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Feed is substantial in the production of animal food products and subject to regulations about traceability in the European Union. The geographical origin as one feature of authenticity was approached by spectroscopic techniques such as Fourier transform infrared spectroscopy. This fast and non-destructive method may be used to identify suspicious samples and initiate further investigations. The aim of the feasibility study was the development of classification models based on authentic grain maize samples from three different countries. Grain maize was used as demonstrator for unprocessed feed materials due to its wide cultivation and trade. Attenuated total reflexion Fourier transform infrared spectroscopy and multivariate analyses were applied to differentiate grain maize samples by their geographical origin (Ukraine, USA, Peru). Several sample preparations and data preprocessings were tested based on the results of a hard and a soft classification method. Model validation was performed with separate test sets and permutation tests. The use of an optimal data preprocessing resulted in 100% sensitivity for solid samples in both classification models, whereas oil extraction resulted in 100% and 70% sensitivity, respectively. These findings indicate the feasibility of FTIR spectroscopy combined with multivariate classification to verify the geographical origin of grain maize.

Wine authentication is vital in identifying malpractice and fraud, and various physical and chemical analytical techniques have been employed for this purpose. Besides wet chemistry, these include chromatography, isotopic ratio mass spectrometry, optical spectroscopy, and nuclear magnetic resonance (NMR) spectroscopy, which have been applied in recent years in combination with chemometric approaches. For many years, 2 H NMR spectroscopy was the method of choice and achieved official recognition in the detection of sugar addition to grape products. Recently, 1 H NMR spectroscopy, a simpler and faster method (in terms of sample preparation), has gathered more and more attention in wine analysis, even if it still lacks official recognition. This technique makes targeted quantitative determination of wine ingredients and nontargeted detection of the metabolomic fingerprint of a wine sample possible. This review summarizes the possibilities and limitations of 1 H NMR spectroscopy in analytical wine authentication, by reviewing its applications as reported in the literature. Examples of commercial and open-source solutions combining NMR spectroscopy and chemometrics are also examined herein, together with its opportunities of becoming an official method.

Non-targeted Nuclear Magnetic Resonance (NMR) spectroscopy is a promising approach for wine authentication. A prerequisite for its application in official control is the inter-laboratory comparability, which has rarely been the subject of research studies so far. For the first time, in this study an instrument comparison was carried out in order to investigate the extent and impact of instrumental differences on the classification of wine grape varieties. Following a harmonized protocol for sample preparation, spectra acquisition and processing, the same sample set, comprising of 101 red and 100 white wine samples, was analyzed with two NMR spectrometers of the same vendor in different laboratories. Observed spectral differences (intensity, baseline, chemical shift of pH-sensitive signals) were accounted for by applying an instrument-specific conversion factor or normalization to total intensity, respectively. Partial Least Squares – Discriminant Analysis (PLS-DA) and Principal Component Analysis – Linear Discriminant Analysis (PCA-LDA) classification models built with data from the individual instruments and with combined data from both instruments achieved comparable accuracies in the internal validation (79–87%). When classification models of the individual instruments were used to predict data of the respective other instrument, results were inconsistent, whereas the use of combined data from both instruments for training and test sets produced more robust and reliable results. This study represents an important step towards harmonization of non-targeted analytical approaches and may help in the development of jointly used databases for food authentication.

Fourier transform infrared spectroscopy becomes increasingly important for detecting adulterations in food due to a minimal sample preparation and a fast nondestructive measurement. Sunflower oil is a popular food ingredient, which might be contaminated or even adulterated by compounds with health concerns such as mineral oil. In this context a feasibility study was performed to compare the suitability of near- and mid-infrared spectroscopy for detecting mineral oil in sunflower oil. For this purpose, sunflower oils spiked with mineral oil in the concentration range of 0.001–1.0% w/w were analyzed by Fourier transform near- and mid-infrared spectroscopy, respectively, and spectra data were preprocessed prior to partial least squares regression. Hereby, the data preparation was optimized for each technique to account for model performance influences. The model performance was fairly similar for both approaches with a slightly better precision and thus limit of detection (near infrared 0.12% w/w, mid infrared 0.16% w/w) for the near-infrared-based model compared to the mid-infrared model. Consequently, both techniques are considered suitable for the determination of mineral oil in sunflower oil in the context of food authentication.

Paprika powder is a widely consumed high-priced product and therefore particularly prone to fraudulent practices. Nuclear magnetic resonance spectroscopy combined with one-class classification was applied for non-targeted detection of paprika adulteration and further chemometric tools were tested for diagnostic purposes. The 1H NMR spectra of 186 commercial paprika powders and 216 spiked samples were used to develop and comprehensively validate a data-driven soft independent modelling of class analogy model. The established one-class model yielded 92% sensitivity and exemplary adulterants were detected with 100% specificity at concentration levels of 0.1%, 0.1%, 10% and 20% by weight for Azorubine, Ponceau 4R, beetroot and sumac powder, respectively. After successful classification, visualization tools of robust principal component analysis and orthogonal partial least squares analysis were explored to uncover fingerprints of unusual (atypical and spiked) paprika powders. One-class classifiers based on 1H NMR spectroscopic data seem to be suitable for adulteration screening and, in combination with an outlier diagnosis, may improve prioritization of suspicious products for further confirmatory analysis in food authentication.

Background Consumers are increasingly interested in the provenance of the foods and European laws require protection against the mislabelling of premium foods. Methods for testing authenticity require robust analytical techniques that can be utilised by the various regulatory authorities. Of the many techniques, the most widely-used method is stable isotope ratio analysis. Scope and approach Focus is on the use of stable isotope ratios of H, C, N, O, S and Sr for verifying the geographical origin of food, cross-referencing it with examples of legal cases. State of the art including rules for building an authentic sample reference database (commonly called databank) and for interpreting the results obtained in actual cases is described. The overall objective is to provide stakeholders and competent authorities dealing with fraud, with a best-practice guide for its use. Key findings and conclusions Stable isotope ratios can differentiate foods on the basis of their geographical origin and, especially for light elements, can be measured reliably in routine work in different matrices and compared successfully between different laboratories. Examples of legal applications are grape products, orange juices, olive oil, cheese, butter, caviar. Sometimes, the cases are not brought directly to the court, but before further verifications (e.g. paper traceability, forensic accounting) are conducted. The system can satisfy the court when a robust databank of authentic samples exists, the methods used are officially recognized, validated and accredited, and the expert demonstrates that the conclusions are sufficiently robust and reliable to stand up to the required level of proof.

Die Authentizitatsprufung von Lebens- und Futtermitteln umfasst eine grose Bandbreite an Fragestellungen, Matrizes, analytischen Techniken und Forschungsansatzen, weshalb die Vernetzung von offentlichen Instituten eine immer grosere Rolle spielt. Um den Austausch zwischen Experten auf eine breitere Basis zu stellen, organisierte das Bundesinstitut fur Risikobewertung (BfR) erstmals eine bundesweite Veranstaltung fur offentliche Institute mit dem Titel „Status quo der Authentizitatsprufung von Lebens- und Futtermitteln - Analytische Routineverfahren und Forschungsansatze“. Auf Einladung des BfR berichteten Expertinnen und Experten diverser Einrichtungen des Bundes, der Landesuberwachungsbehorden und Universitaten am 13. und 14. Juni 2016 in den Tagungsraumen des BfR uber ihre bisherigen Erfahrungen und Aktivitaten in der Echtheitsuberprufung – Authentizitatsprufung – von Lebens- und Futtermitteln. Das Ziel des BfR, mit dieser Veranstaltung den wissenschaftlichen Austausch in diesem aktuellen Forschungsgebiet durch den Aufbau eines Expertennetzwerkes zu fordern, wurde erreicht.

Omics-based methods are increasingly used in current ecotoxicology. Therefore, a large number of observations for various toxic substances and organisms are available and may be used for identifying modes of action, adverse outcome pathways, or novel biomarkers. For these purposes, good statistical analysis of toxicogenomic data is vital. In contrast to established ecotoxicological techniques, concentration-response modeling is rarely used for large datasets. Instead, statistical hypothesis testing is prevalent, which provides only a limited scope for inference. The present study therefore applied automated concentration-response modeling for 3 different ecotoxicotranscriptomic and ecotoxicometabolomic datasets. The modeling process was performed by simultaneously applying 9 different regression models, representing distinct mechanistic, toxicological, and statistical ideas that result in different curve shapes. The best-fitting models were selected by using Akaike's information criterion. The linear and exponential models represented the best data description for more than 50% of responses. Models generating U-shaped curves were frequently selected for transcriptomic signals (30%), and sigmoid models were identified as best fit for many metabolomic signals (21%). Thus, selecting the models from an array of different types seems appropriate, because concentration-response functions may vary because of the observed response type, and they also depend on the compound, the organism, and the investigated concentration and exposure duration range. The application of concentration-response models can help to further tap the potential of omics data and is a necessary step for quantitative mixture effect assessment at the molecular response level.

Food fingerprinting approaches are expected to become a very potent tool in authentication processes aiming at a comprehensive characterization of complex food matrices. By non-targeted spectrometric or spectroscopic chemical analysis with a subsequent (multivariate) statistical evaluation of acquired data, food matrices can be investigated in terms of their geographical origin, species variety or possible adulterations. Although many successful research projects have already demonstrated the feasibility of non-targeted fingerprinting approaches, their uptake and implementation into routine analysis and food surveillance is still limited. In many proof-of-principle studies, the prediction ability of only one data set was explored, measured within a limited period of time using one instrument within one laboratory. Thorough validation strategies that guarantee reliability of the respective data basis and that allow conclusion on the applicability of the respective approaches for its fit-for-purpose have not yet been proposed. Within this review, critical steps of the fingerprinting workflow were explored to develop a generic scheme for multivariate model validation. As a result, a proposed scheme for "good practice" shall guide users through validation and reporting of non-targeted fingerprinting results. Furthermore, food fingerprinting studies were selected by a systematic search approach and reviewed with regard to (a) transparency of data processing and (b) validity of study results. Subsequently, the studies were inspected for measures of statistical model validation, analytical method validation and quality assurance measures. In this context, issues and recommendations were found that might be considered as an actual starting point for developing validation standards of non-targeted metabolomics approaches for food authentication in the future. Hence, this review intends to contribute to the harmonization and standardization of food fingerprinting, both required as a prior condition for the authentication of food in routine analysis and official control.

A method for the non-targeted detection of paprika adulteration was developed using Fourier transform mid-infrared (FT-MIR) spectroscopy and one-class soft independent modelling of class analogy (OCSIMCA). One-class models based on commercially available paprika powders were developed and optimised to provide >80% sensitivity by external validation. The performances of the established models for adulteration detection were tested by predicting spiked paprika samples with various types of fraudulent material and levels of adulterations including 1% (w/w) Sudan I, 1% (w/w) Sudan IV, 3% (w/w) lead chromate, 3% (w/w) lead oxide, 5% (w/w) silicon dioxide, 10% (w/w) polyvinyl chloride, and 10% (w/w) gum arabic. Further, the influence of data preprocessing on the model performance was investigated. Relationship between classification results and data preprocessing was identified and specificity >80% was achieved for all adulterants by applying different preprocessing methods including standard normal variate (SNV), first and second derivatives, smoothing, and combinations thereof.

An untargeted analytical approach combined with chemometrics using the volatiles of German white wine was investigated regarding the usefulness for verifying botanical origin. A total of 198 wine samples of Riesling, Müller-Thurgau, Silvaner, Pinot Gris, and Pinot Blanc were examined applying headspace solid-phase microextraction online coupled with gas chromatography mass spectrometry. The resultant three-dimensional raw data were processed by available metabolomics software. After data treatment, a partial least-squares discriminant analysis (PLS-DA) model was validated. External samples were correctly classified for 97% Silvaner, 93% Riesling, 91% Pinot Gris/Blanc, and 80% Müller-Thurgau. This model was related to monoterpenoids, C13-norisoprenoids, and esters. Further, 100% prediction for a two-class model of Riesling versus Pinot Gris/Blanc was confirmed by 74 additional samples measured independently. Hence, the strategy applied was, in particular, reliable and relevant for white wine varietal classification. In addition, the superior classification performance of the Riesling class was revealed.

The investigation of the so-called food fingerprints provides high potential with regard to the characterization and identity verification of food. Therefore, this kind of non-targeted analysis obtained increasingly importance during the recent years. These applications are usually based on spectroscopic and spectrometric data providing the capability for a comprehensive characterization of the investigated matrices. The subsequent statistical multivariate data analysis enables a general identification of many deviations from the expected product composition. Besides the classical tests of authenticity of foods, a comprehensive analysis that also allows the detection of hazardous or safety-relevant manipulations and violations of the respective laws e.g. with regard to non-authorized food additives or a prohibited use of technological processes is of urgent need in food control. In the literature, several approaches are already pursuing the non-targeted observation of abnormalities in various foods covering a broad variety of analytical methods. This review highlights a current overview of the applicability of this approach using classic spectroscopic as well as spectrometric analytical techniques on the basis of examples of the three most investigated food matrices: honey, olive oil and wine. Furthermore, difficulties as well as challenges regarding the use of food fingerprinting in official food control are discussed.

Hydraulic fracturing is increasingly being used to produce gas from unconventional resource sites for energy supply. Therefore, concerns about risks of this technology related to human health and the environment have to be addressed. Among the major issues is the potential contamination of surrounding water systems by chemical additives used in fracturing fluids. In this study, the ecotoxicological hazards of fracturing fluids, both, their individual components (chemicals) as well as their mixtures (product) were assessed using a component-based mixture approach. For five exemplary fracturing fluids, 40–90 wt% of the contained substances could unambiguously be defined in their chemical identity. The concentrations used in the applied fluid mixture were considered as (maximum) exposure concentrations. For components with mass fractions between 10 and 74 wt%, the effect concentrations for acute and chronic toxicity of fish, daphnia and algae were retrieved from experimental databases and through predictive modeling. The hazard indices calculated from the ratio of exposure to effect concentration were >1 for all fracturing fluids, using different scenarios. This indicated a hazard from the undiluted fracturing fluids. The assessment framework presented in this study allows for dealing with data gaps and uncertainties in a tiered fashion and in particular accommodates for combined effects resulting from chemical mixtures. It might be employed for ecotoxicological risk assessment of products containing chemical mixtures and optimization of their environmental performance.

The investigation of submerged aquatic macrophytes in their natural habitat using metabolic fingerprinting may represent a comprehensive and sensitive approach in environmental sciences. The potential of metabolic fingerprinting for discriminating Myriophyllum spicatum individuals growing in different lakes under varying environmental conditions was explored to determine its applicability in an ecological context. Low molecular weight compounds from hydrophilic and lipophilic extracts of leaves from individual plants were analyzed using gas chromatography–mass spectrometry (GC–MS) with subsequent data preprocessing of the mass-to-charge fragments of metabolites using XCMS. Multivariate analysis was used to compare the metabolite composition of the plants in space and time at a fingerprint level. The technical variation in the signals was low compared with the biological variation between individuals. Variation of the metabolite composition of aquatic macrophytes retrieved at one site at a particular time was within the same range as described for algae and plants grown under controlled conditions in the laboratory. Thus, metabolic fingerprinting of M. spicatum growing in the field is reproducible and biological responses can be detected. As a “proof-of-principle”, the approach was applied to study individuals from two lake populations, as well as one population over a three week flowering period. Distinct metabolic fingerprints were found for the individuals from two lakes and were shown to be associated with the nutrient status of these lakes. For the population characterized over time, metabolic fingerprints of individuals seem to be related to their state of ontogenetic development. These results suggest that the lake water chemistry is reflected in the metabolite composition of the submerged macrophytes and that developmental changes may lead to metabolic alterations. We consider metabolic fingerprinting to be suitable for studying physiological processes of aquatic macrophytes in their habitat and potentially applicable for environmental monitoring.

The potential of metabolomics for toxicity analysis with synchronized algal populations during growth was explored in a proof of principle study. Low molecular weight compounds from hydrophilic and lipophilic extracts of algal populations of the unicellular green alga Scenedesmus vacuolatus were analyzed using gas chromatography-mass spectrometry (GC-MS) and subsequent multivariate analysis to identify time-related patterns. Algal metabolite responses were studied under control and exposure conditions for the photosystem II-inhibiting herbicide prometryn. To define the typical metabolic profile of control S. vacuolatus cultures seven time points over a growth period of 14 h were evaluated. The results show a clear time-related trend in metabolite levels and a distinct separation of exposed and reference algal populations. The results suggest an impairment of the energy metabolism associated with an activation of catabolic processes and a retardation of carbohydrate biosynthesis in treated algae. Metabolite results were compared to observation parameters, currently used in phytotoxicity assessment, showing that metabolites respond faster to exposure than algal growth. The potential of metabolomics for toxicity evaluation, especially to identify physiological markers and to detect effects at an early state of exposure, are discussed. Therefore, we suggest a metabolomics approach utilizing synchronous algal cultures to be a suitable future tool in ecotoxicology.

A major goal of ecotoxicology is the prediction of adverse outcomes for populations from sensitive and early physiological responses. A snapshot of the physiological state of an organism can be provided by metabolic fingerprints. However, to inform chemical risk assessment, multivariate metabolic fingerprints need to be converted to readable end points suitable for effect estimation and comparison. The concentration- and time-dependent responsiveness of metabolic fingerprints to the PS-II inhibitor isoproturon was investigated by use of a Myriophyllum spicatum bioassay. Hydrophilic and lipophilic leaf extracts were analyzed with gas chromatography-mass spectrometry (GC-MS) and preprocessed with XCMS. Metabolic changes were aggregated in the quantitative metabolic effect level index (MELI), allowing effect estimation from Hill-based concentration-response models. Hereby, the most sensitive response on the concentration scale was revealed by the hydrophilic MELI, followed by photosynthetic efficiency and, 1 order of magnitude higher, by the lipophilic MELI and shoot length change. In the hydrophilic MELI, 50% change compares to 30% inhibition of photosynthetic efficiency and 10% inhibition of dry weight change, indicating effect development on different response levels. In conclusion, aggregated metabolic fingerprints provide quantitative estimates and span a broad response spectrum, potentially valuable for establishing adverse outcome pathways of chemicals in environmental risk assessment.

Omics-based methods are increasingly used in current ecotoxicology. Therefore, a large number of observations for various toxic substances and organisms are available and may be used for identifying modes of action, adverse outcome pathways, or novel biomarkers. For these purposes, good statistical analysis of toxicogenomic data is vital. In contrast to established ecotoxicological techniques, concentration-response modeling is rarely used for large datasets. Instead, statistical hypothesis testing is prevalent, which provides only a limited scope for inference. The present study therefore applied automated concentration-response modeling for 3 different ecotoxicotranscriptomic and ecotoxicometabolomic datasets. The modeling process was performed by simultaneously applying 9 different regression models, representing distinct mechanistic, toxicological, and statistical ideas that result in different curve shapes. The best-fitting models were selected by using Akaike's information criterion. The linear and exponential models represented the best data description for more than 50% of responses. Models generating U-shaped curves were frequently selected for transcriptomic signals (30%), and sigmoid models were identified as best fit for many metabolomic signals (21%). Thus, selecting the models from an array of different types seems appropriate, because concentration-response functions may vary because of the observed response type, and they also depend on the compound, the organism, and the investigated concentration and exposure duration range. The application of concentration-response models can help to further tap the potential of omics data and is a necessary step for quantitative mixture effect assessment at the molecular response level.

Growth assays with unicellular green algae are an established tool in ecotoxicological effect assessment for chemicals and environmental samples. From an ecological perspective it seems appropriate to use the growth rate as a process variable rather than a measure of biomass gain for calculating inhibitory effects of contaminants. The notion of simple exponential growth for the description of the population increase in undisturbed suspension cultures of unicellular green algae, however, seems to be an oversimplification. Experimental findings describe the increase in biomass, cell number, the development of cell volume distributions of populations, and the relationship between cell size and chlorophyll content for individual cells over one generation at a time resolution of 2-h intervals. It was observed that algal populations of Desmodesmus subspicatus show a time pattern of cell size growth; the average cell volume increases about sixfold, without corresponding increase in population size. This is followed by a distinct cell division phase with little gain in biomass. This synchronous growth behavior despite continuous illumination may be explained by the multiple fission characteristic of unicellular green algae which is an adaptation to cyclic light-dark changes in the environment. It might be controlled by an independent cell cycle clock. For routine regulatory testing fluorescence-based measurements rather than cell counting minimizes the confounding effect on toxicity determination. For investigations of time-dependent effects, e.g., by pulsed exposure, an alternative mechanistically based growth function for unicellular algae is proposed that accommodates for the observed growth pattern.

N-phenyl-2-naphthylamine, a sediment contaminant previously identified as a major toxicant of site-specific importance was investigated for its mode of toxic action. From short-term bioassays with daphnids, fish eggs, bacteria, and algae it appears that this compound has specific phytotoxic properties at concentrations below 100 microg/L, which cannot be explained assuming an unspecific narcosis type of action in plants. Also, hydroxy-, nitro-, and methylderivatives show clear excess toxicity as compared to baseline toxic effects. Of several plant-specific growth and development processes investigated, only photosynthesis could be demonstrated to be affected at short exposure times and low concentrations. Disturbance of primary photosynthetic reactions such as oxygen evolution and fluorescence quenching, however, becomes only apparent after 2-3 h of exposure, which is in sharp contrast to known specific inhibitors targeting processes such as electron transport or ATP production. This, and concentration-time-effect modeling lead to the suggestion that N-phenyl-2-naphthylamine acts intracellular as a reactive compound in cell membranes producing irreversible, and thus cumulative, damage over time in algae. The effects may become first apparent in membrane-rich compartments such as the algal chloroplast.

In the last years many efforts were made to transform standardized algal test protocols into low-cost microplate assays. While advantages were pointed out frequently, limitations are not systematically addressed, thus hindering a widespread utilisation. In this study a group of organic substances with a wide distribution of volatility (logKAW from -6.53 to -2.13) and lipophilicity (logKOW from 1.26 to 4.92) was investigated with respect to the influence of these physicochemical properties on their algal toxicity in different assays. Therefore the EC50 values were determined with a microplate assay based on ISO 8692 protocol and the results were compared with those of an established algal growth inhibition test conducted in air tight glass vessels. Using the ratio of the EC50 values, a clear connection between biological response and volatility as well as lipophilicity of test substances could be detected. Chemicals with a logKOW higher than 3 or a Henry coefficient logKAW higher than -4 were identified as less effective in the microplate assay than in the comparative assay. The loss in nominal concentration due to physicochemical properties could be shown to contribute to this using HPLC analysis. Consequently, when using microplate assay's one should be aware that lipophilic and volatile chemicals might be underestimated in their toxicity, which could be indicated from evaluating related physicochemical properties modelled from structural information prior to an experimental investigation.