Tracing the geographical origin of food: The application of multi-element and multi-isotope analysis

Decoding of the isotopic fingerprint of tequila 100% agave silver class and image analysis to evaluate differences between spirits

This communication shows the decoding of Isotopic Fingerprint of Tequila 100% agave silver class (IFTequila100% agave) in three areas corresponding to isotopic variations due to: plant used as raw material, fermentation and distillation process, and hydrolysis process. Isotopic tracers that make them up correspond to the δ13CVPDB ethanol-δ13CVPDB ethyl acetate-δ13CVPDB isoamyl alcohol, δ13CVPDB ethyl acetate-δ13CVPDB isoamyl alcohol-δ13CVPDB n-propanol and δ13CVPDB ethyl acetate-δ13CVPDB n-propanol-δ13CVPDB methanol, respectively. Once the IFTequila100%agave has been decoded, an image comparison was performed against isotopic fingerprints of spirits (Tequila, Bacanora, Raicilla, Sotol, and Mezcal). Results show that it is possible classifies 100% of samples analyzed. Likewise, from decoding it is possible to determine the critical process stage to determine variations with respect to the IFTequila100%agave. The chemometric analysis developed corresponds to an auxiliary analytical tool useful for the inspection processes currently carried out by the authorities to determine the authenticity of the beverage.

Development of metabolomic biomarkers to discriminate the geographical origin of Korean and Russian snow crabs using CE-TOF/MS

The quantity of snow crabs (Chionoecetes opilio) harvested in Korea is subject to seasonal restrictions; therefore, snow crabs are imported from Russia. Metabolites in snow crabs from two geographic origins were compared. The metabolites were subjected to metabolomic analysis to prevent fraudulent sales of marine products from a particular country. Capillary electrophoresis time-of-flight mass spectrometry was used. Seventy-seven target metabolites were identified using a mass spectral library. Through orthogonal partial least squares discriminant analysis, the top 25 biomarker candidates were evaluated based on p-values and fold changes. A total of 246 peaks (187 and 59 in the cation and anion modes, respectively) were identified. Among the biomarker candidates, 2-oxovaleric acid, asymmetric dimethylarginine, hypotaurine, and allo-threonine were selected as final biomarkers to unequivocally determine the geographic origin. Overall, metabolic analyses allowed us to differentiate snow crabs from different geographic origins. This method could also be extended of other marine products.

Investigating isotopic markers for hazelnut geographical authentication: Promising variables and potential applications

Hazelnuts' features and price are influenced by their geographical origin, making them susceptible to fraud, especially counterfeit claims regarding their provenance. Stable isotope analysis is a recognised approach to establish the geographical origin of foods, yet its potential in hazelnut authentication remains unexplored. In this prospective study, we assessed multiple isotopic markers in hazelnuts from different origins and evaluated the most promising variables for geographical authentication by chemometric tools. Our findings indicate that bulk δ18O, along with δ2H and δ13C in the main fatty acid methyl esters, exhibit significant potential in discriminating geographical origins, and 87Sr/86Sr analysis could serve as a proficient confirmatory tool. Though no single marker alone can differentiate between all the studied origins, employing a multi-isotopic approach based on PLS-DA models achieved up to 92.5 % accuracy in leave-10 %-out cross-validation. These findings will probably lay the groundwork for developing robust models for hazelnut geographical authentication based on larger datasets.

Elemental Profiling for the Detection of Food Mixtures: A Proof of Principle Study on the Detection of Mixed Walnut Origins Using Measured and Calculated Data

Element profiling is a powerful tool for detecting fraud related to claims of geographical origin. However, these methods must be continuously developed, as mixtures of different origins in particular offer great potential for adulteration. This study is a proof of principle to determine whether elemental profiling is suitable for detecting mixtures of the same food but from different origins and whether calculated data from walnut mixtures could help to reduce the measurement burden. The calculated data used in this study were generated based on measurements of authentic, unadulterated samples. Five different classification models and three regression models were applied in five different evaluation approaches to detect adulteration or even distinguish between adulteration levels (10% to 90%). To validate the method, 270 mixtures of walnuts from different origins were analyzed using inductively coupled plasma mass spectrometry (ICP-MS). Depending on the evaluation approach, different characteristics were observed in mixtures when comparing the calculated and measured data. Based on the measured data, it was possible to detect admixtures with an accuracy of 100%, even at low levels of adulteration (20%), depending on the country. However, calculated data can only contribute to the detection of adulterated walnut samples in exceptional cases.

Assessing Vodka Authenticity and Origin in Vietnam's Market: An Analytical Approach Using FTIR and ICP-MS with Multivariate Statistics

Vodka constitutes a significant sector of Vietnam's alcohol industry, including both domestic and imported varieties. However, this diversity faces challenges from illegal imports and adulterated products, threatening consumer health and brand integrity. This study employs Fourier transform infrared spectroscopy (FTIR) and inductively coupled plasma mass spectrometry (ICP-MS) to analyze 300 vodka samples from five brands collected across Hanoi. Significant variations were found in elemental compositions, with sodium concentrations ranging from 205.67 μg/L to 1269.24 μg/L and magnesium levels from 65.57 μg/L to 1453.34 μg/L. Principal Component Analysis (PCA) of the FTIR and ICP-MS data effectively differentiated the samples, with the first two principal components explaining 84.78% and 73.02% of the total variance, respectively. The PCA plots revealed distinct chemical profiles, notably isolating Rocket Vodka. These findings enhance food safety enforcement, protect consumer rights, and preserve brand reputations. The study underscores the importance of advanced analytical tools in combating beverage adulteration, ensuring public health, and maintaining market integrity, offering a replicable model for similar research in other regions.

Stable Isotope Ratio Analysis for the Geographic Origin Discrimination of Greek Beans "Gigantes-Elefantes" (Phaseolus coccineus L.)

Adulteration of high-value agricultural products is a critical issue worldwide for consumers and industries. Discrimination of the geographical origin can verify food authenticity by reducing risk and detecting adulteration. Between agricultural products, beans are a very important crop cultivated worldwide that provides food rich in iron and vitamins, especially for people in third-world countries. The aim of this study is the construction of a map of the locally characteristic isotopic fingerprint of giant beans, "Fasolia Gigantes-Elefantes PGI", a Protected Geographical Indication product cultivated in the region of Kastoria and Prespes, Western Macedonia, Greece, with the ultimate goal of the discrimination of beans from the two areas. In total, 160 samples were collected from different fields in the Prespes region and 120 samples from Kastoria during each cultivation period (2020-2021 and 2021-2022). The light element (C, N, and S) isotope ratios were measured using Isotope Ratio Mass Spectrometry (IRMS), and the results obtained were analyzed using chemometric techniques, including a one-way ANOVA and Binomial logistic regression. The mean values from the one-way ANOVA were δ15NAIR = 1.875‱, δ13CV-PDB = -25.483‱, and δ34SV-CDT = 4.779‱ for Kastoria and δ15NAIR = 1.654‱, δ13CV-PDB = -25.928‱, and δ34SV-CDT = -0.174‱ for Prespes, and showed that stable isotope ratios of C and S were statistically different for the areas studied while the Binomial logistic regression analysis that followed correctly classified more than 78% of the samples.

Evaluation of antibiotic residues in honey: a systematic review and meta-analysis

Nowadays, incorrect apply of antibiotics to treat infections in honey has led to health risks for humans and antibiotic resistance. Current systematic review and meta-analysis conducted to study antibiotic residues in honey. Data were obtained through searching the databases, including Scopus, Web of Science, PubMed, and other internal databases. The pooled concentration of antibiotic residues was 5.032 (µg/kg) that ranged from 4.72 to 5.33 (µg/kg). The ranking of antibiotics concentration was found in order of fluoroquinolone (8.59 µg/kg) > tetracycline (5.68 µg/kg) > sulfonamides (5.54 µg/kg) > macrolides (4.19µg/kg), respectively. Liquid chromatography-mass spectrometry (LC-MS) method (37.9.7%), high-performance liquid chromatography (HPLC) method (34.4%), and enzyme-linked immunosorbent assay (ELISA) method (27.5.8%) were the most used methods in various studies. In order to avoid contamination, proper use of antibiotics, placement of hives at a suitable distance from agricultural environment, and regular control of antibiotic residues in honey seems to be necessary.

Support vector regression for prediction of stable isotopes and trace elements using hyperspectral imaging on coffee for origin verification

The potential of using rapid and non-destructive near-infrared - hyperspectral imaging (HSI-NIR) for the prediction of an integrated stable isotope and multi-element dataset was explored for the first time with the help of support vector regression. Speciality green coffee beans sourced from three continents, eight countries, and 22 regions were analysed using a push-broom HSI-NIR (700-1700 nm), together with five isotope ratios (δ13C, δ15N, δ18O, δ2H, and δ34S) and 41 trace elements. Support vector regression with the radial basis function kernel was conducted using X as the HSI-NIR data and Y as the geochemistry markers. Model performance was evaluated using root mean squared error, coefficient of determination, and mean absolute error. Three isotope ratios (δ18O, δ2H, and δ34S) and eight elements (Zn, Mn, Ni, Mo, Cs, Co, Cd, and La) had an R2predicted 0.70 - 0.99 across all origin scales (continent, country, region). All five isotope ratios were well predicted at the country and regional levels. The wavelength regions contributing the most towards each prediction model were highlighted, including a discussion of the correlations across all geochemical parameters. This study demonstrates the feasibility of using HSI-NIR as a rapid and non-destructive method to estimate traditional geochemistry parameters, some of which are origin-discriminating variables related to altitude, temperature, and rainfall differences across origins.

A robust interpolation-based method for forensic soil provenancing: A Bayesian likelihood ratio approach

Soil is a complex and spatially variable material that has a demonstrated potential as a useful evidence class in forensic casework and intelligence operations. Here, the capability to spatially constrain police search areas and prioritise resources by triaging areas as low and high interest is advantageous. Conducted between 2017 and 2021, a forensically relevant topsoil survey (0-5 cm depth; 1 sample per 1 km2) was carried out over Canberra, Australia, aiming to document the distribution of chemical elements in an urban/suburban environment, and of acting as a testbed for investigating various aspects of forensic soil provenancing. Geochemical data from X-Ray Fluorescence (XRF; for total major oxides) and Inductively Coupled Plasma-Mass Spectrometry (ICP-MS; for trace elements) following a total digestion (HF + HNO3) of the fused XRF beads were obtained from the survey's 685 topsoil samples (plus 138 additional quality control samples and six "Blind" simulated evidentiary samples). Using those "Blind" samples, we document a likelihood ratio approach where for each grid cell the analytical similarity between the grid cell and evidentiary sample is attributed from a measure of overlap between the two Cauchy distributions, including appropriate uncertainties. Unlike existing methods that base inclusion/exclusion on an arbitrary threshold (e.g., ± three standard deviations), our approach is free from strict binary or Boolean thresholds, providing an unconstrained gradual transition dictated by the analytical similarity. Using this provenancing model, we present and evaluate a new method for upscaling from a fine (25 m x 25 m) interpolated grid to a more appropriate coarser (500 m x 500 m) grid. In addition, an objective method using Random Match Probabilities for ranking individual variables to be used for provenancing prior to receiving evidentiary material was demonstrated. Our results show this collective procedure generates more consistent and robust provenance maps when applied to two different interpolation algorithms (e.g., inverse distance weighting, and natural neighbour), with different grid placements (e.g., grid shifts to the north or east) and by different theoretical users (e.g., different computer systems, or forensic geoscientists).

Influence of Geographical Origin on Isotopic and Elemental Compositions of Pork Meat

Pigs are a primary source of meat, accounting for over 30% of global consumption. Consumers' preferences are determined by health considerations, paying more attention to foodstuffs quality, animal welfare, place of origin, and swine feeding regime, and being willing to pay a higher price for a product from a certain geographical region. In this study, the isotopic fingerprints (δ2H, δ18O, and δ13C) and 29 elements of loin pork meat samples were corroborated with chemometric methods to obtain the most important variables that could classify the samples' geographical origin. δ2H and δ18O values ranged from -71.0 to -21.2‱, and from -9.3 to -2.8‱, respectively. The contents of macro- and micro-essential elements are presented in the following order: K > Na > Mg > Ca > Zn > Fe > Cu > Cr. The LDA model assigned in the initial classification showed 91.4% separation of samples, while for the cross-validation procedure, a percentage of 90% was obtained. δ2H, K, Rb, and Pd were identified as the most representative parameters to differentiate the pork meat samples coming from Romania vs. those from abroad. The mean values of metal concentrations were used to estimate the potential health risks associated with the consumption of pork meat The results showed that none of the analyzed metals (As, Cd, Sn, Pb, Cu, and Zn) pose a carcinogenic risk.

Towards Verifying the Imported Soybeans of China Using Stable Isotope and Elemental Analysis Coupled with Chemometrics

Verifying the geographical origin of soybeans (Glycine max [Linn.] Merr.) is a major challenge as there is little available information regarding non-parametric statistical origin approaches for Chinese domestic and imported soybeans. Commercially procured soybean samples from China (n = 33) and soybeans imported from Brazil (n = 90), the United States of America (n = 6), and Argentina (n = 27) were collected to characterize different producing origins using stable isotopes (δ2H, δ18O, δ15N, δ13C, and δ34S), non-metallic element content (% N, % C, and % S), and 23 mineral elements. Chemometric techniques such as principal component analysis (PCA), linear discriminant analysis (LDA), and BP-artificial neural network (BP-ANN) were applied to classify each origin profile. The feasibility of stable isotopes and elemental analysis combined with chemometrics as a discrimination tool to determine the geographical origin of soybeans was evaluated, and origin traceability models were developed. A PCA model indicated that origin discriminant separation was possible between the four soybean origins. Soybean mineral element content was found to be more indicative of origin than stable isotopes or non-metallic element contents. A comparison of two chemometric discriminant models, LDA and BP-ANN, showed both achieved an overall accuracy of 100% for testing and training sets when using a combined isotope and elemental approach. Our findings elucidate the importance of a combined approach in developing a reliable origin labeling method for domestic and imported soybeans in China.

Bulk and Compound-Specific Stable Isotope Analysis for the Authentication of Walnuts (Juglans regia) Origins

Walnuts are grown in various countries, and as product origin information is becoming more important to consumers, new techniques to differentiate walnut geographical authenticity are needed. We conducted bulk stable isotope analysis (BSIA) and compound-specific stable isotope analysis (CSIA) on walnuts grown in seven countries. The BSIA consisted of δ13Cbulk, δ15Nbulk, and δ34Sbulk, and CSIA covered δ2Hfatty acid, δ13Cfatty acid, δ13Camino acid, δ15Namino acid, and δ2Hamino acid. Analysis of variance (ANOVA) and linear discriminant analysis (LDA) were used for statistical analysis to compare samples from the USA and China. Parameters that yielded significant variations are δ2HC18:1n-9, δ13CC18:2n-6, δ13CC18:3n-3, δ13CGly, δ13CLeu, δ13CVal, δ2HGlu, δ2HIle, δ2HLeu, and δ2HThr. Our findings suggested that CSIA of fatty acids and amino acids can be useful to differentiate the geographical provenance of walnuts.

A review of recent compound-specific isotope analysis studies applied to food authentication

Compound-specific stable isotope analysis (CSIA) of food products is a relatively new and novel technique used to authenticate food and detect adulteration. This paper provides a review of recent on-line and off-line CSIA applications of plant and animal origin foods, essential oils and plant extracts. Different food discrimination techniques, applications, scope, and recent studies are discussed. CSIA δ13C values are widely used to verify geographical origin, organic production, and adulteration. The δ15N values of individual amino acids and nitrate fertilizers have proven effective to authenticate organic foods, while δ2H and δ18O values are useful to link food products with local precipitation for geographical origin verification. Most CSIA techniques focus on fatty acids, amino acids, monosaccharides, disaccharides, organic acids, and volatile compounds enabling more selective and detailed origin and authentication information than bulk isotope analyses.. In conclusion, CSIA has a stronger analytical advantage for the authentication of food compared to bulk stable isotope analysis, especially for honey, beverages, essential oils, and processed foods.

Stable isotope and trace element analyses with non-linear machine-learning data analysis improved coffee origin classification and marker selection

BACKGROUND

This study investigated the geographical origin classification of green coffee beans from continental to country and regional levels. An innovative approach combined stable isotope and trace element analyses with non-linear machine learning data analysis to improve coffee origin classification and marker selection. Specialty green coffee beans sourced from three continents, eight countries, and 22 regions were analyzed by measuring five isotope ratios (δ¹³ C, δ¹⁵ N, δ¹⁸ O, δ² H, and δ³⁴ S) and 41 trace elements. Partial least squares discriminant analysis (PLS-DA) was applied to the integrated dataset for origin classification.

RESULTS

Origins were predicted well at the country level and showed promise at the regional level, with discriminating marker selection at all levels. However, PLS-DA predicted origin poorly at the continental and Central American regional levels. Non-linear machine learning techniques improved predictions and enabled the identification of a higher number of origin markers, and those that were identified were more relevant. The best predictive accuracy was found using ensemble decision trees, random forest and extreme gradient boost, with accuracies of up to 0.94 and 0.89 for continental and Central American regional models, respectively.

CONCLUSION

The potential for advanced machine learning models to improve origin classification and the identification of relevant origin markers was demonstrated. The decision-tree-based models were superior with their embedded variable identification features and visual interpretation. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Near-infrared reflectance spectroscopy accurately predicted isotope and elemental compositions for origin traceability of coffee

Stable isotope ratios and trace elements are well-established tools that act as signatures of the product's environmental conditions and agricultural processes; but they involve time, money, and environmentally destructive chemicals. In this study, we tested for the first time the potential of near-infrared reflectance spectroscopy (NIR) to estimate/predict isotope and elemental compositions for the origin verification of coffee. Green coffee samples from two continents, 4 countries, and 10 regions were analysed for five isotope ratios (δ¹³C, δ¹⁵N, δ¹⁸O, δ²H, and δ³⁴S) and 41 trace elements. NIR (1100-2400 nm) calibrations were developed using pre-processing with extended multiplicative scatter correction (EMSC) and mean centering and partial-least squares regression (PLS-R). Five elements (Mn, Mo, Rb, B, La) and three isotope ratios (δ¹³C, δ¹⁸O, δ²H) were moderately to well predicted by NIR (R²: 0.69 to 0.93). NIR indirectly measured these parameters by association with organic compounds in coffee. These parameters were related to altitude, temperature and rainfall differences across countries and regions and were previously found to be origin discriminators for coffee.

Cost-effective colorimetric sensor for authentication of protected designation of origin (PDO) Longjing green tea

Traceability and authentication of protected designation of origin (PDO) tea is an important prerequisite to safeguard its production and distribution system. Here, indicator displacement array (IDA) sensors consisting of natural anthocyanidins and edible metal ions were developed to authenticate PDO and non-PDO Longjing from different origins. Five IDA elements were selected for constructing sensors, achieved by an indicator displacement reaction after adding epigallocatechin gallate solution. The obtained sensors were subsequently used for real tea samples. Unsupervised algorithms were used for data exploration among PDO and non-PDO teas. The supervised support vector machine (SVM) model further achieved accurate authentication of PDO and non-PDO Longjing with a correct classification rate of 100% for the 26 validated samples. The developed IDA sensor thus achieves accurate authentication of PDO tea in a hazard-free and cost-efficient way, providing a useful tool for origin authentication of other agricultural products.

Traceability of Croatian extra virgin olive oils to the provenance soils by multielement and carbon isotope composition and chemometrics

A capacity to determine the provenance of high-value food products is of high scientific and economic interest. With the aim to develop a tool for geographical traceability of Croatian extra virgin olive oils (EVOO), multielement composition and ¹³C/¹²C isotope ratio in EVOO as well as the geochemistry of the associated soils were analysed in samples collected from three regions along the Croatian Adriatic coast. Soil geochemistry was shown to influence the transfer and elemental composition of EVOO. The most discriminating variables to distinguish EVOO from different regions were S, Mo, Rb, Mg, Pb, Mn, Sn, K, V and δ¹³C. The predictive models achieved high sensitivity and specificity, especially when carbon isotope composition was added. The results suggest that interregional geographical traceability of Croatian EVOO is possible based on matching their multielement composition with that of the soils in the provenance area.

Microcavity- and Microlaser-Based Optical Barcoding: A Review of Encoding Techniques and Applications

Optical microbarcodes have recently received a great deal of interest because of their suitability for a wide range of applications, such as multiplexed assays, cell tagging and tracking, anticounterfeiting, and product labeling. Spectral barcodes are especially promising because they are robust and have a simple readout. In addition, microcavity- and microlaser-based barcodes have very narrow spectra and therefore have the potential to generate millions of unique barcodes. This review begins with a discussion of the different types of barcodes and then focuses specifically on microcavity-based barcodes. While almost any kind of optical microcavity can be used for barcoding, currently whispering-gallery microcavities (in the form of spheres and disks), nanowire lasers, Fabry-Pérot lasers, random lasers, and distributed feedback lasers are the most frequently employed for this purpose. In microcavity-based barcodes, the information is encoded in various ways in the properties of the emitted light, most frequently in the spectrum. The barcode is dependent on the properties of the microcavity, such as the size, shape, and the gain materials. Various applications of these barcodes, including cell tracking, anticounterfeiting, and product labeling are described. Finally, the future prospects for microcavity- and microlaser-based barcodes are discussed.

Towards a Standardized Approach for the Geographical Traceability of Plant Foods Using Inductively Coupled Plasma Mass Spectrometry (ICP-MS) and Principal Component Analysis (PCA)

This paper presents a systematic literature review focused on the use of inductively coupled plasma mass spectrometry (ICP-MS) combined with PCA, a multivariate technique, for determining the geographical origin of plant foods. Recent studies selected and applied the ICP-MS analytical method and PCA in plant food geographical traceability. The collected results from many previous studies indicate that ICP-MS with PCA is a useful tool and is widely used for authenticating and certifying the geographic origin of plant food. The review encourages scientists and managers to discuss the possibility of introducing an international standard for plant food traceability using ICP-MS combined with PCA. The use of a standard method will reduce the time and cost of analysis and improve the efficiency of trade and circulation of goods. Furthermore, the main steps needed to establish the standard for this traceability method are reported, including the development of guidelines and quality control measures, which play a pivotal role in providing authentic product information through each stage of production, processing, and distribution for consumers and authority agencies. This might be the basis for establishing the standards for examination and controlling the quality of foods in the markets, ensuring safety for consumers.

Application of Stable Isotope Analysis for Detecting the Geographical Origin of the Greek Currants "Vostizza": A Preliminary Study

There is a plethora of food products with geographical indications registered in the European Union without any study about their discrimination from other similar products. This is also the case for Greek currants. This paper aims to analyze if stable isotope analysis of C, N, and S could discriminate the Greek currants "Vositzza", registered as a product of Protected Designation of Origin, from two other currants registered as products of Protected Geographical Indication coming from neighboring areas. The first results show that the stable isotope ratio of sulfur is not detectable due to the very low sulfur content in the samples, and the analysis should be based on the stable isotope ratios of carbon and nitrogen to discriminate these products. The mean value of δ¹⁵N (1.38‱) of PDO "Vostizza" currants is lower than that of currants grown outside the PDO zone (2.01‱), while the mean value of δ¹³C of PDO "Vostizza" currants is higher (-23.93‱) in comparison to that of currants grown outside the PDO zone (-24.83‱). Nevertheless, the results indicate that with only two isotopic ratios, discrimination could not be achieved, and further analysis is required.

Incidence of foliar treatments and geographical origin on the geochemical fingerprints of leaves and fruits in olive growing

Recently, food quality and safety has become of great interest, with a consequent demand for geographical identification of agri-food products and eco-friendly agricultural practices. In this study geochemical analyses of soils, leaves and olives from two areas in the Emilia-Romagna Region (Italy), Montiano and San Lazzaro were performed aiming at identifying geochemical fingerprints able to (1) univocally determine the locality of provenance and (2) the effect of different foliar treatments (control, dimethoate, and alternating of natural zeolitite and dimethoate in MN; Spinosad + Spyntor fly, natural zeolitite and NH₄⁺-enriched zeolitite in SL). PCA and PLS-DA (including VIP analysis) were used to discriminate between localities and different treatments. Bioaccumulation and Translocation Coefficients (BA and TC) were studied to evaluate differences in the uptake of trace elements by plants. The PCA performed on soil data highlighted a total variance of 88.81%, allowing a good distinction between the two sites. Leaves and olives PCA showed that using trace elements it is possible to discriminate different foliar treatments (total variance: 95.64% and 91.08% in MN; 71.31% and 85.33% in SL of leaves and olives, respectively) better than the identification of their geographical origin (87.46% of leaves and 83.50% of total variance of olives). PLS-DA of all samples gave the largest contribution to the discrimination of different treatments and geographical identification. Among all elements, only Lu and Hf were able to correlate soil, leaf, and olive for geographical identification through VIP analyses, but also Rb and Sr were significant in the plant uptake (BA and TC). For the discrimination of different foliar treatments, Sm and Dy were identified in MN site, whereas Rb, Zr, La and Th correlated leaves and olives from SL. Based on trace element analyses, it can be put forward that (1) the geographical origin could be discriminated and (2) different foliar treatments applied for crop protection can be recognized, which means, reversing the reasoning that each farmer can develop a method to pinpoint his own product.

Elemental and spectral chemometric analyses of Octopus vulgaris beaks as reliable markers of capture location

The high demand and economic relevance of cephalopods make them prone to food fraud, including related to harvest location. Therefore, there is a growing need to develop tools to unequivocally confirm their capture location. Cephalopod beaks are nonedible, making this material ideal for traceability studies as it can also be removed without a loss of commodity economic value. Within this context, common octopus (Octopus vulgaris) specimens were captured in five fishing areas along the Portuguese coast. Untargeted multi-elemental total X-ray fluorescence analysis of the octopus beaks revealed a high abundance of Ca, Cl, K, Na, S, and P, concomitant with the keratin and calcium phosphate nature of the material. We tested a suite of discrimination models on both elemental and spectral data, where the elements contributing most to discriminate capture location were typically associated with diet (As), human-related pressures (Zn, Se, and Mn), or geological features (P, S, Mn, and Zn). Among the six different chemometrics approaches used to classify individuals to their capture location according to their beaks' element concentration, classification trees attained a classification accuracy of 76.7%, whilst reducing the number of explanatory variables for sample classification and highlighting variable importance for group discrimination. However, using X-ray spectral features of the octopus beaks further improved classification accuracy, with the highest classification of 87.3% found with partial least-squares discriminant analysis. Ultimately, element and spectral analyses of nonedible structures such as octopus beaks can provide an important, complementary, and easily accessible means to support seafood provenance and traceability, whilst integrating anthropogenic and/or geological gradients.

Determining the Authenticity of Spirulina Dietary Supplements Based on Stable Isotope and Elemental Composition

While the demand for Spirulina dietary supplements continues to grow, product inspection in terms of authenticity and safety remains limited. This study used the stable isotope ratios of light elements (C, N, S, H, and O) and the elemental composition to characterize Spirulina dietary supplements available on the Slovenian market. Forty-six samples were labelled as originating from the EU (1), non-EU (6), Hawaii (2), Italy (2), Japan (1), Portugal (2), Taiwan (3), India (4), and China (16), and nine products were without a declared origin. Stable isotope ratio median values were -23.9‱ (-26.0 to -21.8‱) for δ¹³C, 4.80‱ (1.30-8.02‱) for δ¹⁵N, 11.0‱ (6.79-12.7‱) for δ³⁴S, -173‱ (- 190 to -158‱) for δ²H, and 17.2‱ (15.8-18.8‱) for δ¹⁸O. Multivariate statistical analyses achieved a reliable differentiation of Hawaiian, Italian, and Portuguese (100%) samples and a good separation of Chinese samples, while the separation of Indian and Taiwanese samples was less successful, but still notable. The study showed that differences in isotopic and elemental composition are indicative of sample origins, cultivation and processing methods, and environmental conditions such that, when combined, they provide a promising tool for determining the authenticity of Spirulina products.

The intra-laboratory study of EIM-IRMS® (Ethanol Isotope Measurement–Isotope Ratio Mass Spectrometry) method performance with CS-qNMR (Cumulative Screening–quantitative NMR)

In our earlier publications we proposed the possibility of using new EIM–IRMS® instrumental technique and analytical parameter δDn wine ethanol, which represents δ2H value of non-exchangeable hydrogen stable isotope ratio in ethanol, to improve detection of illegal wine production practices such as addition of sugar and/or water to grape must prior to alcoholic fermentation. This article shows a comparative performance study between EIM-IRMS® and CS-qNMR methods in order to evaluate potential and demonstrate the precision of two isotopic concepts and their degree of detection of illegal wine production practices. Three batches of laboratory wine samples were prepared from three different types of grapes (N=24 samples). Sample results obtained from both methods are compared and a statistical data processing was carried out. Based on the results obtained using the EIM-IRMS® method we detected 11 out of 21 of previously adulterated wine samples which represents a success rate of 52.4% detection of illegal practices (beet sugar, water or beet sugar syrup additions). In addition, wines from the Republic of Serbia (90 samples) were examined as well using the EIM-IRMS® method and appropriate equipment to confirm their authenticity.

Isotope Geochemistry for Seafood Traceability and Authentication: The Northern Adriatic Manila Clams Case Study

In Italy, the production of manila clams (Ruditapes philippinarum, Adams and Reeve, 1850) is mainly localized in northern Adriatic lagoons in the Po River delta, where shellfish farming provides important socio-economic revenue. However, in our globalized world, the seafood market is threated by fraudulent activities, in which agri-food products whose provenance is not certified are sold, posing a risk to consumer health. Multi-isotope ratio analysis is commonly used to trace the provenance of goods produced in different countries with different climatic and environmental conditions. Here, we investigated the reliability of this approach in terms of tracing the exact provenance of manila clams harvested in three Adriatic northern lagoons that are close to each other. We also verified the origin of samples bought at a local supermarket with a certificate of provenance. We carried out elemental analyses of carbon (C), nitrogen (N), and sulfur (S) and the respective isotopic ratios (¹³C/¹²C; ¹⁵N/¹⁴N; ³⁴S/³²S) on manila clam tissues, plus isotopic analyses of carbon (¹³C/¹²C), oxygen (¹⁸O/¹⁶O), and strontium (⁸⁷Sr/⁸⁶Sr) on manila clam shells. Each isotopic parameter can be used to identify the marine and continental contributions of water and/or nutrient supplies occurring in the lagoons. Therefore, the combination of isotopic parameters in a linear discriminant analysis (LDA) allowed for the identification of the lagoons in which the manila clams were produced.

Stable Isotope Analysis for the Discrimination of the Geographical Origin of Greek Bottarga ‘Avgotaracho Messolongiou’: A Preliminary Research

Consumers are increasingly interested in the geographical origin of the foodstuff they consume as an important characteristic of food authenticity and quality. To assure the authenticity of the geographical origin, various methods have been proposed. Stable isotope analysis is a method that has been extensively used for products such as wine, oil, meat, while only a few studies have been conducted for the discrimination of seafood origin and especially for mullet roes or bottarga products. Analysis of the stable isotopes of C, N and S of Bottarga samples from four different origins were carried out. The values of δ¹⁵N (5.45‰) and δ³⁴S (4.66‰) for the Greek Bottarga Product named ‘Avgotaracho Messolongiou’, from Messolongi lagoon were lower than other areas while δ¹³C values were higher (−14.84‰). The first results show that the stable isotopes ratios of carbon, nitrogen and sulphur could be used to discriminate the Greek Protected Designations of Origin Bottarga product ‘Avgotaracho Messolongiou’ from other similar products.

Assessment of the Authenticity of Whisky Samples Based on the Multi-Elemental and Multivariate Analysis

Two hundred and five samples of whisky, including 170 authentic and 35 fake products, were analyzed in terms of their elemental profiles in order to distinguish them according to the parameter of their authenticity. The study of 31 elements (Ag, Al, B, Ba, Be, Bi, Cd, Co, Cr, Cu, Li, Mn, Mo, Ni, Pb, Sb, Sn, Sr, Te, Tl, U, V, Ca, Fe, K, Mg, P, S, Ti and Zn) was performed using the Inductively Coupled Plasma Mass Spectrometry (ICP-MS), Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) and Cold Vapor-Atomic Absorption (CVAAS) techniques. Additionally, the pH values of all samples were determined by pH-meter, and their isotopic ratios of ⁸⁸Sr/⁸⁶Sr, ⁸⁴Sr/⁸⁶Sr, ⁸⁷Sr/⁸⁶Sr and ⁶³Cu/⁶⁵Cu were assessed, based on the number of counts by ICP-MS. As a result of conducted research, elements, such as Mn, K, P and S, were identified as markers of whisky adulteration related to the age of alcohol. The concentrations of manganese, potassium and phosphorus were significantly lower in the fake samples (which were not aged, or the aging period was much shorter than legally required), compared to the original samples (in all cases subjected to the aging process). The observed differences were related to the migration of these elements from wooden barrels to the alcohol contained in them. On the other hand, the sulfur concentration in the processed samples was much higher in the counterfeit samples than in the authentic ones. The total sulfur content, such as that of alkyl sulfides, decreases in alcohol with aging in the barrels. Furthermore, counterfeit samples can be of variable origin and composition, so they cannot be characterized as one group with identical or comparable features. Repeatedly, the element of randomness dominates in the production of these kinds of alcohols. However, as indicated in this work, the extensive elemental analysis supported by statistical tools can be helpful, especially in the context of detecting age-related adulteration of whisky. The results presented in this paper are the final part of a comprehensive study on the influence of selected factors on the elemental composition of whisky.

Harnessing the Full Power of Chemometric-Based Analysis of Total Reflection X-ray Fluorescence Spectral Data to Boost the Identification of Seafood Provenance and Fishing Areas

Provenance and traceability are crucial aspects of seafood safety, supporting managers and regulators, and allowing consumers to have clear information about the origin of the seafood products they consume. In the present study, we developed an innovative spectral approach based on total reflection X-ray fluorescence (TXRF) spectroscopy to identify the provenance of seafood and present a case study for five economically relevant marine species harvested in different areas of the Atlantic Portuguese coast: three bony fish-Merluccius merluccius, Scomber colias, and Sparus aurata; one elasmobranch-Raja clavata; one cephalopod-Octopus vulgaris. Applying a first-order Savitzky-Golay transformation to the TXRF spectra reduced the potential matrix physical effects on the light scattering of the X-ray beam while maintaining the spectral differences inherent to the chemical composition of the samples. Furthermore, a variable importance in projection partial least-squares discriminant analysis (VIP-PLS-DA), with k - 1 components (where k is the number of geographical origins of each seafood species), produced robust high-quality models of classification of samples according to their geographical origin, with several clusters well-evidenced in the dispersion plots of all species. Four of the five species displayed models with an overall classification above 80.0%, whereas the lowest classification accuracy for S. aurata was 74.2%. Notably, about 10% of the spectral features that significantly contribute to class differentiation are shared among all species. The results obtained suggest that TXRF spectra can be used for traceability purposes in seafood species (from bony and cartilaginous fishes to cephalopods) and that the presented chemometric approach has an added value for coupling with classic TXRF spectral peak deconvolution and elemental quantification, allowing characterization of the geographical origin of samples, providing a highly accurate and informative dataset in terms of food safety.

Geographical origin discrimination of Agaricus bisporus produced by the complete medium: A pilot study in South Korea

The complete medium used for mushroom cultivation is important for reliable crop production. We aimed to identify how the origin of Agaricus bisporus grown in Korea was affected by complete media produced in different geographical regions using stable isotope ratios (SIRs). We found that the SIR features of A. bisporus significantly depended on the complete media origin used; in particular, it appeared the high δ³⁴S in the Chinese complete medium, low δ³⁴S in the Dutch complete medium, and high δ¹⁵N in the Korean complete medium (P < 0.05). The support vector machine method appeared better geo-origin classification of A. bisporus by the complete media compared to a linear discriminant analysis. A large-scale study should be conducted to establish a reliable origin identification model for A. bisporus grown in complete media to improve the global mushroom marketplace.

Rapidly detecting fennel origin of the near-infrared spectroscopy based on extreme learning machine

Fennel contains many antioxidant and antibacterial substances, and it has very important applications in food flavoring and other fields. The kinds and contents of chemical substances in fennel vary from region to region, which can affect the taste and efficacy of the fennel and its derivatives. Therefore, it is of great significance to accurately classify the origin of the fennel. Recently, origin detection methods based on deep networks have shown promising results. However, the existing methods spend a relatively large time cost, a drawback that is fatal for large amounts of data in practical application scenarios. To overcome this limitation, we explore an origin detection method that guarantees faster detection with classification accuracy. This research is the first to use the machine learning algorithm combined with the Fourier transform-near infrared (FT-NIR) spectroscopy to realize the classification and identification of the origin of the fennel. In this experiment, we used Rubberband baseline correction on the FT-NIR spectral data of fennel (Yumen, Gansu and Turpan, Xinjiang), using principal component analysis (PCA) for data dimensionality reduction, and selecting extreme learning machine (ELM), Convolutional Neural Network (CNN), recurrent neural network (RNN), Transformer, generative adversarial networks (GAN) and back propagation neural network (BPNN) classification model of the company realizes the classification of the sample origin. The experimental results show that the classification accuracy of ELM, RNN, Transformer, GAN and BPNN models are above 96%, and the ELM model using the hardlim as the activation function has the best classification effect, with an average accuracy of 100% and a fast classification speed. The average time of 30 experiments is 0.05 s. This research shows the potential of the machine learning algorithm combined with the FT-NIR spectra in the field of food production area classification, and provides an effective means for realizing rapid detection of the food production area, so as to merchants from selling shoddy products as good ones and seeking illegal profits.

Microchemical provenancing of prey remains in cormorant pellets reveals the use of diverse foraging grounds

Piscivorous birds in aquatic ecosystems exert predation pressure on fish populations. But the site-specific impact on fish populations, including stocked and commercially used fish species, remains disputed. One of the key questions for the management of piscivorous birds and fish is determining the origin of prey and thus which fish populations are targeted by the birds. We addressed this question by provenancing otoliths (earstones) of fish obtained from regurgitated pellets of piscivorous birds by otolith microchemistry analysis. We retrieved otoliths from regurgitated pellets of great cormorants (Phalacrocorax carbo sinensis) collected every 2 weeks for 2 years from breeding and roosting colonies at Chiemsee in Bavaria, Germany, and classified them according to family or species. We collected water samples from Chiemsee and potential surrounding foraging grounds. We measured the strontium (Sr) 87Sr/86Sr isotope ratio and Sr mass fraction of water and otoliths using (laser ablation) inductively coupled plasma-mass spectrometry. We assigned otoliths from regurgitated pellets to habitat clusters of origin by comparing the Sr isotopic and elemental composition of otoliths and waterbodies. In 36% of cormorant pellets collected at Chiemsee, prey was assigned to waterbodies distinct from Chiemsee. Furthermore, cormorants used different foraging sites during 1 day. Microchemical provenancing of prey remains can contribute to identifying foraging sites of piscivorous birds and to what extend the birds switched among foraging sites.

Isotopic and elemental characterization of Romanian pork meat in corroboration with advanced chemometric methods: A first exploratory study

In this study 93 pork meat samples (tenderloin) were analyzed via isotope ratios mass spectrometry (δ2H, δ18O, δ13C) and inductively coupled plasma - Mass spectrometry (55 elements). The meat samples are coming from Romania and abroad. Those from Romania are originating from conventional farms and yard rearing system. The analytical results in conjunction with linear discriminant analysis (LDA) and artificial neural networks (ANNs) were used to assess: The geographical origin, and animal diet. The most powerful markers which could differentiate pork meat samples concerning the geographical origin were δ18O, terbium, and tin. The results of chemometric models showed that, along with 13C signature, rubidium concentration, and a few rare earth-elements (lanthanum, and cerium) were efficient to discriminate animal diet in a percent of 97.8% (initial classification) and 94.6% (cross-validation), respectively. Some of predictors for feeding regime differentiation by using LDA were identified also to be the best markers to distinguish corn-based diet by using ANNs (δ13C, Rb, La).

Grain carbon isotope composition is a marker for allocation and harvest index in wheat

The natural ¹³ C abundance (δ¹³ C) in plant leaves has been used for decades with great success in agronomy to monitor water-use efficiency and select modern cultivars adapted to dry conditions. However, in wheat, it is also important to find genotypes with high carbon allocation to spikes and grains, and thus with a high harvest index (HI) and/or low carbon losses via respiration. Finding isotope-based markers of carbon partitioning to grains would be extremely useful since isotope analyses are inexpensive and can be performed routinely at high throughput. Here, we took the advantage of a set of field trials made of more than 600 plots with several wheat cultivars and measured agronomic parameters as well as δ¹³ C values in leaves and grains. We find a linear relationship between the apparent isotope discrimination between leaves and grain (denoted as Δδ_corr ), and the respiration use efficiency-to-HI ratio. It means that overall, efficient carbon allocation to grains is associated with a small isotopic difference between leaves and grains. This effect is explained by postphotosynthetic isotope fractionations, and we show that this can be modelled by equations describing the carbon isotope composition in grains along the wheat growth cycle. Our results show that ¹³ C natural abundance in grains could be useful to find genotypes with better carbon allocation properties and assist current wheat breeding technologies.

Probabilistic Machine Learning for the Authentication of the Protected Designation of Origin of Greek Bottarga from Messolongi: A Generic Methodology to Cope with Very Small Number of Samples

Consumers are increasingly interested in the geographical origin of foodstuff, as an important characteristic of food authenticity and quality. To assure the authenticity of the geographical origin, various methods have been proposed. Stable isotope analysis is a method that has been extensively used for products like wine, oil, and meat by using large datasets and analysis. On the other hand, only few studies have been conducted for the discrimination of seafood origin and especially for mullet roes or bottarga products, and even fewer investigate a small number of samples and datasets. Stable isotopes of Carbon (C), Nitrogen (N), and Sulfur (S) analysis of bottarga samples from four different origins were carried out. The first results show that the stable isotopes ratios of C, N, and S could be used to discriminate the Greek PDO Bottarga (Messolongi) from other similar products by using a probabilistic machine learning methodology. That could use limited sample data to fit/estimate their parameters, while, at the same time, being capable of describing accurately the population and discriminate individual samples regarding their origin.

Identifying the Geographical Origin of Tobacco Leaf by Strontium and Lead Isotopic with Mineral Elemental Fingerprint

The primary aim of this paper was to identifying the geographical origin of tobacco leaves based on stable isotopic and mineral elemental fingerprint. We collected eighty-one tobacco leaf samples from Argentina, Brazil, Zimbabwe, the U.S., Zambia, and China. And nine mineral element contents and four strontium and lead isotope ratios of the tobacco leaves were determined by thermal ionization mass spectrometry (TIMS) and inductively coupled plasma mass spectrometry (ICP-MS). After variance and stepwise discriminant analysis, the discriminant functions of the tobacco leaf’s geographical origin were established. The results indicate that: (1) the contents of six mineral elements including Cu, Zn, Cr, Ni, Cd, and Pb, together with four strontium and lead isotope ratios containing 87Sr/86Sr, 208Pb/204Pb, 207Pb/204Pb, and 206Pb/204Pb, were significantly different among six countries. (2) Different countries presented some characteristic mineral elemental and isotopic fingerprint. The even contents of mineral elements from Zambian tobacco leaf were much lower than the other countries, among which four elements consisting of Zn, Cr, As, and Cd were not detected. The three average lead isotope ratios including 208Pb/204Pb, 207Pb/204Pb, and 206Pb/204Pb from Zimbabwe tobacco leaves were far higher than the other countries, and the range of which was unoverlapped. (3) The effective identification of the geographical origin of tobacco leaf was accomplished by Fisher stepwise discriminant analysis and the characteristic tracing elements consisted of Cu, Zn, Cr, Ni, Cd, Pb, 87Sr/86Sr, 207Pb/204Pb, and 206Pb/204Pb. Based on the established discriminant functions, the original and cross-validation accuracy towards different geographical origins of tobacco leaves were 98.8% and 95.1%, respectively. The study shows that the strontium and lead isotopic with mineral elemental fingerprints is a potential effective method to identify the geographical origin of tobacco leaves from different countries.

Determination of the geographical origin of Trachinotus ovatus and Pampus argenteus in China by multi-element and stable isotope analysis

This study aimed to determine whether Trachinotus ovatus and Pampus argenteus could be differentiated by multi-element composition or stable isotope ratio analysis of δ¹³C and δ¹⁵N. The multi-element analysis values of a total of 60 Pampus argenteus samples from three sites and 60 Trachinotus ovatus samples from four sites around China were determined, and the other 60 samples of Pampus argenteus and 51 samples of Trachinotus ovatus were tested by principal component analysis (PCA) to estimate the accuracy of origin identification. The results showed that the cross-validation accuracy rate is 92.2% for the Pampus argenteus and 98.3% for the Trachinotus ovatus. Stable isotope analysis of δ¹³C and δ¹⁵N also can distinguish Trachinotus ovatus from different geographical origins. These results showed that the usefulness of multi-element and stable isotope analysis as indicators for authenticating the geographical origin of two pomfrets in China.

Application of stable isotopic and mineral elemental fingerprints in identifying the geographical originof concentrated apple juice in China

Food traceability is an important component of food safety and quality. Currently, there is no authentic established technique to identify the origin of concentrated apple juice (CAJ) in China. In this study, the isotopes of δ¹³C, δ¹⁸O and the contents of 32 elements in CAJ from five production areas (BHB, NWR, SCH, LP and YRAR) were determined. The δ¹³C, δ¹⁸O and 28 elements were significantly different (P < 0.05: post-hoc Duncan's test) in the five production areas. PCA, PLS-DA and OPLS-DA were employed for regional classification of samples. The results show that ten key variables (Tl, Se, δ¹⁸O, B, Mg, Sr, Nd, Mo, As, and Na) are more relevant for discrimination of the samples. These findings contribute to understanding the variations of stable isotopic and element compositions in Chinese CAJ depending on geographic origins and offer valuable insight into the control of fraudulent labeling regarding the geographic origins of CAJ.

Mass spectrometry in food authentication and origin traceability

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

Determining the Geographical Origin of Fuji Apple from China by Multivariate Analysis Based on Soluble Sugars, Organic Acids, and Stable Isotopes

The aim of this study was to explore the regional characteristics of soluble sugars, organic acids, and stable isotopes (δ2H, δ18O, and δ13C) in Fuji apple and the viability of tracing the geographical origin. Totally, 181 Fuji apple samples from 2017 and 2018 from three main apple production regions in China, Bohai Bay (BHB), Loess Plateau (LP), and Northwest region (NW) were collected. The parameters of soluble sugars, organic acids, and stable isotopes in samples were analyzed with HPLC, IC, and IRMS, respectively. The results of regional difference analysis, multiway variance analysis, and correlation analysis indicated that sorbitol (Sor), glucose (Glu), fructose (Fru), sucrose (Sucr), δ2H, and δ13C can be used to distinguish the samples from the three regions. Stepwise linear discriminant analysis (SLDA) showed that the correct discriminant rate of samples from the advantageous production areas of apples in China (BHB and LP) was 82.2%, and the most effective indexes were Glu, Fru, Sucr, and δ2H. Moreover, satisfactory classification can be achieved in samples from BHB and NW, with a correct classification rate of 90.0%, and Sor, Glu, and Fru were included in the discrimination model. Furthermore, the validity of the discriminant model was verified by the prediction set. The study also found that organic acids were not suitable to distinguish the apple samples from the three regions. In addition, soluble sugars and stable isotopes could not effectively distinguish LP and NW samples, which was also the reason that the samples from the three main apple production regions could not be distinguished well.