The scientific challenges in moving from targeted to non-targeted mass spectrometric methods for food fraud analysis: A proposed validation workflow to bring about a harmonized approach

Rapid identification of fragrant rice using starch flavor compound via NIR spectroscopy coupled with GC-MS and Badh2 genotyping

The identification of fragrant rice varieties using near-infrared reflectance spectroscopy (NIRS) models has attracted extensive attention from regulatory authorities worldwide. In this study, 138 fragrant and 54 nonfragrant rice varieties were planted in the same region and distinguished using sensory evaluation, gas chromatography-mass spectrometry analysis, and betaine aldehyde dehydrogenase 2 (Badh2) genotyping. Then, the 2-acetyl-1-pyrroline (2-AP) content was assessed based on partial least-squares discriminant (PLS-DA) models generated after 2nd individually or combined with SNV/MSC/smoothing preprocessing successfully classified fragrant rice both in the calibration and predictive sets. Moreover, design of experiments (DoE)-based preprocessing selection was employed as an effective strategy to optimize the calibration models compared with the one variable at a time (OVAT) method. Further, Badh2 genotype sample screening assisted with identifying authentic fragrant rice and guaranteed the NIRS model's prediction accuracy in identifying fragrant rice. In conclusion, the high throughput PLS-DA multivariate method coupled with NIRS data was applied to identify fragrant rice varieties in routine monitoring and was effective, accurate, and rapid.

Rapid detection of markers in green coffee beans with different primary processing treatments of Coffea arabica L. from Yunnan

The characteristic flavor of Coffea arabica from Yunnan is largely attributed to the primary processing treatments through affecting the VOCs accumulation. Therefore, a rapid and comprehensive detection technique is needed to accurately recognize VOCs in green coffee beans with different pretreatment methods. Hence, we conducted volatile profiles and identified nine markers of three different primary processed green coffee beans from the major production areas in Yunnan with the combined of HS-SPME-GC-MS and PTR-TOF-MS. The relationships between the chemical composition and the content of VOCs in green coffee beans were elucidated. Among the markers, palmitic acid (F3), linoleic acid (F6), α-ethylidene phenylacetaldehyde (T4), and phytane (T8) contributed to the antioxidant activity of sun-exposed green coffee beans. In conclusion, the analytical technology presented here provided a general tool for an overall and rapid understanding of a detailed volatile profiles of green coffee beans in Yunnan.

Food authenticity and the interactions with human health and climate change

Food authenticity and fraud, as well as the interest in food traceability have become a topic of increasing interest not only for consumers but also for the research community and the food manufacturing industry. Food authenticity and fraud are becoming prevalent in both the food supply and value chains since ancient times where different issues (e.g., food spoilage during shipment and storage, mixing decay foods with fresh products) has resulted in foods that influence consumers health. The effect of climate change on the quality of food ingredients and products could also have the potential to influence food authenticity. However, this issue has not been considered. This article focused on the interactions between consumer health and the potential effects of climate change on food authenticity and fraud. The role of technology and development of risk management tools to mitigate these issues are also discussed. Where applicable papers that underline the links between the interactions of climate change, human health and food fraud were referenced.

Metabolomics analysis based on UHPLC-QqQ-MS/MS to discriminate grapes and wines from different geographical origins and climatological characteristics

With the proliferation of the consumer's awareness of wine provenance, wines with unique origin characteristics are increasingly in demand. This study aimed to investigate the influence of geographical origins and climatological characteristics on grapes and wines. A total of 94 anthocyanins and 78 non-anthocyanin phenolic compounds in grapes and wines from five Chinese viticultural vineyards (CJ, WH, QTX, WW, and XY) were identified by UHPLC-QqQ-MS/MS. Chemometric methods PCA and OPLS-DA were established to select candidate differential metabolites, including flavonols, stilbenes, hydroxycinnamic acids, peonidin derivatives, and malvidin derivatives. CCA showed that malvidin-3-O-glucoside had a positive correlation with mean temperature, and quercetin-3-O-glucoside had a negative correlation with precipitation. In addition, enrichment analysis elucidated that the metabolic diversity in different origins mainly occurred in flavonoid biosynthesis. This study would provide some new insights to understand the effect of geographical origins and climatological characteristics on phenolic compounds in grapes and wines.

Fingerprinting black tea: When spectroscopy meets machine learning a novel workflow for geographical origin identification

Food fraud, along with many challenges to the integrity and sustainability, threatens the prosperity of businesses and society as a whole. Tea is the second most commonly consumed non-alcoholic beverage globally. Challenges to tea authenticity require the development of highly efficient and rapid solutions to improve supply chain transparency. This study has produced an innovative workflow for black tea geographical indications (GI) discrimination based on non-targeted spectroscopic fingerprinting techniques. A total of 360 samples originating from nine GI regions worldwide were analysed by Fourier Transform Infrared (FTIR) and Near Infrared spectroscopy. Machine learning algorithms (k-nearest neighbours and support vector machine models) applied to the test data greatly improved the GI identification achieving 100% accuracy using FTIR. This workflow will provide a low-cost and user-friendly solution for on-site and real-time determination of black tea geographical origin along supply chains.

Authenticity assessment of ground black pepper by combining headspace gas-chromatography ion mobility spectrometry and machine learning

Currently, the authentication of ground black pepper is a major concern, creating a need for a rapid, highly sensitive and specific detection tool to prevent the introduction of adulterated batches into the food chain. To this aim, head space gas-chromatography ion mobility spectrometry (HS-GC-IMS), combined with machine learning, is tested in this initial, proof-of-concept study. A broad variety of authentic samples originating from eight countries and three continents were collected and spiked with a range of adulterants, both endogenous sub-products and an assortment of exogenous materials. The method is characterized by no sample preparation and requires 20 min for chromatographic separation and ion mobility data acquisition. After an explorative analysis of the data, those were submitted to two different machine learning algorithms (partial least squared discriminant analysis-PLS-DA and support vector machine-SVM). While the PLS-DA model did not provide fully satisfactory performances, the combination of HS-GC-IMS and SVM successfully classified the samples as authentic, exogenously-adulterated or endogenously-adulterated with an overall accuracy of 90 % and 96 % on withheld test set 1 and withheld test set 2, respectively (at a 95 % confidence level). Some limitations, expected to be mitigated by further research, were encountered in the correct classification of endogenously adulterated ground black pepper. Correct categorization of the ground black pepper samples was not adversely affected by the operator or the time span of data collection (the method development and model challenge were carried out by two operators over 6 months of the study, using ground black pepper harvested between 2015 and 2019). Therefore, HS-GC-IMS, coupled to an intelligent tool, is proposed to: (i) aid in industrial decision-making before utilization of a new batch of ground black pepper in the production chain; (ii) reduce the use of time-consuming conventional analyses and; (iii) increase the number of ground black pepper samples analyzed within an industrial quality control frame.

Metabolomics of natural samples: A tutorial review on the latest technologies

Metabolomics is the study of metabolites present in a living system. It is a rapidly growing field aimed at discovering novel compounds, studying biological processes, diagnosing diseases, and ensuring the quality of food products. Recently, the analysis of natural samples has become important to explore novel bioactive compounds and to study how environment and genetics affect living systems. Various metabolomics techniques, databases, and data analysis tools are available for natural sample metabolomics. However, choosing the right method can be a daunting exercise because natural samples are heterogeneous and require untargeted approaches. This tutorial review aims to compile the latest technologies to guide an early-career scientist on natural sample metabolomics. First, different extraction methods and their pros and cons are reviewed. Second, currently available metabolomics databases and data analysis tools are summarized. Next, recent research on metabolomics of milk, honey, and microbial samples is reviewed. Finally, after reviewing the latest trends in technologies, a checklist is presented to guide an early-career researcher on how to design a metabolomics project. In conclusion, this review is a comprehensive resource for a researcher planning to conduct their first metabolomics analysis. It is also useful for experienced researchers to update themselves on the latest trends in metabolomics.

Cocoa quality: Chemical relationship of cocoa beans and liquors in origin identitation

In this study, HS-SPME-GC-MS was applied in combination with machine learning tools to the identitation of a set of cocoa samples of different origins. Untargeted fingerprinting and profiling approaches were tested for their informative, discriminative and classification ability provided by the volatilome of the raw beans and liquors inbound at the factory in search of robust tools exploitable for long-time studies. The ability to distinguish the country of origin on both beans and liquors is not so obvious due to processing steps accompanying the transformation of the beans, but this capacity is of particular interest to the chocolate industry as both beans and liquors can enter indifferently into the processing of chocolate. Both fingerprinting (untargeted) and profiling (targeted) strategies enable to decipher of the information contained in the complex dataset and the cross-validation of the results, affording to discriminate between the origins with effective classification models.

Robot versus human barista: Comparison of volatile compounds and consumers' acceptance, sensory profile, and emotional response of brewed coffee

The increasing trend of integrating robots into the food industry has sparked debates regarding their potential influence on consumer attitudes toward food technology. This study investigated volatile compound profiles via gas chromatography-mass spectrometry (GC-MS), consumer acceptability, sensory profiling, and emotional responses of consumers toward coffee samples brewed by robot and human baristas. Moreover, the effect of the robot experience on food technology neophobia (FTN) was investigated. The principal component analysis of the volatile compound profiles revealed that the samples by the robot barista exhibited a higher degree of similarity compared to those prepared by the human barista. The range of relative standard deviations of volatile compounds from the robot barista brewed coffee was 1.4-83.1% and the variation was smaller than that of the human barista, which was 5.0-118.3%. Participants had a significant decrease in FTN scores after evaluating the robot-brewed coffee (p < 0.05), but there was no significant difference in FTN scores before and after evaluating the coffee brewed by the human barista (p > 0.05). Sensory evaluation studies revealed no significant differences in acceptability ratings and purchase intentions between the two groups (p > 0.05). However, emotional responses to the coffee samples significantly varied, with the robot-brewed coffee inducing more dynamic and positive emotions and the human-brewed coffee inducing more static and positive emotions (p < 0.05). Overall, this study provides valuable insights into consumer attitudes toward food robot service to humans and indicates that consumer's experience with food robots may significantly reduce FTN (p < 0.001).

Incidents and Potential Adverse Health Effects of Serious Food Fraud Cases Originated in Asia

Food fraud has long been regarded as a major issue within the food industry and is associated with serious economic and public health concerns. Economically motivated adulteration, the most common form of food fraud, has consequences for human health, ranging from mild to life-threatening conditions. Despite the potential harm and public health threats posed by food fraud, limited information on incidents causing illness has been reported. Enhancing the food control system on the Asian continent has become crucial for global health and trade considerations. Food fraud databases serve as valuable tools, assisting both the food industry and regulatory bodies in mitigating the vulnerabilities associated with fraudulent practices. However, the availability of accessible food fraud databases for Asian countries has been restricted. This review highlights detrimental food fraud cases originating in Asian countries, including sibutramine in dietary supplements, plasticizer contamination, gutter oil, and the adulteration of milk. This comprehensive analysis encompasses various facets, such as incident occurrences, adverse health effects, regulatory frameworks, and mitigation strategies.

Revealing adulterated olive oils by triacylglycerol screening methods: Beyond the official method

Official control methods to detect olive oil (OO) adulteration fail to provide satisfactory consumer protection. Thus, faster and more sensitive screening tools are needed to increase their effectiveness. Here, the official method for adulterant detection in OO was compared with three untargeted screening methods based on triacylglycerol analysis using high-throughput (FIA-HESI-HRMS; HT-GC-MS; HPLC-RID) and pattern recognition techniques (PLS-DA). They were assayed on a set of genuine and adulterated samples with a high natural variability (n = 143). The sensitivity of the official method was 1 for high linoleic (HL) blends at ≥2 % but only 0.39 for high oleic (HO) blends at ≥5 %, while specificity was 0.96. The sensitivity of the screening methods in external validation was 0.90-0.99 for the detection of HL and 0.82-0.88 for HO blends. Among them, HT-GC-MS offered the highest sensitivity (0.94) and specificity (0.76), proving to be the most suitable screening tool for OO authentication.

A Metabolomics and Big Data Approach to Cannabis Authenticity (Authentomics)

With the increasing accessibility of cannabis (Cannabis sativa L., also known as marijuana and hemp), its products are being developed as extracts for both recreational and therapeutic use. This has led to increased scrutiny by regulatory bodies, who aim to understand and regulate the complex chemistry of these products to ensure their safety and efficacy. Regulators use targeted analyses to track the concentration of key bioactive metabolites and potentially harmful contaminants, such as metals and other impurities. However, the metabolic complexity of cannabis metabolic pathways requires a more comprehensive approach. A non-targeted metabolomic analysis of cannabis products is necessary to generate data that can be used to determine their authenticity and efficacy. An authentomics approach, which involves combining the non-targeted analysis of new samples with big data comparisons to authenticated historic datasets, provides a robust method for verifying the quality of cannabis products. To meet International Organization for Standardization (ISO) standards, it is necessary to implement the authentomics platform technology and build an integrated database of cannabis analytical results. This study is the first to review the topic of the authentomics of cannabis and its potential to meet ISO standards.

Untargeted metabolomics analysis based on LC-IM-QTOF-MS for discriminating geographical origin and vintage of Chinese red wine

Identifying wine geographical origin and vintage is vital due to the abundance of fraudulent activity associated with wine mislabeling of region and vintage. In this study, an untargeted metabolomic approach based on liquid chromatography/ion mobility quadrupole time-of-flight mass spectrometry (LC-IM-QTOF-MS) was used to discriminate wine geographical origin and vintage. Wines were well discriminated according to region and vintage with orthogonal partial least squares-discriminant analysis (OPLS-DA). The differential metabolites subsequently were screened by OPLS-DA with pairwise modeling. 42 and 48 compounds in positive and negative ionization modes were screened as differential metabolitesfor the discrimination of different wine regions, and 37 and 35 compounds were screened for wine vintage. Furthermore, new OPLS-DA models were performed using these compounds, and the external verification trial showed excellent practicality with an accuracy over 84.2%. This study indicated that LC-IM-QTOF-MS-based untargeted metabolomics was a feasible tool for wine geographical origin and vintage discrimination.

Wearable Passive Samplers for Assessing Environmental Exposure to Organic Chemicals: Current Approaches and Future Directions

PURPOSE OF REVIEW

We are continuously exposed to dynamic mixtures of airborne contaminants that vary by location. Understanding the compositional diversity of these complex mixtures and the levels to which we are each exposed requires comprehensive exposure assessment. This comprehensive analysis is often lacking in population-based studies due to logistic and analytical challenges associated with traditional measurement approaches involving active air sampling and chemical-by-chemical analysis. The objective of this review is to provide an overview of wearable passive samplers as alternative tools to active samplers in environmental health research. The review highlights the advances and challenges in using wearable passive samplers for assessing personal exposure to organic chemicals and further presents a framework to enable quantitative measurements of exposure and expanded use of this monitoring approach to the population scale.

RECENT FINDINGS

Overall, wearable passive samplers are promising tools for assessing personal exposure to environmental contaminants, evident by the increased adoption and use of silicone-based devices in recent years. When combined with high throughput chemical analysis, these exposure assessment tools present opportunities for advancing our ability to assess personal exposures to complex mixtures. Most designs of wearable passive samplers used for assessing exposure to semi-volatile organic chemicals are currently uncalibrated, thus, are mostly used for qualitative research. The challenge with using wearable samplers for quantitative exposure assessment mostly lies with the inherent complexity in calibrating these wearable devices. Questions remain regarding how they perform under various conditions and the uncertainty of exposure estimates. As popularity of these samplers grows, it is critical to understand the uptake kinetics of chemicals and the different environmental and meteorological conditions that can introduce variability. Wearable passive samplers enable evaluation of exposure to hundreds of chemicals. The review presents the state-of-the-art of technology for assessing personal exposure to environmental chemicals. As more studies calibrate wearable samplers, these tools present promise for quantitatively assessing exposure at both the individual and population levels.

A multi-center validation study on the discrimination of Legionella pneumophila sg.1, Legionella pneumophila sg. 2-15 and Legionella non-pneumophila isolates from water by FT-IR spectroscopy

This study developed and validated a method, based on the coupling of Fourier-transform infrared spectroscopy (FT-IR) and machine learning, for the automated serotyping of Legionella pneumophila serogroup 1, Legionella pneumophila serogroups 2-15 as well as their successful discrimination from Legionella non-pneumophila. As Legionella presents significant intra- and inter-species heterogeneities, careful data validation strategies were applied to minimize late-stage performance variations of the method across a large microbial population. A total of 244 isolates were analyzed. In details, the method was validated with a multi-centric approach with isolates from Italian thermal and drinking water (n = 82) as well as with samples from German, Italian, French, and British collections (n = 162). Specifically, robustness of the method was verified over the time-span of 1 year with multiple operators and two different FT-IR instruments located in Italy and Germany. Moreover, different production procedures for the solid culture medium (in-house or commercial) and different culture conditions (with and without 2.5% CO₂) were tested. The method achieved an overall accuracy of 100, 98.5, and 93.9% on the Italian test set of Legionella, an independent batch of Legionella from multiple European culture collections, and an extra set of rare Legionella non-pneumophila, respectively.

Essential terminology and considerations for validation of non-targeted methods

Through their suggestive name, non-targeted methods (NTMs) do not aim at a predefined "needle in the haystack." Instead, they exploit all the constituents of the haystack. This new type of analytical method is increasingly finding applications in food and feed testing. However, the concepts, terms, and considerations related to this burgeoning field of analytical testing need to be propagated for the benefit of those associated with academic research, commercial development, or official control. This paper addresses frequently asked questions regarding terminology in connection with NTMs. The widespread development and adoption of these methods also necessitate the need to develop innovative approaches for NTM validation, i.e., evaluating the performance characteristics of a method to determine if it is fit-for-purpose. This work aims to provide a roadmap for approaching NTM validation. In doing so, the paper deliberates on the different considerations that influence the approach to validation and provides suggestions therefor.

Untargeted metabolomics and proteomics approach using liquid chromatography-Orbitrap high resolution mass spectrometry to detect pork adulteration in Pangasius hypopthalmus meat

Pangasius hypopthalmus is well known as a good source of protein. However, Pangasius hypopthalmus meat (PHM) can be adulterated with pork for economic concern, thus, analytical methods for authentication are required. Untargeted metabolomics and proteomics using liquid chromatography-high resolution mass spectrometry (LC-HRMS) and chemometrics of principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA) was successfully used to differentiate authentic and adulterated PHM with the good of fitness (R > 0.95) and good of predictivity (Q > 0.5). Metabolites of PC(o-18:0/18:2(9Z,12Z)) was found to be a potential marker for pork whereas DMPC (dimyristoylphosphatidylcholine) was a potential marker for PHM. Meanwhile, pork peptide marker of myoglobin (HPGDFGADAQGAMSK) and β-hemoglobin (FFESFGDLSNADAVMGNPK) could be identified. Both metabolomics and proteomics using LC-HRMS could detect pork at the lowest concentration level (0.5% w/w). In conclusion, untargeted metabolomics and proteomics using LC-HRMS in combination with chemometrics could be used as powerful methods to detect pork adulteration in fish meat.

Mass spectrometry-based electronic nose to authenticate 100% Italian durum wheat pasta and characterization of volatile compounds

Headspace solid-phase microextraction (HS-SPME) coupled with mass spectrometry-based electronic nose (MS-eNose), in combination with multivariate statistical analysis was used as untargeted method for the rapid authentication of 100% Italian durum wheat pasta. Among the tested classification models, i.e. PCA-LDA, PLS-DA and SVMc, SVMc provided the highest accuracy results in both calibration (90%) and validation (92%) processes. Potential markers discriminating pasta samples were identified by HS-SPME/GC-MS analysis. Specifically, the content of a pattern of 8 out of 59 volatile organic compounds (VOCs) was significantly different between samples of 100% Italian durum wheat pasta and pasta produced with durum wheat of different origins, most of which were related to different lipidic oxidation in the two classes of pasta. The proposed MS-eNose method is a rapid and reliable tool to be used for authenticating Italian pasta useful to promote its typicity and preserving consumers from fraudulent practices.

Authentication of Edible Insects’ Powders by the Combination of DART-HRMS Signatures: The First Application of Ambient Mass Spectrometry to Screening of Novel Food

This feasibility study reports the use of direct analysis in real-time high-resolution mass spectrometry (DART-HRMS) in profiling the powders from edible insects, as well as the potential for the identification of different insect species by classification modeling. The basis of this study is the revolution that has occurred in the field of analytical chemistry, with the improved capability of ambient mass spectrometry to authenticate food matrices. In this study, we applied DART-HRMS, coupled with mid-level data fusion and a learning method, to discriminate between Acheta domesticus (house cricket), Tenebrio molitor (yellow mealworm), Locusta migratoria (migratory locust), and Bombyx mori (silk moth). A distinct metabolic fingerprint was observed for each edible insect species, while the Bombyx mori fingerprint was characterized by highly abundant linolenic acid and quinic acid; palmitic and oleic acids are the statistically predominant fatty acids in black soldier fly (Hermetia illucens). Our chemometrics also revealed that the amino acid proline is a discriminant molecule in Tenebrio molitor, whereas palmitic and linoleic acids are the most informative molecular features of the house cricket (Acheta domesticus). Good separation between the four different insect species was achieved, and cross-validation gave 100% correct identification for all training samples. The performance of the random forest classifier was examined on a test set and produced excellent results, in terms of overall accuracy, sensitivity, and specificity. These results demonstrate the reliability of the DART-HRMS as a screening method in a future quality control scenario to detect complete substitution of insect powders.

Approaches for assessing performance of high-resolution mass spectrometry-based non-targeted analysis methods

Non-targeted analysis (NTA) using high-resolution mass spectrometry has enabled the detection and identification of unknown and unexpected compounds of interest in a wide range of sample matrices. Despite these benefits of NTA methods, standardized procedures do not yet exist for assessing performance, limiting stakeholders’ abilities to suitably interpret and utilize NTA results. Herein, we first summarize existing performance assessment metrics for targeted analyses to provide context and clarify terminology that may be shared between targeted and NTA methods (e.g., terms such as accuracy, precision, sensitivity, and selectivity). We then discuss promising approaches for assessing NTA method performance, listing strengths and key caveats for each approach, and highlighting areas in need of further development. To structure the discussion, we define three types of NTA study objectives: sample classification, chemical identification, and chemical quantitation. Qualitative study performance (i.e., focusing on sample classification and/or chemical identification) can be assessed using the traditional confusion matrix, with some challenges and limitations. Quantitative study performance can be assessed using estimation procedures developed for targeted methods with consideration for additional sources of uncontrolled experimental error. This article is intended to stimulate discussion and further efforts to develop and improve procedures for assessing NTA method performance. Ultimately, improved performance assessments will enable accurate communication and effective utilization of NTA results by stakeholders.

Untargeted metabolomic analysis of Chinese red wines for geographical origin traceability by UPLC-QTOF-MS coupled with chemometrics

Identifying geographical origins of red wines made in specific regions is of significance since the false claim of geographical origins has been frequently exposed in China's wine industry. In this work, an untargeted metabolomic approach based on UPLC-QTOF-MS was established to discriminate geographical origins of Chinese red wines. The principal component analysis (PCA) showed significant differences between wine samples from three famous geographical origins in China. The metabolites contributing to the differentiation were screened by orthogonal partial least squares-discriminant analysis (OPLS-DA) with pairwise modeling. 40 and 46 differential metabolites in positive and negative ionization modes were putatively identified as chemical markers. Furthermore, heatmap visualization and OPLS-DA models were constructed based on these identified markers and external verification wine samples from different regions were successfully discriminated, with recognition rate up to 96.7%. This study indicated that UPLC-QTOF-MS-based untargeted metabolomics has great potential for the geographical origin traceability of Chinese red wines.

Present-Day Practice of Non-Target Chemical Analysis

Abstract

We review the main techniques, procedures, and information products used in non-target analysis (NTA) to reveal the composition of substances. Sampling and sample preparation methods are preferable that ensure the extraction of analytes from test samples in a wide range of analyte properties with the most negligible loss. The necessary techniques of analysis are versions of chromatography–high-resolution tandem mass spectrometry (HRMS), yielding individual characteristics of analytes (mass spectra, retention properties) to accurately identify them. The prioritization of the analytical strategy discards unnecessary measurements and thereby increases the performance of the NTA. Chemical databases, collections of reference mass spectra and retention characteristics, algorithms, and software for processing HRMS data are indispensable in NTA.

Assessing food authenticity through protein and metabolic markers

This chapter aims to address an issue of ancient origins, but more and more topical in a globalized world in which consumers and stakeholders are increasingly aware: the authenticity of food. Foods are systems that can also be very complex, and verifying the correspondence between what is declared and the actual characteristics of the product is often a challenging issue. The complexity of the question we want to answer (is the food authentic?) means that the answer is equally articulated and makes use of many different analytical techniques. This chapter will consider the chemical analyses of foods aimed at guaranteeing their authenticity and will focus on frontier methods that have been developed in recent years to address the need to respond to ever-increasing guarantees of authenticity. Targeted and non-targeted approaches will be considered for verifying the authenticity of foods, through the study of different classes of constituents (proteins, metabolites, lipids, flavors). The numerous approaches available (proteomics, metabolomics, lipidomics) and the related analytical techniques (LC-MS, GC-MS, NMR) are first described from a more general point of view, after which their specific application for the purposes of authentication of food is addressed.

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.

Baobab pulp authenticity and quality control by multi-imaging high-performance thin-layer chromatography

Globalization of trade and increasing demand for baobab fruit pulp powder (Adansonia digitata) has led to more adulteration incidence with physically similar products, e.g. sifted cereal flours. In this study, 135 baobab samples drawn from trees in Kitui and Kilifi (Kenya) and North and South Kordofan (Sudan) were used as the reference and compared with adulterated (with 10-30% sifted rice, maize and wheat flours) baobab samples using multi-imaging by high-performance thin-layer chromatography. The ethanol - water extracts were separated on a normal phase. Any differences were detected via multi-imaging (UV/Vis/FLD) including diphenylamine alanine o-phosphoric acid, p-anisaldehyde sulfuric acid and p-amino benzoic acid reagents. Raffinose was identified as a marker compound for cereal-based adulteration. The method accuracy (recovery of 95%) and detection from 10-30% flour addition onwards are sufficient to curb economically motivated adulteration, to control product quality and to ensure consumer protection for local and international trade.

Comparative metabolite fingerprinting of chia, flax and sesame seeds using LC-MS untargeted metabolomics

Chia, flax, and sesame seeds are well known for their nutritional quality and are commonly included in bakery products. So far, the development of methods to verify their presence and authenticity in foods is a requisite and a raised need. In this work we applied untargeted metabolomics to propose authenticity markers. Seeds were analyzed by HPLC-MS/MS and 9938 features in negative mode and 9044 in positive mode were obtained by Mzmine. After isotopes grouping, alignment, gap-filling, filtering adducts, and normalization, PCA was applied to explore the dataset and recognize pre-existent classification patterns. OPLS-DA analysis and S-Plots were used as supervised methods. Twenty-five molecules (12 in negative mode and 13 in positive mode) were selected as discriminant for the three seeds, polyphenols and lignans were identified among them. To the best of our knowledge, this is the first approach using non-target HPLC-MS/MS for the authentication of chia, flax and sesame seeds.

Exploring Chemical Markers Related to the Acceptance and Sensory Profiles of Concentrated Liquid Coffees: An Untargeted Metabolomics Approach

In this study, we aimed to apply an untargeted LC/QTOF-MS analysis for the identification of compounds that positively and negatively affect the acceptance of coffee beverages from liquid coffee concentrates (CLCs) before and after storage. The metabolomic results were integrated with physicochemical and sensory parameters, such as color, pH, titratable acidity, and oxygen contents, by a bootstrapped version of partial least squares discriminant analysis (PLS-DA) to select and classify the most relevant variables regarding the rejection or acceptance of CLC beverages. The OPLS-DA models for metabolite selection discriminated between the percent sensory acceptance (the Accepted group) and rejection (the Rejected group). Eighty-two molecular features were considered statistically significant. Our data suggest that coffee sample rejection is associated with chlorogenic acid hydrolysis to produce ferulic and quinic acids, consequently generating methoxybenzaldehydes that impact the perceived acidity and aroma. Furthermore, acceptance was correlated with higher global scores and sweetness, as with lactones such as feruloyl-quinolactone, caffeoyl quinolactone, and 4-caffeoyl-1,5-quinolactone, and significant oxygen levels in the headspace.

Detecting adulteration of stingless bee honey using untargeted 1H NMR metabolomics with chemometrics

As stingless bee honey (SBH) is gaining in popularity in the Malaysian market, it is now prone to adulteration. The higher price of SBH compared to floral honey has led to the use of unusual adulterants such as vinegar and even floral honey to mimic the unique taste and appearance of SBH. Since the current AOAC 998.12 method fails to detect these adulterants as their δ¹³C values are in the range for C₃ plants, untargeted ¹H NMR metabolomics was proposed. Principal component analysis of SBH ¹H NMR fingerprints was able to distinguish authentic SBHs from adulterated ones down to 1% adulteration level for selected adulterants. Discriminant analysis showed promising results in distinguishing the preliminary datasets of authentic SBHs from the adulterated ones, including discriminating SBHs adulterated with different adulterants derived from C₃ and C₄ plants. Hence, to assure any emerging adulterant can be detected, all ¹H NMR regions should be considered.

Alternating in-source fragmentation with single-stage high-resolution mass spectrometry with high annotation confidence in non-targeted metabolomics

Non-targeted metabolomics is increasingly applied in various applications for understanding biological processes and finding novel biomarkers in living organisms. However, high-confidence identity confirmation of metabolites in complex biological samples is still a significant bottleneck, especially when using single-stage mass analysers. In the current study, a complete workflow for alternating in-source fragmentation on a time-of-flight mass spectrometry (TOFMS) instrument for non-targeted metabolomics is presented. Hydrophilic interaction liquid chromatography (HILIC) was employed to assess polar metabolites in yeast following ESI parameter optimization using experimental design principles, which revealed the key influence of fragmentor voltage for this application. Datasets from alternating in-source fragmentation high resolution mass spectrometry (HRMS) were evaluated using open-source data processing tools combined with public reference mass spectral databases. The significant influence of the selected fragmentor voltages on the abundance of the primary analyte ion of interest and the extent of in-source fragmentation allowed an optimum selection of qualifier fragments for the different metabolites. The new acquisition and evaluation workflow was implemented for the non-targeted analysis of yeast extract samples whereby more than 130 metabolites were putatively annotated with more than 40% considered to be of high confidence. The presented workflow contains a fully elaborated acquisition and evaluation methodology using alternating in-source fragmentor voltages suitable for peak annotation and metabolite identity confirmation for non-targeted metabolomics applications performed on a single-stage HRMS platform.

An Introduction to the Benchmarking and Publications for Non-Targeted Analysis Working Group

Non-targeted analysis (NTA) encompasses a rapidly evolving set of mass spectrometry techniques aimed at characterizing the chemical composition of complex samples, identifying unknown compounds, and/or classifying samples, without prior knowledge regarding the chemical content of the samples. Recent advances in NTA are the result of improved and more accessible instrumentation for data generation and analysis tools for data evaluation and interpretation. As researchers continue to develop NTA approaches in various scientific fields, there is a growing need to identify, disseminate, and adopt community-wide method reporting guidelines. In 2018, NTA researchers formed the Benchmarking and Publications for Non-Targeted Analysis Working Group (BP4NTA) to address this need. Consisting of participants from around the world and representing fields ranging from environmental science and food chemistry to 'omics and toxicology, BP4NTA provides resources addressing a variety of challenges associated with NTA. Thus far, BP4NTA group members have aimed to establish a consensus on NTA-related terms and concepts and to create consistency in reporting practices by providing resources on a public Web site, including consensus definitions, reference content, and lists of available tools. Moving forward, BP4NTA will provide a setting for NTA researchers to continue discussing emerging challenges and contribute to additional harmonization efforts.

Chromatographic Fingerprinting and Food Identity/Quality: Potentials and Challenges

Chromatograms are a valuable source of information about the chemical composition of the food being analyzed. Sometimes, this information is not explicit and appears in a hidden or not obvious way. Thus, the use of chemometric tools and data-mining methods to extract it is required. The fingerprint provided by a chromatogram offers the possibility to perform both identity and quality testing of foodstuffs. This perspective is aimed at providing an updated opinion of chromatographic fingerprinting methodology in the field of food authentication. Furthermore, the limitations, its absence in official analytical methods, and the future directions of this methodology are discussed.