DNA-based methods.