Food Adulteration Research Articles

Combating Olive Oil Fraud Using GC–IMS and FGC-Enose

The adulteration of high-quality foods is big business. Typical adulteration of olive oil involves the use of other types of oil, such as seed oils or pomace oils, the introduction of re-esterified oils, or the creation of mixtures with refined oils to create a lower quality product that can still be sold at a premium price. Fortunately, these processes can be easily detected using standard methods. However, fraudsters now seek more advanced methods using soft refined oils or oils with a tailored composition, making detection with existing procedures difficult. LCGC spoke to Michele Suman about novel screening and confirmatory analytical strategies he has investigated to regain the upper hand in the fight against olive oil adulteration.

Newly marketed seed oils. What we can learn from the current status of authentication of edible oils

The actual concerns regarding the quality and authenticity of newly-marketed seed oils are based on a high risk of identity theft, tampering with purity, and adulteration. Seed oils are a rich source of dietary fat and various phytochemicals, and as such are an object of health-related and commercial interest. However, the lipid composition of these oils is poorly investigated and there is a lack of information on what methods are appropriate for detecting adulteration in this kind of oils. Therefore, in this review, the current knowledge on the problem of detecting adulteration of well-known edible oils was collected and systematized. Most methods of identification and determination of lipidomic profiles of oils are based on infrared and Raman spectroscopy as well as chromatographic separation combined with mass spectrometry. Quantitative and qualitative determination of fatty acids, tri- and di-glycerols, phospholipids, glycolipids, sterols and tocopherols can contribute to overcoming the problem of food adulteration.

Discrimination of Four Cinnamomum Species with Physico-Functional Properties and Chemometric Techniques: Application of PCA and MDA Models.

Discrimination of highly valued and non-hepatotoxic Cinnamomum species (C. verum) from hepatotoxic (C. burmannii, C. loureiroi, and C. cassia) is essential for preventing food adulteration and safety problems. In this study, we developed a new method for the discrimination of four Cinnamomum species using physico-functional properties and chemometric techniques. The data were analyzed through principal component analysis (PCA) and multiclass discriminant analysis (MDA). The results showed that the cumulative variability of the first three principal components was 81.70%. The PCA score plot indicated a clear separation of the different Cinnamomum species. The training set was used to build the discriminant MDA model. The testing set was verified by this model. The prediction rate of 100% proved that the model was valid and reliable. Therefore, physico-functional properties coupled with chemometric techniques constitute a practical approach for discrimination of Cinnamomum species to prevent food fraud.

Design of Mismatch Primers to Identify and Differentiate Closely Related (Sub)Species: Application to the Authentication of Meat Products.

Single-nucleotide polymorphisms (SNPs) are powerful molecular markers for the identification and differentiation of closely related organisms. A variety of methods can be used to determine the allele that is present at a specific locus in the genome, including real-time PCR by using an allele-specific primer. In order to increase the selectivity for the target allele, deliberate mismatch bases at the 3' end of the allele-specific primer may be introduced. This strategy has already been used for the identification and differentiation of microorganisms and plants. We have recently developed real-time PCR assays involving mismatch primers for the identification and differentiation of closely related deer species (red deer, fallow deer, sika deer) or the discrimination of wild boar and domestic pig in game meat products. These methods are applicable to detect meat species adulteration in food products.In this chapter, we offer a protocol for the design of PCR primer/probe systems suitable for meat species authentication in food. We address the retrieval and alignment of sequences, primer design by using a commercial software and the introduction of deliberate mismatch bases. In addition, we describe how the suitability of primer/probe systems can be tested in silico and in practice. We use the design of PCR primer/probe systems for wild boar and domestic pig as example.

Incidence of fraud and adulterations in ASEAN food/feed exports: A 20-year analysis of RASFF's notifications.

This paper explored the occurrence of food fraud and adulterations (FFA) in exports from the Association of South- East Asia Nations (ASEAN), with implications on food chain and international trade. Data from European Union Rapid Alert System for Food and Feed (EU RASFF) about FFA notifications on ASEAN exports for a period of 20 years (2000–2020) were extracted and analyzed. Results from this study revealed that of all ten ASEAN member countries, seven had cases of FFA notified in the database with Thailand (n = 47, 32%) and the Philippines (n = 37, 26%) receiving the highest frequency of notifications in the region. There was a statistical significance difference in frequency of notifications received on products from these seven countries with herbs and spices ranking highest (n = 22, 15%). Highest notifications of FFA on ASEAN exports came from the United Kingdom (n = 31, 21%). All the seven countries experienced border rejections and consequent destruction of food products especially on exports from Indonesia where 95% of product with FFA were border rejected. Border rejections on products from these countries were significantly different. Therefore, a thorough implementation system, appropriate testing and constantly updating each country’s FFA database could aid actions in curtailing future events.

DNA Accounting: Tallying Genomes to Detect Adulterated Saffron.

The EU General Food Law not only aims at ensuring food safety but also to ‘prevent fraudulent or deceptive practices; the adulteration of food; and any other practices which may mislead the consumer’. Especially the partial or complete, deliberate, and intentional substitution of valuable ingredients (e.g., Saffron) for less valuable ones is of concern. Due to the variety of products on the market an approach to detect food adulteration that works well for one species may not be easily applicable to another. Here we present a broadly applicable approach for the detection of substitution of biological materials based on digital PCR. By simultaneously measuring and forecasting the number of genome copies in a sample, fraud is detectable as a discrepancy between these two values. Apart from the choice of target gene, the procedure is identical across all species. It is scalable, rapid, and has a high dynamic range. We provide proof of concept by presenting the analysis of 141 samples of Saffron (Crocus sativus) from across the European market by DNA accounting and the verification of these results by NGS analysis.

Manuka honey authentication by a compact atmospheric solids analysis probe mass spectrometer

Manuka honey is a high-value and commonly adulterated food commodity that is produced by Apis mellifera bees from the nectar of the bush Leptospermum scoparium found exclusively in New Zealand and some parts of Australia. In this study, a rapid method using a compact atmospheric solid analysis probe (ASAP) mass spectrometer (MS) and chemometric modelling was used for the authentication of Manuka honey. Class separation was observed for Manuka honey of different production batches, brands, and grades, whereas no class separation was observed for Manuka honey of the same production batch. A one-class model training set of a production batch of honey was built (R2 = 0.845; Q2 = 0.686). Model validation with a prediction set of Manuka honey consisting of samples of the same production batch not used in the model, different production batches, brands, and grades gave and a perfect classifier (AUC = 1) for the receiver operator characteristic (ROC) indicating 100 % correct classification of class samples and outliers. Additionally, validation of the model against adulterated Manuka honey gave an AUC = 0.96 for the ROC. The results in this study indicate that compositional differences between production batches of Manuka honey could be used as unique chemical fingerprints for Manuka honey authentication for the purpose of brand protection and fraud detection.

Real-Time PCR Assay for the Detection and Quantification of Roe Deer to Detect Food Adulteration-Interlaboratory Validation Involving Laboratories in Austria, Germany, and Switzerland.

Game meat products are particularly prone to be adulterated by replacing game meat with cheaper meat species. Recently, we have presented a real-time polymerase chain reaction (PCR) assay for the identification and quantification of roe deer in food. Quantification of the roe deer content in % (w/w) was achieved relatively by subjecting the DNA isolates to a reference real-time PCR assay in addition to the real-time PCR assay for roe deer. Aiming at harmonizing analytical methods for food authentication across EU Member States, the real-time PCR assay for roe deer has been tested in an interlaboratory ring trial including 14 laboratories from Austria, Germany, and Switzerland. Participating laboratories obtained aliquots of DNA isolates from a meat mixture containing 24.8% (w/w) roe deer in pork, roe deer meat, and 12 meat samples whose roe deer content was not disclosed. Performance characteristics included amplification efficiency, level of detection (LOD95%), repeatability, reproducibility, and accuracy of quantitative results. With a relative reproducibility standard deviation ranging from 13.35 to 25.08% (after outlier removal) and recoveries ranging from 84.4 to 114.3%, the real-time PCR assay was found to be applicable for the detection and quantification of roe deer in raw meat samples to detect food adulteration.

Paper based low-cost flexible SERS sensor for food adulterant detection

A simple in-situ one-step silver mirror reaction (SMR) was utilized for the growth of silver nanostructures (AgNS) on filter paper (P1,0.01; P2,0.1; and P3,0.2M AgNO 3 ) for SERS substrates. Concentration dependent growth of AgNS, with the evolution of multifaceted structures have been achieved. Further, SERS substrates were employed to detect Methylene blue (MB) and Crystal violet (CV) and fM sensitivity was achieved. Water spiked with melamine and urea were detected at limit of detection (LOD) of 0.1 mM; 0.2 mM concentrations, and for milk samples detection level was found to be 1.69 mM; 0.48mM respectively. Reproducibility of SERS sensor was achieved, with RSD values of <8% for water spiked samples, and for whole milk it was <12.28%. The achieved sensitivity and LODs for water and whole milk samples were found to be either superior or comparable with reported literature. Moreover, the stability of P3 SERS substrate was examined after 4 months for MB, CV, whole milk spiked with melamine and urea, and found to be ¿94% efficiency. Thus, the fabricated paper based SERS substrate, offers high sensitivity, selectivity, stability, and cost-effective approach, for on-site detection of chemical contaminant in water and milk samples. • Paper Based Low-cost Flexible SERS Sensor for Food Adulterant was demonstrated. • The paper sensor validated with MB and CV (LODs: 7.9 and 11.9) fM respectively. • Water spiked melamine and urea demonstrate sensitivity with 0.1 and 0.2 mM. • Whole milk spiked melamine and urea demonstrate sensitivity with 1.69 and 0.48 mM. • Stability of sensor was tested after 4 months and found >94% efficiency.

Omics-Based Analytical Approaches for Assessing Chicken Species and Breeds in Food Authentication.

Chicken is known to be the most common meat type involved in food mislabeling and adulteration. Establishing a method to authenticate chicken content precisely and identifying chicken breeds as declared in processed food is crucial for protecting consumers’ rights. Categorizing the authentication method into their respective omics disciplines, such as genomics, transcriptomics, proteomics, lipidomics, metabolomics, and glycomics, and the implementation of bioinformatics or chemometrics in data analysis can assist the researcher in improving the currently available techniques. Designing a vast range of instruments and analytical methods at the molecular level is vital for overcoming the technical drawback in discriminating chicken from other species and even within its breed. This review aims to provide insight and highlight previous and current approaches suitable for countering different circumstances in chicken authentication.

Siamese network‐based computer vision approach to detect papaya seed adulteration in black peppercorns

Food adulteration is a growing peril for consumers, traders, and manufacturers worldwide. Food fraud costs the economy a fortune and creates mistrust among consumers and merchants. Black Pepper is a valuable and heavily adulterated spice. This study explores the ability of deep learning coupled with image processing to identify black pepper contaminated with its common adulterant papaya seeds. Prevalent methods work on a relatively small sample, requiring specialization and resources. Our research proposes a system to quickly and economically obtain a method to trace added impurities. It is a problematic venture for one-shot image classification systems to differentiate between identical entities. Therefore, we implement a Siamese network that is proficient in it. A data set of 2,000 images of each class is created. After experimentation, the training and validation accuracy of 0.96 and 0.92 is obtained, while human eye judgment of the same images gave a correctness score of 0.97. Novelty impact statement A novel non-destructive, agile, cost-effective, user-friendly technique to trace papaya seeds' adulteration in black peppercorns using Artificial Intelligence. The proposed system is better in accuracy than the majority of existing approaches.

Numerical design of SERS-active substrate for analyte detection using gold-hexagonal patterns

Surface enhanced Raman spectroscopy (SERS) is a rapid, sensitive, and efficient tool for detection/analysis of molecules based on enhancement of its Raman signals. Raman active molecules can be detected in trace quantities using SERS by coupling the electromagnetic energy at optical frequency. In this study, design of a robust SERS-active substrate has been proposed that is composed of gold hexagonal nano-patterns on SiO2. High enhancement of electric field strength (≈1010) was demonstrated using optimised substrate with standard laser sources (λ = 632 nm, and λ = 785 nm) for illumination. The proposed SERS substrate was found to respond to different analytes such as water, urea, and blood, with high sensitivity (Enhancement factor≈ 108-109). It is a versatile design that may find its application in bioanalysis/bio diagnostics, explosive detection, food adulteration etc. It was observed that variation of sharpness of corners of hexagonal patterns does not degrade the enhancement factor immensely. Fabrication of the proposed SERS substrate can be done by using existing nano-lithography tools such as e-beam lithography, focussed ion beam etc.

Investigation of pork meat in chicken- and beef-based commercial products by ELISA and real-time PCR sold at retail in Kosovo

Food adulteration and fraudulent practices are widely observed in the food industry worldwide and are of great concern for Balkan countries. This study aims at investigating the level of undeclared pork meat in commercial beef and chicken meat products sold in Kosovo by implying one commercial enzyme-linked immunosorbent assay (ELISA) and two confirmatory real-time polymerase chain reaction (PCR) approaches [ready-to-use real-time PCR and real-time PCR with primers specific for pork mitochondrial deoxyribonucleic acid (DNA)]. In supermarkets in the capital city, Prishtina, 62 meat products were randomly sampled, and the three methods were applied. Additionally, these three approaches were evaluated for their practicability, reproducibility, and cost. The results showed that pork was present in 32% of beef- and 8% chicken-based products. ELISA and real-time PCR with pork specific primers showed 100% of reproducibility for beef- and chicken-based products. In contrast, the ready-to-use real-time PCR kit showed 100% reproducibility in chicken-, but only 75% in beef-based samples. ELISA was more rapid than both real-time PCR approaches, but it was more challenging when large numbers of samples were processed. The real-time PCR approach with pork specific primers was the cheapest, while the ready-to-use real-time PCR was the most practical method. Commercial ELISA, in combination with real-time PCR with pork specific primers, provides a reliable and affordable testing methodology that can be implemented for rapid detection and monitoring of pork adulteration in diverse commercial foods.

Identification and quantification of fox meat in meat products by liquid chromatography–tandem mass spectrometry

Over the years, food adulteration has become an important global problem, threatening public health safety and the healthy development of food industry. This study established a liquid chromatography–tandem mass (LC-MS/MS) method for accurate identification and quantitative analysis of fox meat products. High-resolution mass was used for data collection, and Proteome Discoverer was used for data analysis to screen fox-specific peptides. Multivariate statistical analysis was conducted using the data obtained from the label-free analysis of different contents of simulated samples. Samples with different contents were distinguished without interfering with each other, suggesting the feasibility of quantitative analysis of fox meat content. The linear correlation coefficient and recovery rate were calculated to determine the fox peptides that can be used for accurate quantification. The established LC-MS/MS method can be used for the accurate identification and quantification of actual samples. In addition, this method can provide technical support for law enforcement departments.

Utilizing AgNPt-SALDI to Classify Edible Oils by Multivariate Statistics of Triacylglycerol Profile.

Edible oils are valuable sources of nutrients, and their classification is necessary to ensure high quality, which is essential to food safety. This study reports the establishment of a rapid and straightforward SALDI-TOF MS platform used to detect triacylglycerol (TAG) in various edible oils. Silver nanoplates (AgNPts) were used to optimize the SALDI samples for high sensitivity and reproducibility of TAG signals. TAG fingerprints were combined with multivariate statistics to identify the critical features of edible oil discrimination. Eleven various edible oils were discriminated using principal component analysis (PCA). The results suggested the creation of a robust platform that can examine food adulteration and food fraud, potentially ensuring high-quality foods and agricultural products.

Enabling Food Safety Entrepreneurship: Exploratory Case Studies From Nepal, Senegal, and Ethiopia

Today, formal and informal enterprises are increasingly contributing to the safety and nutritional ramifications of their food business activities. Enabling entrepreneurship in a sustainable manner means making profits, striving to prevent ingress of harmful substances, and increasing the efficiency of using local natural resources and thus mitigating food hazardous footprints. Using examples from Nepal, Senegal and Ethiopia, this review provides information on microbial and chemical contamination and food adulteration that lead to having unsafe food in the market and on factors that are limiting growing food businesses. Four examples for how to accelerate food safety entrepreneurship are presented that include safely diversifying markets with animal sourced foods, sustainably using neglected and underutilized animal sources, expanding, and integrating innovative technologies with traditional practice and using digital technology to improving monitoring and safety along the food supply chain.

Using one-class autoencoder for adulteration detection of milk powder by infrared spectrum

Food adulteration detection requires quick and simple methods. Spectral detection can significantly reduce the analysis time, but it needs to construct a detection model. In this study, a one-class classification method based on an autoencoder is proposed for the detection of food adulteration by spectroscopy. In the proposed method, the autoencoder is constructed to extract low-dimensional features from high-dimensional spectral data and reconstruct the original spectrum. Then the coding error and reconstruction error are used to determine the food sample is adulterated or not. The infrared spectral data of milk powder and its adulterated forms are used to verify the performance of the proposed model. Experimental results show that the proposed method has similar effects to soft independent modeling of class analogy and one-class partial least squares, and is significantly better than support vector data description. The proposed method can be flexibly applied to the spectral detection of food adulteration.

Amplification Refractory Mutation System (ARMS)-PCR for Waxy Sorghum Authentication with Single-Nucleotide Resolution.

Waxy sorghum has greater economic value than wild sorghum in relation to their use in food processing and the brewing industry. Thus, the authentication of the waxy sorghum species is an important issue. Herein, a rapid and sensitive Authentication Amplification Refractory Mutation System-PCR (aARMS-PCR) method was employed to identify sorghum species via its ability to resolve single-nucleotide in genes. As a proof of concept, we chose a species of waxy sorghum containing the wxc mutation which is abundantly used in liquor brewing. The aARMS-PCR can distinguish non-wxc sorghum from wxc sorghum to guarantee identification of specific waxy sorghum species. It allowed to detect as low as 1% non-wxc sorghum in sorghum mixtures, which ar one of the most sensitive tools for food authentication. Due to its ability for resolving genes with single-nucleotide resolution and high sensitivity, aARMS-PCR may have wider applicability in monitoring food adulteration, offering a rapid food authenticity verification in the control of adulteration.

Food Metabolites as Tools for Authentication, Processing, and Nutritive Value Assessment.

Secondary metabolites are molecules with unlimited applications that have been gaining importance in various industries and studied from many angles. They are mainly used for their bioactive capabilities, but due to the improvement of sensibility in analytical chemistry, they are also used for authentication and as a quality control parameter for foods, further allowing to help avoid food adulteration and food fraud, as well as helping understand the nutritional value of foods. This manuscript covers the examples of secondary metabolites that have been used as qualitative and authentication molecules in foods, from production, through processing and along their shelf-life. Furthermore, perspectives of analytical chemistry and their contribution to metabolite detection and general perspectives of metabolomics are also discussed.

Portable through Bottle SORS for the Authentication of Extra Virgin Olive Oil

The authenticity of olive oil has been a significant long-term challenge. Extra virgin olive oil (EVOO) is the most desirable of these products and commands a high price, thus unscrupulous individuals often alter its quality by adulteration with a lower grade oil. Most analytical methods employed for the detection of food adulteration require sample collection and transportation to a central laboratory for analysis. We explore the use of portable conventional Raman and spatially-offset Raman spectroscopy (SORS) technologies as non-destructive approaches to assess the adulteration status of EVOO quantitatively and for SORS directly through the original container, which means that after analysis the bottle is intact and the oil would still be fit for use. Three sample sets were generated, each with a different adulterant and varying levels of chemical similarity to EVOO. These included EVOO mixed with sunflower oil, pomace olive oil, or refined olive oil. Authentic EVOO samples were stretched/diluted from 0% to 100% with these adulterants and measured using two handheld Raman spectrometers (excitation at 785 or 1064 nm) and handheld SORS (830 nm). The PCA scores plots displayed clear trends which could be related to the level of adulteration for all three mixtures. Conventional Raman (at 785 or 1064 nm) and SORS (at 830 nm with a single spatial offset) conducted in sample vial mode resulted in prediction errors for the test set data ranging from 1.9–4.2% for sunflower oil, 6.5–10.7% for pomace olive oil and 8.0–12.8% for refined olive oil; with the limit of detection (LOD) typically being 3–12% of the adulterant. Container analysis using SORS produced very similar results: 1.4% for sunflower, 4.9% for pomace, and 10.1% for refined olive oil, with similar LODs ranging from 2–14%. It can be concluded that Raman spectroscopy, including through-container analysis using SORS, has significant potential as a rapid and accurate analytical method for the non-destructive detection of adulteration of extra virgin olive oil.