Analytical authentication of organic products: an overview of markers

Development of a Quantitative GC-MS Method for the Detection of Cyclopropane Fatty Acids in Cheese as New Molecular Markers for Parmigiano Reggiano Authentication

Cyclopropane fatty acids (CPFA), as lactobacillic acid and dihydrosterculic acid, are components of bacterial membranes and have been recently detected in milk and in dairy products from cows fed with corn silage. In this paper, a specific quantitative gas chromatography-mass spectrometry (GC-MS) method for the detection of CPFA in cheeses was developed, and the quality parameters of the method (limit of detection, limit of quantitation, and intralaboratory precision) were assessed. Limit of detection and quantitation of CPFA were, respectively, 60 and 200 mg/kg of cheese fat, and the intralaboratory precision, determined on three concentration levels, satisfied the Horwitz equation. This method was applied to 304 samples of PDO cheeses of certified origin, including Parmigiano Reggiano (Italy), Grana Padano (Italy), Fontina (Italy), Comté (France), and Gruyère (Switzerland). Results showed that CPFA were absent in all of the cheeses whose Production Specification Rules expressly forbid the use of silages (Parmigiano Reggiano, Fontina, Comté, and Gruyère). CPFA were instead present in variable concentrations (300-830 mg/kg of fat) in all of the samples of Grana Padano cheese (silages admitted). A mix of grated Parmigiano Reggiano and Grana Padano was also prepared, showing that the method is able to detect the counterfeiting of Parmigiano Reggiano with other cheeses up to 10-20% Grana Padano content. These results support the hypothesis that CPFA can be used as a marker of silage feedings for cheeses, and the data reported can be considered a first attempt to create a database for CPFA presence in PDO cheeses.

OMICS Technologies and Applications in Sugar Beet

Sugar beet is a species of the Chenopodiaceae family. It is an important sugar crop that supplies approximately 35% of the sugar in the world. Sugar beet M14 line is a unique germplasm that contains genetic materials from Beta vulgaris L. and Beta corolliflora Zoss. And exhibits tolerance to salt stress. In this review, we have summarized OMICS technologies and applications in sugar beet including M14 for identification of novel genes, proteins related to biotic and abiotic stresses, apomixes and metabolites related to energy and food. An OMICS overview for the discovery of novel genes, proteins and metabolites in sugar beet has helped us understand the complex mechanisms underlying many processes such as apomixes, tolerance to biotic and abiotic stresses. The knowledge gained is valuable for improving the tolerance of sugar beet and other crops to biotic and abiotic stresses as well as for enhancing the yield of sugar beet for energy and food production.

Determination of geographical origin of alcoholic beverages using ultraviolet, visible and infrared spectroscopy: A review

The identification of the geographical origin of beverages is one of the most important issues in food chemistry. Spectroscopic methods provide a relative rapid and low cost alternative to traditional chemical composition or sensory analyses. This paper reviews the current state of development of ultraviolet (UV), visible (Vis), near infrared (NIR) and mid infrared (MIR) spectroscopic techniques combined with pattern recognition methods for determining geographical origin of both wines and distilled drinks. UV, Vis, and NIR spectra contain broad band(s) with weak spectral features limiting their discrimination ability. Despite this expected shortcoming, each of the three spectroscopic ranges (NIR, Vis/NIR and UV/Vis/NIR) provides average correct classification higher than 82%. Although average correct classification is similar for NIR and MIR regions, in some instances MIR data processing improves prediction. Advantage of using MIR is that MIR peaks are better defined and more easily assigned than NIR bands. In general, success in a classification depends on both spectral range and pattern recognition methods. The main problem still remains the construction of databanks needed for all of these methods.

Stable isotope ratio analysis: A potential analytical tool for the authentication of South African lamb meat

Stable isotope ratios ((13)C/(12)C and (15)N/(14)N) of South African Dorper lambs from farms with different vegetation types were measured by isotope ratio mass spectrometry (IRMS), to evaluate it as a tool for the authentication of origin and feeding regime. Homogenised and defatted meat of the Longissimus lumborum (LL) muscle of lambs from seven different farms was assessed. The δ(13)C values were affected by the origin of the meat, mainly reflecting the diet. The Rûens and Free State farms had the lowest (p ⩽ 0.05) δ(15)N values, followed by the Northern Cape farms, with Hantam Karoo/Calvinia having the highest δ(15)N values. Discriminant analysis showed δ(13)C and δ(15)N differences as promising results for the use of IRMS as a reliable analytical tool for lamb meat authentication. The results suggest that diet, linked to origin, is an important factor to consider regarding region of origin classification for South African lamb.

Compound-Specific δ¹⁵N and δ¹³C Analyses of Amino Acids for Potential Discrimination between Organically and Conventionally Grown Wheat

We present a study deploying compound-specific nitrogen and carbon isotope analysis of amino acids to discriminate between organically and conventionally grown plants. We focused on grain samples of common wheat and durum wheat grown using synthetic nitrogen fertilizers, animal manures, or green manures from nitrogen-fixing legumes. The measurement of amino acid δ(15)N and δ(13)C values, after protein hydrolysis and derivatization, was carried out using gas chromatography-combustion-isotope ratio mass spectrometry (GC-C-IRMS). Our results demonstrated that δ(13)C of glutamic acid and glutamine in particular, but also the combination of δ(15)N and δ(13)C of 10 amino acids, can improve the discrimination between conventional and organic wheat compared to stable isotope bulk tissue analysis. We concluded that compound-specific stable isotope analysis of amino acids represents a novel analytical tool with the potential to support and improve the certification and control procedures in the organic sector.

Learning to Classify Organic and Conventional Wheat - A Machine Learning Driven Approach Using the MeltDB 2.0 Metabolomics Analysis Platform

We present results of our machine learning approach to the problem of classifying GC-MS data originating from wheat grains of different farming systems. The aim is to investigate the potential of learning algorithms to classify GC-MS data to be either from conventionally grown or from organically grown samples and considering different cultivars. The motivation of our work is rather obvious nowadays: increased demand for organic food in post-industrialized societies and the necessity to prove organic food authenticity. The background of our data set is given by up to 11 wheat cultivars that have been cultivated in both farming systems, organic and conventional, throughout 3 years. More than 300 GC-MS measurements were recorded and subsequently processed and analyzed in the MeltDB 2.0 metabolomics analysis platform, being briefly outlined in this paper. We further describe how unsupervised (t-SNE, PCA) and supervised (SVM) methods can be applied for sample visualization and classification. Our results clearly show that years have most and wheat cultivars have second-most influence on the metabolic composition of a sample. We can also show that for a given year and cultivar, organic and conventional cultivation can be distinguished by machine-learning algorithms.

Impact of Production Location, Production System, and Variety on the Volatile Organic Compounds Fingerprints and Sensory Characteristics of Tomatoes

Consumers have more and more interest in where and how their foods are produced. However, it is often challenging to discriminate products from different production locations and systems. The objective of this study was to examine fingerprinting of volatile organic compounds (VOCs) as an approach for characterization and discrimination of tomatoes by their production location, production system, and variety using Proton Transfer Reaction Mass Spectrometry combined with multivariate statistics. Sensory analysis was complementing the VOC analyses. The study was part of the EU CORE Organic II project AuthenticFood. Tomato sample batches cultivated in two locations in Italy, according to the organic and conventional production system, comprising two varieties, and produced in two consecutive years were examined. Both factors production location and production system impacted considerably the VOC fingerprints, but compared to these two factors, minor differences were observed between the two varieties of tomatoes studied. VOC data were successfully used to predict the origin and production system for this sample set. Sensory data also primarily indicated the differences between origin and production systems, and several sensory attributes could be predicted from the VOC fingerprints. Therefore, VOC fingerprints reflect production conditions and are promising for substantiation and authentication of special tomato traits.

Differentiation of organic and non-organic winter wheat cultivars from a controlled field trial by crystallization patterns

BACKGROUND

There is a need for authentication tools in order to verify the existing certification system. Recently, markers for analytical authentication of organic products were evaluated. Herein, crystallization with additives was described as an interesting fingerprint approach which needs further evidence, based on a standardized method and well-documented sample origin.

RESULTS

The fingerprint of wheat cultivars from a controlled field trial is generated from structure analysis variables of crystal patterns. Method performance was tested on factors such as crystallization chamber, day of experiment and region of interest of the patterns. Two different organic treatments and two different treatments of the non-organic regime can be grouped together in each of three consecutive seasons. When the k-nearest-neighbor classification method was applied, approximately 84% of Runal samples and 95% of Titlis samples were classified correctly into organic and non-organic origin using cross-validation.

CONCLUSION

Crystallization with additive offers an interesting complementary fingerprint method for organic wheat samples. When the method is applied to winter wheat from the DOK trial, organic and non-organic treated samples can be differentiated significantly based on pattern recognition. Therefore crystallization with additives seems to be a promising tool in organic wheat authentication.

Characterization of conventional, biodynamic, and organic purple grape juices by chemical markers, antioxidant capacity, and instrumental taste profile

The objectives of this study were to characterize organic, biodynamic, and conventional purple grape juices (n = 31) produced in Europe based on instrumental taste profile, antioxidant activity, and some chemical markers and to propose a multivariate statistical model to analyze their quality and try to classify the samples from the 3 different crop systems. Results were subjected to ANOVA, correlation, and regression analysis, principal component analysis (PCA), hierarchical cluster analysis (HCA), soft independent modeling of class analogy (SIMCA), and partial least-squares discriminant analysis (PLSDA). No statistical significant differences (P > 0.05) were observed among juices from the 3 crop systems. Using PCA and HCA, no clear separation among crop systems was observed, corroborating the ANOVA data. However, PCA showed that the producing region highly affects the chemical composition, electronic tongue parameters, and bioactivity of grape juices. In this sense, when organic and biodynamic were grouped as "nonconventional" juices, SIMCA model was able to discriminate 12 out of 13 organic/biodynamic juices and 17 out of 18 conventional juices, presenting an efficiency of 93.5%, while 11 out of 13 non-conventional and 100% conventional grape juices were correctly classified using PLSDA. The use of electronic tongue and the determination of antioxidant properties and major phenolic compounds have shown to be a quick and accurate analytical approach to assess the quality of grape juices.

The influence of organic production on food quality - research findings, gaps and future challenges

Although several meta-analysis studies have been published comparing the quality of food derived from organic and non-organic origin, it is still not clear if food from organic production per se can guarantee product-related added value to consumers. This paper aims to summarize the status quo in order to identify research gaps and suggest future research challenges. Organic food is described according to a quality model already published. The influence of organic production on food quality is structured in primary production and processing. Furthermore, organic food authentication is discussed. Organic food seems to contain fewer pesticide residues and statistically more selected health-related compounds such as polyphenols in plant products and polyunsaturated fatty acids in milk and meat products, but the health relevance for consumers is not clear yet. Comparing food from organic origin with so called 'conventional' food seems not to be appropriate, because 'conventional' is not defined. In organic food quality research a system approach is needed from which systemic markers can be selected. Research on the impact of processing technologies on the quality according to organic principles seems of high relevance, since most of the food is processed.

Metabolite profiling on wheat grain to enable a distinction of samples from organic and conventional farming systems

BACKGROUND

Identification of biomarkers capable of distinguishing organic and conventional products would be highly welcome to improve the strength of food quality assurance. Metabolite profiling was used for biomarker search in organic and conventional wheat grain (Triticum aestivum L.) of 11 different old and new bread wheat cultivars grown in the DOK system comparison trial. Metabolites were extracted using methanol and analysed by gas chromatography-mass spectrometry.

RESULTS

Altogether 48 metabolites and 245 non-identified metabolites (TAGs) were detected in the cultivar Runal. Principal component analysis showed a sample clustering according to farming systems and significant differences in peak areas between the farming systems for 10 Runal metabolites. Results obtained from all 11 cultivars indicated a greater influence of the cultivar than the farming system on metabolite concentrations. Nevertheless, a t-test on data of all cultivars still detected 5 metabolites and 11 TAGs with significant differences between the farming systems.

CONCLUSION

Based on individual cultivars, metabolite profiling showed promising results for the categorization of organic and conventional wheat. Further investigations are necessary with wheat from more growing seasons and locations before definite conclusions can be drawn concerning the feasibility to evolve a combined set of biomarkers for organically grown wheat using metabolite profiles.

Beetroot (Beta vulgaris L.) and naturally fermented beetroot juices from organic and conventional production: metabolomics, antioxidant levels and anticancer activity

BACKGROUND

The aim of the paper was to determine the level of antioxidants and metabolomic fingerprinting in both raw beetroots and naturally fermented beetroot juices from organic (ORG) versus conventional (CONV) production. In addition, the anticancer properties of the fermented beetroot juices were evaluated.

RESULTS

The obtained results showed that ORG fresh beetroots contained significantly more dry matter, vitamin C and some individual phenolic compounds than CONV beetroots. The content of total phenolic acids was significantly higher in CONV beetroots compared with the ORG ones. The level of flavonoids was similar in ORG and CONV beetroots. There were only slight differences in the chemical composition of ORG and CONV beetroot juices. Metabolomic analysis provided a possibility to distinguish clearly between ORG and CONV fermented beetroot juices. However, this method was less useful in the case of fresh whole beetroots. It was found that anticancer activity was stronger in the case of ORG fermented juices when compared with CONV ones.

CONCLUSION

The obtained results indicate that ORG- and CONV-produced beetroots and fermented beetroot juices have different chemical properties and different impacts on cancer cells. It is necessary to continue research on this topic in order to confirm and understand the achieved results.

Status quo and future research challenges on organic food quality determination with focus on laboratory methods

Organic food quality determination needs multi-dimensional evaluation tools. The main focus is on the authentication as an analytical verification of the certification process. New fingerprinting approaches such as ultra-performance liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry, direct analysis in real time-high-resolution mass spectrometry as well as crystallization with and without the presence of additives seem to be promising methods in terms of time of analysis and detecting organic system-related parameters. For further methodological development, a system approach is recommended, which also takes into account food structure aspects. Furthermore, the authentication of processed organic samples needs more consciousness, hence most of organic food is complex and processed.

Discrimination of conventional and organic white cabbage from a long-term field trial study using untargeted LC-MS-based metabolomics

The influence of organic and conventional farming practices on the content of single nutrients in plants is disputed in the scientific literature. Here, large-scale untargeted LC-MS-based metabolomics was used to compare the composition of white cabbage from organic and conventional agriculture, measuring 1,600 compounds. Cabbage was sampled in 2 years from one conventional and two organic farming systems in a rigidly controlled long-term field trial in Denmark. Using Orthogonal Projection to Latent Structures–Discriminant Analysis (OPLS-DA), we found that the production system leaves a significant (p = 0.013) imprint in the white cabbage metabolome that is retained between production years. We externally validated this finding by predicting the production system of samples from one year using a classification model built on samples from the other year, with a correct classification in 83 % of cases. Thus, it was concluded that the investigated conventional and organic management practices have a systematic impact on the metabolome of white cabbage. This emphasizes the potential of untargeted metabolomics for authenticity testing of organic plant products.