Recent applications of omics-based technologies to main topics in food authentication

Identification of pasteurized mare milk and powder adulteration with bovine milk using quantitative proteomics and metabolomics approaches

Adulteration in dairy products presents food safety challenges, driven by economic factors. Processing may change specific biomarkers, thus affecting their effectiveness in detection. In this study, proteomics and metabolomics approaches were to investigate the detection of bovine milk (BM) constituents adulteration in pasteurized mare milk (PMM) and mare milk powder (MMP). Several bovine proteins and metabolites were identified, with their abundances in PMM and MMP increasing upon addition of BM. Proteins like osteopontin (OPN) and serotransferrin (TF) detected adulteration down to 1 % in PMM, whereas these proteins in MMP were utilized to identify 10 % adulteration. Biotin and N6-Me-adenosine were effective in detecting adulteration in PMM as low as 10 % and 1 % respectively, while in MMP, their detection limits extend down to 0.1 %. These findings offer insights for authenticating mare milk products and underscore the influence of processing methods on biomarker levels, stressing the need to consider these effects in milk product authentication.

An integrative review of analytical techniques used in food authentication: A detailed description for milk and dairy products

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.

Research advances in detection of food adulteration and application of MALDI-TOF MS: A review

Food adulteration and illegal supplementations have always been one of the major problems in the world. The threat of food adulteration to the health of consumers cannot be ignored. Food of questionable origin causes economic losses to consumers, but the potential health risks cannot be ignored. However, the traditional detection methods are time-consuming and complex. This review mainly discusses the types of adulteration and technologies used to detect adulteration. Matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS) is also emphasized in the detection of adulteration and authenticity of origin analysis of various types of food (milk, meat, edible oil, etc.), and the future application direction and feasibility of this technology are analyzed. On this basis, MALDI-TOF MS was compared with other detection methods, highlighting the advantages of this technology in the detection of food adulteration. The future development prospect and direction of this technology are also emphasized.

Untargeted metabolomics using liquid chromatography-high resolution mass spectrometry and chemometrics for analysis of non-halal meats adulteration in beef meat

OBJECTIVE

The adulteration of raw beef (BMr) with dog meat (DMr) and pork (PMr) becomes a serious problem because it is associated with halal status, quality, and safety of meats. This research aimed to develop an effective authentication method to detect non-halal meats (dog meat and pork) in beef using metabolomics approach.

METHODS

Liquid chromatography-high resolution mass spectrometry (LC-HRMS) using untargeted approach combined with chemometrics was applied for analysis non-halal meats in BMr.

RESULTS

The untargeted metabolomics approach successfully identified various metabolites in BMr DMr, PMr, and their mixtures. The discrimination and classification between authentic BMr and those adulterated with DMr and PMr were successfully determined using partial least square-discriminant analysis (PLS-DA) with high accuracy. All BMr samples containing non-halal meats could be differentiated from authentic BMr. A number of discriminating metabolites with potential as biomarkers to discriminate BMr in the mixtures with DMr and PMr could be identified from the analysis of variable importance for projection value. Partial least square (PLS) and orthogonal PLS (OPLS) regression using discriminating metabolites showed high accuracy (R2>0.990) and high precision (both RMSEC and RMSEE <5%) in predicting the concentration of DMr and PMr present in beef indicating that the discriminating metabolites were good predictors. The developed untargeted LC-HRMS metabolomics and chemometrics successfully identified non-halal meats adulteration (DMr and PMr) in beef with high sensitivity up to 0.1% (w/w).

CONCLUSION

A combination of LC-HRMS untargeted metabolomic and chemometrics promises to be an effective analytical technique for halal authenticity testing of meats. This method could be further standardized and proposed as a method for halal authentication of meats.

A proof-of-concept study on the versatility of liquid chromatography coupled to high-resolution mass spectrometry to screen for various contaminants and highlight markers of floral and geographical origin for different honeys

The high-resolution mass spectrometry is a powerful analytical tool for improving food safety and authenticity, but still underused in official control laboratories. The present work is a proof-of-concept study overviewing how liquid-chromatography coupled to high-resolution mass spectrometry could be used simultaneously for large-scale screening of contaminants and differentiation of honey samples. Within this study, the samples were extracted using all-in-one QuEChERS-based protocol that allowed for analysis of various anthropogenic contaminants and endogenous compounds. First, targeted-analysis of 52 honey samples led to unequivocal identification of 23 chemicals, including neonicotinoids, triazole fungicides and synergist. Then, suspect-screening using MSDial software allowed for tentative identification of 30 chemicals including plasticizers, flame-retardants and additives. Suspect-screening also made it possible to highlight tentative markers of chestnut honey (deoxyvasicinone, 2-quinolone, indoleacrylic acid and kynurenic acid) and citrus honey (caffeine, 2-oxindole and indole-3-carbinol). Lastly, non-targeted analysis enabled to separate honeys by their type, floral and geographical origins.

Analysis of dog meat adulteration in beef meatballs using non-targeted UHPLC-Orbitrap HRMS metabolomics and chemometrics for halal authentication study

Due to the different price and high quality, halal meat such as beef can be adulterated with non-halal meat with low price to get an economical price. The objective of this research was to develop an analytical method for halal authentication testing of beef meatballs (BM) from dog meat (DM) using a non-targeted metabolomics approach employing liquid chromatography-high-resolution mass spectrometry (LC-HRMS) and chemometrics. The differentiation of authentic BM from that adulterated with DM was successfully performed using partial least square-discriminant analysis (PLS-DA) with high accuracy (R2X = 0.980, and R2Y = 0.980) and good predictivity (Q2 = 0.517). In addition, partial least square (PLS) and orthogonal PLS (OPLS) were successfully used to predict the DM added (% w/w) in BM with high accuracy (R2 > 0.990). A number of metabolites, potential for biomarker candidates, were identified to differentiate BM and that adulterated with DM. It showed that the combination of a non-targeted LC-HRMS Orbitrap metabolomics and chemometrics could detect up to 0.1% w/w of DM adulteration. The developed method was successfully applied for analysis of commercial meatball samples (n = 28). Moreover, pathway analysis revealed that beta-alanine, histidine, and ether lipid metabolism were significantly affected by dog meat adulteration. In summary, this developed method has great potential to be developed and used as an alternative method for analysis of non-halal meats in halal meat products.

Determination of adulteration, geographical origins, and species of food by mass spectrometry

Food adulteration, mislabeling, and fraud, are rising global issues. Therefore, a number of precise and reliable analytical instruments and approaches have been proposed to ensure the authenticity and accurate labeling of food and food products by confirming that the constituents of foodstuffs are of the kind and quality claimed by the seller and manufacturer. Traditional techniques (e.g., genomics-based methods) are still in use; however, emerging approaches like mass spectrometry (MS)-based technologies are being actively developed to supplement or supersede current methods for authentication of a variety of food commodities and products. This review provides a critical assessment of recent advances in food authentication, including MS-based metabolomics, proteomics and other approaches.

Meat Authenticity Made Easy: DNA Extraction-Free Rapid Onsite Detection of Duck and Pork Ingredients in Beef and Lamb Using Dual-Recombinase-Aided Amplification and Multiplex Lateral Flow Strips

Meat adulteration is a major global concern that poses a threat to public health and consumer rights. However, current detection techniques, such as quantitative polymerase chain reaction (qPCR) and gas chromatography-mass spectrometry, are time-consuming and require sophisticated equipment. In this study, we developed a rapid onsite identification method for animal-derived ingredients by utilizing a fast nucleic acid lysis buffer to expedite the release of sample nucleic acids and combined it with dual-recombinase-aided amplification (dual-RAA) technology and visual multiplex lateral flow strips (MLFSs). Our method successfully detected duck- and bovine-derived, porcine- and bovine-derived, duck- and ovine-derived, and porcine- and ovine-derived meat in a rapid 20 min onsite detection assay, with a detection limit of 101 copies/50 μL reaction system for target genes. Moreover, our method accurately detected adulterated meat with proportions as low as 1:999. These findings have significant implications for food safety and the protection of consumer rights.

Analysis of Pork in Beef Sausages Using LC-Orbitrap HRMS Untargeted Metabolomics Combined with Chemometrics for Halal Authentication Study

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.

Atmospheric solids analysis probe-mass spectrometry (ASAP-MS) as a rapid fingerprinting technique to differentiate the harvest seasons of Tieguanyin oolong teas

Atmospheric solids analysis probe-mass spectrometry (ASAP-MS), an ambient mass spectrometry technique, was used to differentiate spring and autumn Tieguanyin teas. Two configurations were used to obtain their chemical fingerprints - ASAP attached to a high-resolution quadrupole time-of-flight mass spectrometer (i.e., ASAP-QTOF) and to a single-quadrupole mass spectrometer (i.e., Radian™ ASAP™ mass spectrometer). Then, orthogonal projections to latent structures-discriminant analysis was conducted to identify features that held promise in differentiating harvest seasons. Four machine learning models - decision tree, linear discriminant analysis, support vector machine, and k-nearest neighbour - were built using these features, and high classification accuracy of up to 100% was achieved. The markers were putatively identified using their accurate masses and MS/MS fragmentation patterns from ASAP-QTOF. This approach was successfully transferred to the Radian ASAP MS, which is more deployable in the field. Overall, this study demonstrated the potential of ASAP-MS as a rapid fingerprinting tool for differentiating spring and autumn Tieguanyin.

Microbial spoilage mechanisms of vacuum-packed lamb meat: A review

Lamb meat is an important export commodity, however chilled vacuum-packed (VP) lamb has approximately half the shelf-life of beef under the same storage conditions. This makes the industry more vulnerable to financial losses due to long shipping times and unexpected spoilage. Understanding the spoilage mechanisms of chilled VP lamb in relation to VP beef is important for developing effective strategies to extend the shelf-life of lamb. This review has discussed various key factors (i.e., pH, fat, and presence of bone) that have effects on microbial spoilage of VP lamb contributing to its shorter shelf-life relative to VP beef. A range of bacterial organisms and their metabolisms in relevance to lamb spoilage are also discussed. The data gap in the literature regarding the potential mechanisms of spoilage in VP red meat is highlighted. This review has provided the current understanding of key factors affecting the shelf-life of VP lamb relative to VP beef. It has also identified key areas of research to further understand the spoilage mechanisms of VP lamb. These include investigating the potential influence of fat and bone (including bone marrow) on the shelf-life, as well as assessing changes in the meat metabolome as the spoilage microbial community is developing using an integrated approach. Such new knowledge would aid the development of effective approaches to extend the shelf-life of VP lamb.

Determinants of the willingness to buy products certified by omics technology: differences between regular and occasional consumers of organic food

Consumers' demand for organic food has increased in the last years, together with a growing request for food authentication and safety. Omics technologies represent a viable analytical strategy to respond to such needs, strengthen food safety information transmission between consumers and industry, and differentiate between organic and conventional products. However, little is known about consumers' perception of such a novel certification approach. The present research ought to provide insights into the perspectives of consumers, exploring the antecedents of their intention to purchase organic vegetables certified through omics technologies and differentiating between regular and occasional consumers of organic foods. Data were collected from a representative sample of 807 Italian respondents who completed a self-report questionnaire, and Structural Equation Modeling was performed to analyze the data. Results show that several factors influence consumers' approach to omics technology, among which trust in actors in the food industry, attitudes towards the technology and environmental food concerns. In addition, the study drew attention to the differential path impacting consumers with distinct eating habits. Indeed, the degree of importance attributed to food in one's life and the interest towards innovative food are significant predictors of the intention to adopt omics technology only for people consuming organic products with higher frequency. Also, trust in industry actors follows a different path for regular and occasional organic food consumers. The present study sheds light on consumers' perspective on omics technologies, a relatively unexplored topic. Moreover, it allowed to differentiate consumers based on their organic consumption habits, which has been rarely done in previous research. The evidence collected suggests the need for tailored communication programs to stimulate the adoption of omics technologies and foster consumers' confidence in novel food technologies.

Metabolomics during the spontaneous fermentation in cocoa (Theobroma cacao L.): An exploraty review

Spontaneous fermentation is a process that depends on substrates' physical characteristics, crop variety, and postharvest practices; it induces variations in the metabolites that are responsible for the taste, aroma, and quality. Metabolomics makes it possible to detect key metabolites using chemometrics and makes it possible to establish patterns or identify biomarker behaviors under certain conditions at a given time. Therefore, sensitive and highly efficient analytical techniques allow for studying the metabolomic fingerprint changes during fermentation; which identify and quantify metabolites related to taste and aroma formation of an adequate processing time. This review shows that studying metabolomics in spontaneous fermentation permits the characterization of spontaneous fermentation in different stages. Also, it demonstrates the possibility of modulating the quality of cocoa by improving the spontaneous fermentation time (because of volatile aromatic compounds formation), thus standardizing the process to obtain attributes and quality that will later impact the chocolate quality.

Essential terminology and considerations for validation of non-targeted methods

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

Real-time qPCR for the detection of puffer fish components from Lagocephalus in food: L. inermis, L. lagocephalus, L. gloveri, L. lunaris, and L. spadiceus

Puffer fish is a type of precious high-end aquatic product, is widely popular in Asia, especially in China and Japan, even though it naturally harbors a neurotoxin known as tetrodotoxin (TTX) that is poisonous to humans and causes food poisoning. With the increasing trade demand, which frequently exceeds existing supply capacities, fostering fraudulent practices, such as adulteration of processed products with non-certified farmed wild puffer fish species. To determine the authenticity of puffer fish processed food, we developed a real-time qPCR method to detect five common puffer fish species in aquatic products: Lagocephalus inermis, Lagocephalus lagocephalus, Lagocephalus gloveri, Lagocephalus lunaris, and Lagocephalus spadiceus. The specificity, cross-reactivity, detection limit, efficiency, and robustness of the primers and probes created for five species of puffer fish using TaqMan technology have been determined. No cross-reactivity was detected in the DNA of non-target sample materials, and no false-positive signal was detected; the aquatic products containing 0.1% of a small amount of wild puffer fish materials without certification can be reliably tracked; the statistical p-value for each method's Ct value was greater than 0.05. The developed qPCR method was sensitive, highly specific, robust, and reproducibility, which could be used to validate the authenticity of wild puffer fish in aquatic products sold for commercial purposes.

Detection of Pork in Beef Meatballs Using LC-HRMS Based Untargeted Metabolomics and Chemometrics for Halal Authentication

Adulteration of high-quality meat products using lower-priced meats, such as pork, is a crucial issue that could harm consumers. The consumption of pork is strictly forbidden in certain religions, such as Islam and Judaism. Therefore, the objective of this research was to develop untargeted metabolomics using liquid chromatography-high resolution mass spectrometry (LC-HRMS) combined with chemometrics for analysis of pork in beef meatballs for halal authentication. We investigated the use of non-targeted LC-HRMS as a method to detect such food adulteration. As a proof of concept using six technical replicates of pooled samples from beef and pork meat, we could show that metabolomics using LC-HRMS could be used for high-throughput screening of metabolites in meatballs made from beef and pork. Chemometrics of principal component analysis (PCA) was successfully used to differentiate beef meatballs and pork meatball samples. Partial least square-discriminant analysis (PLS-DA) clearly discriminated between halal and non-halal beef meatball samples with 100% accuracy. Orthogonal projection to latent structures-discriminant analysis (OPLS-DA) perfectly discriminated and classified meatballs made from beef, pork, and a mixture of beef-pork with a good level of fitness (R²X = 0.88, R²Y = 0.71) and good predictivity (Q² = 0.55). Partial least square (PLS) and orthogonal PLS (OPLS) were successfully applied to predict the concentration of pork present in beef meatballs with high accuracy (R² = 0.99) and high precision. Thirty-five potential metabolite markers were identified through VIP (variable important for projections) analysis. Metabolites of 1-(1Z-hexadecenyl)-sn-glycero-3-phosphocholine, acetyl-l-carnitine, dl-carnitine, anserine, hypoxanthine, linoleic acid, and prolylleucine had important roles for predicting pork in beef meatballs through S-line plot analysis. It can be concluded that a combination of untargeted metabolomics using LC-HRMS and chemometrics is promising to be developed as a standard analytical method for halal authentication of highly processed meat products.

An Integrative Glycomic Approach for Quantitative Meat Species Profiling

It is estimated that food fraud, where meat from different species is deceitfully labelled or contaminated, has cost the global food industry around USD 6.2 to USD 40 billion annually. To overcome this problem, novel and robust quantitative methods are needed to accurately characterise and profile meat samples. In this study, we use a glycomic approach for the profiling of meat from different species. This involves an O-glycan analysis using LC-MS qTOF, and an N-glycan analysis using a high-resolution non-targeted ultra-performance liquid chromatography-fluorescence-mass spectrometry (UPLC-FLR-MS) on chicken, pork, and beef meat samples. Our integrated glycomic approach reveals the distinct glycan profile of chicken, pork, and beef samples; glycosylation attributes such as fucosylation, sialylation, galactosylation, high mannose, α-galactose, Neu5Gc, and Neu5Ac are significantly different between meat from different species. The multi-attribute data consisting of the abundance of each O-glycan and N-glycan structure allows a clear separation between meat from different species through principal component analysis. Altogether, we have successfully demonstrated the use of a glycomics-based workflow to extract multi-attribute data from O-glycan and N-glycan analysis for meat profiling. This established glycoanalytical methodology could be extended to other high-value biotechnology industries for product authentication.

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

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

MALDI-TOF-MS integrated workflow for food authenticity investigations: An untargeted protein-based approach for rapid detection of PDO feta cheese adulteration

Advances in Matrix-assisted Laser Desorption/Ionization -Time-Of-Flight Mass Spectrometry (MALDI-TOF-MS) have led to its supremacy for complex assessment of food authenticity studies, like dairy products fraud, holding promise for the discovery of potential authenticity (bio)markers. In this study, an integrated untargeted protein-based workflow in combination with advanced chemometrics is presented, to address authenticity challenges in PDO feta cheese which is legally manufactured by the mixture of sheep/goat milk. Potential markers attributed to specific animal origin were found from protein profiles acquired for authentic feta and white cheeses (prepared from cow milk), belonging to 4 kDa-18.5 kDa mass area. Rapid detection of feta cheese adulteration from cow milk was also achieved down to 1% adulteration level. The discriminative models showed high predictive ability for feta cheese authenticity (Q² = 0.920, RMSEE = 0.053) and its adulteration (Q² = 0.835, RMSEE = 0.121), introducing a reliable approach in routine analysis. The methodology was successfully applied in detection of cow milk in sheep yoghurt.

From sample preparation to NMR-based metabolic profiling in food commodities: The case of table olives

INTRODUCTION

Nuclear magnetic resonance (NMR)-based metabolic profiling has been widely used in food and plant sciences. Despite its simplicity and inherent reproducibility, the determination of the appropriate pre-processing procedures greatly affects the obtained metabolic profile.

OBJECTIVES

The current study represents a detailed guide of use for untargeted NMR-based metabolic profiling of table olives (Olea europaea L.).

METHODS

Greek Kalamon table olives from different geographical origins were selected as reference materials. Differently treated samples were extracted using different solvents and/or solvent systems. Chemical profiles were evaluated with high-performance thin layer chromatography (HPTLC). Different deuterated solvents and sample concentrations were evaluated for the recording of optimal quality spectra.

RESULTS

The methanol extract of freeze-dried table olives was found to contain the most representative secondary metabolites, in higher concentrations, as well. The optimal deuterated solvent for the NMR analysis was methanol-d₄ , while final sample concentration should be within the range of 10 to 15 mg/mL. Multivariate data analysis was also used to estimate and confirm the variation and clustering caused by different characteristics of the samples.

CONCLUSIONS

Results of the present study make evident the necessity for thorough planning and method development prior to any extensive metabolomic study based on NMR spectroscopy. Pre-processing and sample preparation stages seemed to greatly affect the metabolic profile and spectral quality in the case of table olives, which by extrapolation could apply to other food commodities. Nevertheless, the nature of the samples must be fully described in general, in order to proceed to solid conclusions.

Application of the Metabolomics Approach in Food Authentication

The authentication of food products is essential for food quality and safety. Authenticity assessments are important to ensure that the ingredients or contents of food products are legitimate and safe to consume. The metabolomics approach is an essential technique that can be utilized for authentication purposes. This study aimed to summarize food authentication through the metabolomics approach, to study the existing analytical methods, instruments, and statistical methods applied in food authentication, and to review some selected food commodities authenticated using metabolomics-based methods. Various databases, including Google Scholar, PubMed, Scopus, etc., were used to obtain previous research works relevant to the objectives. The review highlights the role of the metabolomics approach in food authenticity. The approach is technically implemented to ensure consumer protection through the strict inspection and enforcement of food labeling. Studies have shown that the study of metabolomics can ultimately detect adulterant(s) or ingredients that are added deliberately, thus compromising the authenticity or quality of food products. Overall, this review will provide information on the usefulness of metabolomics and the techniques associated with it in successful food authentication processes, which is currently a gap in research that can be further explored and improved.

Foodomic-Based Approach for the Control and Quality Improvement of Dairy Products

The food quality assurance before selling is a needed requirement intended for protecting consumer interests. In the same way, it is also indispensable to promote continuous improvement of sensory and nutritional properties. In this regard, food research has recently contributed with studies focused on the use of 'foodomics'. This review focuses on the use of this technology, represented by transcriptomics, proteomics, and metabolomics, for the control and quality improvement of dairy products. The complex matrix of these foods requires sophisticated technology able to extract large amounts of information with which to influence their aptitude for consumption. Thus, throughout the article, different applications of the aforementioned technologies are described and discussed in essential matters related to food quality, such as the detection of fraud and/or adulterations, microbiological safety, and the assessment and improvement of transformation industrial processes (e.g., fermentation and ripening). The magnitude of the reported results may open the door to an in-depth transformation of the most conventional analytical processes, with the introduction of new techniques that allow a greater understanding of the biochemical phenomena occurred in this type of food.

Species identification of ruminant milk by genotyping of the κ-casein gene

The development of molecular genetic and bioinformatic systems for identifying the species of milk and the raw material composition of dairy products is of great scientific and practical importance with the purpose of introducing developments in the system for controlling the turnover of falsified products. The aim of the research is to develop a method of PCR-RFLP analysis for species identification of milk and dairy products from agricultural ruminant animals by the κ-casein gene (CSN3) with the possibility of qualitative and relative quantitative assessment of species-specific DNA of the tested biomaterial. The objects of research were samples of raw milk and milk powder, pasteurized cream, and hard and semi-hard cheeses. The developed method of species identification of milk and dairy products includes sample preparation of the studied samples, nucleic acid extraction, combined PCR-RFLP technique, detection of obtained results by the method of horizontal electrophoresis in agarose gel and their analysis, including using the developed mathematical algorithms and software. The synergistic effect established in combined operation of 2 restriction enzymes ensured their application in a mix with increased performance in an ergonomic way in the context of DNA authentication of cow, goat, and sheep milk and dairy products based on them. The specificity and sensitivity of the proposed method is potentially suitable for implementing the development of a system to control the turnover of falsified and counterfeit goods.

Rapid Full-Cycle Technique to Control Adulteration of Meat Products: Integration of Accelerated Sample Preparation, Recombinase Polymerase Amplification, and Test-Strip Detection

Verifying the authenticity of food products is essential due to the recent increase in counterfeit meat-containing food products. The existing methods of detection have a number of disadvantages. Therefore, simple, cheap, and sensitive methods for detecting various types of meat are required. In this study, we propose a rapid full-cycle technique to control the chicken or pig adulteration of meat products, including 3 min of crude DNA extraction, 20 min of recombinase polymerase amplification (RPA) at 39 °C, and 10 min of lateral flow assay (LFA) detection. The cytochrome B gene was used in the developed RPA-based test for chicken and pig identification. The selected primers provided specific RPA without DNA nuclease and an additional oligonucleotide probe. As a result, RPA-LFA, based on designed fluorescein- and biotin-labeled primers, detected up to 0.2 pg total DNA per μL, which provided up to 0.001% w/w identification of the target meat component in the composite meat. The RPA-LFA of the chicken and pig meat identification was successfully applied to processed meat products and to meat after heating. The results were confirmed by real-time PCR. Ultimately, the developed analysis is specific and enables the detection of pork and chicken impurities with high accuracy in raw and processed meat mixtures. The proposed rapid full-cycle technique could be adopted for the authentication of other meat products.

Metabolomics-based screening analysis of PPCPs in water pretreated with five different SPE columns

The selection of solid phase extraction (SPE) columns in the pretreatment process plays a decisive role in the screening and quantification of pharmaceutical and personal care products (PPCPs). As growing PPCPs have frequently been detected in the aquatic environment, it is a burdensome task through one-by-one recovery comparison to judge which column presents relatively ideal pretreatment results for PPCPs. In view of this, we developed a novel metabolomics-based screening method based on ultrahigh-performance liquid chromatography-tandem mass spectrometer (UHPLC-MS/MS) results to accurately, rapidly and comprehensively choose a suitable column from 5 different kinds to handle 64 PPCPs in two water environments (50 μg L^-1/pH ≅ 7.0/pure water and 1 μg L^-1/pH ≅ 7.0/reservoir water) through seeking 'biomarkers', for which multivariate and univariate analyses were adopted. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) play a crucial role in multivariate analysis, and the pairwise t-test and fold change judgement in univariate analysis. Each column group was fully separated from the other 4 groups in PCA and OPLS-DA plots, laying a foundation to distinguish 'biomarkers' between groups. The S-Plot, permutation and variable importance in projection (VIP) in OPLS-DA were employed to screen and identify 'biomarkers', which were further verified by a pairwise t-test and fold change judgement. Eventually, the 64 PPCPs as 'biomarkers' were divided into 5 groups, which correspond to 5 column groups, consistent with the findings of traditional PPCP recovery comparison, proving the validity of the metabolomics-based screening method. This novel method will exhibit greater superiority in choosing suitable SPE columns to handle a growing and larger number of PPCPs in water environments and beyond.

The Role of Chromatographic and Electromigration Techniques in Foodomics

Foodomics is the discipline aimed at studying the prevention of diseases by food, identifying chemical, biological and biochemical food contaminants, determining changes in genetically modified foods, identifying biomarkers able to confirm the authenticity and quality of foods or studying the safety, quality and traceability of foods, among other issues. It is mainly based on the use of genomic, transcriptomic, proteomic and metabolomic tools, among others, in order to understand the effect of food on animals and humans at the level of genes, messenger ribonucleic acid, proteins and metabolites. Since the first definition of Foodomics, a reasonable number of works have shown the extremely high possibilities of this discipline, which is highly based on the use of advanced analytical hyphenated techniques - especially for proteomics and metabolomics. This book chapter aims at providing a general description of the role of chromatographic and electromigration techniques that are currently being applied to achieve the main objectives of Foodomics, particularly in the proteomic and metabolomic fields, since most published works have been focused on these approaches, and to highlight relevant applications.

Utilizing Big Data to Identify Tiny Toxic Components: Digitalis

The botanical genus Digitalis is equal parts colorful, toxic, and medicinal, and its bioactive compounds have a long history of therapeutic use. However, with an extremely narrow therapeutic range, even trace amounts of Digitalis can cause adverse effects. Using chemical methods, the United States Food and Drug Administration traced a 1997 case of Digitalis toxicity to a shipment of Plantago (a common ingredient in dietary supplements marketed to improve digestion) contaminated with Digitalis lanata. With increased accessibility to next generation sequencing technology, here we ask whether this case could have been cracked rapidly using shallow genome sequencing strategies (e.g., genome skims). Using a modified implementation of the Site Identification from Short Read Sequences (SISRS) bioinformatics pipeline with whole-genome sequence data, we generated over 2 M genus-level single nucleotide polymorphisms in addition to species-informative single nucleotide polymorphisms. We simulated dietary supplement contamination by spiking low quantities (0-10%) of Digitalis whole-genome sequence data into a background of commonly used ingredients in products marketed for "digestive cleansing" and reliably detected Digitalis at the genus level while also discriminating between Digitalis species. This work serves as a roadmap for the development of novel DNA-based assays to quickly and reliably detect the presence of toxic species such as Digitalis in food products or dietary supplements using genomic methods and highlights the power of harnessing the entire genome to identify botanical species.

An untargeted metabolomics approach to identify markers to distinguish duck eggs that come from different poultry breeding systems by ultra high performance liquid chromatography-high resolution mass spectrometry

Untargeted metabolomics approach based on ultra high performance liquid chromatography coupled with high resolution mass spectrometry (UHPLC-HRMS) was used to investigate the differences in cage duck eggs and sea duck eggs that from different poultry breeding system, which could help to combat fraud within the egg industry. High dimensions and complex data collected by UHPLC-HRMS were analyzed by multivariate statistical analysis. Identification model of sea duck eggs based on was established. After matching with the chemical databases, four potential markers were putatively matched. Further analysis showed that three of them were confirmed by reference standards. All these three markers (n-behenoyl-d-erythro-sphingosine, 1,2-dipalmitoyl-sn-glycero-3-phosphocholine and n-nervonoyl-d-erythro-sphingosine) have higher content in sea duck eggs. The quantitative analysis showed that the content difference of three markers in farm samples were in highly consistent with the concentration changes measured in experimental samples, which indicate that these three markers are reliable.

Metabolomics: An analytical technique for food processing evaluation

This review aimed to retrieve the most recent research with strong impact concerning the application of metabolomics analysis in food processing. The literature reveals the high capacity of this methodology to evaluate chemical and organoleptic transformations that occur during food production. Current and potential applications of metabolomics analysis will be addressed, focusing on process-composition-function relationships. The use of the metabolomics approach to evaluate transformations in foods submitted to minimal processes, heat or cold treatments, drying, fermentation, chemical and enzymatic treatments and processes using innovative technologies will be discussed. Moreover, the main strategies and advantages of metabolomics-based approaches are reviewed, as well as the most used analytical platforms. Overall, metabolomics can be seen as an important tool to support academia and industry on pursuing knowledge about the transformation of raw animal or plant materials into ready-to-eat products.

Authentication of carioca common bean cultivars (Phaseolus vulgaris L.) using digital image processing and chemometric tools

Bean authentication can result in higher quality products for commerce. Partial least squares discriminant analysis (PLS-DA) was applied to digital images in order to develop a methodology that allows the non-destructive discrimination of three Phaseolus vulgaris L. cultivars (Agro ANfc9, IPR-Andorinha, and IPR-Sabiá) having different technological characteristics. Principal component analysis resulted in a separation of these cultivars, but with a certain amount of overlap. Supervised analysis showed that three PLS1-DA models, each for two cultivars, was moderately better than the simultaneous treatment of all three cultivars (PLS2-DA). Permutation test evaluated statistical significance of PLS-DA models. The classification models were more accurate for Agro ANfc9 and IPR-Sabiá cultivars than for IPR-Andorinha. The Agro ANfc9-IPR-Sabiá model correctly classified 100% of the two bean classes in both training and test sets. This analytical strategy is fast, inexpensive, environmentally friendly, and can be applied for bean quality control helping cultivar authenticity for commerce.

Development of a handheld liquid extraction pen for on-site mass spectrometric analysis of daily goods

We present a handheld liquid extraction pen (LEP) combined with a self-sustaining electrospray ionization platform for ambient mass spectrometry within a laboratory-independent workspace. The LEP enables direct sampling from various surfaces and textures, independent of sample shape without precise sample positioning or dedicated sample preparation. The combination of liquid extraction of analytes through the pen and electrospray ionization (ESI) opens a broad field of applications. Qualitative and semi-quantitative analysis is presented for pesticides, plasticizers and drugs which were analyzed from representative consumer goods, such as fruits, toys and pills. Food authentication via metabolomic fingerprinting and multivariate statistics is demonstrated for the analysis of fish fillets and coffee. The LEP source uses a rechargeable battery to power a compressor. Ambient air is used for solvent nebulization in ESI. Through a pressure pump with integrated solvent reservoir, a solvent flow through the LEP and ESI source is generated. Measurement times of more than three hours are possible. The ion source is adaptable to any kind of mass spectrometer equipped with an atmospheric pressure interface. Measurements were performed on orbital trapping instruments and on a miniature mass spectrometer. Coupled to the miniaturized mass spectrometer, the completely portable LEP-MS instrument has dimensions of 48.4 × 27.0 × 18.0 cm (l × w × h).

LIQUID CHROMATOGRAPHY-MASS SPECTROMETRY BOTTOM-UP PROTEOMIC METHODS IN ANIMAL SPECIES ANALYSIS OF PROCESSED MEAT FOR FOOD AUTHENTICATION AND THE DETECTION OF ADULTERATIONS

This review offers an overview of the current status and the most recent advances in liquid chromatography-mass spectrometry (LC-MS) techniques with both high-resolution and low-resolution tandem mass analyzers applied to the identification and detection of heat-stable species-specific peptide markers of meat in highly processed food products. We present sets of myofibrillar and sarcoplasmic proteins, which turned out to be the source of 105 heat-stable peptides, detectable in processed meat using LC-MS/MS. A list of heat-stable species-specific peptides was compiled for eleven types of white and red meat including chicken, duck, goose, turkey, pork, beef, lamb, rabbit, buffalo, deer, and horse meat, which can be used as markers for meat authentication. Among the 105 peptides, 57 were verified by multiple reaction monitoring, enabling identification of each species with high specificity and selectivity. The most described and monitored species by LC-MS/MS so far are chicken and pork with 26 confirmed heat-stable peptide markers for each meat. In thermally processed samples, myosin, myoglobin, hemoglobin, l-lactase dehydrogenase A and β-enolase are the main protein sources of heat-stable markers. © 2019 John Wiley & Sons Ltd. Mass Spec Rev.

Meat differentiation between pasture-fed and concentrate-fed sheep/goats by liquid chromatography quadrupole time-of-flight mass spectrometry combined with metabolomic and lipidomic profiling

Animal feeding method is a crucial factor in influencing meat quality. Consumers would preferentially select meat obtained from pasture-fed animals. In this study, an untargeted metabolomic and lipidomic method based on ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) combined with chemometric analysis was utilized to investigate the differences between meat from free-range and intensively-fed sheep/goats. Distinct separation between these two kinds of sheep/goats meat obtained were identified by principal component analysis. Analysis of variance, fold change and orthogonal projection to latent structures discriminant analysis were then conducted to determine specific potential markers. A total of 46 potential markers were selected according to online chemical databases. The support vector machine (SVM) method was used to process the responses of the selected potential markers, and the results of metabolomics and lipidomics from an additional 59 samples revealed the discrimination rate of 89.3% and 98.3%. These findings provided a basis for differentiation of meat from sheep/goats fed in the two methods.

Probabilistic topic modelling in food spoilage analysis: A case study with Atlantic salmon (Salmo salar)

Probabilistic topic modelling is frequently used in machine learning and statistical analysis for extracting latent information from complex datasets. Despite being closely associated with natural language processing and text mining, these methods possess several properties that make them particularly attractive in metabolomics applications where the applicability of traditional multivariate statistics tends to be limited. The aim of the study was thus to introduce probabilistic topic modelling - more specifically, Latent Dirichlet Allocation (LDA) - in a novel experimental context: volatilome-based (sea) food spoilage characterization. This was realized as a case study, focusing on modelling the spoilage of Atlantic salmon (Salmo salar) at 4 °C under different gaseous atmospheres (% CO₂/O₂/N₂): 0/0/100 (A), air (B), 60/0/40 (C) or 60/40/0 (D). First, an exploratory analysis was performed to optimize the model tunings and to consequently model salmon spoilage under 100% N₂ (A). Based on the obtained results, a systematic spoilage characterization protocol was established and used for identifying potential volatile spoilage indicators under all tested storage conditions. In conclusion, LDA could be used for extracting sets of underlying VOC profiles and identifying those signifying salmon spoilage, giving rise to an extensive discussion regarding the key points associated with model tuning and/or spoilage analysis. The identified compounds were well in accordance with a previously established approach based on partial least squares regression analysis (PLS). Overall, the outcomes of the study not only reflect the promising potential of LDA in spoilage characterization, but also provide several new insights into the development of data-driven methods for food quality analysis.