Review on metabolomics for food authentication

Alternative data mining/machine learning methods for the analytical evaluation of food quality and authenticity - A review

In recent years, the variety and volume of data acquired by modern analytical instruments in order to conduct a better authentication of food has dramatically increased. Several pattern recognition tools have been developed to deal with the large volume and complexity of available trial data. The most widely used methods are principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), soft independent modelling by class analogy (SIMCA), k-nearest neighbours (kNN), parallel factor analysis (PARAFAC), and multivariate curve resolution-alternating least squares (MCR-ALS). Nevertheless, there are alternative data treatment methods, such as support vector machine (SVM), classification and regression tree (CART) and random forest (RF), that show a great potential and more advantages compared to conventional ones. In this paper, we explain the background of these methods and review and discuss the reported studies in which these three methods have been applied in the area of food quality and authenticity. In addition, we clarify the technical terminology used in this particular area of research.

Metabolomics Provides Valuable Insight for the Study of Durum Wheat: A Review

Metabolomics is increasingly being applied in various fields offering a highly informative tool for high-throughput diagnostics. However, in plant sciences, metabolomics is underused, even though plant studies are relatively easy and cheap when compared to those on humans and animals. Despite their importance for human nutrition, cereals, and especially wheat, remain understudied from a metabolomics point of view. The metabolomics of durum wheat has been essentially neglected, although its genetic structure allows the inference of common mechanisms that can be extended to other wheat and cereal species. This review covers the present achievements in durum wheat metabolomics highlighting the connections with the metabolomics of other cereal species (especially bread wheat). We discuss the metabolomics data from various studies and their relationships to other "-omics" sciences, in terms of wheat genetics, abiotic and biotic stresses, beneficial microbes, and the characterization and use of durum wheat as feed, food, and food ingredient.

Quality and Authenticity Control of Fruit Juices-A Review

Food fraud, being the act of intentional adulteration of food for financial advantage, has vexed the consumers and the food industry throughout history. According to the European Committee on the Environment, Public Health and Food Safety, fruit juices are included in the top 10 food products that are most at risk of food fraud. Therefore, reliable, efficient, sensitive and cost-effective analytical methodologies need to be developed continuously to guarantee fruit juice quality and safety. This review covers the latest advances in the past ten years concerning the targeted and non-targeted methodologies that have been developed to assure fruit juice authenticity and to preclude adulteration. Emphasis is placed on the use of hyphenated techniques and on the constantly-growing role of MS-based metabolomics in fruit juice quality control area.

A Comprehensive Comparative Study for the Authentication of the Kadsura Crude Drug

The stems and roots of Kadsura species have been used as the folk medicine in Traditional Chinese medicine (TCM) and have good traditional efficacy and medicinal application with a long history. Among these species, K. coccinea, K. heteroclita and K. longipedunculata are the most widely distributed species in the regions of south and southwest China. Owing to their similar appearance, the crude drugs are often confusedly used by some folk doctors, even some pharmaceutical factories. To discriminate the crude drugs, haplotype analysis based on cpDNA markers and ITS was firstly employed in this study. Generic delimitation, interspecific interrelationships, and the identification of medicinal materials between K. longipedunculata and K. heteroclita remained unresolved by the existing molecular fragments. The original plant could be identified through the morphological character of flower, fruit and leaf. However, in most situation collectors have no chance to find out these characters due to lack of reproductive organs, and have no experience with the minor difference and transitional variation of leaf morphology. The chemical characterization show that the chemometric of chemical composition owned higher resolution to discriminate three herbs of Kadsura species. In conclusion, this integrative approach involving molecular phylogeny, morphology and chemical characterization could be applied for authentication of the Kadusra. Our study suggests the use of this comprehensive approach for accurate characterization of this closely related taxa as well as identifying the source plant and confused herbs of TCM.

Discrimination of geographical origin of oranges (Citrus sinensis L. Osbeck) by mass spectrometry-based electronic nose and characterization of volatile compounds

An untargeted method using headspace solid-phase microextraction coupled to electronic nose based on mass spectrometry (HS-SPME/MS-eNose) in combination with chemometrics was developed for the discrimination of oranges of three geographical origins (Italy, South Africa and Spain). Three multivariate statistical models, i.e. PCA/LDA, SELECT/LDA and PLS-DA, were built and relevant performances were compared. Among the tested models, SELECT/LDA provided the highest prediction abilities in cross-validation and external validation with mean values of 97.8% and 95.7%, respectively. Moreover, HS-SPME/GC-MS analysis was used to identify potential markers to distinguish the geographical origin of oranges. Although 28 out of 65 identified VOCs showed a different content in samples belonging to different classes, a pattern of analytes able to discriminate simultaneously samples of three origins was not found. These results indicate that the proposed MS-eNose method in combination with multivariate statistical analysis provided an effective and rapid tool for authentication of the orange's geographical origin.

Assessing fish authenticity by direct analysis in real time-high resolution mass spectrometry and multivariate analysis: discrimination between wild-type and farmed salmon

The constant increase in seafood consumption worldwide has led to a parallel growth of the incidence of products obtained by aquaculture on the market, but also of the fraudulent commercialization of farmed products as wild-type ones. A careful characterization of the lipid component of seafood products based on chromatography-mass spectrometry techniques has been reported as a promising approach to reliably differentiate farmed from wild-type products. In this context, a fast method based on Direct Analysis in Real Time (DART) coupled to High Resolution Mass Spectrometry (HRMS) based on a single stage Orbitrap mass analyzer, integrated by Principal Component Analysis (PCA), was developed in the present study and applied to scout for spectral features useful to discriminate wild-type from farmed salmon of Salmo salar species. In particular, normalized intensities obtained for the 30 most intense signals (all referred to fatty acids, FA) detected in negative ion DART-HRMS spectra of the lipid extracts of salmon fillets [26 wild-type from Canada, 74 farmed from Canada (25), Norway (25) and Chile (24)] were considered as the variables for PCA. The scatterplot referred to the first two principal components showed a clear distinction between wild-type and farmed salmon, which gathered as a unique cluster, despite the remarkable differences in their geographical origin. In accordance with previous studies based on more complex and time-demanding analytical approaches, three saturated (14:0, 16:0 and 18:0) FA, along with unsaturated ones having 20 or 22 carbon atoms, were found as the main discriminating variables for wild-type salmons, whereas FA with compositions 18:1, 18:2, 18:3 and several oxidized forms arising from them were found to have a significantly higher incidence in farmed salmon. The method was further validated by Discriminant Analysis (DA) performed on the same dataset used for PCA integrated by data obtained from 6 commercial samples, putatively referred to farmed Norwegian salmon. Results showed that 100% of the latter were correctly classified as farmed type. Relative abundances of DART-HRMS signals related to specific FA appear then very promising for the differentiation of wild-type salmon from farmed ones, a very relevant issue in the context of consumers' protection from seafood frauds.

Metabotyping of 30 maize hybrids under early-sowing conditions reveals potential marker-metabolites for breeding

INTRODUCTION

In Northern Europe, maize early-sowing used to maximize yield may lead to moderate damages of seedlings due to chilling without visual phenotypes. Genetic studies and breeding for chilling tolerance remain necessary, and metabolic markers would be particularly useful in this context.

OBJECTIVES

Using an untargeted metabolomic approach on a collection of maize hybrids, our aim was to identify metabolite signatures and/or metabolites associated with chilling responses at the vegetative stage, to search for metabolites differentiating groups of hybrids based on silage-earliness, and to search for marker-metabolites correlated with aerial biomass.

METHODS

Thirty genetically-diverse maize dent inbred-lines (Zea mays) crossed to a flint inbred-line were sown in a field to assess metabolite profiles upon cold treatment induced by a modification of sowing date, and characterized with climatic measurements and phenotyping.

RESULTS

NMR- and LC-MS-based metabolomic profiling revealed the biological variation of primary and specialized metabolites in young leaves of plants before flowering-stage. The effect of early-sowing on leaf composition was larger than that of genotype, and several metabolites were associated to sowing response. The metabolic distances between genotypes based on leaf compositional data were not related to the genotype admixture groups, and their variability was lower under early-sowing than normal-sowing. Several metabolites or metabolite-features were related to silage-earliness groups in the normal-sowing condition, some of which were confirmed the following year. Correlation networks involving metabolites and aerial biomass suggested marker-metabolites for breeding for chilling tolerance.

CONCLUSION

After validation in other experiments and larger genotype panels, these marker-metabolites can contribute to breeding.

Tetraplex real-time PCR assay for the simultaneous identification and quantification of roe deer, red deer, fallow deer and sika deer for deer meat authentication

Analytical methods are needed for the identification and quantification of meat species to detect food adulteration. Since game meat is more expensive than meat from domesticated animal species, it is a potential target for adulteration. We present a tetraplex real-time PCR assay that allows the simultaneous determination of the content of roe deer, red deer, fallow deer and sika deer. The tetraplex assay showed only moderate cross-reactivity with closely related species. After optimization the tetraplex assay had a limit of detection of 0.1% (w/w) and a limit of quantification of 0.5% (w/w) for each of the four deer species. The tetraplex assay was found to be robust, slight modifications of the experimental setup did not lower its performance. Recoveries obtained by analyzing DNA mixtures and DNA isolates from model game sausages were similar to those obtained with the singleplex assays.

Identification of volatile markers for the detection of adulterants in red ginseng (Panax ginseng) juice using headspace stir-bar sorptive extraction coupled with gas chromatography and mass spectrometry

Red ginseng (Panax ginseng) products are frequently adulterated by manufacturers with cheaper medicinal plant products including deodeok (Codonopsis lanceolata) and doraji (Platycodon grandiflorum) to increase profits. To identify possible volatile markers for the adulteration of red ginseng juices with deodeok or doraji, a headspace stir-bar sorptive extraction method was developed. Gas chromatography with mass spectrometry and untargeted metabolomics analysis revealed that 1-hexanol, cis-3-hexen-1-ol, and trans-2-hexen-1-ol are abundantly present in deodeok and doraji but not red ginseng. The peak area ratios in gas chromatograms of these compounds in red ginseng juices mixed with deodeok or doraji indicate that these volatile chemicals can be used as markers to detect the adulteration of red ginseng juice.

Classification of Bitter Orange Essential Oils According to Fruit Ripening Stage by Untargeted Chemical Profiling and Machine Learning

The quality and composition of bitter orange essential oils (EOs) strongly depend on the ripening stage of the citrus fruit. The concentration of volatile compounds and consequently its organoleptic perception varies. While this can be detected by trained humans, we propose an objective approach for assessing the bitter orange from the volatile composition of their EO. The method is based on the combined use of headspace gas chromatography–mass spectrometry (HS-GC-MS) and artificial neural networks (ANN) for predictive modeling. Data obtained from the analysis of HS-GC-MS were preprocessed to select relevant peaks in the total ion chromatogram as input features for ANN. Results showed that key volatile compounds have enough predictive power to accurately classify the EO, according to their ripening stage for different applications. A sensitivity analysis detected the key compounds to identify the ripening stage. This study provides a novel strategy for the quality control of bitter orange EO without subjective methods.

Differentiation of Apple Varieties and Investigation of Organic Status Using Portable Visible Range Reflectance Spectroscopy

Food fraud, the sale of goods that have in some way been mislabelled or tampered with, is an increasing concern, with a number of high profile documented incidents in recent years. These recent incidents and their scope show that there are gaps in the food chain where food authentication methods are not applied or otherwise not sufficient and more accessible detection methods would be beneficial. This paper investigates the utility of affordable and portable visible range spectroscopy hardware with partial least squares discriminant analysis (PLS-DA) when applied to the differentiation of apple types and organic status. This method has the advantage that it is accessible throughout the supply chain, including at the consumer level. Scans were acquired of 132 apples of three types, half of which are organic and the remaining non-organic. The scans were preprocessed with zero correction, normalisation and smoothing. Two tests were used to determine accuracy, the first using 10-fold cross-validation and the second using a test set collected in different ambient conditions. Overall, the system achieved an accuracy of 94% when predicting the type of apple and 66% when predicting the organic status. Additionally, the resulting models were analysed to find the regions of the spectrum that had the most significance. Then, the accuracy when using three-channel information (RGB) is presented and shows the improvement provided by spectroscopic data.

Baseline levels of metabolites in different tissues of mussel Mytilus galloprovincialis (Bivalvia: Mytilidae)

The Mediterranean mussel Mytilus galloprovincialis (Lamarck 1819) is a popular shellfish commonly included in human diet and is routinely used as bioindicator in environmental monitoring programmes worldwide. Recently, metabolomics has emerged as a powerful tool both in food research and ecotoxicology for monitoring mussels' freshness and assessing the effects of environmental changes. However, there is still a paucity of data on complete metabolic baseline of mussel tissues. To mitigate this knowledge gap, similarities and differences in metabolite profile of digestive gland (DG), gills (G), and posterior adductor muscle (PAM) of aquaculture-farmed M. galloprovincialis were herein investigated by a proton nuclear magnetic resonance (¹H NMR)-based metabolomic approach and discussed considering their physiological role. A total of 44 metabolites were identified in mussel tissues and grouped in amino acids, energy metabolites, osmolytes, neurotransmitters, nucleotides, alkaloids, and miscellaneous metabolites. A PCA showed that mussel tissues clustered separately from each other, suggesting a clear differentiation in their metabolic profiles. A Venn diagram revealed that mussel DG, G and PAM shared 27 (61.36%) common metabolites, though with different concentrations. Osmolytes were found to dominate the metabolome of all tissues. The DG exhibited higher level of glutathione and carbohydrates. The G showed greater level of osmolytes and the exclusive presence of neurotransmitters, namely acetylcholine and serotonin. In PAM higher levels of energetics-related metabolites were found. Overall, findings from this study are helpful for a better understanding of mussel tissue-specific physiological functions as well as for future NMR-based metabolomic investigations of marine mussel health and safety.

Real-Time Food Authentication Using a Miniature Mass Spectrometer

Food adulteration is a threat to public health and the economy. In order to determine food adulteration efficiently, rapid and easy-to-use on-site analytical methods are needed. In this study, a miniaturized mass spectrometer in combination with three ambient ionization methods was used for food authentication. The chemical fingerprints of three milk types, five fish species, and two coffee types were measured using electrospray ionization, desorption electrospray ionization, and low temperature plasma ionization. Minimum sample preparation was needed for the analysis of liquid and solid food samples. Mass spectrometric data was processed using the laboratory-built software MS food classifier, which allows for the definition of specific food profiles from reference data sets using multivariate statistical methods and the subsequent classification of unknown data. Applicability of the obtained mass spectrometric fingerprints for food authentication was evaluated using different data processing methods, leave-10%-out cross-validation, and real-time classification of new data. Classification accuracy of 100% was achieved for the differentiation of milk types and fish species, and a classification accuracy of 96.4% was achieved for coffee types in cross-validation experiments. Measurement of two milk mixtures yielded correct classification of >94%. For real-time classification, the accuracies were comparable. Functionality of the software program and its performance is described. Processing time for a reference data set and a newly acquired spectrum was found to be 12 s and 2 s, respectively. These proof-of-principle experiments show that the combination of a miniaturized mass spectrometer, ambient ionization, and statistical analysis is suitable for on-site real-time food authentication.

Plant Metabolomics: Maximizing Metabolome Coverage by Optimizing Mobile Phase Additives for Nontargeted Mass Spectrometry in Positive and Negative Electrospray Ionization Mode

Nontargeted screening methods with ultrahigh-performance liquid chromatography-electrospray ionization/quadrupole-time-of-flight mass spectrometry have been extensively applied to plant metabolomics to very diverse scientific issues in plant metabolomics. In this study, different mobile phase additives were tested in order to improve the electrospray ionization process and to detect as many metabolites as possible with high peak intensities in positive and negative ionization mode. Influences of modifiers were examined for nonpolar and polar compounds, as optimal conditions are not always the same. By combining different additives, metabolite coverage could be significantly increased. The best results for polar metabolites in positive ionization mode were achieved by using 0.1% acetic acid and 0.1% formic acid in negative ionization mode. For measurements of nonpolar metabolites in positive ionization mode, the application of 10 mmol/L ammonium formate led to the best findings, while the use of 0.02% acetic acid was more appropriate in negative ionization mode.

Combination of mass spectrometry-based targeted lipidomics and supervised machine learning algorithms in detecting adulterated admixtures of white rice

The mixing of extraneous ingredients with original products is a common adulteration practice in food and herbal medicines. In particular, authenticity of white rice and its corresponding blended products has become a key issue in food industry. Accordingly, our current study aimed to develop and evaluate a novel discrimination method by combining targeted lipidomics with powerful supervised learning methods, and eventually introduce a platform to verify the authenticity of white rice. A total of 30 cultivars were collected, and 330 representative samples of white rice from Korea and China as well as seven mixing ratios were examined. Random forests (RF), support vector machines (SVM) with a radial basis function kernel, C5.0, model averaged neural network, and k-nearest neighbor classifiers were used for the classification. We achieved desired results, and the classifiers effectively differentiated white rice from Korea to blended samples with high prediction accuracy for the contamination ratio as low as five percent. In addition, RF and SVM classifiers were generally superior to and more robust than the other techniques. Our approach demonstrated that the relative differences in lysoGPLs can be successfully utilized to detect the adulterated mixing of white rice originating from different countries. In conclusion, the present study introduces a novel and high-throughput platform that can be applied to authenticate adulterated admixtures from original white rice samples.

Isotope ratio mass spectrometry in combination with chemometrics for characterization of geographical origin and agronomic practices of table grape

BACKGROUND

Although table grape is one of the most cultivated and consumed fruits worldwide, no study has been reported on its geographical origin or agronomic practice based on stable isotope ratios. This study aimed to evaluate the usefulness of isotopic ratios (i.e. ² H/¹ H, ¹³ C/¹² C, ¹⁵ N/¹⁴ N and ¹⁸ O/¹⁶ O) as possible markers to discriminate the agronomic practice (conventional versus organic farming) and provenance of table grape.

RESULTS

In order to quantitatively evaluate which of the isotopic variables were more discriminating, a t test was carried out, in light of which only δ¹³ C and δ¹⁸ O provided statistically significant differences (P ≤ 0.05) for the discrimination of geographical origin and farming method. Principal component analysis (PCA) showed no good separation of samples differing in geographical area and agronomic practice; thus, for classification purposes, supervised approaches were carried out. In particular, general discriminant analysis (GDA) was used, resulting in prediction abilities of 75.0 and 92.2% for the discrimination of farming method and origin respectively.

CONCLUSION

The present findings suggest that stable isotopes (i.e. δ¹⁸ O, δ² H and δ¹³ C) combined with chemometrics can be successfully applied to discriminate the provenance of table grape. However, the use of bulk nitrogen isotopes was not effective for farming method discrimination. © 2016 Society of Chemical Industry.

Metabolomics for organic food authentication: Results from a long-term field study in carrots

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The agricultural origin of carrots could be predicted.

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Using yearly harvested samples allowed 100% correct classification of unknowns.

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Metabolomic fingerprinting showed potential for organic food authentication.

Increasing demand for organic products and their premium prices make them an attractive target for fraudulent malpractices. In this study, a large-scale comparative metabolomics approach was applied to investigate the effect of the agronomic production system on the metabolite composition of carrots and to build statistical models for prediction purposes. Orthogonal projections to latent structures-discriminant analysis (OPLS-DA) was applied successfully to predict the origin of the agricultural system of the harvested carrots on the basis of features determined by liquid chromatography–mass spectrometry. When the training set used to build the OPLS-DA models contained samples representative of each harvest year, the models were able to classify unknown samples correctly (100% correct classification). If a harvest year was left out of the training sets and used for predictions, the correct classification rates achieved ranged from 76% to 100%. The results therefore highlight the potential of metabolomic fingerprinting for organic food authentication purposes.