Food Authentication: Small-Molecule Profiling as a Tool for the Geographic Discrimination of German White Asparagus

Exploring the potential of high-resolution LC-MS in combination with ion mobility separation and surrogate minimal depth for enhanced almond origin authentication

Almonds (Prunus dulcisMill.) are consumed worldwide and their geographical origin plays a crucial role in determining their market value. In the present study, a total of 250 almond reference samples from six countries (Australia, Spain, Iran, Italy, Morocco, and the USA) were non-polar extracted and analyzed by UPLC-ESI-IM-qToF-MS. Four harvest periods, more than 30 different varieties, including both sweet and bitter almonds, were considered in the method development. Principal component analysis showed that there are three groups of samples with similarities: Australia/USA, Spain/Italy and Iran/Morocco. For origin determination, a random forest achieved an accuracy of 88.8 %. Misclassifications occurred mainly between almonds from the USA and Australia, due to similar varieties and similar external influences such as climate conditions. Metabolites relevant for classification were selected using Surrogate Minimal Depth, with triacylglycerides containing oxidized, odd chained or short chained fatty acids and some phospholipids proven to be the most suitable marker substances. Our results show that focusing on the identified lipids (e. g., using a QqQ-MS instrument) is a promising approach to transfer the origin determination of almonds to routine analysis.

Food authentication, current issues, analytical techniques, and future challenges: A comprehensive review

Food authentication and contamination are significant concerns, especially for consumers with unique nutritional, cultural, lifestyle, and religious needs. Food authenticity involves identifying food contamination for many purposes, such as adherence to religious beliefs, safeguarding health, and consuming sanitary and organic food products. This review article examines the issues related to food authentication and food fraud in recent periods. Furthermore, the development and innovations in analytical techniques employed to authenticate various food products are comprehensively focused. Food products derived from animals are susceptible to deceptive practices, which can undermine customer confidence and pose potential health hazards due to the transmission of diseases from animals to humans. Therefore, it is necessary to employ suitable and robust analytical techniques for complex and high-risk animal-derived goods, in which molecular biomarker-based (genomics, proteomics, and metabolomics) techniques are covered. Various analytical methods have been employed to ascertain the geographical provenance of food items that exhibit rapid response times, low cost, nondestructiveness, and condensability.

Opening the Random Forest Black Box of 1H NMR Metabolomics Data by the Exploitation of Surrogate Variables

The untargeted metabolomics analysis of biological samples with nuclear magnetic resonance (NMR) provides highly complex data containing various signals from different molecules. To use these data for classification, e.g., in the context of food authentication, machine learning methods are used. These methods are usually applied as a black box, which means that no information about the complex relationships between the variables and the outcome is obtained. In this study, we show that the random forest-based approach surrogate minimal depth (SMD) can be applied for a comprehensive analysis of class-specific differences by selecting relevant variables and analyzing their mutual impact on the classification model of different truffle species. SMD allows the assignment of variables from the same metabolites as well as the detection of interactions between different metabolites that can be attributed to known biological relationships.

Cationic Conjugated Polymer Fluorescence Resonance Energy Transfer for DNA Methylation Assessment to Discriminate the Geographical Origins of Lonicerae japonicae flos

The flavor and taste of Lonicerae japonicae flos (LJF) products are heavily influenced by geographical origin. Tracing the geographical origin is an important aspect of LJF quality assessment. Here, DNA methylation analysis coupled with chemometrics revealed that, in 10 CpG islands upstream of genes in the chlorogenic acid and iridoid biosynthetic pathways, DNA methylation differences appear close association with LJF geographical origin. DNA methylation status in these CpG islands was determined using the cationic conjugated polymer fluorescence resonance energy transfer method. As a result, LJFs from 39 geographical origins were classified into four groups corresponding to Northern China, Central Plain of China, Southeast China, and Western China, according to cluster analysis and principal component analysis. Our findings contribute to an understanding of the modulation of LJF taste and can assist in understanding how DNA methylation in LJF varies with geographical origin.

Metabolomics: A suitable foodomics approach to the geographical origin traceability of Ethiopian Arabica specialty coffees

Coffee arabica, originated in Ethiopia, is considered a quality bean for its high sensory qualities, and has a special price in the world coffee market. The country is a pool of genetic diversity for Arabica coffee, and coffee from different regions has a distinct flavor profile. Their exceptional quality is attributed to their genetic diversity, favorable environmental conditions, and agroforestry-based production system. However, the country still needs to benefit from its single-origin product due to a lack of appropriate traceability information to register for its geographical indication. Certification of certain plants or plant-derived products emerged to inform consumers about their exceptional qualities due to their geographical origin and protect the product from fraud. The recently emerging foodomics approaches, namely proteomics, genomics, and metabolomics, are reported as suitable means of regional agri-food product authentication and traceability. Particularly, the metabolomics approach provides truthful information on product traceability. Despite efforts by some researchers to trace the geographical origin of Ethiopian Arabica coffees through stable isotope and phenolic compound profiling and elemental analysis, foodomics approaches are not used to trace the geographical origin of Arabica specialty coffees from various parts of the country. A metabolomics-based traceability system that demonstrates the connection between the exceptional attributes of Ethiopian Arabica specialty coffees and their geographic origin is recommended to maximize the benefit of single-origin coffees.

Food Fingerprinting: LC-ESI-IM-QTOF-Based Identification of Blumeatin as a New Marker Metabolite for the Detection of Origanum majorana Admixtures to O. onites/vulgare

Oregano (Origanum vulgare and O. onites) is one of the most frequently counterfeited herbs in the world and is diluted with the leaves of a wide variety of plants. In addition to olive leaves, marjoram (O. majorana) is often used for this purpose in order to achieve a higher profit. However, apart from arbutin, no marker metabolites are known to reliably detect marjoram admixtures in oregano batches at low concentrations. In addition, arbutin is relatively widespread in the plant kingdom, which is why it is of great relevance to look for further marker metabolites in order to secure the analysis accordingly. Therefore, the aim of the present study was to use a metabolomics-based approach to identify additional marker metabolites with the aid of an ion mobility mass spectrometry instrument. The focus of the analysis was on the detection of non-polar metabolites, as this study was preceded by nuclear magnetic resonance spectroscopic investigations of the same samples based mainly on the detection of polar analytes. Using the MS-based approach, numerous marjoram specific features could be detected in admixtures of marjoram >10% in oregano. However, only one feature was detectable in admixtures of >5% marjoram. This feature was identified as blumeatin, which belongs to the class of flavonoid compounds. Initially, blumeatin was identified based on MS/MS spectra and collision cross section values using a database search. In addition, the identification of blumeatin was confirmed by a reference standard. Moreover, dried leaves of olive, myrtle, thyme, sage and peppermint, which are also known to be used to adulterate oregano, were measured. Blumeatin could not be detected in these plants, so this substance can be considered as an excellent marker compound for the detection of marjoram admixtures.

Unravelling the seasonal dynamics of the metabolome of white asparagus spears using untargeted metabolomics

INTRODUCTION

The white asparagus season lasts 4 months while the harvest period per field is 8 weeks. Different varieties are better suited for harvesting early or late in the season. Little is known of the dynamics of secondary metabolites of white asparagus during the production season.

OBJECTIVE

Characterization of the metabolome of white asparagus spears covering volatile and non-volatile composition in relation to quality aspects.

METHODS

Eight varieties, harvested repeatedly during two consecutive seasons were analysed following an untargeted metabolomics workflow using SPME GC-MS and LC-MS. Linear regression, cluster and network analyses were used to explore the profile dynamics, unravel patterns and study the influence of genotype and environment.

RESULTS

The metabolite profiles were influenced by the harvest moment and genetic background. Metabolites that significantly changed over time were distributed across seven clusters based on their temporal patterns. Two clusters including monoterpenes, benzenoids and saponins showed the most prominent seasonal changes. The changes depicted by the other five clusters were mainly ≤ 2-fold relative to the harvest start. Known asparagus aroma compounds were found to be relatively stable across the season/varieties. Heat-enhanced cultivation appeared to yield spears early in season with a similar metabolome to those harvested later.

CONCLUSION

The dynamics of the white asparagus metabolome is influenced by a complex relationship between the onset of spear development, the moment of harvest and the genetic background. The typical perceived asparagus flavour profile is unlikely to be significantly affected by these dynamics.

Food metabolomics: Latest hardware-developments for nontargeted food authenticity and food safety testing

The analytical requirements for food testing have increased significantly in recent years. On the one hand, because food fraud is becoming an ever-greater challenge worldwide, and on the other hand because food safety is often difficult to monitor due to the far-reaching trade chains. In addition, the expectations of consumers on the quality of food have increased, and they are demanding extensive information. Cutting-edge analytical methods are required to meet these demands. In this context, non-targeted metabolomics strategies using mass and nuclear magnetic resonance spectrometers (mass spectrometry [MS]) have proven to be very suitable. MS-based approaches are of particular importance as they provide a comparatively high analytical coverage of the metabolome. Accordingly, the efficiency to address even challenging issues is high. A variety of hardware developments, which are explained in this review, have contributed to these advances. In addition, the potential of future developments is highlighted, some of which are currently not yet commercially available or only used to a comparatively small extent but are expected to gain in importance in the coming years.

Mass spectrometry in food authentication and origin traceability

Food authentication and origin traceability are popular research topics, especially as concerns about food quality continue to increase. Mass spectrometry (MS) plays an indispensable role in food authentication and origin traceability. In this review, the applications of MS in food authentication and origin traceability by analyzing the main components and chemical fingerprints or profiles are summarized. In addition, the characteristic markers for food authentication are also reviewed, and the advantages and disadvantages of MS-based techniques for food authentication, as well as the current trends and challenges, are discussed. The fingerprinting and profiling methods, in combination with multivariate statistical analysis, are more suitable for the authentication of high-value foods, while characteristic marker-based methods are more suitable for adulteration detection. Several new techniques have been introduced to the field, such as proton transfer reaction mass spectrometry, ambient ionization mass spectrometry (AIMS), and ion mobility mass spectrometry, for the determination of food adulteration due to their fast and convenient analysis. As an important trend, the miniaturization of MS offers advantages, such as small and portable instrumentation and fast and nondestructive analysis. Moreover, many applications in food authentication are using AIMS, which can help food authentication in food inspection/field analysis. This review provides a reference and guide for food authentication and traceability based on MS.

Opening the Random Forest Black Box of the Metabolome by the Application of Surrogate Minimal Depth

For the untargeted analysis of the metabolome of biological samples with liquid chromatography–mass spectrometry (LC-MS), high-dimensional data sets containing many different metabolites are obtained. Since the utilization of these complex data is challenging, different machine learning approaches have been developed. Those methods are usually applied as black box classification tools, and detailed information about class differences that result from the complex interplay of the metabolites are not obtained. Here, we demonstrate that this information is accessible by the application of random forest (RF) approaches and especially by surrogate minimal depth (SMD) that is applied to metabolomics data for the first time. We show this by the selection of important features and the evaluation of their mutual impact on the multi-level classification of white asparagus regarding provenance and biological identity. SMD enables the identification of multiple features from the same metabolites and reveals meaningful biological relations, proving its high potential for the comprehensive utilization of high-dimensional metabolomics data.

Wood profiling by non-targeted liquid chromatography high-resolution mass spectrometry: Part 2, Detection of the geographical origin of spruce wood (Picea abies) by determination of metabolite pattern

A non-targeted metabolomics-based approach using liquid chromatography high-resolution mass spectrometry was used to authenticate spruce wood (Picea abies) from two geographic source areas. The two sample sites were located in Germany and only 250 km apart. In order to achieve the highest possible metabolite coverage, the spruces samples were measured with four different methods using liquid chromatography high-resolution mass spectrometry. In this way, a total of approximately 4,100 features were detected, which included non-polar, polar, and intermediate-polar metabolites. Using supervised multivariate methods, a distinction between the two sample groups could be achieved on the basis of non-polar data sets. The major metabolites contributing to differentiation were identified by MS/MS experiments and were from the following classes of compounds: ceramides, fatty acids, glycerolipids, and phytosterols. Based on the soil descriptions of the two sites, it was concluded that there is probably a close relationship between nutrient availability and the differences in concentration of the marker compounds. The results show that a metabolomics-based approach is also suitable for differentiation of origin, even if the sample sites are close to each other.

Metabolomics Reveals Heterogeneity in the Chemical Composition of Green and White Spears of Asparagus (A. officinalis)

Green and white asparagus are quite different crops but can be harvested from the same plant. They have distinct morphological differences due to their mode of cultivation and they are characterised by having contrasting appearance and flavour. Significant chemical differences are therefore expected. Spears from three varieties of both green and white forms, harvested in two consecutive seasons were analysed using headspace GC-MS and LC-MS with an untargeted metabolomic workflow. Mainly C5 and C8 alcohols and aldehydes, and phenolic compounds were more abundant in green spears, whereas benzenoids, monoterpenes, unsaturated aldehydes and steroidal saponins were more abundant in white ones. Previously reported key asparagus volatiles and non-volatiles were detected at similar or not significantly different levels in the two asparagus types. Spatial metabolomics revealed also that many volatiles with known positive aroma attributes were significantly more abundant in the upper parts of the spears and showed a decreasing trend towards the base. These findings provide valuable insights into the metabolome of raw asparagus, the contrasts between green and white spears as well as the different chemical distributions along the stem.

Non-Targeted LC-MS Metabolomics Approach towards an Authentication of the Geographical Origin of Grain Maize (Zea mays L.) Samples

Safety along the food and feed supply chain is an emerging topic and closely linked to the ability to analytical trace the geographical origin of food or feed. In this study, ultra-performance liquid chromatography coupled with electrospray ionization quadrupole-time-of-flight mass spectrometry was used to trace back the geographical origin of 151 grain maize (Zea mays L.) samples from seven countries using a high resolution non-targeted metabolomics approach. Multivariate data analysis and univariate statistics were used to identify promising marker features related to geographical origin. Classification using only 20 selected markers with the Random Forest algorithm led to 90.5% correctly classified samples with 100 times repeated 10-fold cross-validation. The selected markers were assigned to the class of triglycerides, diglycerides and phospholipids. The marker set was further evaluated for its ability to separate between one sample class and the rest of the dataset, yielding accuracies above 89%. This demonstrates the high potential of the non-polar metabolome to authenticate the geographic origin of grain maize samples. Furthermore, this suggests that focusing on only a few lipids with high potential for grain maize authentication could be a promising approach for later transfer of the method to routine analysis.

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.

Wood profiling by non-targeted high-resolution mass spectrometry: Part 1, Metabolite profiling in Cedrela wood for the determination of the geographical origin

The determination of the geographical origin of wood can be highly relevant for several reasons: On the one hand, it can help to prevent illegal logging and timber trade, on the other hand, it is of special interest for archaeological artefacts made of wood, as well as for a variety of biological questions. For this reason, different extraction methods were first tested for the analysis of polar and non-polar metabolites using liquid chromatography coupled electrospray ionization quadrupole time-of-flight mass spectrometry (UHPLC-ESI-QTOF-MS). A two-phase extraction with chloroform, methanol and water proved to be particularly successful. Subsequently, cedrela (Cedrela odorata) samples from South America were measured to distinguish geographic origin. Using multivariate data analysis, numerous origin-dependent differences could be extracted. The identification of the marker substances indicated that several metabolic pathways were affected by the geographical influences, some of them probably indicating pest infections.

Determination of the Geographical Origin of Asparagus officinalis L. by 1H NMR Spectroscopy

Food authenticity concerning the geographical origin becomes increasingly important for consumers, food industries, and food authorities. In this study, nontargeted ¹H NMR metabolomics combined with machine learning methodologies was applied to successfully distinguish the geographical origin of 237 samples of white asparagus from Germany, Poland, The Netherlands, Spain, Greece, and Peru. Support vector classification of the geographical origin achieved an accuracy of 91.5% for the entire sample set and 87.8% after undersampling the majority class. Important regions of the spectra could be identified and assigned to potential chemical markers. A subset of samples was compared to isotope-ratio mass spectrometry (IRMS), an established method for the determination of origin of white asparagus in Germany. Here, SVM classification led to accuracies of 79.4% for NMR and 70.9% for IRMS. Finally, the classification of asparagus from different German regions was evaluated, and the influence of year and variety was analyzed.

Mass-Spectrometry-Based Food Metabolomics in Routine Applications: A Basic Standardization Approach Using Housekeeping Metabolites for the Authentication of Asparagus

The low reproducibility of non-targeted liquid chromatography-mass spectrometry-based metabolomics approaches represents a major challenge for their implementation in routine analyses, because it is impossible to compare individual measurements directly with each other, if they were not analyzed in the same batch. This study describes a normalization process based on housekeeping metabolites in plant-based raw materials, which are present in comparatively constant concentrations and are subject to no or only minor deviations as a result of exogenous influences. As a model, an authenticity study was selected to determine the origin of white asparagus (Asparagus officinalis). Using three model data sets and one test data set, we were able to show that samples that have been measured independently of one another can be correctly assigned in terms of origin after the normalization with housekeeping metabolites. The procedure does not require internal standards or the measurements of further reference samples and can also be applied to other matrices and scientific issues.

Food Phenotyping: Recording and Processing of Non-Targeted Liquid Chromatography Mass Spectrometry Data for Verifying Food Authenticity

Experiments based on metabolomics represent powerful approaches to the experimental verification of the integrity of food. In particular, high-resolution non-targeted analyses, which are carried out by means of liquid chromatography-mass spectrometry systems (LC-MS), offer a variety of options. However, an enormous amount of data is recorded, which must be processed in a correspondingly complex manner. The evaluation of LC-MS based non-targeted data is not entirely trivial and a wide variety of strategies have been developed that can be used in this regard. In this paper, an overview of the mandatory steps regarding data acquisition is given first, followed by a presentation of the required preprocessing steps for data evaluation. Then some multivariate analysis methods are discussed, which have proven to be particularly suitable in this context in recent years. The publication closes with information on the identification of marker compounds.

Metabolic imaging: Analysis of different sections of white Asparagus officinalis shoots using high-resolution mass spectrometry

Many plant metabolism processes are currently not completely understood despite the numerous studies. These include the events in plant shoots and especially in the apical meristem. To understand the various mechanisms on a molecular level, a combined approach of target and non-targeted fingerprinting analysis was worked out on different white asparagus spear segments using high resolution mass spectrometry. By means of various multivariate analysis strategies, numerous distinctions within diverse substance classes were observed. While most of the investigated metabolites were present in relatively higher concentrations in the tip of the asparagus spears, others were more accumulated at the bottom, some, in turn, did not show any concentration differences along the shoot. Using pathway analysis, the most significant metabolites were classified in the biological context. To our knowledge for the first time, a non-targeted metabolomics approach is used with the aim of metabolic profiling of plant sprouts.

Food authentication in real life: How to link nontargeted approaches with routine analytics?

In times of increasing globalization and the resulting complexity of trade flows, securing food quality is an increasing challenge. The development of analytical methods for checking the integrity and, thus, the safety of food is one of the central questions for actors from science, politics, and industry. Targeted methods, for the detection of a few selected analytes, still play the most important role in routine analysis. In the past 5 years, nontargeted methods that do not aim at individual analytes but on analyte profiles that are as comprehensive as possible have increasingly come into focus. Instead of investigating individual chemical structures, data patterns are collected, evaluated and, depending on the problem, fed into databases that can be used for further nontargeted approaches. Alternatively, individual markers can be extracted and transferred to targeted methods. Such an approach requires (i) the availability of authentic reference material, (ii) the corresponding high-resolution laboratory infrastructure, and (iii) extensive expertise in processing and storing very large amounts of data. Probably due to the requirements mentioned above, only a few methods have really established themselves in routine analysis. This review article focuses on the establishment of nontargeted methods in routine laboratories. Challenges are summarized and possible solutions are presented.

Characterization of metabolite profiles of white and green spears of asparagus officinalis L. from Caoxian, East China

China is the largest planting country of asparagus (Asparagus officinalis L.) in the world. Caoxian, as the famous asparagus township in China, enjoys a reputation for producing asparagus with high yield and good quality, due to its unique geological characteristic. In this study, a method of reverse-phase ultraperformance liquid chromatography coupled with electrospray tandem mass spectrometry (RP-UPLC-ESI-MS/MS) was established for profiling metabolites from three segments (tip, mid, and base) of 'Caoxian white and green Asparagus'. A total of 114 metabolites were identified, among them, 43 were found for the first time in this vegetable. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) was applied to provide an overview of the metabolite profiles of Caoxian asparagus and to separate different segments of spears. The variables most decisive to discriminate among segments included 9 of the metabolites tentatively identified. This study will help to improve the protection of Caoxian asparagus geographical indication.