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Untargeted metabolomic approaches in food authenticity: A review that showcases biomarkers
Abstract
The assessment of food authenticity is a topic that gained a lot of interest at the international level. This term includes misidentification of variety, origin, production system, processing but also adulteration. These frauds all have an analytical component, and research tends to offer new analytical solutions to manage them. One of them is non-targeted approaches, which get around the limitations of targeted analysis by detecting the unexpected. A wide range of products are studied such as wine, rice, olive oil, spices, and honey among the top five. Geographic origin is by far the fraud with the most attention. The main reason is probably the complexity to consider terroir effect and every other variable to determine an area of production. This review offers an overview of the potential of non-targeted analysis to assess food authenticity. These results also illustrate the capability to look for environmental terroir markers that could be cross-matrixes.
... These include the requirement for technical knowledge and specialized equipment, the time-consuming sample extraction process, the high cost of equipment and specific reagents, and other components in the food matrix that can impact result accuracy. These challenges underscore the need for more efficient and cost-effective authentication techniques (Danezis, Tsagkaris, Camin, et al., 2016;Lo & Shaw, 2018;Mialon, Roig, Capodanno, & Cadiere, 2023;Qian et al., 2020). ...
... A suitable detector allows each sample component to be immediately identified and quantified with high precision and accuracy, even in small quantities. Chromatographic techniques more frequently reported were high-performance liquid chromatography with an ultraviolet-visible detector , gas chromatography with a mass spectroscopy detector (Mialon et al., 2023), and ultra-performance liquid chromatography with a mass spectroscopy detector (Liu et al., 2022), among others. Chromatographic techniques enable the selection of the most suitable method for food authentication due to their versatility, which is highly applicable and recognized by regulatory authorities. ...
... Amplified fragment length polymorphism (PCR-AFLP) and singlestrand conformational polymorphism (PCR-SSCP) have been highlighted among the most widely used biological techniques for food authentication. Their applications can have some objectives, such as the authentication of foods claiming to be organic (Böhme, Calo-Mata, Barros-Velázquez, & Ortea, 2019), foods claimed to be consistent with halal production (Rohman et al., 2020), differentiation of animal species that give rise to meat products (Khalil et al., 2021), day products (Abbas et al., 2018;Baptista, Cunha, & Domingues, 2021) and the so-called "blue foods" , and also vegetables and products derived from them (Francois, Fabrice, & Didier, 2020;Wadood et al., 2022;Wadood, Boli, Xiaowen, Hussain, & Yimin, 2020), authentication of foods with geographical indication appeal (Mialon et al., 2023;Singh, Lee, & Lee, 2017). In addition, reports of the combined use of different techniques are standard to optimize the analytical results of authentication, associating the selectivity conferred by biological techniques with applications of electroanalytical techniques (Mohamad et al., 2022), chromatographic techniques (Saadat, Pandya, Dey, & Rawtani, 2022;Valdés, Beltrán, Mellinas, Jiménez, & Garrigós, 2018) and spectroscopic techniques (Khalil et al., 2021;Wadood et al., 2022). ...
The use of suitable analytical techniques for the detection of adulteration, falsification, deliberate substitution, and mislabeling of foods has great importance in the industrial, scientific, legislative, and public health contexts. This way, this work reports an integrative review with a current analytical approach for food authentication, indicating the main analytical techniques to identify adulteration and perform the traceability of chemical components in processed and non-processed foods, evaluating the authenticity and geographic origin. This work presents results from a systematic search in Science Direct® and Scopus® databases using the keywords “authentication” AND “food”, “authentication,” AND “beverage”, from published papers from 2013 to, 2024. All research and reviews published were employed in the bibliometric analysis, evaluating the advantages and disadvantages of analytical techniques, indicating the perspectives for direct, quick, and simple analysis, guaranteeing the application of quality standards, and ensuring food safety for consumers. Furthermore, this work reports the analysis of natural foods to evaluate the origin (traceability), and industrialized foods to detect adulterations and fraud. A focus on research to detect adulteration in milk and dairy products is presented due to the importance of these products in the nutrition of the world population. All analytical tools discussed have advantages and drawbacks, including sample preparation steps, the need for reference materials, and mathematical treatments. So, the main advances in modern analytical techniques for the identification and quantification of food adulterations, mainly milk and dairy products, were discussed, indicating trends and perspectives on food authentication.
... Becchi, Rocchetti, Vezzulli, Lambri, and Lucini (2023) recently used the same analytical approach to unravel the peculiarities of mountain grassland-based Parmigiano Reggiano PDO cheese production . Overall, as carefully reviewed (Mialon, Roig, Capodanno, & Cadiere, 2023), those products most susceptible to fraud and authenticity issues are mainly olive oil, wine, honey, meat, fruits, and vegetables. They also report that the most important fraud is mislabeling, followed by adulteration and unapproved treatments or processes. ...
... They also report that the most important fraud is mislabeling, followed by adulteration and unapproved treatments or processes. Among the mislabeling practices, the most common are products wrongfully labeled as organic, from premium quality (e.g., extra virgin olive oil instead of lampant or virgin), or a wrong geographical origin or area (Mialon et al., 2023). As far as the geographical origin is concerned, the research studies reviewed suggest that a new trend is emerging in food authenticity dealing with the strong association between one or a class of metabolites with a certain geographical area, thus broadening the concept of terroir to other food products rather than the only wine (Lucini et al., 2020;Mialon et al., 2023;Roullier-Gall, Lucio, Noret, Schmitt-Kopplin, & Gougeon, 2014). ...
... Among the mislabeling practices, the most common are products wrongfully labeled as organic, from premium quality (e.g., extra virgin olive oil instead of lampant or virgin), or a wrong geographical origin or area (Mialon et al., 2023). As far as the geographical origin is concerned, the research studies reviewed suggest that a new trend is emerging in food authenticity dealing with the strong association between one or a class of metabolites with a certain geographical area, thus broadening the concept of terroir to other food products rather than the only wine (Lucini et al., 2020;Mialon et al., 2023;Roullier-Gall, Lucio, Noret, Schmitt-Kopplin, & Gougeon, 2014). ...
Background
The advances in NMR and mass spectrometry metabolomics allows a comprehensive profiling of foods, potentially covering geographical origin, authenticity, quality and integrity issues. However, mining specific effects within the corresponding datasets is challenging due to the presence of a set of interacting factors that finally determine metabolomics signatures.
Scope and approach
This review provides an overview of the different metabolomics approaches used in food quality and authenticity, then focusing on different chemometric approaches for data interpretation. In particular, data interpretation is hierarchically presented, starting from unsupervised (PCA, hierarchical clusters) to supervised multivariate statistics like OPLS and AMOPLS multiblock ANOVA discriminant approaches. Finally, machine learning approaches like Artificial Neural Networks are discussed as the novel and emerging tool to support food integrity issues.
Key findings and conclusions
Tailored data mining approaches are advisable, rather than unique solutions, with unsupervised statistics that naively provide qualitative recognition of patterns, and supervised modelling that support markers identification. Nonetheless, machine learning approaches are emerging as a novel approach able to interpretate complex metabolomics signatures.
... PCA and OPLS-DA were utilized to assess the differences in liver metabolic profiles among the Control, Model, and Lf groups ( Figures 8A,B and 9A,B). PCA is an unsupervised multivariate statistical method, which projects the data into the principal component space by linear transformation to visualize the similarities and differences in the overall sample [42]. Unlike PCA, PLS-DA and OPLS-DA are "supervised mode" multivariate statistical analysis methods that can exclude unnecessary intra-group errors and better analyze differences between groups [43]. ...
... PCA and OPLS-DA were utilized to assess the differences in liver metabolic profiles among the Control, Model, and Lf groups ( Figures 8A,B and 9A,B). PCA is an unsupervised multivariate statistical method, which projects the data into the principal component space by linear transformation to visualize the similarities and differences in the overall sample [42]. Unlike PCA, PLS-DA and OPLS-DA are "supervised mode" multivariate statistical analysis methods that can exclude unnecessary intra-group errors and beIer analyze differences between groups [43]. ...
As a probiotic strain isolated from Hongqu rice wine (a traditional Chinese fermented food), Limosilactobacillus fermentum FZU501 (designated as Lf) demonstrates exceptional gastric acid and bile salt tolerance, showing potential application as a functional food. The aim of this study was to investigate the protective effect of dietary Lf intervention on alcohol-induced liver injury (ALI) in mice. The results demonstrated that oral administration of Lf effectively ameliorated alcohol-induced lipid metabolism disorders by reducing the serum levels of TC, TG and LDL-C and increasing the serum levels of HDL-C. In addition, oral administration of Lf effectively prevented alcohol-induced liver damage by increasing the hepatic activities of antioxidant enzymes (CAT, SOD, GSH-Px) and alcohol-metabolizing enzymes (ADH and ALDH). Interestingly, 16S amplicon sequencing showed that oral administration of Lf increased the number of Prevotella, Lachnospiraceae_NK4A136_group and Lactobacillus, but decreased the proportion of Faecalibaculum, Adlercreutzia and Alistipes in the intestines of mice that consumed excessive alcohol, which was highly associated with improved liver function. As revealed by liver untargeted metabolomics studies, oral Lf clearly changed liver metabolic profiles, with the signature biomarkers mainly involving purine metabolism, taurine metabolism, tryptophan, alanine, aspartic acid and glutamate metabolism, etc. Additionally, Lf intervention regulated liver gene transcription in over-drinking mice for cholesterol metabolism, bile acid metabolism, fatty acid β-oxidation, alcohol metabolism and oxidative stress. Taken together, the above research results provide solid scientific support for the biological activity of Lf in ameliorating alcohol-induced liver metabolism disorder and intestinal microbiota imbalance.
... Flavor and aroma/odor compounds, which reflect some of the sensory properties of honey, serve as chemical markers for honey of different botanical and entomological origins [8,10], making them valuable metabolites in bioorganic research. Metabolomic analysis reveals intriguing chemical diversity among different honey types [5], while discriminatory molecules are referred to as biomarkers [15]. Some secondary metabolites such as flavonoids, phenolic acids, including cinnamic acids, and other bioactive phenolic compounds and terpenes emerged as chemical markers for botanical origin discrimination [16]. ...
... Several reviews on metabolomics involving honey and other honey bee products have been published [15,17,25,26], but there is no recent detailed review explicitly focusing on honey chromatography-based metabolomics from the perspective of bioorganic research. Moreover, the chemical structures of some key metabolites are presented in this review, demonstrating the chemical diversity of the metabolites identified in the chromatographic profiles. ...
This review presents the latest research on chromatography-based metabolomics for bioorganic research of honey, considering targeted, suspect, and untargeted metabolomics involving metabolite profiling and metabolite fingerprinting. These approaches give an insight into the metabolic diversity of different honey varieties and reveal different classes of organic compounds in the metabolic profiles, among which, key metabolites such as biomarkers and bioactive compounds can be highlighted. Chromatography-based metabolomics strategies have significantly impacted different aspects of bioorganic research, including primary areas such as botanical origins, honey origin traceability, entomological origins, and honey maturity. Through the use of different tools for complex data analysis, these strategies contribute to the detection, assessment, and/or correlation of different honey parameters and attributes. Bioorganic research is mainly focused on phytochemicals and their transformation, but the chemical changes that can occur during the different stages of honey formation remain a challenge. Furthermore, the latest user- and environmentally friendly sample preparation methods and technologies as well as future perspectives and the role of chromatography-based metabolomic strategies in honey characterization are discussed. The objective of this review is to summarize the latest metabolomics strategies contributing to bioorganic research onf honey, with emphasis on the (i) metabolite analysis by gas and liquid chromatography techniques; (ii) key metabolites in the obtained metabolic profiles; (iii) formation and accumulation of biogenic volatile and non-volatile markers; (iv) sample preparation procedures; (v) data analysis, including software and databases; and (vi) conclusions and future perspectives. For the present review, the literature search strategy was based on the PRISMA guidelines and focused on studies published between 2019 and 2024. This review outlines the importance of metabolomics strategies for potential innovations in characterizing honey and unlocking its full bioorganic potential.
... The "fingerprinting" of food products has been used to protect consumers from food fraud, confirm the provenance of a food, and identify indicators of quality or functional properties of value (Capozzi & Bordoni, 2013;Ellis et al., 2016;Mialon et al., 2023). Fingerprinting involves characterising a food at the molecular level and recent advancements in high-throughput omics technologies and multivariate statistics has allowed this to be done at lower costs and greater accuracy (Capozzi & Bordoni, 2013). ...
... To date, fingerprinting research on edible insect species has focused on using technologies such as polymerase chain reaction (PCR), proteomics, FTIR and MS, to differentiate between species (Belghit et al., 2019;Debode et al., 2017;Dhami et al., 2011;Francis et al., 2020;Marien et al., 2018;Mellado-Carretero et al., 2020;Tramuta et al., 2018;Zagon et al., 2018). In other non-insect contexts, due to the high precision and ability to distinguish a wide range of metabolites, metabolomics has been used in the detection of food fraud and geographical provenance (Capozzi & Bordoni, 2013;Mialon et al., 2023;Sobolev et al., 2017). ...
Edible insects have been proposed as a sustainable future protein source for a growing global population. Overseas, interest has seen investment in start-up companies specialising in producing insects for human consumption. Yet little is known of the edible insect species in Aotearoa New Zealand or whether New Zealanders are willing to consume insects. To address this, I investigated the current landscape of edible insects in Aotearoa. I created a catalogue of species with traditional and contemporary uses, the companies that are producing insect-based products and the social and legislative landscape they exist in. I then explored the social licence for insects as food, using a questionnaire to examine the perceptions and experiences of 620 participants, finding an openness to insects as food, influenced by prior experience, knowledge, and a low food neophobia score. However, this was tempered by sentiment analysis showing insect ingredients were viewed more negatively than other food ingredients suggesting an unconscious bias. The huhu beetle, Prionoplus reticularis, was widely listed by participants as an edible insect in Aotearoa, suggesting a potential candidate for future farming. However, surprisingly little is known about the biology of this well-known beetle. To address this, I used GCMS to explore the metabolomic profile of this species along a latitudinal gradient. I found that a random forest model trained on this data was able to predict the geographic location of a larvae with reasonable accuracy, providing a possible tool to detect the provenance of edible insects in the future. I investigated the beetles' scramble competition mating system, and, using static allometry and flight mills, I found evidence of the interplay of sexual selection and stabilising selection on traits associated with male fitness in this species. I also found evidence of sexual dimorphism in the species, indicating the importance of female chemical cues to male mate searching. Using scanning electron microscopy, I investigated the antennal ultrastructure of male and female P. reticularis, describing unique, sexually dimorphic sensilla in this species. This thesis serves as a starting point for the development of edible insects in Aotearoa.
... In food authentication, two primary methods of analysis are employed: targeted and untargeted approaches. Targeted methods concentrate on analyzing specific metabolites or groups of metabolites, with the limitation that markers do not present in databases may go unnoticed (Mialon et al., 2023). Approximately, 10% of the food supply could potentially be linked to food fraud, yet only a small fraction of food fraud cases are actually detected based on targeted methods. ...
... Initially, food authentication was primarily explored through targeted methods. However, the inadequacies of control plans were exposed by the melamine scandal, prompting the development of untargeted methods (Mialon et al., 2023). Non-targeted methodology for food fraud detection uses interdisciplinary areas of expertise, including analytical chemistry, metabolomics, statistics, data science, and food science. ...
Background
This review highlights the critical need for innovative biomarkers in authenticating milk and its derivatives to ensure quality, authenticity and safety in the food industry. The limitations of traditional methods in detecting sophisticated adulteration techniques underscore the urgency for advanced solutions.
Scope and approach
The review explores biomarker discovery as a promising avenue, utilizing specific molecular indicators to provide credible evidence about the origin, genuineness, and overall quality of milk and its products. It critically examines the challenges and shortcomings of existing authentication methods, emphasizing the necessity for novel biomarkers. The study encompasses various strategies in biomarker discovery, including genomics, proteomics, metabolomics, and other -omics approaches, contextualized within high-throughput technologies.
Key findings and conclusions
Assessing potential biomarker sources within milk and dairy, such as intrinsic components, microbial communities, processing markers, and environmental factors, the review rigorously evaluates their applicability, sensitivity, specificity, and practical utility in ensuring authenticity and safety. A critical analysis addresses challenges related to biomarker validation, including standardization, reproducibility, and integration with existing authentication methods. The incorporation of these novel biomarkers holds the promise of reinforcing the authentication process, providing heightened protection against fraudulent practices and strengthening consumer trust and satisfaction within the milk and dairy sector.
... Recent debates about instrumental efficacy have led to more comparisons between benchtop and handheld instruments using advanced mathematical techniques known as chemometrics. These techniques help to access the performance of NIR instruments through exploratory data analysis [27], classification [28], and prediction [29]. Through these techniques, parameters of quality control such as sensitivity, specificity, precision, detection and quantification limits, and various predictive modeling parameters can be accessed. ...
... LOD and LOQ values were thoroughly calculated for a large number of models, and the NIRS6500 spectrometer could reach the lowest values, approximating the 0.1% limit in the case of urea and melamine detection. Balabin et al. (2011) [27] could reach sub-ppm LOD values for melamine in milk products by combining NIRS with non-linear machine learning tools, including support vector regression and artificial neural networks, implying the possibility to build models with extremely high sensitivity given a suitable dataset and the right algorithm. The authors identified non-linear patterns between NIR spectral data and complex pseudo-protein (melamine) concentrations while investigating milk products, encouraging the use of algorithms that can bypass the issues of non-linearity. ...
Protein adulteration is a common fraud in the food industry due to the high price of protein sources and their limited availability. Total nitrogen determination is the standard analytical technique for quality control, which is incapable of distinguishing between protein nitrogen and nitrogen from non-protein sources. Three benchtops and one handheld near-infrared spectrometer (NIRS) with different signal processing techniques (grating, Fourier transform, and MEM—micro-electro-mechanical system) were compared with detect adulteration in protein powders at low concentration levels. Whey, beef, and pea protein powders were mixed with a different combination and concentration of high nitrogen content compounds—namely melamine, urea, taurine, and glycine—resulting in a total of 819 samples. NIRS, combined with chemometric tools and various spectral preprocessing techniques, was used to predict adulterant concentrations, while the limit of detection (LOD) and limit of quantification (LOQ) were also assessed to further evaluate instrument performance. Out of all devices and measurement methods compared, the most accurate predictive models were built based on the dataset acquired with a grating benchtop spectrophotometer, reaching R²P values of 0.96 and proximating the 0.1% LOD for melamine and urea. Results imply the possibility of using NIRS combined with chemometrics as a generalized quality control tool for protein powders.
... Targeted approaches focus on a predefined set of known chemical parameters, enabling precise quantification and direct comparisons. In contrast, non-targeted methods [5] rely on comprehensive profiling techniques to generate an analytical fingerprint of the food product, allowing for the detection of subtle compositional variations. In targeted analyses, the selected indicators can be further categorized into primary and secondary indicators. ...
This study investigates the potential of strontium isotopes as a geographical tracer for Vignola cherries. Despite several studies having employed this indicator to trace the origin of food products, the mechanisms underlying the fractionation and translocation of strontium from soil to edible parts remain poorly understood. In this study, the ⁹¹Zr/⁹⁰Zr ratio was used as a normalization pair to correct measurements of ⁸⁷Sr/⁸⁶Sr and ⁸⁸Sr/⁸⁶Sr (δ⁸⁸Sr). Soil, cherry branches, and fruit samples were collected from various producers and locations. Isotopic analyses were carried out using a double-focusing multi-collector–inductively coupled plasma/mass spectrometer (MC-ICP/MS). External correction was applied using the ⁹¹Zr/⁹⁰Zr ratio, assuming both equal and different fractionation factors for Sr and Zr isotopes. Results from both correction models showed improved accuracy by accounting for fluctuations in instrumental mass bias. Regarding the translocation of strontium, the data indicate an increase in ⁸⁸Sr of approximately 0.2‰ from soil to plant tissue. This trend was consistent across all sampled locations.
... Food fraud is a long-standing concern, with the earliest documented occurrences tracing back to Ancient Rome, where laws were enacted to prevent the adulteration of wine with dyes and flavorings (Shears, 2010;Sumar & Ismail, 1995). As commerce expanded, anti-fraud regulations were developed to encompass transnational areas, particularly from the 13th century onwards (Mialon et al., 2023). However, globalization has introduced increasingly sophisticated forms of food fraud. ...
Food fraud raises significant concerns to consumer health and economic integrity, with the adulteration of honey by sugary syrups representing one of the most prevalent forms of economically motivated adulteration. This study presents a novel framework that combines data from multiple analytical techniques with specialized deep learning models (convolutional neural networks), integrated via meta-learning, in order to differentiate between pure honey and samples adulterated with sugar cane molasses, glucose syrup, or caramel-flavored ice cream topping. Unlike traditional chemometric methods, this approach expands the input feature space, leading to enhanced predictive performance. The resulting deep heterogeneous ensemble learner exhibited considerable generalization capability, achieving an average classification accuracy of 98.53 % and a Matthews correlation coefficient of 0.9710. Furthermore, the ensemble demonstrated exceptional robustness, maintaining an accuracy of 73 %, even when 90 % of the input data were corrupted, underscoring its unparalleled capacity to generalize under both subtle and extreme data variability. This adaptable and scalable solution underscores the transformative potential of ensemble-meta-learning strategy for addressing complex challenges in analytical chemistry. The model, its constituents and other additional resources were made available in an open repository.
... Besides, untargeted metabolomics that focused on unbiased analysis covering as many metabolites as possible was reported as a powerful strategy for detecting food authentication, especially in screening markers [14]. It has been reported as successfully applied for the authentication of oil, milk, juice, honey, seafood, meat, and other foods [15]. There have been several studies focusing on the differentiation of geographical origin or cultivar identification based on untargeted metabolomics [16]. ...
Virgin extra olive oil as a high‐value edible oil is a potential object of adulteration. Refined camellia oil (RCO) could be one of the most challenging potential adulterants of olive oil to detect due to its high similarity in the fatty acid composition. In this study, an untargeted metabolomics strategy based on data from ultra‐performance liquid chromatography‐electrospray ionization‐quadrupole‐time of flight (UPLC‐ESI‐qTOF) measurements combined with statistical methods was applied to identify the unauthorized addition of RCO to extra virgin olive oil (EVOO). Untargeted fingerprints of the olive oil and RCO could be classified into two groups via unsupervised principal component analysis (PCA) that shows the significant difference of the fingerprints of polar components extracted from olive oil and CAOs, respectively. Orthogonal partial least squares‐discriminant analysis (OPLS‐DA) and volcano plots were used to identify markers with significant difference between these two oils. The results show that 927 and 780 features (positive and negative ESI modes), respectively, were higher regulated in virgin extra olive oil, whereas 439 and 479 features, respectively, were higher regulated in RCO. From these features, 28 markers for olive oil and 7 markers for CAO were tentatively identified. Further adulteration experiments showed that virgin extra olive oil containing more than 15% RCO could be distinguished from the olive oil by this untargeted UPLC‐ESI‐qTOF measurement, followed by unsupervised PCA. Furthermore, camelliagenin A (519.3695/12.22, [M + FA − H] ⁻ ) could still be detected when EVOOs were mixed with at least 5% CAO.
... Metabolomics also emerges as a crucial tool in the authentication of natural products, ensuring their quality and integrity. By analyzing the metabolite profiles of natural products and comparing them to established reference libraries, metabolomics can confirm the authenticity of a given product, or identify specific biomarkers, such as unique secondary metabolites or metabolic signatures (Drouet et al. 2018b;Mialon et al. 2023). ...
The increasing demand for natural ingredients in cosmetics raises new challenges for the characterization of highly complex natural extracts and the production of standardized extracts. Metabolomics has spread in a variety of research areas with great success for metabolite identification, product discrimination, and the control of quality, authenticity and adulteration. This review aims to present recent trends in the development of metabolomics tools and their potential to address new issues in natural cosmetics for more effective selection and control of natural ingredients. The current sources of natural ingredients used in skincare products are summarized. The actual metabolomics approaches are delineated, encompassing the pivotal steps of metabolomic workflow from sample extraction and metabolite identification to data mining and statistical analysis. Outlined are diverse cosmeceutical activities, ranging from antioxidant effectiveness and anti-inflammatory potential to rejuvenating properties, ultraviolet radiation protection, and the brightening attributes inherent in natural extracts. Finally, we presented the contributions of metabolomics to the quality assessment of natural cosmetics ingredients by identification of bioactive compounds, dereplication of bio-sourced ingredients, quality and safety assessment of extracts, authentication, and traceability, and the detection of adulteration. This review presents systematic approaches to studying the complex phytochemical mixtures found in natural ingredients, linking analytical techniques to biological assay systems, and thereby opening new opportunities for discovering active principles and assisting in the production of standardized compositions.
... Numerous analytical methods applying some physico-chemical constants and instrumental analyses have been explored and reported for the authentication of expensive and high-quality edible oils. The sophisticated instruments such as near infrared, FTIR, Raman, and NMR spectrophotometers [17], chromatograph hyphenated with mass spectrometer like GC-MS and LC-MS mainly through metabolomics approach [18], thermal analyses like differential scanning calorimetry [19] and some rapid methods like electronic nose and electronic tongue [20] have been developed and validated for the authentication of fats and oils. For routine analysis, FTIR spectroscopy combined with chemometrics or multivariate data analysis could be the ideal method for the authentication of edible fats and oils. ...
... This can be achieved by machine learning techniques, using training samples to calibrate and validation samples to challenge the classification model. This approach closely mirrors the workflows recommended in clinical biomarker discovery studies for patient stratification (Glaab et al., 2021), and has also been applied, albeit more discreetly, in food authenticity testing (Mialon et al., 2023). ...
Provence ros ́e wines have gained global popularity, making them vulnerable to fraud. This study aimed toidentify specific chemical markers to detect counterfeit Provence ros ́e wines. An untargeted LC-MS-basedmetabolomics analysis was performed on a set of 30 wines classified as “Provence,” “Non-Provence,” and“Provence imitations.” Using the Molnotator workflow, 1300 potential metabolites were generated, and five keychemomarkers were selected through a machine learning pipeline. Further targeted analysis and bioinformaticsusing in silico MS/MS fragmentation systems confidently annotated three specific chemomarkers for “Provence”ros ́e: acuminoside, tetrahydroxydimethoxyflavone, and 5′-methoxycastavinol. A composite score using a PLSmodel combining the 3 chemomarkers effectively distinguished authentic wines, with high accuracy (sensitivity83.3 %, specificity 100 %, accuracy 93.3 %).
(PDF) In vino veritas: A metabolomics approach for authenticating Provence Rosé wines. Available from: https://www.researchgate.net/publication/385691095_In_vino_veritas_A_metabolomics_approach_for_authenticating_Provence_Rose_wines [accessed Nov 18 2024].
... However, overlapping was observed in the classes for all developed models in Fig. 8, which indicates that samples had similar characteristics. These similarities could make it challenging to notice differences in LDA plots, consequently, confusion tables were used to quantify such differences 24 . ...
Calcium carbide is prohibited as a fruit ripening agent in many countries due to its harmful effects. Current methods for detecting calcium carbide in fruit involve time-consuming and destructive chemical analysis techniques, necessitating the need for non-destructive and rapid detection techniques. This study combined near infrared (NIR) spectroscopy with chemometrics to detect two banana varieties ripened with calcium carbide in different forms when they are peeled or unpeeled. Sixteen linear discriminant analysis (LDA) models were developed with high average classification accuracies for classifying banana based on the mode used to ripen banana, type of carbide treatment and the duration of soaking banana in carbide solution. Banana colour was predicted with partial least squared regression (PLSR) models with R2CV > 0.74, RMSECV and <5.4 and RPD close to 3. NIR coupled with chemometrics has good potential as a technique for detecting carbide ripened banana even if the banana is peeled or not.
... The overlaps indicate that samples were similar irrespective of the retail location and the condition of the location since they were all chocolate samples and thus share similar structural and chemical properties. Similarities in samples could make it difficult to notice differences in PLS-DA plots; consequently, a confusion matrix and table (Table 3) may be used to quantify such differences since the linear discriminant plots help mostly in visualization (qualitative data) (Mialon et al., 2023). The confusion matrix from Table 3 shows the average recognition and average cross-validation for the discrimination of the retail location Columns represent the actual class membership (%), and the rows represent the predicted class membership (%). ...
Chocolates sold in Ghana are stored under different conditions that are suspected to affect their appearance, flavour and texture. Rapid and non-invasive techniques such as near-infrared spectroscopy (NIRS) have been lauded for their reliability and cost-effectiveness that can be very useful for chocolate monitoring. This study developed a rapid and non-destructive method to predict the quality of chocolate obtained from three different sales outlets based on the location and conditions of retail. Data for physicochemical analysis (total color change, total phenolics, free fatty acid, peroxide value, moisture, hardness, and aluminum content) and mold count of chocolate were collected using standard protocols. These data and results obtained from NIRS in the wavelength range 900-1700 nm, were used to develop chemometric models to predict the parameters measured and classified the chocolate samples. Chocolate from the street recorded the highest mold count of 10.00 ± 18.92 cfu/g. Although the physicochemical analysis showed that different retail conditions had no significant effect on the chocolate quality parameters, the NIRS models could classify the chocolates based on retail conditions, with an average recognition and prediction accuracy of 75.41 % and 71.59 %, respectively. The regression model could predict the total color change with R 2 CV of 0.503 and RMSECV of 4.96 w/w. The findings suggest that NIRS combined with chemometrics could be used to classify chocolate sold under different conditions at different retail locations. However, the models could not predict other physicochemical quality parameters.
... Various analytical tools have been developed and applied in food authentication, ranging from simple spot inspections to intensive supply chain monitoring and customer complaint investigations when issues arise. Sophisticated techniques based on chromatography (Górska-Horczyczak, Zalewska, and Wierzbicka 2022), mass spectrometry (Dou et al. 2023), proteomics (Afzaal et al. 2022), metabolomics (Mialon et al. 2023), and genomics (Kumar et al. 2022) have demonstrated high accuracies and precision in identifying food fraud. However, these methods are time-consuming, labor-intensive, destructive, and require well-trained staff. ...
Food fraud, a pervasive issue in the global food industry, poses significant challenges to consumer health, trust, and economic stability, costing an estimated $10-15 billion annually. Therefore, there is a rising demand for developing portable and miniature sensors that facilitate food authentication throughout the supply chain. This review explores the recent advancements and applications of portable and miniature sensors, including portable/miniature near-infrared (NIR) spectroscopy, e-nose and colorimetric sensors based on nanozyme for food authentication within the supply chain. After briefly presenting the architecture and mechanism, this review discusses the application of these portable and miniature sensors in food authentication, addressing the challenges and opportunities in integrating and deploying these sensors to ensure authenticity. This review reveals the enhanced utility of portable/miniature NIR spectroscopy, e-nose, and nanozyme-based colorimetric sensors in ensuring food authenticity and enabling informed decision-making throughout the food supply chain.
... It has been demonstrated to be excellent for analysing complex food compositions, Metabolomics for quality assessment of poultry meat and eggs 3 including biological samples such as poultry meat and eggs (Wu et al., 2022b). Moreover, the importance of metabolomic analysis in identifying essential biomarkers across diverse food matrices for food authentication has been extensively discussed by Mialon et al. (2023). This review will explore and discuss the parameters used in assessing the quality of poultry meat and eggs, factors affecting meat and egg qualities, and various applications of metabolomics in determining the quality of poultry meat and eggs. ...
The poultry industry is experiencing rapid growth worldwide. This accelerated growth has led to multiple food fraud incidents across the food supply chain, which consequently created a demand for precise determination of quality poultry production. This increase in demand for precise poultry production quality has necessitated advanced solutions. Metabolomics has emerged as a viable solution by offering detailed differentiation of biochemical indicators throughout the poultry supply chain. Additionally, this study provides a means to address risk factors affecting the poultry industry without compromising animal welfare, which is a critical concern. This review focuses on important issues related to poultry product quality assessment. Food adulteration has escalated in recent years as it is driven by the increasing focus on consuming high-quality and nutritious food. However, there is no specific guideline for such determinations, especially when appearance, texture, and taste can be manipulated by substituting for food components. Metabolomics can pave the way for a deeper understanding of existing and novel biochemical indicators responsible for determining the quality of poultry meat and eggs. This approach holds the potential to enhance the overall quality of poultry meat and egg products while also preventing food fraud.
... Metabolomics is de ned as the qualitative and quantitative analysis of metabolites contained in samples to be tested [7-9], as a whole, under speci c conditions [10]. Among metabolomic assay techniques, traditional targeted and untargeted metabolomics have progressed to widely targeted metabolomics [11,12]. Widely targeted metabolomics refers to an approach that employs a platform that combines liquid chromatography and tandem mass spectrometry (LC-MS/MS) to identify compounds; this approach is capable of quantitatively and qualitatively detecting more than 1000 known compounds and has a strong advantage in the detection of organic acids, amino acids, alkaloids, phenols and avonoids in plants [13,14]. ...
Background Propolis is a substance derived from plant resin (tree gum), and its antibacterial activity is one of its most important biological activities. However, the key differential components between propolis and tree gum and their antibacterial mechanisms remain unclear. Therefore, this study aimed to reveal the key active differential metabolites and potential antibacterial mechanisms of these differential metabolites utilizing widely targeted metabolomics and network pharmacology.
Methods Widely targeted metabolomics was employed to identify metabolites in poplar propolis and poplar gum. The antibacterial activities of poplar propolis and poplar gum against methicillin-resistant Staphylococcus aureus(MRSA) were compared using the inhibition zone diameter (DIZ), minimum inhibitory concentration (MIC), and minimum bactericidal concentration (MBC), and the data were analyzed using one-way analysis of variance (ANOVA). Additionally, network pharmacology was utilized to screen key active differential metabolites and their core targets. The potential antibacterial mechanisms of these key active differential metabolites were determined through GO enrichment and KEGG pathway analysis.
Results Using widely targeted metabolomics, we identified 1646 metabolites in two poplar propolis samples and one poplar gum sample, of which 942 were common differential metabolites that were enriched in nine KEGG enrichment pathways, including flavonoid biosynthesis. In vitro antibacterial experiments also revealed that the diameter of the zone of inhibition of MRSA by poplar gum was significantly different from that of poplar propolis (p < 0.05). Furthermore, the MIC for poplar propolis was 0.15625 mg/ml, the MBC was 0.3125 mg/ml, the MIC for poplar gum was 0.3125 mg/ml, and the MBC was 0.625 mg/ml. Poplar propolis exhibited stronger antibacterial activity than poplar gum. Subsequently, network pharmacology analysis identified 72 key active differential metabolites and 23 core targets associated with the treatment of bacterial infection. Finally, GO enrichment and KEGG pathway analyses revealed that the core targets were predominantly enriched in signaling pathways related to immune regulation and the inflammatory response.
Conclusions In summary, although propolis is derived from tree gum, its metabolites are significantly different from those of tree propolis. In addition, the key active differential metabolites of propolis and poplar propolis determined the differences in their antibacterial efficacy, and the potential antibacterial mechanisms of poplar propolis and poplar gum might include immune regulation and the inflammatory response. Moreover, the screening of key active differential metabolites might lead to the identification of reliable biomarkers for poplar propolis and poplar gum. This work provides valuable insights for future quality control of poplar propolis and treatment of associated bacterial infections.
... This approach allows for the discovery of unique molecular markers that can aid in the precise identification of insect species. To harness the full potential of these techniques and transform raw data into actionable insights, the pivotal role of chemometrics comes into play (Mialon et al., 2023). In this context, chemometrics serves as the bridge between the analytical instrument's output and the meaningful information that informs decision-making in edible insect authentication. ...
... Currently, the metabolome is considered the most predictive of the phenotype, and the study of metabolites is key for studying an organism (Olmo-García & Carrasco-Pancorbo, 2021). In food sector, metabolomics and non-targeted metabolomics fingerprinting are steadily growing to address challenges regarding the issues of food safety, quality, and authenticity, and therefore not only with the aim to obtain a detailed characterization (Ballin & Laursen, 2019;Conte et al., 2020;Lioupi et al., 2020;Mialon et al., 2023). ...
... In this sense, these signals may relate to the identity of a food, to its physico-chemical or other natural properties, as well as to the presence or quantity of compounds in the chemical composition of the food [22,23]. Despite of this, instrumental fingerprints contain hidden information about it, which normally requires the use of chemometric tools in particular or machine learning methods in general [24][25][26]. ...
Nowadays, the combination of fingerprinting methodology with friendly environmental and economical analytical instrumentation are becoming increasingly relevant in the food sector. In this study, a highly versatile portable analyser based on Spatially Offset Raman Spectroscopy (SORS) to obtain the edible vegetable oils (sunflower and olive oils) fingerprints was used to evaluate the capability of such fingerprints, obtained quickly, reliable and without any sample treatment, to discriminate/classify the analysed samples. After data treatment, not only HCA and PCA as unsupervised pattern recognition techniques but also SVM, kNN and SIMCA as supervised pattern recognition techniques, showed that the main effect over the discrimination/classification was associated to those regions of RAMAN fingerprint related to the free fatty acids content, especially oleic and linoleic acid. These facts allowed the discrimination attending to the original raw material used in the oil's elaboration. In all the model established, reliable qualimetric parameters were obtained.
... These should provide a comprehensive description of the complex matrix under investigation, enabling the detection of both known and unknown compounds contained in the mixture. The resulting indications of samples should be primarily intended not only for the identification of analytes but also for the assessment of sample authenticity via the use of suitable classification tools [7]. ...
Verifying the quality and authenticity of agri-food products is essential to guaranteeing adequate food safety for consumers [...]
... It has been used in various field of research including food analysis and authentication because of its ability to determine metabolite composition in foods. The changes in metabolites' composition in food products due to factors such as adulteration, contamination, and substitution can be observed using a metabolomics approach [22,23]. Several analytical techniques, such as gas chromatography and liquid chromatography coupled with mass spectrometry detection and nuclear magnetic resonance spectrometry (NMR), have been utilized for metabolomics research [24,25]. ...
Beef sausage (BS) is one of the most favored meat products due to its nutrition and good taste. However, for economic purposes, BS is often adulterated with pork by unethical players. Pork consumption is strictly prohibited for religions including Islam and Judaism. Therefore, advanced detection methods are highly required to warrant the halal authenticity of BS. This research aimed to develop a liquid chromatography–high-resolution mass spectrometry (LC–HRMS) method to determine the halal authenticity of BS using an untargeted metabolomics approach. LC–HRMS was capable of detecting various metabolites in BS and BS containing pork. The presence of pork in BS could be differentiated using principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) with high accuracy. PLS-DA perfectly classified authentic BS and BS containing pork in all concentration levels of pork with R²X = (0.821), R²Y(= 0.984), and Q² = (0.795). The level of pork in BS was successfully predicted through partial least squares (PLS) and orthogonal PLS (OPLS) chemometrics. Both models gave high R² (>0.99) actual and predicted values as well as few errors, indicating good accuracy and precision. Identification of discriminating metabolites’ potential as biomarker candidates through variable importance for projections (VIP) value revealed metabolites of 2-arachidonyl-sn-glycero-3-phosphoethanolamine, 3-hydroxyoctanoylcarnitine, 8Z,11Z,14Z-eicosatrienoic acid, D-(+)-galactose, oleamide, 3-hydroxyhexadecanoylcarnitine, arachidonic acid, and α-eleostearic acid as good indicators to detect pork. It can be concluded that LC–HRMS metabolomics combined with PCA, PLS-DA, PLS, and OPLS was successfully used to detect pork adulteration in beef sausages. The results imply that LC–HRMS untargeted metabolomics in combination with chemometrics is a promising alternative as an analytical technique to detect pork in sausage products. Further analysis of larger samples is required to warrant the reproducibility.
... The potential offered by chemometric methods using the non-targeted approach in spectroscopic techniques such as NIR is remarkable (Karunathilaka et al., 2016). Through this approach, the full non-specific signal is used as an instrumental fingerprint providing relevant chemical information to characterize the material (Mialon et al., 2023). Advantages of non-targeted over targeted approaches for food authentication purposes have already been highlighted in literature (Sarkar et al., 2022;Hassoun et al., 2023). ...
Food fraud in olive oil is a major concern for consumers and authorities due to the health risks and economic impacts. Common frauds include blending with other cheaper non-olive oils, or misleading labelling. The main issue is that legislation and methods presently used in routine laboratories are not always up to date with current fraudulent practices, making detection difficult, so new analytical methods development is required.
This study focuses on developing an affordable and non-destructive analysis method based on NIR spectroscopy and chemometrics for EVOO quality assessment, specifically by monitoring 7 parameters of interest in EVOO measured by official methods and used to develop calibrations through NIR data. For this, two NIR low-cost portable instruments were employed, studied in-depth and compared with a NIR benchtop instrument. Calibration results enabled detection of atypical olive oils and excellent accuracy, especially for palmitic and oleic acid predictions, demonstrating the potential of the instruments.
Within-species diversity of microorganisms in food systems significantly shapes community function. While next-generation sequencing (NGS) methods have advanced our understanding of microbiomes at the community level, it is essential to recognize the importance of within-species variation for understanding and predicting the functional activities of these communities. This review highlights the substantial variation observed among microbial species in food systems and its implications for their functionality. We discuss a selection of key species in fermented foods and food systems, highlighting examples of strain-level variation and its influence on quality and safety. We present a comprehensive roadmap of methodologies aimed at uncovering this often overlooked underlying diversity. Technologies like long-read marker-gene or shotgun metagenome sequencing offer enhanced resolution of microbial communities and insights into the functional potential of individual strains and should be integrated with techniques such as metabolomics, metatranscriptomics, and metaproteomics to link strain-level microbial community structure to functional activities. Furthermore, the interactions between viruses and microbes that contribute to strain diversity and community stability are also critical to consider. This article highlights existing research and emphasizes the importance of incorporating within-species diversity in microbial community studies to harness their full potential, advance fundamental science, and foster innovation.
Organic foods have become popular, with growing attention to healthier, safer foods. Yet, the comprehensive knowledge of chemical composition and metabolic network rewiring in organic food crops remains limited, hindering a thorough understanding of their nutritional values and health effects. This study investigates chemical truth behind organic production and knowledge gaps and proposes metabolomics as a tool to systemically explore the chemical diversity of organic food crops. Three essential tasks using metabolomics are given to achieve the goal. Based on previously published literature on organic crop metabolomics research, one of the suggested tasks, functional enrichment analysis was applied to demonstrate its usefulness for the identification of metabolic network and the necessity of future research on organic food crops. The proposed actions will be a breakthrough to better understand the overall metabolism and quality traits of organic foods, thereby contributing to the quality assurance, authentication, and traceability of organic food products.
Table olives are one of the most widespread fermented foods in the Mediterranean area, presenting an exponential increase in global consumption in the latest years. As a fermented product, its microbiota consists of a complex ecosystem, the composition of which depends on a multitude of factors and affects the quality attributes of the final product. The swiftly developing and constantly evolving field of omics technologies is being applied to unravel the profile of the microbial ecosystem and enable a deeper understanding of the fermentation process. In particular, the use of amplicon metagenomics facilitates the thorough analysis of the microbiota involved as it encompasses both culturable and unculturable microorganisms. Volatilomics aims at the identification and quantification of the volatile metabolites formed during fermentation with a direct involvement in the safety and quality evaluation of the food product. The integration of metagenomic and volatilomic data, through the application of bioinformatics can enhance the understanding of the interplay between the microbial profile and volatilome, resulting in a more comprehensive view of the system. This review summarized the overall amplicon metagenomics and volatilomics analytical approaches, along with the currently available bioinformatics tools for the data analysis in the field of table olives. Emphasis is given to the integration of amplicon metagenomic and volatilomic data employed to characterize the diversity of microbial populations and reveal the relationships between them and the volatile compounds. The latter may provide an extensive view of the microbial community dynamics, which is key in table olive fermentation and the microbiota’s functional properties. The potentiality to evaluate their effect in shaping the quality and unique features of the final product is highlighted.
Country of origin is defined as the country where food or feed is entirely grown, produced, or manufactured, or, if produced in more than one country, where it last underwent a substantial change. In the UK, EU-assimilated legislation states that indication of the country of origin is a mandatory labelling requirement for food and feed, including products such as meat, vegetables, eggs, honey and wine.
The country of origin claim plays an important role for consumers who tend to relate certain country of origin labelling to superior quality or brand identity. Patriotism (or ethnocentrism) can also play a role in consumer food choice. In Europe, there are 3500 products with a specific geographical origin and their production methods are officially protected (Protected Designation of Origin = PDO; Protected Geographical Indication = PGI; Geographical Indication (for spirit drinks) = GI). These goods often carry a premium price. In addition to customer preference and sale price, country of origin claims are important to businesses when they seek to (i) monitor food miles (carbon footprint), (ii) ensure sustainable sourcing of, for example soy and palm oil (including new Regulation (EU) 2023/1115 on deforestation-free products), (iii) avoid trading of goods which are subject to sanctions, (iv) reassure consumers over concerns of farming and animal welfare standards, (v) avoid foods which are linked to exploitation of farm workers, enforced, or child labour.
‘Verification’ of geographical origin involves testing against a database to confirm that the data for a sample are consistent with those for that geographical location as claimed on a product label. Verification therefore does not involve testing a sample from an unknown location to unequivocally identify its provenance, as such methods are not available or are extremely limited in scope.
Recent publications in the field of food authentication have reported using analytical methods which measure changes in sample composition. These changes can be due to a variety of causes such as the presence of adulterants, different production methods, or varying geographical origins of food. While the increasing use of marker-based approaches is beneficial in combating food fraud, there is a pressing need to adopt a harmonized approach for validating these markers. In this article, we make recommendations for harmonized terminologies and general definitions related to food authenticity markers. First, we propose the terms “primary” and “secondary” markers to distinguish between direct and indirect authentication. The terms “single” and “dual” authenticity markers, and authentic “profiles” and “fingerprints” are suggested to distinguish between the number of analytical targets used. We also recommend that the terms: “threshold”, “binary”, and “interval” markers are applied depending on how they discriminate authentic from non-authentic samples. Second, we advocate for harmonization in marker discovery approaches. A summary of the main analytical techniques, published guidelines, data repositories and data analysis approaches is presented for various marker classes while also stating their applicability and limitations. Finally, we propose guidelines for the analytical community concerning marker validation. In our view, the validation of the authentication method should include the following steps: 1) applicability statement; 2) experimental design; 3) marker selection and analysis; 4) analytical method validation; 5) method release; 6) method monitoring. Implementing these approaches will represent a significant step towards establishing a wide range of fully validated and accredited methodologies that can be applied effectively in food authenticity monitoring and control programs.
Rationale
Vinegar is an everyday condiment made from fermented grains or fruits. It contains acetic acid which is the main organic material produced by fermentation. Vinegar suffers from the authenticity problem of exogenous adulteration due to the indistinguishability of low‐cost chemical sources of synthetic acetic acid from acetic acid produced by fermentation. It is necessary to establish a simple and rapid measurement technique.
Methods
Determination was according to the total acid content of vinegar diluted with acetone to a certain concentration. Online separation and determination of acetic acid δD in vinegar were carried out using gas chromatography–pyrolysis–isotope ratio mass spectrometry.
Results
An HP‐Plot/U column was selected for online separation of acetic acid and water with molecular sieve characteristics. At the same time, combined with the instrument blowback function to remove water. Dilute solvent acetone was treated with a molecular sieve to remove trace water. The reproducibility of this method is less than 3‰. The long‐term stability is within a reasonable error range. The accuracy correlation coefficient is greater than 0.99. The δD values of acetic acid in vinegar (−264.5 ± 20.3‰) and from chemical sources (−30.5 ± 90.8‰) were obtained.
Conclusions
A rapid method was developed for identification of different sources of acetic acid. These different sources of acetic acid exhibited significant hydrogen isotope distribution characteristics. Additionally, it was observed that the carboxyl hydrogen of acetic acid exhibited facile exchange with water. In future investigations, we aim to mitigate this interference.
Currently, the combination of fingerprinting methodology and environmentally friendly and economical analytical instrumentation is becoming increasingly relevant in the food sector. In this study, a highly versatile portable analyser based on Spatially Offset Raman Spectroscopy (SORS) obtained fingerprints of edible vegetable oils (sunflower and olive oils), and the capability of such fingerprints (obtained quickly, reliably and without any sample treatment) to discriminate/classify the analysed samples was evaluated. After data treatment, not only unsupervised pattern recognition techniques (as HCA and PCA), but also supervised pattern recognition techniques (such as SVM, kNN and SIMCA), showed that the main effect on discrimination/classification was associated with those regions of the Raman fingerprint related to free fatty acid content, especially oleic and linoleic acid. These facts allowed the discernment of the original raw material used in the oil’s production. In all the models established, reliable qualimetric parameters were obtained.
For economic reasons, high-price milk, such as horse milk (HM), can be adulterated using lower-price milk by unethical producers and traders. Milk adulteration is a serious problem because it is closely associated with the product's quality and safety, which could harm consumers. The purpose of this research was to develop a non-targeted metabolomics approach using liquid chromatography-high resolution mass spectrometry (LC-HRMS) in combination with chemometrics for the detection of goat milk (GM) adulterated in horse milk (HM). Principal component analysis (PCA) successfully differentiated between authentic HM and adulterated HM with GM. In addition, discrimination and classification between authentic and adulterated HM with GM were successfully performed using partial least square-discriminant analysis (PLS-DA). The PLS-DA was confirmed for its accuracy, precision, and validity. Moreover, multivariate regression of partial least squares (PLS) and orthogonal PLS (OPLS) could predict the amount of GM added in HM with high accuracy. Analysis of the variable importance for projection (VIP) and S-line plot found that metabolites of ( ±)9(10)-EpOME, 1,2:5,6-dianhydro-3,4-dideoxy-1-dodecyl-6-[12-(5-methyl-2-oxo-2,5-dihydro-3-furanyl)dodecyl]hexitol, 1-Palmitoleoyl-2-oleoyl-sn-glycerol, 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoethanolamine, DG (14:0/18:3(9Z,12Z,15Z)/0:0), DG (16:1(9Z)/18:3(9Z,12Z,15Z)/0:0), ditridecanoin, DMG (dimyristoyl glycerol), ethyl palmitoleate, giganin, octadec-9-ynoic acid, and palmitoleic acid were responsible for the discrimination of HM and GM as well as for the prediction of GM in HM. It can be concluded that a non-targeted metabolomics approach using LC-HRMS combined with chemometrics is potential for milk authentication.
Ginger (Zingiber officinale Roscoe) is a high-value food and herb worldwide. The quality of ginger is often related to its production regions. In this study, stable isotopes, multiple elements, and metabolites were investigated together to realize ginger origin traceability. Chemometrics showed that ginger samples could be preliminarily separated, and 4 isotopes (δ13C, δ2H, δ18O, and δ34S), 12 mineral elements (Rb, Mn, V, Na, Sm, K, Ga, Cd, Al, Ti, Mg, and Li), 1 bioelement (%C), and 143 metabolites were the most important variables for discrimination. Furthermore, three algorithms were introduced, and the fused dataset based on VIP features led to the highest accuracies for origin classification, with predictive rates of 98% for K-nearest neighbor and 100% for support vector machine and random forest. The results demonstrated that isotopic, elemental, and metabolic fingerprints were useful indicators for the geographical origins of Chinese ginger.
Non-targeted approaches (NTAs) are being increasingly developed and adopted to detect food fraud and identify the authenticity of the food. Method validation is a critical step before bringing the NTAs to the routine, to be assured of method performance and trust that its outcomes are reliable. However, the paucity of well structured and harmonized validation strategies has been one of the hurdles, withholding the exploitation of NTAs. This report aims to describe a validation framework for methods that involve binary classification, which are prevalent in non-targeted workflows. We foresee this work to contribute to filling the current gap in the provisions for NTA method validation; perhaps also push the dialogue further to collectively resolve existing challenges.
Wheat is the staple food for the world’s major populations. However, chemical characters of geographically authentic wheat samples, especially for the lipids, have not been deeply studied. The present research aimed to investigate lipid compositions of Chinese wheat samples and clarify the major markers that contribute to the geographical differences. A total of 94 wheat samples from eight main wheat-producing provinces in China were evaluated to differentiate their lipid compositions. Based on the data collected from ultra-high-performance-liquid-chromatography tandem time-of-flight mass spectrometry (UPLC-Q/TOF MS), an optimized non-targeted lipidomic method was utilized for analyses. As the results, 62 lipid compounds, including fatty acids, phospholipids, galactolipids, triglycerides, diglycerides, alkylresorcinol, and ceramide were tentatively identified. Partial least squares discriminant analysis (PLS-DA) demonstrated a more satisfying performance in distinguishing wheat samples from different origins compared with principal component analysis (PCA). Further, the abundances of triglycerides and glycerophospholipids with more unsaturated fatty acids were found greater in wheat samples from northern origins of China, while more glycolipids and unsaturated fatty acids arose in southern original wheat samples. These findings describe the lipid profiles of wheat samples in China and could contribute to the quality and safety control for the wheat flour products.
Cheese maturation and flavor development results from complex interactions between milk substrates, cheese microbiota and their metabolites. In this study, bacterial 16S rRNA-gene sequencing, untargeted metabolomics (gas chromatography-mass spectrometry) and data integration analyses were used to characterize and differentiate commercial Cheddar cheeses of varying maturity made by the same and different manufacturers. Microbiota and metabolite compositions varied between cheeses of different ages and brands, and could be used to distinguish the cheeses. Individual amino acids and carboxylic acids were positively correlated with the ripening age for some brands. Integration and Random Forest analyses revealed numerous associations between specific bacteria and metabolites including a previously undescribed positive correlation between Thermus and phenylalanine and a negative correlation between Streptococcus and cholesterol. Together these results suggest that multi-omics analyses has the potential to be used for better understanding the relationships between cheese microbiota and metabolites during ripening and for discovering biomarkers for validating cheese age and brand authenticity.
Introduction
In the last years, consumers increased the demand for high-quality and healthy beverages, including coffee. To date, among the techniques potentially available to determine the overall quality of coffee beverage, metabolomics is emerging as a valuable tool.
Objective
In this study, 47 ground coffee samples were selected during the 2018 Edition of the "International Coffee Tasting" (ICT) in order to provide discrimination based on both chemical and sensory profiles. In particular, 20 samples received a gold medal ("high quality" group), while lower sensory scores characterized 27 samples (without medal).
Methods
Untargeted metabolomics based on ultra-high pressure liquid chromatography coupled with quadrupole-time-of-flight (UHPLC-QTOF) and head space-gas chromatography coupled with mass spectrometry platforms followed by multivariate statistical approaches (i.e. both supervised and unsupervised) were used to provide new insight into the searching of potential markers of sensory quality.
Results
Several compounds were identified, including polyphenols, alkaloids, diazines, and Maillard reaction products. Also, the headspace/GC-MS highlighted the most important volatile compounds. Polyphenols were scarcely correlated to the sensory parameters, whilst the OPLS-DA models built using typical coffee metabolites and volatile/Maillard compounds possessed prediction values > 0.7. The "high quality" group showed specific metabolomic signatures, thus corroborating the results from sensory analysis. Overall, methyl pentanoate (ROC value = 0.78), 2-furfurylthiol (ROC value = 0.75), and L-Homoserine (ROC value = 0.74) established the higher number of significant (p < 0.05) correlations with the sensory parameters.
Conclusion
Although ad-hoc studies are advisable to further confirm the proposed markers, this study demonstrate the suitability of untargeted metabolomics for evaluating coffee quality and the potential correlations with the sensory attributes.
Introduction:
LC-MS based untargeted metabolomics are the main untargeted methods used for juice metabolomics to solve the authentication problem faced in fruit juice industry.
Objectives:
To evaluate the performances of different untargeted metabolomics methods on fruit juices metabolomics and authentication, orange and apple fruit juices were selected for this study.
Methods:
IDA-MS and SWATH-MS based on UHPLC-QTOF were used for the metabolomics and authenticity determination of apple and orange juices, including the lab-made samples of oranges (Citrus sinensis Osb.) from Jiangxi Province, apples (Malus domestica Borkh) from Shandong Province, and different brands of commercial orange and apple juice samples from markets.
Results:
IDA-MS and SWATH-MS could both acquire numerous MS1 features and MS2 information of juice components, while SWATH-MS excels at the acquisition rate of MS2. Distinctive separation between authentic orange juice and not authentic orange juice could be seen from principal component analysis and hierarchical clustering analysis based on both IDA-MS and SWATH-MS. After analysis of variance, fold change analysis and orthogonal projection to latent structures discriminant mode, 53 and 46 potential markers were defined by IDA-MS and SWATH-MS (with 77.4% and 100% MS2 acquisition rate) separately. Subsequently, these potential markers were putatively annotated using general chemical databases with 6 more annotated by SWATH-MS. Furthermore, 7 of the potential markers, l-asparagine, umbelliferone, glucosamine, phlorin, epicatechin, phytosphingosine and chlorogenic acid, were identified with standards. For the consideration of model simplicity, two determined makers (umbelliferone and chlorogenic acid) were selected to construct the DD-SIMCA model in commercial samples because of their good correlation with apple adulteration proportion, and the sensitivity and specificity of the model were 100% and 95%.
Conclusion:
SWATH-MS excels at the MS2 acquisition of juice components and potential markers. This study provides an overall performance comparison between IDA-MS and SWATH-MS, and guidance for the method selection on fruit juice metabolomics and juice authenticity determination. Two of the potential markers determined, umbelliferone and chlorogenic acid, could be used as apple juice indicators in orange juice.
BACKGROUND
Coffee is one of the most popular beverages around the world, consumed as an infusion of ground roasting coffee beans with a characteristic taste and flavor. Two main varieties, Arabica and Robusta, are produced worldwide. Furthermore, interest of consumers in quality attributes related to coffee production region and varieties is increasing. Thus, it is necessary to encourage the development of simple methodologies to authenticate and guarantee the coffee origin, variety and roasting degree, aiming to prevent fraudulent practices.
RESULTS
C18 high‐performance liquid chromatography with fluorescence detection (HPLC‐FLD) fingerprints obtained after brewing coffees without any sample treatment other than filtration (i.e. considerably reducing sample manipulation) were employed as sample chemical descriptors for subsequent coffee characterization and classification by principal component analysis (PCA) and partial least squares regression‐discriminant analysis (PLS‐DA). PLS‐DA showed good classification capabilities regarding coffee origin, variety and roasting degree when employing HPLC‐FLD fingerprints, although overlapping occurred for some sample groups. However, the discrimination power increased when selecting HPLC‐FLD fingerprinting segments richer in discriminant features, which were deduced from PLS‐DA loading plots. In this case, excellent separation was observed and 100% classification rates for both PLS‐DA calibrations and predictions were obtained (all samples were correctly classified within their corresponding groups).
CONCLUSION
HPLC‐FLD fingerprinting segments were3 found to be suitable chemical descriptors for discriminating the origin (country of production), variety (Arabica and Robusta) and roasting degree of coffee. Therefore, HPLC‐FLD fingerprinting can be proposed as a feasible, simple and cheap methodology to address coffee authentication, especially for developing coffee production countries. © 2020 Society of Chemical Industry
Food science continually requires the development of novel analytical methods to prevent fraudulent actions and guarantee food authenticity. Greek table olives, one of the most emblematic and valuable Greek national products, are often subjected to economically motivated fraud. In this work, a novel ultra-high-performance liquid chromatography–quadrupole time of flight tandem mass spectrometry (UHPLC-QTOF-MS) analytical method was developed to detect the mislabeling of Greek PDO Kalamata table olives, and thereby establish their authenticity. A non-targeted screening workflow was applied, coupled to advanced chemometric techniques such as Principal Component Analysis (PCA) and Partial Least Square Discriminant Analysis (PLS-DA) in order to fingerprint and accurately discriminate PDO Greek Kalamata olives from Kalamata (or Kalamon) type olives from Egypt and Chile. The method performance was evaluated using a target set of phenolic compounds and several validation parameters were calculated. Overall, 65 table olive samples from Greece, Egypt, and Chile were analyzed and processed for the model development and its accuracy was validated. The robustness of the chemometric model was tested using 11 Greek Kalamon olive samples that were produced during the following crop year, 2018, and they were successfully classified as Greek Kalamon olives from Kalamata. Twenty-six characteristic authenticity markers were indicated to be responsible for the discrimination of Kalamon olives of different geographical origins.
The metabolome of an organism depends on environmental factors and intracellular regulation and provides information about the physiological conditions. Metabolomics helps to understand disease progression in clinical settings or estimate metabolite overproduction for metabolic engineering. The most popular analytical metabolomics platform is mass spectrometry (MS). However, MS metabolome data analysis is complicated, since metabolites interact nonlinearly, and the data structures themselves are complex. Machine learning methods have become immensely popular for statistical analysis due to the inherent nonlinear data representation and the ability to process large and heterogeneous data rapidly. In this review, we address recent developments in using machine learning for processing MS spectra and show how machine learning generates new biological insights. In particular, supervised machine learning has great potential in metabolomics research because of the ability to supply quantitative predictions. We review here commonly used tools, such as random forest, support vector machines, artificial neural networks, and genetic algorithms. During processing steps, the supervised machine learning methods help peak picking, normalization, and missing data imputation. For knowledge-driven analysis, machine learning contributes to biomarker detection, classification and regression, biochemical pathway identification, and carbon flux determination. Of important relevance is the combination of different omics data to identify the contributions of the various regulatory levels. Our overview of the recent publications also highlights that data quality determines analysis quality, but also adds to the challenge of choosing the right model for the data. Machine learning methods applied to MS-based metabolomics ease data analysis and can support clinical decisions, guide metabolic engineering, and stimulate fundamental biological discoveries.
The aim of this project was to register in a LC-MS-based untargeted single-batch analysis, the metabolome of 11 single-cultivar, single-vintage Italian red wines (Aglianico, Cannonau, Corvina, Montepulciano, Nebbiolo, Nerello, Primitivo, Raboso, Sagrantino, Sangiovese and Teroldego) from 12 regions across Italy, each one produced in their terroirs under ad hoc legal frameworks to guarantee their quality and origin. The data provided indications regarding the similarity between the cultivars, and highlighted a rich list of putative Biomarkers of Origin Wines (pBOWs) characterizing each individual cultivar-terroir combination, where Primitivo, Teroldego and Nebbiolo had the maximum number of unique pBOWs. The pBOWs included anthocyanins (Teroldego), flavanols (Aglianico, Sangiovese, Nerello and Nebbiolo), amino acids and N-containing metabolites (Primitivo), hydroxycinnamates (Cannonau) and flavonols (Sangiovese). The raw data generated in this study are publicly available and, therefore, accessible and reusable as a baseline dataset for future investigations.
BACKGROUND
There is an urgent need to strengthen the testing and certification of geographically iconic foods, as well as to use discriminatory science and technology for their regulation and verification. Multi‐element and stable isotope analyses were combined to provide a new chemometric approach for improving the discrimination tea samples from different geographical origins. Different stoichiometric methods [principal component analysis (PCA), hierarchical cluster analysis (HCA), partial least squares‐discriminant analysis (PLS‐DA), back propagation based artificial neural network (BP‐ANN) and linear discriminant analysis (LDA)] were used to demonstrate this discrimination approach using Yongchuanxiuya tea samples in an experimental test.
RESULTS
Multi‐element and stable isotope analyses of tea samples using inductively coupled plasma mass spectrometry and inductively coupled plasma optical emission spectrometry easily distinguished the geographical origins. However, the clustering ability of the two unsupervised learning methods (PCA and HCA) were worse compared to that of the three supervised learning methods (PLS‐DA, BP‐ANN and LDA). BP‐ANN and LDA, with 100% recognition and prediction abilities, were found to be better than PLS‐DA. ⁸⁶Sr and ¹¹²Cd were the markers enabling the successful classification of tea samples according to their geographical origins. Under the validation by ‘blind’ dataset, the prediction accuracies of the BP‐ANN and LDA methods were all greater than 90%. The LDA method showed the best performance, with an accuracy of 100%.
CONCLUSION
In summary, determination of mineral elements and stable isotopes using inductively coupled plasma mass spectrometry and inductively coupled plasma optical emission spectrometry techniques coupled with chemometric methods, especially the LDA method, is a good approach for improving the authentication of a diverse range of tea. The present study contributes toward generalizing the use of fingerprinting mineral elements and stable isotopes as a promising tool for testing the geographic roots of tea and food worldwide. © 2020 Society of Chemical Industry
A rapid Ultra Performance Liquid Chromatography coupled with Quadrupole/Time Of Flight Mass Spectrometry (UPLC-QTOF-MS) method was designed to quickly acquire high-resolution mass spectra metabolomics fingerprints for rosé wines. An original statistical analysis involving ion ratios, discriminant analysis, and genetic algorithm (GA) was then applied to study the discrimination of rosé wines according to their origins. After noise reduction and ion peak alignments on the mass spectra, about 14 000 different signals were detected. The use of an in-house mass spectrometry database allowed us to assign 72 molecules. Then, a genetic algorithm was applied on two series of samples (learning and validation sets), each composed of 30 commercial wines from three different wine producing regions of France. Excellent results were obtained with only four diagnostic peaks and two ion ratios. This new approach could be applied to other aspects of wine production but also to other metabolomics studies.
Fat-spread products are a stabilized emulsion of water and vegetable oils. The whole fat content can vary from 10 to 90% (w/w). There are different kinds, which are differently named, and their composition depends on the country in which they are produced or marketed. Thus, having analytical solutions to determine geographical origin is required. In this study, some multivariate classification methods are developed and optimised to differentiate fat-spread-related products from different geographical origins (Spain and Morocco), using as an analytical informative signal the instrumental fingerprints, acquired by liquid chromatography coupled with a diode array detector (HPLC-DAD) in both normal and reverse phase modes. No sample treatment was applied, and, prior to chromatographic analysis, only the samples were dissolved in n‑hexane. Soft independent modelling of class analogy (SIMCA) and partial least squares-discriminant analysis (PLS-DA) were used as classification methods. In addition, several classification strategies were applied, and performance of the classifications was evaluated applying proper classification metrics. Finally, 100% of samples were correctly classified applying PLS-DA with data collected in reverse phase.
Introduction
The consumption of high quality coffee such as specialty Arabica and fine Robusta coffee is increasing steadily in recent years. Development of single origin coffee is an important strategy to maintain coffee quality, grade and high cupping score. Indonesia is a top exporting country for Arabica coffee with high variety of specialty coffees from different origins. Despite its long standing reputation in global coffee market, very few is known about the variability among Indonesian specialty coffees.
Objectives
This study aims to observe metabolite variability among Indonesian coffees from different species and geographical origins by means of non-targeted GC/MS metabolite profiling.
Methods
Sixty-four compounds were tentatively identified from 16 green and roasted coffee beans from different species and cultivation areas in Indonesia and were subjected to principal component analysis (PCA). Ten Specialty Arabica coffee and five Fine Robusta representing all important high quality coffees of Indonesia were also analyzed independently to further classify Indonesian coffee according to their origin.
Results
PCA results of 16 green and roasted coffee beans of different species and cultivation areas showed that samples were separated along PC1 based on different roasting condition (green and roasted) with 52.9% variance and were separated along PC2 based on different species with 19.3% variance. The result from this study showed the clustering of samples based on three major cultivation areas in Indonesia (western, central, eastern part). Metabolites showing higher concentration in Sulawesi, Papua, Flores and Sumatra samples were glycerol, glucuno-1,5-lactone, gluconic acid and sorbitol. A clear distinction in galactitol and galactinol concentration between all samples from eastern part of Indonesia and western and middle part of Indonesia was also observed.
Conclusions
Our results showed that each region (western, central and eastern part of Indonesia) has signature compounds that may serve as discriminant markers for coffee authentication. This is the first report on the classification of Indonesian specialty coffee based on their metabolic profiles and can act as a basis for marker identification for routine procedures in industry.
BACKGROUND
In the present study a metabolomics‐based approach was used to discriminate among different hazelnut cultivars and to trace their geographical origins. Ultra‐high‐pressure liquid chromatography coupled to quadrupole‐time‐of‐flight mass spectrometry (UHPLC‐ESI/QTOF‐MS) was used to profile phenolic and sterolic compounds.
RESULTS
Compounds were identified against an in‐house database using accurate monoisotopic mass and isotopic patterns. The screening approach was designed to discern 15 hazelnut cultivars and to discriminate among the geographical origins of six cultivars from the four main growing regions (Chile, Georgia, Italy, and Turkey).
This approach allowed more than 1000 polyphenols and sterols to be annotated. The metabolomics data were elaborated with both unsupervised (hierarchical clustering) and supervised (orthogonal projections to latent structures discriminant analysis, OPLS‐DA) statistics. These multivariate statistical tools allowed hazelnut samples to be discriminated, considering both ‘cultivar type’ and ‘geographical origin’.
Flavonoids (anthocyanins, flavanols and flavonols – VIP scores 1.34–1.49), phenolic acids (mainly hydroxycinnamics – VIP scores 1.35–1.55) together with cholesterol, ergosterol, and stigmasterol derivatives (VIP scores 1.34–1.49) were the best markers to discriminate samples according to geographical origin.
CONCLUSIONS
This work illustrates the potential of untargeted profiling of phenolics and sterols based on UHPLC‐ESI/QTOF mass spectrometry to discriminate hazelnut and support authenticity and origin. © 2019 Society of Chemical Industry
In 2008, the European Commission highlighted the risk of wine mislabelling regarding the geographical origin and varietal identification. While analytical methods for the identification of wine by geographical origin exist, a reliable strategy for authentication of wine variety is still missing. Here, we investigate the suitability of the metabolomic fingerprinting of ethyl acetate wine extracts, using ultra-high-performance liquid chromatography coupled to high-resolution tandem mass spectrometry. In total, 43 white wine samples (three varieties) were analysed within our study. The generated data were processed by principal component analysis and then by partial least squares discriminant analysis. The resulting statistical models were validated and assessed according to their R2 (cum) and Q2 (cum) parameters. The most promising models were based on positive ionisation data, enabling successful classification of 92% of wine samples.
Verification of food authenticity establishes consumer trust in food ingredients and components of processed food. Next to genetic or protein markers, chemicals are unique identifiers of food components. Non-targeted metabolomics is ideally suited to screen food markers when coupled to efficient data analysis. This study explored feasibility of random forest (RF) machine learning, specifically its inherent feature extraction for non-targeted metabolic marker discovery. The distinction of chia, linseed, and sesame that have gained attention as “superfoods” served as test case. Chemical fractions of non-processed seeds and of wheat cookies with seed ingredients were profiled. RF technology classified original seeds unambiguously but appeared overdesigned for material with unique secondary metabolites, like sesamol or rosmarinic acid in the Lamiaceae, chia. Most unique metabolites were diluted or lost during cookie production but RF technology classified the presence of the seed ingredients in cookies with 6.7% overall error and revealed food processing markers, like 4-hydroxybenzaldehyde for chia and succinic acid monomethylester for linseed additions. RF based feature extraction was adequate for difficult classifications but marker selection should not be without human supervision. Combination with alternative data analysis technologies is advised and further testing of a wide range of seeds and food processing methods.
Direct-injection electron spray ionization mass spectrometry (DIESI-MS) can be used to quantify the whole set of positive and negative ions in complex biological samples. A cherry tomato cultivar was grown inside a greenhouse in soil pots supplemented with different nitrogen sources. Organic cultivation increased fruit dry matter while conventional chemical fertilizers increased yield due to higher water content. While soluble sugars were unaltered, secondary metabolism of tomato fruit was highly sensitive to compost soil supplied to the roots. From a total of ~1647 DIESI-MS signals, 725 revealed significant differences between treatments. Heatmap biclustering showed that ionomic differences were robustly maintained in independent experiments carried out during three consecutive years. The ionomic fingerprints allowed reproducible sample classification, reflecting the effect of organic farming on tomato fruit quality. Specific biomarker ions could be identified for various nitrogen sources. We propose DIESI-MS as an up-front strategy for plant food characterization aiming to identify the ions with the most significant differences across genotypes or agronomic conditions.
Extra virgin olive oil (EVOO) is characterized by its aroma and other sensory attributes. These are determined by the geographical origin of the oil, extraction process, place of cultivation, soil, tree varieties, and storage conditions. In the present work, an array of metal oxide gas sensors (called S3), in combination with the SPME-GC-MS technique, was applied to the discrimination of different types of olive oil (phase 1) and to the identification of four varieties of Garda PDO extra virgin olive oils coming from west and east shores of Lake Garda (phase 2). The chemical analysis method involving SPME-GC-MS provided a complete volatile component of the extra virgin olive oils that was used to relate to the S3 multisensory responses. Furthermore, principal component analysis (PCA) and k-Nearest Neighbors (k-NN) analysis were carried out on the set of data acquired from the sensor array to determine the best sensors for these tasks and to assess the capability of the system to identify various olive oil samples. k-NN classification rates were found to be 94.3% and 94.7% in the two phases, respectively. These first results are encouraging and show a good capability of the S3 instrument to distinguish different oil samples.
Nontargeted workflows for chemical hazard analyses are highly desirable in the food safety and integrity fields to ensure human health. Two different analytical strategies, nontargeted metabolomics and chemical database filtering, can be used to screen unknown contaminants in food matrices. Sufficient mass and chromatographic resolutions are necessary for the detection of compounds and subsequent componentization and interpretation of candidate ions. Analytical chemistry-based technologies, including gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), nuclear magnetic resonance (NMR), and capillary electrophoresis-mass spectrometry (CE-MS), combined with chemometrics analysis are being used to generate molecular formulas of compounds of interest. The construction of a chemical database plays a crucial role in nontargeted detection. This review provides an overview of the current sample preparation, analytical chemistry-based techniques, and data analysis as well as the limitations and challenges of nontargeted detection methods for analyzing complex food matrices. Improvements in sample preparation and analytical platforms may enhance the relevance of food authenticity, quality, and safety.
Saw palmetto (Serenoa repens, SP) is the most expensive oil source of the pharmaceutical and healthfood market, and its high cost and recurrent shortages have spurred the development of designer blends of fatty acids to mimic its phytochemical profile and fraudulently comply with the current authentication assays. To detect this adulteration, the combined use of isotopic fingerprint and omic analysis has been investigated, using Principal Component Analysis (PCA) to handle the complex databases generated by these techniques and to identify the possible source of the adulterants. Surprisingly, the presence of fatty acids of animal origin turned out to be widespread in commercial samples of saw palmetto oil.
Paprika production under the protected designation of origin (PDO) standardized procedures leads to more quality products. However, it is also related to higher retail prices, making them susceptible to adulteration with low-quality paprika or its agricultural origin’s mislabeling. Therefore, in this study, high-performance liquid chromatography with fluorescence detection (HPLC-FLD) fingerprints, strongly related to phenolic acid and polyphenolic compounds, were proposed as chemical markers to assess the classification of paprika from five European regions (three Spanish PDO, Hungary, and the Czech Republic), through a classification decision tree constructed by partial least squares-regression discriminant analysis (PLS-DA) models. After external validation, an excellent classification accuracy of 97.9% was achieved. Moreover, the chromatographic fingerprints were also proposed to detect and quantitate two different paprika geographical origin blend scenarios by partial least squares (PLS) regression. Low external validation and prediction errors —with values below 1.6 and 10.7%, respectively— were obtained.
The authentication and traceability of food attract more attention due to the increasing consumer awareness regarding nutrition and health, being a new hotspot of food science. Infrared spectroscopy (IRS) combined with shallow neural network has been widely proven to be an effective food analysis technology. As an advanced deep learning technology, deep neural network has also been explored to analyze and solve food-related IRS problems in recent years. The present review begins with brief introductions to IRS and artificial neural network (ANN), including shallow neural network and deep neural network. More notably, it emphasizes the comprehensive overview of the advances of the technology combined IRS with ANN for the authentication and traceability of food, based on relevant literature from 2014 to early 2020. In detail, the types of IRS and ANN, modeling processes, experimental results, and model comparisons in related studies are described to set forth the usage and performance of the combined technology for food analysis. The combined technology shows excellent ability to authenticate food quality and safety, involving chemical components, freshness, microorganisms, damages, toxic substances, and adulteration. As well, it shows excellent performance in the traceability of food variety and origin. The advantages, current limitations, and future trends of the combined technology are further discussed to provide a thoughtful viewpoint on the challenges and expectations of online applications for the authentication and traceability of food.
Honey traceability is an important topic, especially for honeydew honeys, due to the increased incidence of adulteration. This study aimed to establish specific markers to quantify proteins in honey. A proteomics strategy to identify marker peptides from bracatinga honeydew honey was therefore developed. The proteomics approach was based on initial untargeted identification of honey proteins and peptides by LC-ESI-Triple-TOF-MS/MS, which identified the major royal jelly proteins (MRJP) presence. Afterwards, the peptides were selected by the in silico digestion. The marker peptides were quantified by the developed targeted LC-QqQ-MS/MS method, which provided good linearity and specificity, besides recoveries between 92 and 100% to quantify peptides from bracatinga honeydew honey. The uniqueness and high response in mass spectrometry were backed by further complementary protein analysis (SDS-PAGE). The selected marker peptides EALPHVPIFDR (MRJP 1), ILGANVK (MRJP 2), TFVTIER (MRJP 3), QNIDVVAR (MRJP 4), FINNDYNFNEVNFR (MRJP 5) and LLQPYPDWSWTK (MRJP 7), quantified by LC-QqQ-MS/MS, highlighted that the content of QNIDVVAR from MRJP 4 could be used to differentiate bracatinga honeydew honey from floral honeys (p < 0.05) as a potential marker for its authentication. Finally, principal components analysis highlighted the QNIDVVAR content as a good descriptor of the analyzed bracatinga honeydew honey samples.
A metabolite profiling method was developed for rice samples under conventional and organic agricultural practices. In this study, an ultra-high performance liquid chromatography combined with quadrupole time-of-flight MS based (UHPLC-QTOF based) metabolite approach in combination with multivariate statistical analyses, including principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA) and hierarchical clustering analysis (HCA), was applied to determine metabolite patterns among rice samples. In addition, an orthogonal partial least squares-discriminant analysis (OPLS-DA) was applied to identify key constituents to efficiently distinguish between cultivation methods. In total, 30 discriminant components were chosen from these two kinds of rice samples, in which 8 secondary metabolites could be considered to be potential biomarkers for the discrimination of organic and conventional rice. These results suggest that a metabolomics approach could be a reliable, precise, and effective method for the identification of rice under different cultivation practices.
Virgin olive oil (VOO) is a highly valued vegetable oil often subjected to fraud practices such as adulteration with lower prized oils such as seed oils and refined olive oil. Thus, there is a need to provide rapid tools that allow high-throughput authentication and quality control of VOO as well as other valued oils. Mass spectrometry offers unique features -such as specificity, sensitivity and speed of analysis-that map well against this challenge, either those based on atmospheric pressure ionization methods (ESI and APCI) or those occurring under vacuum conditions such as MALDI for nonvolatile species or headspace sampling-mass spectrometry using electron impact ionization (HS-MS) or chemical ionization (proton transfer reaction mass spectrometry (PTR-MS) and selected ion flow tube mass spectrometry (SIFT-MS)) for volatile fraction analysis. In addition, more recent atmospheric pressure methods (Ambient MS) enable direct analysis with minor or even no sample manipulation. The aim of this article is to provide a critical overview on all these methods and their potential use for vegetable oil characterization, highlighting the strengths and weaknesses of the different approaches.
Beef is a common staple food in many countries, and there is a growing concern over misinformation of beef products, such as false claims of origin, species and production methods. In this study, we used a mass spectrometry-based metabolomics approach to study the metabolite profiles of beef samples purchased from local retailers in Hong Kong. Using multivariate analysis, beef samples from different a) geographical origins, namely the United States (US), Japan and Australia, and b) feeding regimes could be differentiated. We identified twenty-four metabolites to distinguish beef samples from different countries, ten metabolites to identify Angus beef samples from others and seven metabolites to discriminate Australian beef produced by the organic farming from that produced using other farming modes. Based on results of this study, it is concluded that metabolomics provides an efficient strategy for tracing and authenticating beef products to ensure their quality and to protect consumer rights.
Due to constant integrity challenges, ambiguous definitions, and lack of specific guidance, the food industry has been left vulnerable to food fraud. Food fraud is undefined by the EU, resulting in the creation of varying definitions from researchers and regulatory bodies. Not having a definition of food fraud and related terms makes it challenging to comprehend, communicate and ideate on how to prevent it. Furthermore, the food industry is lacking clear guidance on the prevention and mitigation of food fraud. Several documents are available to help guide the food industry; however, they are nonspecific to supply chains, which has left a substantial gap in knowledge that is necessary to protect the food supply. This review analyzed definitions for food fraud and related terms, as well as current guidance on food fraud prevention and mitigation, which can be utilized by the food industry to find commonality and assess where more information is needed. An abundance of literature describing food fraud was found, and although definitions varied, the common themes of intentional acts and economic motivation were widespread. Additionally, general guidance documents for food fraud mitigation and prevention shared general ideas of supply chain transparency, supplier audits, horizon scanning, and vulnerability assessment, which could be utilized throughout the food industry.
Rice is one of the most important cereals for human nutrition and is a basic staple food for half of the global population. The assessment of rice geographical origins in terms of its authenticity is of great interest to protect consumers from misleading information and fraud. In the present study, a head space gas chromatography mass spectrometry (HS-GC-MS) strategy for characterising volatile organic compounds (VOCs) profiles to distinguish rice samples from China, India and Vietnam is described. Partial Least Square Discriminant Analysis (PLS-DA) model exhibited a good discrimination (R² = 0.98182, Q²= 0.9722, and Accuracy= 1.0) for rice samples from China, India and Vietnam. Moreover, Data-Driven Soft Independent Modelling of Class Analogy (DD-SIMCA) and K-nearest neighbors shown good specificity 100% and accuracy 100% in identifying the origin of samples. The present study established VOC fingerprinting as a highly efficient approach to identify the geographical origin of rice.
Untargeted metabolomics was used to investigate the differences between orange juices from concentrate or not. Although no distinctive separation could be observed from principal component analysis for all samples, student’s t-test, fold change, orthogonal projection to latent structures discriminant and detection rate analyses were conducted to find the latent potential markers. Subsequently, 91 and 42 potential markers were defined in positive/negative mode. Thirteen of them, L-glutamine, carvone, erucamide, lysoPE (16:0/0:0), oleic acid, α-linolenic acid and 7 tripeptides (reported for the first time in orange juice) were identified with standards. A partial least squares discriminant analysis model, based on the potential markers in positive mode by direct analysis in real time mass spectrometry-quadrupole time of flight (DART-QTOF) with 9 s acquisition time, was constructed and validated with 97% and 95% accuracy for training and test. The model was applied to commercial samples successfully, and an NFC brand was found highly suspicious.
This work examines the use of stable isotopes and elemental composition for determining geographical origin and authenticity of cow milk from four geographical regions of Slovenian. Samples (277) were collected during summer and winter (2012 to 2014). It was possible to discriminate milk samples according to the year, season and production region using discriminant analysis (DA). The overall temporal prediction variability was 84.6% and 56.4% for regional differences. It was also possible to discriminate milk from three geographic regions, although Alpine samples overlap with Dinaric and Pannonian ones. Prediction ability was the highest for the Pannonian (82.1%) and lowest (26.9%) for the Alpine region. Pairwise comparison using OPLS-DA also displaying good regional predictability (≥0.77) with δ¹³Ccas values and Br content carrying the most variance. A model based on DD-SIMCA was also developed and applied to the control of Slovenian milk. The results revealed the mislabeling of three Slovenian milk products.
The low reproducibility of non-targeted LC-MS-based metabolomics approaches represents a major challenge for their implementation in routine analyses, since it is impossible to compare individual measurements directly with each other, if they were not analysed 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 due to 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.
Source authentication of herbal medicines was essential for ensuring their safety, efficacy and quality consistency, especially those with multiple botanical origins. This study proposed a metabolomics strategy for species discrimination and source recognition. Uncariae Rammulus Cum Uncis, officially stipulating the stems with hooks of five Uncaria species as its origins, was taken as a case study. Firstly, an untargeted MSE method was developed by ultra‐high performance liquid chromatography hyphenated with quadrupole time‐of‐flight mass spectrometry for global metabolite characterization. Subsequently, data pretreatment was conducted by using a Progenesis QI software and screening rules. The obtained metabolite features were defined as variables for statistical analyses. Principal component analysis and chemical fingerprinting spectra suggested that five official species were differentiated from each other except for Uncaria hirsuta and Uncaria sinensis. Furthermore, orthogonal partial least squares discrimination analysis was performed to discriminate confused two species, and resulted in the discovery of nine contributing markers. Ultimately, a Support Vector Machine model was developed to recognize five species and predict origins of commercial materials. The study demonstrated that the developed strategy was effective in discrimination and recognition of confused species, and promising in tracking botanical origins of commercial materials. This article is protected by copyright. All rights reserved
Research on investigation and determination of geographic origins of olive oils is increased by consumers’ demand to authenticated olive oils. Classification algorithms which are machine learning methods can be employed for the authentication of olive oils. In this study, different classification algorithms were evaluated to reveal the most accurate one for authentication of Turkish olive oils. BayesNet, Naive Bayes, Multilayer Perception, IBK, Kstar, SMO, Random Forest, J48, LWL, Logistic Regression, Simple Logistic, LogitBoost algorithms were implemented on 61 chemical analysis parameters of 49 olive oil samples from 6 different locations at Western Turkey. These 61 parameters were obtained from five different chemical analyses which are stable carbon isotope ratio, trace elements, sterol compositions, FAMEs and TAGs. This study is the most comprehensive study to determine the geographical origin of Turkish olive oils in terms of these mentioned features. Classification performances of the algorithms were compared using accuracy, specificity and sensitivity metrics. Random Forest, BayesNet, and LogitBoost algorithms were found as the best classification algorithms for authentication of Turkish olive oils. Using the classification model in this study, geographic origin of an unknown olive oil can be predicted with high accuracy. Besides, similar models can be developed to obtain useful information for authentication of other food products.
Legumes are a well-known source of phytochemicals and are commonly believed to have similar composition between different genera. To date, there are no studies evaluating changes in legumes to discover those compounds that help to discriminate for food quality and authenticity. The aim of this work was to characterize and make a comparative analysis of the composition of bioactive compounds between Cicer arietinum L. (chickpea), Lens culinaris L. (lentil) and Phaseolus vulgaris L. (white bean) through an LC-MS-Orbitrap metabolomic approach to establish which compounds discriminate between the three studied legumes. Untargeted metabolomic analysis was carried out by LC-MS-Orbitrap from extracts of freeze-dried legumes prepared from pre-cooked canned legumes. The metabolomic data treatment and statistical analysis were realized by using MAIT R's package, and final identification and characterization was done using MSn experiments. Fold-change evaluation was made through Metaboanalyst 4.0. Results showed 43 identified and characterized compounds displaying differences between the three legumes. Polyphenols, mainly flavonol and flavanol compounds, were the main group with 30 identified compounds, followed by α-galactosides (n = 5). Fatty acyls, prenol lipids, a nucleoside and organic compounds were also characterized. The fold-change analysis showed flavanols as the wider class of discriminative compounds of lentils compared to the other legumes; prenol lipids and eucomic acids were the most discriminative compounds of beans versus other legumes and several phenolic acids (such as primeveroside salycilic), kaempferol derivatives, coumesterol and α-galactosides were the most discriminative compounds of chickpeas. This study highlights the applicability of metabolomics for evaluating which are the characteristic compounds of the different legumes. In addition, it describes the future application of metabolomics as tool for the quality control of foods and authentication of different kinds of legumes.
Saffron is a high-quality and expensive spice being widely subjected to adulteration. An UHPLC-ESI/QTOF-MS metabolomic-based approach was therefore used to investigate the discrimination potential between adulterated (added with different percentage of other parts of the flower) and authentic saffron, as well as to trace its geographical origin. Both unsupervised (hierarchical clustering) and supervised OPLS- DA multivariate statistics allowed discriminating authentic saffron from styles added of other floral components, as well as PDO (Protected Designation of Origin) vs non PDO saffron samples according to their chemical fingerprints. The proposed markers were then validated through ROC curves. Anthocyanins and glycosidic flavonols were the best markers of the styles' adulteration. However, other flavonoids (mainly free flavonols and flavones), together with protocatechuic aldehyde and isomeric forms of hydroxybenzoic acid, were also validated as markers for the discrimination of PDO vs non PDO saffron samples. This work outlines the potential of untargeted metabolomics based on UHPLC-ESI/QTOF mass spectrometry for saffron authenticity and traceability.
With the growing availability of high throughput methodologies for food characterization and analysis, more and more data are being collected on food products that can be used for the authentication of their quality. In this context, the availability of different multi-block strategies, each with its own peculiarities and providing specific details on the investigated samples, allows to integrate the information from the different sources into a richer model with great flexibility. The aim of the present chapter is to present general perspectives on data-fusion, and to briefly discuss the potentialities of this strategy in the food analysis context. In order to provide an overview on such a wide topic as multi-block analysis, the chapter is conceptually divided into two parts. The first one, where the subject is approached from a theoretical standpoint (from Section 1 to Section 3), and a more practical part, in which selected applications of multi-block methods applied to authenticate or to check quality of foodstuff - such as, e.g., olive oil, wine, beer, vinegar, tea, and dairies - are described (Section 4). Throughout the text, general advantages and disadvantages of data-fusion strategies are depicted with a slight deeper attention into few specific methods.
Species substitution is one of the forms of food fraud for economic gain. The vast majority of existing DNA typing methods are targeted methods that developed for the determination of the specific species in food products. However, these methods are inappropriate for the analysis of unknown food products or a blend of more than one species. This study aimed to investigate the ability of high-throughput DNA metabarcoding to identify mammalian and avian species in mixed products. Next-generation sequencing of a short segment of the 16S ribosomal RNA (16S rRNA) mitochondrial gene was performed. Duplicate samples and two different databases produced very similar results. Although the relative abundance of reads obtained from each species could not make a quantitative assessment of the original species composition, this method still has the potential to determination of high and low contents of animal species in mixed products. Subsequently, we performed a market survey to identify animal species in 27 meat and poultry products sold in China using the developed DNA metabarcoding approach. The results indicated the presence of mislabeling of processed meat and poultry products.
In recent years, food frauds and adulterations have increased significantly. This practice is motivated by fast
economical gains and has an enormous impact on public health, representing an important issue in food science.
In this context, this review has been designed to be a useful guide of potential biomarkers of food authenticity
and safety. In terms of food authenticity, we focused our attention on biomarkers reported to specify different
botanical or geographical origins, genetic diversity or production systems, while at the food safety level, molecular
evidences of food adulteration or spoilage will be highlighted. This report is the first to combine results
from recent studies in a format that allows a ready overview of metabolites (< 1200 Da) and potentially molecular
routes to monitor food authentication and safety. This review has therefore the potential to unveil important
aspects in food adulteration and safety, contributing to improve the current regulatory frameworks.
Due to favourable climate condition, Italy is a prominent producer of different wheat varieties. Several wheat baked goods are produced, but the most typical Italian foods, like pasta, pizza and bread, are made of durum and common wheat flour. Because of the great importance of wheat in the Italian food market, authenticity represents an essential quality parameter not only for the producers and regulatory bodies but also for consumers. The aim of our study was to test the effectiveness of an unconventional non-targeted method for the discrimination of Triticum species using direct analysis real time–high-resolution mass spectrometry (DART–HRMS). For this purpose, 60 wheat samples including durum, common and hulled wheat varieties were collected over two consecutive harvest years. Chemometric evaluation revealed an optimal sample clustering according to the wheat species and the presence of 18 significant markers able to discriminate the groups. The discrimination power obtained is promising since the use of DART–HRMS can significantly reduce the analysis time compared to chromatographic techniques. A plausible future commercial and industrial scenario could see the application of this analytical approach especially to evaluate the risk of substitution of higher value wheat species with lower value flours.
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.
The use of non-targeted analytical methods in food authentication has rapidly increased during the past decade. Non-targeted analyses are now used for a plethora of different food commodities but also across several scientific disciplines. This has brought together a mixture of analytical traditions and terminologies. Consequently, the scientific literature on food authentication often includes different approaches and inconsistently used definitions and nomenclature for both targeted and non-targeted analysis. This commentary paper aims to propose definitions and nomenclature for targeted and non-targeted analytical approaches as a first step towards harmonization.
The capabilities of dynamic headspace entrainment followed by thermal desorption in combination with gas chromatography (GC) coupled to single quadrupole mass spectrometry (MS) have been tested for the determination of volatile components of olive oil. This technique has shown a great potential for olive oil quality classification by using an untargeted approach. The data processing strategy consisted of three different steps: component detection from GC-MS data using novel data treatment software PARADISe, a multivariate analysis using EZ-Info, and the creation of the statistical models. The great number of compounds determined enabled not only the development of a quality classification method as a complementary tool to the official established method “PANEL TEST” but also a correlation between these compounds and different types of defect. Classification method was finally validated using blind samples. An accuracy of 85% in oil classification was obtained, with 100% of extra virgin samples correctly classified.
The purpose of this preliminary study was to discriminate the chemical fingerprints of Protected Designation of Origin (PDO) Grana Padano cheeses from non-PDO “Grana-type” cheeses by means of an untargeted metabolomic approach based on ultra-high-pressure liquid chromatography coupled to quadrupole time-of-flight mass spectrometer (UHPLC/QTOF- MS). Hierarchical cluster analysis and Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) allowed discriminating PDO vs. non-PDO cheeses. Lipids (fatty acids and their derivatives, phospholipids and monoacylglycerols), amino acids and oligopeptides, together with plant-derived compounds were the markers having the highest discrimination potential.
It can be postulated that Grana Padano value chain, as strictly defined in the PDO production specification rules, can drive the biochemical processes involved in cheese making and ripening in a distinct manner, thus leaving a defined chemical signature on the final product. These preliminary findings provide the basis for further authenticity studies, aiming to protect the designation of origin of PDO Grana Padano cheese by applying a comprehensive foodomics-based approach.
Berry fruit juice, which is represented by blueberry and cranberry juice, has become increasingly popular due to its reported nutritional and health benefits. However, in markets, adulteration of berry fruit juice with cheaper substitutes is frequent. In the present study, a metabolomic approach for authentication of berry fruit juices by liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) was established. The global characterization of the berry fruit metabolome by information dependent acquisition (IDA) directed LC-MS/MS coupled to a peak mining workflow by isotope pattern matching was reported. Targeted metabolomics analysis of known juice biomarkers, such as flavonoids, anthocyanins, etc. exhibited a good separation of berry fruit juices from adulterant juices. Moreover, untargeted metabolomics analysis was carried out and subjected to chemometrics analysis. Discrimination of blueberry juice, cranberry juice, and its adulterant apple juice, grape juice was obtained by principal component analysis-discriminant analysis (PCA-DA). 18 characteristic markers discriminating berry fruit juice and its adulterants were selected by comparison of marker abundances in different juice samples. Identification of characteristic markers was accomplished by elemental formula prediction and online database searches based on accurate MS information. These results suggested that the combination of untargeted and targeted metabolomics approach has great potential for authentication of berry fruit juice.
A fast and reproducible protocol for milk Nuclear Magnetic Resonance (NMR) metabolomic fingerprinting was developed, allowing for an accurate discrimination among milk samples from large-scale distribution, as well as among milk sample from different farms located in the same restricted geographical area. Seasonal variations in milk composition and correlations with cows’ nutritional patterns are also assessed, underlining relationships between feeding and metabolites. The most important difference was related to the use of silage feeding. This finding is relevant to assess the suitability of milk for different dairy products. A prominent example is parmesan cheese, the preparation protocol of which excludes milk from silage-fed cows.
Chardonnay is a grape variety widely used to produce different white wine styles. In this work, a metabolomic approach based on ultra- high-performance liquid chromatography coupled to quadrupole time-of- flight mass spectrometry (UHPLC-ESI/QTOF-MS) has been applied to clarify the entire phenolic composition of commercial Chardonnay wines. For this purpose, six wines from different viticultural regions were comprehensively profiled and the phenolic markers discriminating different samples were identified through orthogonal partial least squares discriminant analysis (OPLS-DA). Polyphenols were quantified according to their chemical class and subclass. The most abundant phenolic compounds detected in Chardonnay wines were campesteryl ferulate, caffeic acid and 4-hydroxybenzoic acid (on average 42.4, 5.7, and 5.6 mg/L, respectively). The averaged stilbenes' content detected (as resveratrol equivalents) was 1.3 mg/L. Flavonoids (i.e., flavonols) were the main class of polyphenols contributing to discrimination of wines according to their geographical origin. Furthermore, the OPLS-DA approach provided also an excellent discrimination when considering steel vs. barrel ageing process. These preliminary findings highlight that UHPLC- ESI/QTOF MS-based profiling appears to be a very promising approach in the comprehensive analysis of nutraceuticals, such as polyphenols, in white wine from different geographical origin.
Background: The authenticity of foodstuffs and associated fraud has become an important area. It is estimated that global food fraud costs approximately $US49b annually. In relation to testing for this malpractice, analytical technologies exist to detect fraud but are usually expensive and lab based. However, recently there has been a move towards non-targeted methods as means for detecting food fraud but the question arises if these techniques will ever be accepted as routine. Scope and approach: In this opinion paper, many aspects relating to the role of non-targeted spectroscopy based methods for food fraud detection are considered: (i) a review of the current non-targeted spectroscopic methods to include the general differences with targeted techniques; (ii) overview of in-house validation procedures including samples, data processing and chemometric techniques with a view to recommending a harmonized procedure; (iii) quality assessments including QC samples, ring trials and reference materials; (iv) use of “big data” including recording, validation, sharing and joint usage of databases. Key findings and conclusions: In order to keep pace with those who perpetrate food fraud there is clearly a need for robust and reliable non-targeted methods that are available to many stakeholders. Key challenges faced by the research and routine testing communities include: a lack of guidelines and legislation governing both the development and validation of non-targeted methodologies, no common definition of terms, difficulty in obtaining authentic samples with full traceability for model building; the lack of a single chemometric modelling software that offers all the algorithms required by developers.
The development of analytical strategies to fight against food fraud is currently one of the most developing fields in food science, as the food value chain becomes increasingly complex and global. Food can be certified by clear labeling, but also by objective analytical methods. As shown the last years, especially the omics technologies such as genomics, proteomics, metabolomics and isotopolomics are suitable to prove the geographical origin, the production or cultivation process, and the biological and the overall chemical identity of food. This article describes different analytical approaches beginning with non‐targeted strategies as well as the further developmental stages of transferring the methods to routine laboratories.
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In the last decade, the consumption trend of organic food has increased dramatically worldwide. However, the lack of reliable chemical markers to discriminate between organic and conventional products makes this market susceptible to food fraud in products labeled as “organic”. Metabolomic fingerprinting approach has been demonstrated as the best option for a full characterization of metabolome occurring in plants, since their pattern may reflect the impact of both endogenous and exogenous factors. In the present study, advanced technologies based on high performance liquid chromatography-high-resolution accurate mass spectrometry (HPLC-HRAMS) has been used for marker search in organic and conventional tomatoes grown in greenhouse under controlled agronomic conditions. The screening of unknown compounds comprised the retrospective analysis of all tomato samples throughout the studied period and data processing using databases (mzCloud, ChemSpider and PubChem). In addition, stable nitrogen isotope analysis (δ¹⁵N) was assessed as a possible indicator to support discrimination between both production systems using crop/fertilizer correlations. Pesticide residue analyses were also applied as a well-established way to evaluate the organic production. Finally, the evaluation by combined chemometric analysis of high-resolution accurate mass spectrometry (HRAMS) and δ¹⁵N data provided a robust classification model in accordance with the agricultural practices. Principal component analysis (PCA) showed a sample clustering according to farming systems and significant differences in the sample profile was observed for six bioactive components (L-tyrosyl-L-isoleucyl-L-threonyl-L-threonine, trilobatin, phloridzin, tomatine, phloretin and echinenone).
An optimized and validated LC-ESI-QTOF-MS method with an integrated non-target screening workflow was applied in the investigation of the metabolomic profile of 51 Greek monovarietal extra virgin olive oils (EVOOs) from the varieties: Manaki, Ladoelia, Koroneiki, Amfissis, Chalkidikis and Kolovi. Data processing was carried out with the R language and XCMS package. A local database consisting of 1608 compounds naturally occurring in different organs of Olea Europa L. was compiled in order to accelerate the identification workflow. The preliminary examination of the distribution of EVOOs toward their cultivars was achieved by Principal Component Analysis (PCA). Ant Colony Optimization-Random Forest (ACO-RF) was developed to prioritize over 250 features and to establish a classification tree. Apigenin, vanillic acid, luteolin 7-methyl ether and oleocanthal were suggested as the markers responsible for the classification of Greek EVOOs’ cultivars.