Stable isotope signatures (δ2 H-, δ13 C-, δ15 N-values) of walnuts (Juglans regia L.) from different regions in Germany

Meeting the challenge of varietal and geographical authentication of hazelnuts through lipid metabolite fingerprinting

Hazelnuts are high-quality products with significant economic importance in many European countries. Their market price depends on their qualitative characteristics, which are driven by cultivar and geographical origin, making hazelnuts susceptible to fraud. This study systematically compared two lipidomic fingerprinting strategies for the simultaneous authentication of hazelnut cultivar and provenance, based on the analysis of the unsaponifiable fraction (UF) and triacylglycerol (TAG) profiles by gas chromatography-mass spectrometry coupled with chemometrics. PLS-DA classification models were developed using a large sample set with high natural variability (n = 309) to discriminate hazelnuts by cultivar and origin. External validation results demonstrated the suitability of the UF fingerprint as a hazelnut authentication tool, both tested models showing a high efficiency (>94 %). The correct classification rate of the TAG fingerprinting method was lower (>80 %), but due to its faster analysis time, it is recommended as a complementary screening tool to UF fingerprinting.

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.

Prospective exploration of hazelnut's unsaponifiable fraction for geographical and varietal authentication: A comparative study of advanced fingerprinting and untargeted profiling techniques

This study compares two data processing techniques (fingerprinting and untargeted profiling) to authenticate hazelnut cultivar and provenance based on its unsaponifiable fraction by GC-MS. PLS-DA classification models were developed on a selected sample set (n = 176). As test cases, cultivar models were developed for "Tonda di Giffoni" vs other cultivars, whereas provenance models were developed for three origins (Chile, Italy or Spain). Both fingerprinting and untargeted profiling successfully classified hazelnuts by cultivar or provenance, revealing the potential of the unsaponifiable fraction. External validation provided over 90 % correct classification, with fingerprinting slightly outperforming. Analysing PLS-DA models' regression coefficients and tentatively identifying compounds corresponding to highly relevant variables showed consistent agreement in key discriminant compounds across both approaches. However, fingerprinting in selected ion mode extracted slightly more information from chromatographic data, including minor discriminant species. Conversely, untargeted profiling acquired in full scan mode, provided pure spectra, facilitating chemical interpretability.

Stable Carbon Isotope Ratios (δ13C Values [‰]) of Individual Sterols in the Oils of C3, C4, and CAM Plants

The compound-specific determination of δ13C values [‰] by gas chromatography interfaced with isotope ratio mass spectrometry (GC-IRMS) is a powerful analytical method to indicate minute but relevant variations in the 13C/12C ratio of sample compounds. In this study, the δ13C values [‰] of individual sterols were measured in eleven different oils of C3, C4, and CAM plants (n = 33) by GC-IRMS. For this purpose, a suitable acetylation method was developed for sterols. Nine of the eleven phytosterols identified by GC with mass spectrometry (GC/MS) could be measured by GC-IRMS. The δ13C values [‰] of individual sterols and squalene of C3 plant oils were between 3‰ and >16‰ more negative (lighter in carbon) than in C4 and CAM oils. We also showed that the blending of C4 oils into C3 oils (exemplarily conducted with one olive and one corn oil) would be precisely determined by means of the δ13C value [‰] of β-sitosterol.

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.

Geographical origin classification of peanuts and processed fractions using stable isotopes

This study investigates the use of stable isotopes (C, N, H, and O) to characterize the geographical origin of peanuts along with different peanut fractions including whole peanut kernel, peanut shell, delipidized peanuts and peanut oil. Peanut samples were procured in 2017 from three distinctive growing regions (Shandong, Jilin, and Jiangsu) in China. Peanut processing significantly influenced the δ 13C, δ 2H, and δ 18O values of different peanut fractions, whereas δ 15N values were consistent across all fractions and unaffected by peanut processing. Geographical differences of peanut kernels and associated peanut fractions showed a maximum variance for δ 15N and δ 18O values which indicated their strong potential to discriminate origin. Different geographical classification models (SVM, LDA, and k-NN) were tested for peanut kernels and associated peanut fractions. LDA achieved the highest classification percentage, both on the training and validation sets. Delipidized peanuts had the best classification rate compared to the other fractions.

Multi-chemical analysis combined with chemometrics to characterize PDO and PGI Italian apples

BACKGROUND

The use of PDO (protected designation of origin) and PGI (protected geographical indication) labels allows to protect and promote agricultural products characterized by unique features related to the place of origin and traditional know-how. However, the presence of non-authentic products in the market represents a fraud that can be tackled applying analytical techniques combined with chemometric analysis. In this study, we applied multi-element and multi-isotope analysis to characterize PDO and PGI apples cultivated in northern Italy, comparing them with Italian apples without labels of geographical indications.

RESULTS

The multi-element and multi-isotope approach allowed to characterize the apples cultivated in northern Italy. Despite a significant effect of the sampling sites on the apple composition, the comparison of the multi-chemical fingerprint of the apples significantly varied among cultivation areas. Results of this characterization were used to classify samples according to their cultivation area applying a linear discriminant analysis (LDA). Outputs of the LDA showed that correct sample classification can be successfully achieved (balanced accuracy > 96%). Moreover, using a selection of variables, it was possible to correctly classify samples also at regional level.

CONCLUSION

The presented evidences indicate that the multi-element and multi-isotope fingerprint can be successfully applied to traceability studies. The combination of this characterization with chemometric tools allows the classification of Italian apples based on their origin both on a national and regional scale. This approach represents an interesting tool to enhance and protect PDO and PGI Italian products. © 2021 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Fatty Acid Stable Carbon Isotope Ratios Combined with Oxidation Kinetics for Characterization and Authentication of Walnut Oils

Walnut oil is vulnerable to oxidation due to its high content of polyunsaturated fatty acids and adulteration due to its high price. This study investigated 12 typical walnut oils from six main walnut-producing areas in China, using differential scanning calorimetry (DSC), Rancimat test, gas chromatography (GC), elemental analyzer-isotope ratio mass spectrometry (EA-IRMS) and GC-IRMS combined with oxidation kinetic analysis, Pearson correlation analysis, and principal component analysis (PCA). The melting and crystallization profiles using DSC indicated that walnut oils with a relatively high crystal onset temperature tended to be more stable against oxidation. Oleic acid was found to be the most characteristic fatty acid in walnut oil, with a content ranging from 13.84 to 35.08%. Two walnut oils with the highest oleic acid contents of 35.08 and 32.78% had the highest activation energies in nonisothermal DSC. Their predicted shelf lives based on the Rancimat test were 3.5-4.0 times longer than that of the oil with the highest α-linolenic acid at 4 °C and 3.1-3.5 times longer at 25 °C. The δ¹³C values of walnut oils were determined by EA-IRMS, and the δ¹³C values of fatty acids were determined by GC-IRMS. Fatty acid stable carbon isotope ratios combined with PCA were successfully applied to intuitively discriminate different walnut oils. The results suggested that fatty acid δ¹³C values determined by IRMS combined with chemometrics and lipid compositions are promising as a powerful means of vegetable oil authentication and discrimination.

Determination of the Geographical Origin of Walnuts (Juglans regia L.) Using Near-Infrared Spectroscopy and Chemometrics

The prices of walnuts vary according to their geographical origin and, therefore, offer a financial incentive for adulteration. A reliable analysis method is required to quickly detect possible misdeclarations and thus prevent food fraud. In this study, a method to distinguish between seven geographical origins of walnuts using Fourier transform near-infrared (FT-NIR) spectroscopy combined with chemometrics as a fast, versatile, and easy to handle analytical tool was developed. NIR spectra of 212 ground and afterwards freeze-dried walnut samples, harvested in three consecutive years (2017-2019), were collected. We optimized the data pre-processing by applying and evaluating 50,545 different pre-processing combinations, followed by linear discriminant analysis (LDA) which was confirmed by nested cross-validation. The results show that in the scope of our research minimal pre-processing led to the best results: By applying just multiplicative scatter correction (MSC) and median centering, a classification accuracy of 77.00% ± 1.60% was achieved. Consequently, this complex model can be used to answer economically relevant questions e.g., to distinguish between European and Chinese walnuts. Furthermore, the great influence of the applied pre-processing methods, e.g., the selected wavenumber range, on the achieved classification accuracy is shown which underlines the importance of optimization of the pre-processing strategy.

Geographical and Species Differentiation of Truffles (Tuber spp.) by Means of Stable Isotope Ratio Analysis of Light Elements (H, C, and N)

Truffles (Tuber spp.) are considered the most expensive edible fungi and, therefore, are highly prone to food fraud. In this study, authentic truffles from different countries of origin and species were characterized by the determination of their stable carbon, nitrogen, and hydrogen isotope ratios (bulk δ²H, δ¹³C, and δ¹⁵N values). Chinese truffles from Yunnan or related provinces (n = 19) could be well-separated from all European samples (eight countries; n = 105) by means of their significantly (p < 0.05) more negative δ²H values. Furthermore, samples of the second most expensive European species Tuber magnatum were both more enriched in ¹⁵N and more depleted in ¹³C compared to the remaining samples, which allowed for an unequivocal differentiation. Hence, stable isotope ratio analysis could be of high value in terms of authentication of truffles.

Origin Determination of Walnuts (Juglans regia L.) on a Worldwide and Regional Level by Inductively Coupled Plasma Mass Spectrometry and Chemometrics

To counteract food fraud, this study aimed at the differentiation of walnuts on a global and regional level using an isotopolomics approach. Thus, the multi-elemental profiles of 237 walnut samples from ten countries and three years of harvest were analyzed with inductively coupled plasma mass spectrometry (ICP-MS), and the resulting element profiles were evaluated with chemometrics. Using support vector machine (SVM) for classification, validated by stratified nested cross validation, a prediction accuracy of 73% could be achieved. Leave-one-out cross validation was also applied for comparison and led to less satisfactory results because of the higher variations in sensitivity for distinct classes. Prediction was still possible using only elemental ratios instead of the absolute element concentrations; consequently, a drying step is not mandatory. In addition, the isotopolomics approach provided the classification of walnut samples on a regional level in France, Germany, and Italy, with accuracies of 91%, 77%, and 94%, respectively. The ratio of the model's accuracy to a random sample distribution was calculated, providing a new parameter with which to evaluate and compare the performance of classification models. The walnut cultivar and harvest year had no observable influence on the origin differentiation. Our results show the high potential of element profiling for the origin authentication of walnuts.