Nuclear magnetic resonance and chemometrics to assess geographical origin and quality of traditional food products

Myoglobin as a molecular biomarker for meat authentication and traceability

The global incidence of economically motivated meat adulteration represents a crucial issue for the food industry. Undeclared addition of cheaper or low-quality species to meat products of high commercial value has become a common practice that needs to be countered with specific measures. In this framework, myoglobin (Mb) is a sarcoplasmic haemoprotein, primarily responsible for meat colour and has been successfully used in meat fraud authentication. Mb is highly soluble in water, easily monitored at 409 nm and species-specific. Knowing that various analytical DNA-based and protein-based methods, as well as spectroscopic techniques have been developed over the years for the detection of meat fraud, the aim of the present review is to take stock of the situation regarding the possible use of Mb as a molecular biomarker for the easy and rapid detection of undeclared species in meat products, avoiding the need of sophisticated or expensive equipment and specialised operators.

Application and prospect of metabolomics-related technologies in food inspection

BACKGROUND

Food inspection covers a broad range of topics, including nutrient analysis, food pollutants, food auxiliary materials, additives, and food sensory identification. The foundation of diverse subjects like food science, nutrition, health research, and the food industry, as well as the desired reference for drafting trade and food legislation, makes food inspection highly significant. Because of their high efficiency, sensitivity, and accuracy, instrumental analysis methods have gradually replaced conventional analytical methods as the primary means of food hygiene inspection.

SCOPE AND APPROACH

Metabolomics-based analysis technology, such as nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and capillary electrophoresis-mass spectrometry (CE-MS), has become a widely used analytics platform. This research provides a bird's eye view of the application and future of metabolomics-related technologies in food inspection.

KEY FINDINGS AND CONCLUSIONS

We have provided a summary of the features and the application range of various metabolomics techniques, the strengths and weaknesses of different metabolomics platforms, and their implementation in specific inspection procedures. These procedures encompass the identification of endogenous metabolites, the detection of exogenous toxins and food additives, analysis of metabolite alterations during processing and storage, as well as the recognition of food adulteration. Despite the widespread utilization and significant contributions of metabolomics-based food inspection technologies, numerous challenges persist as the food industry advances and technology continues to improve. Thus, we anticipate addressing these potential issues in the future.

Universal drivers of cheese microbiomes

The culinary value, quality, and safety of cheese are largely driven by the resident bacteria, but comparative analyses of the cheese microbiota across cheese types are scarce. We present the first global synthesis of cheese microbiomes. Following a systematic literature review of cheese microbiology research, we collected 16S rRNA gene amplicon sequence data from 824 cheese samples spanning 58 cheese types and 16 countries. We found a consistent, positive relationship between microbiome richness and pH, and a higher microbial richness in cheeses derived from goat milk. In contrast, we found no relationship between pasteurization, geographic location, or salinity and richness. Milk and cheese type, geographic location, and pasteurization collectively explained 65% of the variation in microbial community composition. Importantly, we identified four universal cheese microbiome types, driven by distinct dominant taxa. Our study reveals notable diversity patterns among the cheese microbiota, which are driven by geography and local environmental variables.

Discrimination of Geographical Origin of Unroasted Kernels Argan Oil (Argania spinosa (L.) Skeels) Using Tocopherols and Chemometrics

Valorisation of Argan oil requires the precise identification of different provenances markers. The concentration of tocopherol is regarded as one of the essential parameters that certifies the quality and purity of Argan oil. In this study, 39 Argan samples from six different geographical origins (Safi, Essaouira, Agadir, Taroudant, Tiznit, and Sidi Ifni) from the central west of Morocco were collected and extracted using cold pressing. The total tocopherol amount was found to range from 783.23 to 1,271.68 mg/kg. Generally, γ-tocopherol has the highest concentration in Argan oil. It should also be noted that the geographical origin was found to have a strong effect on the amounts of all tocopherol homologues studied. Principal component analysis of tocopherol concentrations highlighted a significant difference between the different provenances. The content of tocopherol has also been found to be strongly influenced by the distance from the coast and altitude, whereas no significant effect was found regarding other ecological parameters. The prediction ability of the LDA models was 87.2%. The highest correct classification was revealed in coastal provenances (100%), and the lowest values were from the continental ones (71.4%). These results provide the basis for determining the geographical origins of Argan oil production with well-defined characteristics to increase the product’s value and the income of local populations. In addition, this study provides a very promising basis for developing Argan varieties with a high content of tocopherol homologues, as well as contributing to the traceability and protection of Argan oil’s geographical indication.

Rapid geographical indication of peppercorn seeds using corona discharge mass spectrometry

With increasing demands for more rapid and practical analyses, various techniques of ambient ionization mass spectrometry have gained significant interest due to the speed of analysis and abundance of information provided. Herein, an ambient ionization technique that utilizes corona discharge was applied, for the first time, to analyze and categorize whole seeds of black and white peppers from different origins. This setup requires no solvent application nor gas flow, thus resulting in a very simple and rapid analysis that can be applied directly to the sample without any prior workup or preparation. Combined with robust data pre-processing and subsequent chemometric analyses, this analytical method was capable of indicating the geographical origin of each pepper source with up to 98% accuracies in all sub-studies. The simplicity and speed of this approach open up the exciting opportunity for onsite analysis without the need for a highly trained operator. Furthermore, this methodology can be applied to a variety of spices and herbs, whose geographical indication or similar intellectual properties are economically important, hence it is capable of creating tremendous impact in the food and agricultural industries.

Combination of Stable Isotopes and Fatty Acid Composition for Geographical Origin Discrimination of One Argan Oil Vintage

Quality control and traceability of Argan oil requires precise chemical characterization considering different provenances. The fatty acid profile is an essential parameter that certifies the quality and purity of Argan oil. In addition, stable isotopes were recently shown to be accurate as an indicator for geographical origin. In this study, fatty acid composition by gas chromatography (GC) and stable isotope ratio by isotope ratio mass spectrometry (IRMS) were investigated for classifying Argan oil according to its geographical origin. Forty-one Argan oil samples, belonging to six geographical origins of Moroccan natural Argan population (Safi, Essaouira, Agadir Ida Outanane, Taroudant, Tiznit and Sidi Ifni) were collected and extracted under the same conditions. The results show that the isotope δ¹³C, palmitic acid (C16:0), linoleic acid (C18:2) and unsaturated fatty acids (UFA) were strongly influenced by ecological parameters. Linear discriminant analysis (LDA) was performed to discriminate the six studied provenances. Discriminant models predicted the origin of Argan oil with 92.70% success. Samples from Safi, Essaouira and Agadir Ida Outanane presented the highest classification rate (100%). In contrast, the lowest rate was reported for samples from Tiznit (85.70%). The findings obtained for fatty acids and isotope combination might be considered as an accurate tool for determining the geographical origins of Argan oil. Moreover, they can potentially be used as specific markers for oils labeled with Protected Geographical Indication (PGI).

Fatty-acid Profiles and Fingerprints of Seven Types of Fish Roes as Determined by Chemometric Methods

The fatty acids in seven species of fish roes were determined by GC-MS in combination with principal component and cluster analyses in order to derive their fatty-acid profiles and fingerprints. Twenty-three common chromatography peaks were identified in the fatty-acid fingerprints of the seven fish roes. A total of 19 typical fatty acids were identified in the fish roes studied. The fatty acid contents of the roes were significantly different, with saturated-fatty-acid contents in the seven roes ranging from 26.69% to 41.81%, and the unsaturated-fatty-acid contents ranging from 57.65% to 72.21%, the total EPA and DHA content (37.20%) is high in E. cypselurus roe, especially. The seven roe species were clearly distinguished according to fatty-acid composition and content by principal component analysis (PCA) and divided into two groups by cluster analysis (CA). PCA of the fatty acid data yielded three significant PCs , which together account for 94% of the total variance; with PC1 contributing 54% of the total.

Bioactivity and Bioavailability of the Major Metabolites of Crocus sativus L. Flower

Crocus sativus L. has been cultivated throughout history to obtain its flowers, whose dried stigmas give rise to the spice known as saffron. Crocetin esters, picrocrocin, and safranal are the main metabolites of this spice, which possess a great bioactivity, although the mechanisms of action and its bioavailability are still to be solved. The rest of the flower is composed by style, tepals, and stamens that have other compounds, such as kaempferol and delphinidin, which have an important antioxidant capacity, and these can be applied in foods, phytopharmaceuticals, and cosmetics. The aim of this work was to provide an updated and critical review of the research on the main compounds of Crocus sativus L. flower, including the adequate analytical methods for their identification and quantification, with a focus on their bioactivity and bioavailability.

Nontargeted Detection Methods for Food Safety and Integrity

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.

Fingerprint and authenticity roasted coffees by 1H-NMR: the Brazilian coffee case

With globalization, it has become necessary to adopt policies to regulate the coffee market, addressing problems including the authenticity and traceability of products. It is therefore important to establish methodologies that can help to safeguard the interests of producer countries and add value to products. For this purpose, the use of NMR combined with multivariate statistical procedures can be an attractive option. The aim of this study was to develop a fast and effective technique, using 1H NMR coupled with multivariate statistics, to create a fingerprint of roasted coffees, distinguishing them according to the main Brazilian producer regions. Several compounds suitable for differentiating roasted coffees were identified in the fingerprint. Discriminant analysis revealed good distinction among the samples. The compounds catechol, trigonelline, caffeine, and n-methylpyridine were most important for the differentiation. The findings should assist coffee-producing countries in adopting measures to protect their markets and to add value to coffee products.

Transfer of multivariate regression models between high-resolution NMR instruments: application to authenticity control of sunflower lecithin

In recent years the number of spectroscopic studies utilizing multivariate techniques and involving different laboratories has been dramatically increased. In this paper the protocol for calibration transfer of partial least square regression model between high-resolution nuclear magnetic resonance (NMR) spectrometers of different frequencies and equipped with different probes was established. As the test system previously published quantitative model to predict the concentration of blended soy species in sunflower lecithin was used. For multivariate modelling piecewise direct standardization (PDS), direct standardization, and hybrid calibration were employed. PDS showed the best performance for estimating lecithin falsification regarding its vegetable origin resulting in a significant decrease in root mean square error of prediction from 5.0 to 7.3% without standardization to 2.9-3.2% for PDS. Acceptable calibration transfer model was obtained by direct standardization, but this standardization approach introduces unfavourable noise to the spectral data. Hybrid calibration is least recommended for high-resolution NMR data. The sensitivity of instrument transfer methods with respect to the type of spectrometer, the number of samples and the subset selection was also discussed. The study showed the necessity of applying a proper standardization procedure in cases when multivariate model has to be applied to the spectra recorded on a secondary NMR spectrometer even with the same magnetic field strength. Copyright © 2016 John Wiley & Sons, Ltd.

Metabolomics as a Challenging Approach for Medicinal Chemistry and Personalized Medicine

"Omics" sciences have been developed to provide a holistic point of view of biology and to better understand the complexity of an organism as a whole. These systems biology approaches can be examined at different levels, starting from the most fundamental, i.e., the genome, and finishing with the most functional, i.e., the metabolome. Similar to how genomics is applied to the exploration of DNA, metabolomics is the qualitative and quantitative study of metabolites. This emerging field is clearly linked to genomics, transcriptomics, and proteomics. In addition, metabolomics provides a unique and direct vision of the functional outcome of an organism's activities that are required for it to survive, grow, and respond to internal and external stimuli or stress, e.g., pathologies and drugs. The links between metabolic changes, patient phenotype, physiological and/or pathological status, and treatment are now well established and have opened a new area for the application of metabolomics in the drug discovery process and in personalized medicine.

HR MAS 1H NMR and chemometrics as useful tool to assess the geographical origin of cocoa beans - Comparison with HR 1H NMR

Chocolate and cocoa-based products are among the goods with higher added value. A current trend of the cocoa market is to offer to the consumers high quality cocoa products, namely mono-origin cocoa. However, a reliable analytical method able to trace the geographical origin of cocoa is lacking. In this work we tested the capability of HR MAS ¹H NMR combined with chemometrics to assess the geographical origins of 60 fermented and dried cocoa beans of 23 different cocoa producing countries from the three major crop-growing areas (Africa, Central/South America, Asia/Oceania). Metabolic profiling was determined by HR MAS ¹H NMR directly on cocoa powder after the method optimization. The same samples were also subjected to extraction and analysis with HR ¹H NMR. HR MAS ¹H NMR, as ¹H NMR analysis, allowed the simultaneous detection of amino acids, polyalcohols, organic acids, sugars, methylxanthines, catechins. Moreover, HR MAS allows the detection of lipids, not present in the aqueous extract utilized for ¹H NMR. The data set obtained is therefore representative of all classes of cocoa compounds. Untargeted HR MAS ¹H NMR and ¹H NMR datasets were utilized as fingerprint of the samples and elaborated with multivariate statistical methods. A targeted quantitative approach of selected metabolites was possible only with HR ¹H NMR data, because HR MAS ¹H NMR does not give reliable quantitative results. All the approaches adopted showed a discrimination of the cocoa origins. HR MAS presents the advantages to obtain a very rapid picture of the samples, comprising both lipophilic and hydrophilic components, avoiding any sample manipulation.

On the Traceability of Commercial Saffron Samples Using ¹H-NMR and FT-IR Metabolomics

In previous works on authentic samples of saffron of known history (harvest and processing year, storage conditions, and length of time) some biomarkers were proposed using both FT-IR and NMR metabolomics regarding the shelf life of the product. This work addresses the difficulties to trace back the "age" of commercial saffron samples of unknown history, sets a limit value above which these products can be considered substandard, and offers a useful tool to combat saffron mislabeling and fraud with low-quality saffron material. Investigations of authentic and commercial saffron samples of different origin and harvest year, which had been stored under controlled conditions for different lengths of time, allowed a clear-cut clustering of samples in two groups according to the storage period irrespectively of the provenience. In this respect, the four-year cut off point proposed in our previous work assisted to trace back the "age" of unknown samples and to check for possible mislabeling practices.

An approach for simultaneous determination for geographical origins of Korean Panax ginseng by UPLC-QTOF/MS coupled with OPLS-DA models

Identification of the origins of Panax ginseng has been issued in Korea scientifically and economically. We describe a metabolomics approach used for discrimination and prediction of ginseng roots from different origins in Korea. The fresh ginseng roots from six ginseng cooperative associations (Gangwon, Gaeseong, Punggi, Chungbuk, Jeonbuk, and Anseong) were analyzed by UPLC-MS-based approach combined with orthogonal projections to latent structure-discriminant analysis multivariate analysis. The ginsengs from Gangwon and Gaeseong were easily differentiated. We further analyzed the metabolomics results in subgroups. Punggi, Chungbuk, Jeonbuk, and Anseong ginseng could be easily differentiated by the first two orthogonal components. As a validation of the discrimination model, we performed blind prediction tests of sample origins using an external test set. Our model predicted their geographical origins as 99.7% probability. The robust discriminatory power and statistical validity of our method suggest its general applicability for determining the origins of P. ginseng samples.

¹H NMR and multivariate data analysis of the relationship between the age and quality of duck meat

To contribute to a better understanding of the factors affecting meat quality, we investigated the influence of age on the chemical composition of duck meat. Aging probably affects the quality of meat through changes in metabolism. Therefore, we studied the metabolic composition of duck meat using (1)H nuclear magnetic resonance (NMR) spectroscopy. Comprehensive multivariate data analysis showed significant differences between extracts from ducks that had been aged for four different time periods. Although lactate and anserine increased with age, fumarate, betaine, taurine, inosine and alkyl-substituted free amino acids decreased. These results contribute to a better understanding of changes in duck meat metabolism as meat ages, which could be used to help assess the quality of duck meat as a food.

NMR based geographical characterization of roasted coffee

The increasing attention for food quality and safety led to develop several analytical techniques suitable to address these demands. Coffee has been already demonstrated to represent a worldwide appreciated beverage and its high economical value could induce frauds or adulteration practices involving both compositional and geographical aspects. In the last years, metabolic profiling revealed to be suitable to face the quality determination of food matrices and NMR confirmed its potentiality in metabolites characterization. The present study reports the capability of NMR spectroscopy to investigate the metabolite content of roasted Coffea arabica samples from the three main production areas, America, Africa and Asia. OPLS-DA models performed on (1)H NMR data led to a clear separation of samples according to their origin: fatty acids, chlorogenic acids and lactate and finally acetate and trigonelline resulted the main compounds characterizing the American, African and Asian samples respectively. The analytical approach here presented confirmed the potentiality of the joined NMR analysis and statistical treatments in quality determination of food matrices.

Discrimination of the geographical origin of beef by (1)H NMR-based metabolomics

The geographical origin of beef is of increasing interest to consumers and producers due to "mad cow" disease and the implementation of the Free Trade Agreement (FTA). In this study, (1)H NMR spectroscopy coupled with multivariate statistical analyses was used to differentiate the geographical origin of beef samples. Principal component analysis (PCA) and orthogonal projection to latent structure-discriminant analysis (OPLS-DA) showed significant separation between extracts of beef originating from four countries: Australia, Korea, New Zealand, and the United States. The major metabolites responsible for differentiation in OPLS-DA loading plots were succinate and various amino acids including isoleucine, leucine, methionine, tyrosine, and valine. A one-way ANOVA was performed to statistically certify the difference in metabolite levels. The data suggest that NMR-based metabolomics is an efficient method to distinguish fingerprinting difference between raw beef samples, and several metabolites including various amino acids and succinate can be possible biomarkers for discriminating the geographical origin of beef.

Evaluation of the production year in Italian and Chinese tomato paste for geographical determination using O2PLS models

Nuclear magnetic resonance (NMR) is nowadays largely used as valid tool in metabolomic applications. In this study, the metabolite content of Italian and Chinese tomato paste at different concentration rates of two production years (2007 and 2008) was investigated with the aim of building a robust geographical differentiation statistical model. A total of 119 tomato paste samples were analyzed by (1)H NMR and multivariate data analysis tools, in particular using bidirectional orthogonal projection to latent structures-discriminant analysis (O2PLS-DA). This technique is well-suited for noisy and correlated variables and was recently adopted to obtain robust classification models, having a clear interpretation of the systematic variation useful to characterize each class. In the present study, the analysis of latent space underlying the classification model allowed us to understand the role played by the production year on geographical discrimination. The O2PLS-DA model performed considering only tomato paste samples of 2007 was capable of predicting the geographical origin of all analyzed samples. The effect of the production year therefore resulted in not affecting the geographical origin discrimination.