Authenticity testing of wheat, barley, rye and oats in food and feed market samples by real-time PCR assays

Development and validation of a method for quantification of common wheat, durum wheat, rye and barley by droplet digital PCR

Food fraud is becoming a prominent topic in the food industry. Thus, valid methods for detecting potential adulterations are necessary to identify instances of food fraud in cereal products, a significant component of human diet. In this work, primer–probe systems for real-time PCR and droplet digital PCR (ddPCR) for the detection of these cereal species: bread wheat (together with spelt), durum wheat, rye and barley for real-time PCR and ddPCR were established, optimized and validated. In addition, it was projected to validate a molecular system for differentiation of bread wheat and spelt; however, attempts for molecular differentiation between common wheat and spelt based on the gene GAG56D failed because of the genetic variability of the molecular target. Primer–probe systems were further developed and optimized on the basis of alignments of DNA sequences, as well as already developed PCR systems. The specificity of each system was demonstrated on 10 (spelt), 11 (durum wheat and rye) and 12 (bread wheat) reference samples. Specificity of the barley system was already proved in previous work. The calculated limits of detection (LOD95%) were between 2.43 and 4.07 single genome copies in real-time PCR. Based on the “three droplet rule”, the LOD95% in ddPCR was calculated to be 9.07–13.26 single genome copies. The systems were tested in mixtures of flours (rye and common wheat) and of semolina (durum and common wheat). The methods proved to be robust with regard to the tested conditions in the ddPCR. The developed primer–probe systems for ddPCR proved to be effective in quantitatively detecting the investigated cereal species rye and common wheat in mixtures by taking into account the haploid genome weight and the degree of milling of a flour. This method can correctly detect proportions of 50%, 60% and 90% wholemeal rye flour in a mixture of wholemeal common wheat flour. Quantitative results depend on the DNA content, on ploidy of cereal species and are also influenced by comminution. Hence, the proportion of less processed rye is overestimated in higher processed bread wheat and adulteration of durum wheat by common wheat by 1–5% resulted in underestimation of common wheat.

Wheat authentication：An overview on different techniques and chemometric methods

Wheat (Triticum aestivum L.) is one of the most important cereal crops and is consumed as a staple food around the globe. Wheat authentication has become a crucial issue over the last decades. Recently, many techniques have been applied in wheat authentication including the authentication of wheat geographical origin, wheat variety, organic wheat, and wheat flour from other cereals. This paper collected related literature in the last ten years, and attempted to highlight the recent studies on the discrimination and authentication of wheat using different determination techniques and chemometric methods. The stable isotope analysis and elemental profile of wheat are promising tools to obtain information regarding the origin, and variety, and to differentiate organic from conventional farming of wheat. Image analysis, genetic parameters, and omics analysis can provide solutions for wheat variety, organic wheat, and wheat adulteration. Vibrational spectroscopy analyses, such as NIR, FTIR, and HIS, in combination with multivariate data analysis methods, such as PCA, LDA, and PLS-DA, show great potential in wheat authenticity and offer many advantages such as user-friendly, cost-effective, time-saving, and environment friendly. In conclusion, analytical techniques combining with appropriate multivariate analysis are very effective to discriminate geographical origin, cultivar classification, and adulterant detection of wheat.

The Potential of Selected Agri-Food Loss and Waste to Contribute to a Circular Economy: Applications in the Food, Cosmetic and Pharmaceutical Industries

The food sector includes several large industries such as canned food, pasta, flour, frozen products, and beverages. Those industries transform agricultural raw materials into added-value products. The fruit and vegetable industry is the largest and fastest-growing segment of the world agricultural production market, which commercialize various products such as juices, jams, and dehydrated products, followed by the cereal industry products such as chocolate, beer, and vegetable oils are produced. Similarly, the root and tuber industry produces flours and starches essential for the daily diet due to their high carbohydrate content. However, the processing of these foods generates a large amount of waste several times improperly disposed of in landfills. Due to the increase in the world’s population, the indiscriminate use of natural resources generates waste and food supply limitations due to the scarcity of resources, increasing hunger worldwide. The circular economy offers various tools for raising awareness for the recovery of waste, one of the best alternatives to mitigate the excessive consumption of raw materials and reduce waste. The loss and waste of food as a raw material offers bioactive compounds, enzymes, and nutrients that add value to the food cosmetic and pharmaceutical industries. This paper systematically reviewed literature with different food loss and waste by-products as animal feed, cosmetic, and pharmaceutical products that strongly contribute to the paradigm shift to a circular economy. Additionally, this review compiles studies related to the integral recovery of by-products from the processing of fruits, vegetables, tubers, cereals, and legumes from the food industry, with the potential in SARS-CoV-2 disease and bacterial diseases treatment.

Discovery of food identity markers by metabolomics and machine learning technology

Verification of food authenticity establishes consumer trust in food ingredients and components of processed food. Next to genetic or protein markers, chemicals are unique identifiers of food components. Non-targeted metabolomics is ideally suited to screen food markers when coupled to efficient data analysis. This study explored feasibility of random forest (RF) machine learning, specifically its inherent feature extraction for non-targeted metabolic marker discovery. The distinction of chia, linseed, and sesame that have gained attention as “superfoods” served as test case. Chemical fractions of non-processed seeds and of wheat cookies with seed ingredients were profiled. RF technology classified original seeds unambiguously but appeared overdesigned for material with unique secondary metabolites, like sesamol or rosmarinic acid in the Lamiaceae, chia. Most unique metabolites were diluted or lost during cookie production but RF technology classified the presence of the seed ingredients in cookies with 6.7% overall error and revealed food processing markers, like 4-hydroxybenzaldehyde for chia and succinic acid monomethylester for linseed additions. RF based feature extraction was adequate for difficult classifications but marker selection should not be without human supervision. Combination with alternative data analysis technologies is advised and further testing of a wide range of seeds and food processing methods.

Real-time PCR Detection Methods for Food Allergens (Wheat, Buckwheat, and Peanuts) Using Reference Plasmids

Specific and sensitive real-time qualitative polymerase chain reaction (PCR) methods for the detection of food allergens including wheat, buckwheat, and peanuts were developed that could cancel between instrument effects and avoid risks of false-positives and false-negatives. In these real-time PCR analysis, the cutoff for determination of positive samples was set in every PCR run by using reference plasmids containing known copies of the target sequences. The copy numbers of the plasmids were used to detect the allergenic ingredients corresponding to 10 ppm (w/w) protein in highly processed foods (cooked for more than 30 min at 122 °C). Reference plasmid analysis for each real-time PCR run helped to minimize variability between runs and instruments (7900HT Real-Time PCR systems and Light Cycler Nano). It also helped to avoid false positives due to trace levels of contaminants from the laboratory environment or agricultural products. The specificity of the real-time PCR method was verified using 79 commonly used food materials and some of their relatives. The method was sensitive enough to detect those allergenic ingredients corresponding to 10 ppm (w/w) in seven types of incurred samples. The current official Japanese method was not able to detect the allergens in some of the incurred samples. The developed method can avoid false negatives due to lack of sensitivity and is useful to confirm positive ELISA screening tests.

Quantitation of Specific Barley, Rye, and Oat Marker Peptides by Targeted Liquid Chromatography-Mass Spectrometry To Determine Gluten Concentrations

Celiac disease is triggered by the ingestion of gluten from wheat, barley, rye, and possibly oats. Gluten is quantitated by DNA-based methods or enzyme-linked immunosorbent assays (ELISAs). ELISAs mostly detect the prolamin fraction and potentially over- or underestimate gluten contents. Therefore, a new independent method is required to comprehensively detect gluten. A targeted liquid chromatography-tandem mass spectrometry method was developed to quantitate seven barley, seven rye, and three oat marker peptides derived from each gluten protein fraction (prolamin and glutelin) and type (barley, B-, C-, D-, and γ-hordeins; rye, γ-75k-, γ-40k-, ω-, and HMW-secalins). The quantitation of each marker peptide in the chymotryptic digest of a defined amount of the respective reference gluten protein type resulted in peptide-specific yields, which enabled the conversion of peptide into protein concentrations. This method was applied to quantitate gluten in samples from the brewing process, in raw materials for sourdough fermentation, and in dried sourdoughs.

Critically appraised topic on adverse food reactions of companion animals (5): discrepancies between ingredients and labeling in commercial pet foods

Background

Elimination dietary trials for the diagnosis of adverse food reactions (food allergies) in dogs and cats are often conducted with commercial pet foods while relying on their label to select those not containing previously-eaten ingredients. There are concerns that industrial pet foods might contain unlisted food sources that could negate the usefulness of performing food trials. Furthermore, unidentified ingredients might cause clinical reactions in patients hypersensitive to such items.

Results

We searched two article databases on July 7, 2017 and January 12, 2018 for relevant articles, and we screened abstracts from the leading international veterinary dermatology congresses for suitable material. Additional citations were found in the selected papers. In all, we extracted data from 17 articles and one abstract. The studies varied both in the number of pet foods tested (median: 15; range: 1 to 210) and that of ingredients specifically evaluated (median: 4; range: 1 to 11). Studies most often employed either PCR to detect DNA or ELISA to identify proteins from one or more vegetal or animal species; two studies used mass spectrometry to increase the number of detectable proteins. The various methods found ingredients that were not on the label in 0 to 83% (median: 45%) of tested diets; this percentage varied between 33 and 83% in pet foods with “novel/limited” ingredients proposed for elimination diets. Similarly, ingredients were found to be missing from the label in 0 to 38% (median: 1%) of tested foods. Finally, six studies evaluated, among others, several hydrolysate-containing pet foods: mislabeling with unlabeled or missing ingredients was found only in one diet.

Conclusions

The mislabeling of pet foods appears rather common, even in those with “novel” or “limited” ingredients proposed for elimination diets. Unexpected added ingredients are more frequently detected than those missing from the label. There is insufficient information to determine if the presence of a contaminating component will lead to a clinical reaction in a patient allergic to it, as challenges with the mislabeled foods were not performed in dogs or cats allergic to such ingredients. The testing of hydrolysate-containing pet foods found only one instance of possible mislabeling.

Electronic supplementary material

The online version of this article (10.1186/s12917-018-1346-y) contains supplementary material, which is available to authorized users.