Isotopic and elemental markers for geographical origin and organically grown carrots discrimination

Preliminary study on the geographical origin of Chinese 'Cuiguan' pears using integrated stable isotope and multi-element analyses

Distinguish the geographical origin of the pear is important due to the increasingly valued brand protection and reducing the potential food safety risks. In this study, the profiles of stable isotopes (δ13C, δ15N, δ2H, δ18O) and the contents of 16 elements in pear peer from four production areas were analyzed. The δ13C, δ15N, δ2H, δ18O and 12 elements were significantly different (p < 0.05) in the four production areas. Chemometrics analysis including principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA) and linear discriminant analysis (LDA) were exploited for geographical origin classification of samples. OPLS-DA analysis showed that crucial variables (δ13C, δ18O, δ2H, Ni, Cd, Ca, δ15N, Sr and Ga) are more relevant for the discrimination of the samples. OPLS-DA achieved pear origin accuracy rates of 87.76 % by combining stable isotope ratios and elemental contents. LDA had a higher accuracy rate than OPLS-DA, and the LDA analysis showed that the original discrimination rate reached to 100 %, while the cross-validated rate reached to 95.7 %. These studies indicated that this method could be used to assess the geographical discrimination of pear from different producing areas and could potentially control the fair trade of pear in fruit markets.

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.

Effects of different floral periods and environmental factors on royal jelly identification by stable isotopes and machine learning analyses during non-migratory beekeeping

It is crucial to monitor the authenticity of royal jelly (RJ) because the qualities of RJs produced by different floral periods vary substantially. In the context of non-migratory beekeeping, this study aims to identify rape RJ (RRJ), chaste RJ (CRJ), and sesame RJ (SRJ) based on δ13C, δ15N, δ2H, and δ18O combined with machine learning and to evaluate environmental effect factors. The results showed that δ13C (-27.62‰ ± 0.24‰), δ15N (2.88‰ ± 0.85‰), and δ18O (28.02‰ ± 1.30‰) of RRJ were significantly different from other RJs. The δ13C, δ2H, and δ18O in CRJ and SRJ were strongly correlated with temperature and precipitation, suggesting that these isotopes are influenced by environmental elements such as sunlight and rainfall. In addition, the artificial neural network (ANN) model was superior to the random forest (RF) model in terms of accuracy, sensitivity, and specificity. This study revealed that combining stable isotopes with ANN models and the unique correlation between stable isotopes and environmental factors could provide promising ideas for monitoring the authenticity of RJ.

Differentiation of Transylvanian fruit distillates using supervised statistical tools based on isotopic and elemental fingerprint

BACKGROUND

The spirit drinks industry is one of the largest in the world. Fruit distillates require adequate analysis methods combined with statistical tools to build differentiation models, according to distinct criteria (geographical and botanical origin, producer's fingerprint, respectively). Over time a database of alcoholic beverage fingerprints can be generated, being very important for product safety and authenticity control.

RESULTS

To control the distillates' geographical origin, linear discriminant analysis (LDA) revealed that the cross-validation classification was correct for 88.2% of samples, but partial least squares discriminant analysis (PLS-DA) was slightly better suited for this purpose, with a correct classification rate of 91.2%. LDA effectiveness was proven for the trademark fingerprint differentiation, which was achieved at 93.5%, compared to 89.1% for PLS-DA. The principal predictors obtained by LDA were the same both for geographical origin and producer differentiation: B, δ¹³ C, Na, Cu, Ca and Be; highlighting the fact that in the production process of distillates each producer used fruits coming from the respective specific region. Through PLS-DA, some of the discrimination markers were the same for geographical origin and producer's identification, but others were completely specific: the rare earth elements Eu and Er only for geographical origin differentiation, and Cu solely as predictor for producer's identification. Regarding distillates' fruit variety, the correct discrimination rates of plum spirits from the rest were 84.2% for PLS-DA and 63% for LDA.

CONCLUSION

LDA and PLS-DA were suitable for differentiation models development of fruits spirits according to geographical region, producer and fruit variety based on isotopic and elemental fingerprint. © 2022 Society of Chemical Industry.

The Development of Honey Recognition Models Based on the Association between ATR-IR Spectroscopy and Advanced Statistical Tools

The newly developed prediction models, having the aim to classify Romanian honey samples by associating ATR-FTIR spectral data and the statistical method, PLS-DA, led to reliable differentiations among the samples, in terms of botanical and geographical origin and harvesting year. Based on this approach, 105 out of 109 honey samples were correctly attributed, leading to true positive rates of 95% and 97% accuracy for the harvesting differentiation model. For the botanical origin classification, 83% of the investigated samples were correctly predicted, when four honey varieties were simultaneously discriminated. The geographical assessment was achieved in a percentage of 91% for the Transylvanian samples and 85% of those produced in other regions, with overall accuracy of 88% in the cross-validation procedure. The signals, based on which the best classification models were achieved, allowed the identification of the most significant compounds for each performed discrimination.

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).

Authentication of Transylvanian Spirits Based on Isotope and Elemental Signatures in Conjunction with Statistical Methods

The potential association between stable isotope ratios of light elements and mineral content, in conjunction with unsupervised and supervised statistical methods, for differentiation of spirits, with respect to some previously defined criteria, is reviewed in this work. Thus, based on linear discriminant analysis (LDA), it was possible to differentiate the geographical origin of distillates in a percentage of 96.2% for the initial validation, and the cross-validation step of the method returned 84.6% of correctly classified samples. An excellent separation was also obtained for the differentiation of spirits producers, 100% in initial classification, and 95.7% in cross-validation, respectively. For the varietal recognition, the best differentiation was achieved for apricot and pear distillates, a 100% discrimination being obtained in both classifications (initial and cross-validation). Good classification percentages were also obtained for plum and apple distillates, where models with 88.2% and 82.4% in initial and cross-validation, respectively, were achieved for plum differentiation. A similar value in the cross-validation procedure was reached for the apple spirits. The lowest classification percent was obtained for quince distillates (76.5% in initial classification followed by 70.4% in cross-validation). Our results have high practical importance, especially for trademark recognition, taking into account that fruit distillates are high-value commodities; therefore, the temptation of “fraud”, i.e., by passing regular distillates as branded ones, could occur.

Evaluation of an autoencoder as a feature extraction tool for near-infrared spectroscopic discriminant analysis

The utility of an autoencoder (AE) as a feature extraction tool for near-infrared (NIR) spectroscopy-based discrimination analysis has been explored and the discrimination of the geographic origins of 8 different agricultural products has been performed as the case study. The sample spectral features were broad and insufficient for component distinction due to considerable overlap of individual bands, so AE enabling of extracting the sample-descriptive features in the spectra would help to improve discrimination accuracy. For comparison, four different inputs of AE-extracted features, raw NIR spectra, principal component (PC) scores, and features extracted using locally linear embedding were employed for sample discrimination using support vector machine. The use of AE-extracted feature improved the accuracy in the discrimination of samples in all 8 products. The improvement was more substantial when the sample spectral features were indistinct. It demonstrates that AE is expandable for vibrational spectroscopic discriminant analysis of other samples with complex composition.

A field trials-based authentication study of conventionally and organically grown Chinese yams using light stable isotopes and multi-elemental analysis combined with machine learning algorithms

In this study, stable isotopes and multi-element signatures combined with chemometrics were used to distinguish conventional and organic Chinese yams based on field trials. Four light stable isotopes δD, δ¹³C, δ¹⁵N, δ¹⁸O, and 20 elements (e.g. Li, Na, Mn) were determined, then evaluated using significance analysis and correlation analysis, and modeling of various chemometrics methods. Consequently, the RandomForest model showed the best performance with AUC value of 0.972 and predictive accuracy of 97.3%, and Mn, Cr, Se, Na, δD, As, and δ¹⁵N were screened as significant variables. Moreover, many chemical components and antioxidant activity of yam samples were determined spectrophotometrically. The results indicated that organic yams had advantages in secondary metabolites such as polyphenol, flavonoid and saponin; conversely, conventional samples had more primary metabolites like protein and amino acids. Above all, this work provides a beneficial case in the authentication and quality evaluation of conventional and organic yams.

Fuzzy Divisive Hierarchical Associative-Clustering Applied to Different Varieties of White Wines According to Their Multi-Elemental Profiles

Wine data are usually characterized by high variability, in terms of compounds and concentration ranges. Chemometric methods can be efficiently used to extract and exploit the meaningful information contained in such data. Therefore, the fuzzy divisive hierarchical associative-clustering (FDHAC) method was efficiently applied in this study, for the classification of several varieties of Romanian white wines, using the elemental profile (concentrations of 30 elements analyzed by ICP-MS). The investigated wines were produced in four different geographical areas of Romania (Transylvania, Moldova, Muntenia and Oltenia). The FDHAC algorithm provided not only a fuzzy partition of the investigated white wines, but also a fuzzy partition of considered characteristics. Furthermore, this method is unique because it allows a 3D bi-plot representation of membership degrees corresponding to wine samples and elements. In this way, it was possible to identify the most specific elements (in terms of highest, smallest or intermediate concentration values) to each fuzzy partition (group) of wine samples. The chemical elements that appeared to be more powerful for the differentiation of the wines produced in different Romanian areas were: K, Rb, P, Ca, B, Na.

Isotope chemometrics determines farming methods and geographical origin of vegetables from Yangtze River Delta Region, China

Shanghai city has encountered possible food fraud regarding the geographical mislabeling of vegetables for economic gain. A combination of δ¹³C, δ¹⁵N, δ²H and δ¹⁸O values and partial least squares discrimination analysis and support vector machine (SVM) methods were used for the first time to assess farming methods and determine the origin of vegetables from Shanghai city, Anhui and Zhejiang provinces. The results showed that 65.8% of Shanghai vegetables, 38.2% of Anhui vegetables and 23.6% of Zhejiang vegetables appeared to be grown using green or organic farming methods. The optimal discriminant model was obtained using SVM with a predictive accuracy of 100% for Shanghai vegetables. Zhejiang vegetables had a predictive accuracy of 91.7%, while it was difficult to distinguish Anhui vegetables from Shanghai or Zhejiang vegetables. Therefore, this study provided a useful method to identify vegetable farming methods and discriminate vegetables from Shanghai and Zhejiang.

Applications of emerging stable isotopes and elemental markers for geographical and varietal recognition of Romanian and French honeys

The research towards the identification of new authenticity markers is crucial to fight against fraudulent activities on honey, one of the top ten most falsified food commodities. This work proposes an association of stable isotopes and elemental content as markers for honey authentication, with respect to its floral and geographical origin. Emerging markers like isotopic signature of honey water alongside with carbon and hydrogen isotopic ratios of ethanol obtained from honey fermentation and Rare Earth Elements, were used to develop new recognition models. Thus, the efficiency of the discrimination potential of these emerging markers was discussed individually and in association. This approach proved its effectiveness for geographical differentiation (>98%) and the role of the emerging markers in these classifications was an essential one, especially of: (D/H)_I, δ²H, δ¹⁸O, La, Ce and Pr. Floral recognition was realized in a lower percentage revealing the suitability of these markers mainly for geographical classification.

Differentiation of tomatoes based on isotopic, elemental and organic markers

In this study, 41 tomato samples were investigated by means of stable isotope ratios (δ¹³C, δ¹⁸O and δ²H), elemental content, phenolic compounds and pesticides in order to classify them, according to growing conditions and geographical origin. Using investigated parameters, stepwise linear discriminant analysis was applied and the differences that occurred between tomato samples grown in greenhouses compared to those grown on field, and also between Romanian and abroad purchased samples were pointed out. It was shown that Ti, Ga, Te, δ²H and δ¹³C content were able to differentiate Romanian tomato samples from foreign samples, whereas Al, Sc, Se, Dy, Pb, δ¹⁸O, 4,4'-DDT could be used as markers for growing regime (open field vs. greenhouse). For the discrimination of different tomato varieties (six cherry samples and fourteen common sorts) grown in greenhouse, phenolic compounds of 20 samples were determined. In this regard, dihydroquercetin, caffeic acid, chlorogenic acid, rutin, rosmarinic acid, quercetin and naringin were the major phenolic compounds detected in our samples. The phenolic profile showed significant differences between cherry tomato and common tomato. The contents of the chlorogenic acid and rutin were significantly higher in the cherry samples (90.27-243.00 µg/g DW and 160.60-433.99 µg/g DW respectively) as compared to common tomatoes (21.30-88.72 µg/g DW and 24.84-110.99 µg/g DW respectively). The identification of dihydroquercetin is of particular interest, as it had not been reported previously in tomato fruit.

Traceability of fruits and vegetables

Food safety and traceability are nowadays a constant concern for consumers, and indeed for all actors in the food chain, including those involved in the fruit and vegetable sector. For the EU, the principles and legal requirements of traceability are set out in Regulation 178/2002. Currently however the regulation does not describe any analytical traceability tools. Furthermore, traceability systems for fruits and vegetables face increasing competition due to market globalization. The current challenge for actors in this sector is therefore to be sufficiently competitive in terms of price, traceability, quality and safety to avoid scandal and fraud. For all these reasons, new, flexible, cheap and efficient traceability tools, as isotopic analysis, DNA fingerprinting and metabolomic profiling coupled with chemometrics are needed.

Isotopic and elemental profiling alongside with chemometric methods for vegetable differentiation

In this study, three chemometric models for vegetables growing system (field versus greenhouse), geographical origin and species attribution using stable isotope (δ¹³C, δ¹⁸O, δ²H) and elemental fingerprints of 101 samples (54 squashes and 47 radishes) commercialized on Romanian market were developed. These models were constructed and validated through linear discriminant analysis. Initial validations of 94.4% and 83% were obtained for squash and radish growing systems, respectively, such that one squash and four radish samples declared to be grown in the field were attributed to the greenhouse group. For this purpose, the most powerful differentiation markers appeared to be Sn and δ¹³C for radishes, and Sn, Cu for squashes. Regarding the vegetable origin, four samples, initially considered to originate from Romania (95% for initial classification) were attributed to the foreign group in the cross-validation procedure (93.1%). Romanian radishes and squashes were characterized by a higher content of Na and Cu, respectively, compared with foreign samples, while the mean values for Zn, Sr, Zr and Co concentrations were found to be higher for the vegetables from abroad.

Brown rice authenticity evaluation by spark discharge-laser-induced breakdown spectroscopy

Rice is the most consumed food worldwide, therefore its designation of origin (PDO) is very useful. Laser-induced breakdown spectroscopy (LIBS) is an interesting analytical technique for PDO certification, since it provides fast multielemental analysis requiring minimal sample treatment. In this work LIBS spectral data from rice analysis were evaluated for PDO certification of Argentine brown rice. Samples from two PDOs were analyzed by LIBS coupled to spark discharge. The selection of spectral data was accomplished by extreme gradient boosting (XGBoost), an algorithm currently used in machine learning, but rarely applied in chemical issues. Emission lines of C, Ca, Fe, Mg and Na were selected, and the best performance of classification were obtained using k-nearest neighbor (k-NN) algorithm. The developed method provided 84% of accuracy, 100% of sensitivity and 78% of specificity in classification of test samples. Furthermore, it is simple, clean and can be easily applied for rice certification.

Deuterium-Depleted Water Influence on the Isotope 2H/1H Regulation in Body and Individual Adaptation

This review article presents data about the influence of deuterium-depleted water (DDW) on biological systems. It is known that the isotope abundances of natural and bottled waters are variable worldwide. That is why different drinking rations lead to changes of stable isotopes content in body water fluxes in human and animal organisms. Also, intracellular water isotope ratios in living systems depends on metabolic activity and food consumption. We found the 2H/1H gradient in human fluids (δ2H saliva >> δ2H blood plasma > δ2Hbreast milk), which decreases significantly during DDW intake. Moreover, DDW induces several important biological effects in organism (antioxidant, metabolic detoxification, anticancer, rejuvenation, behavior, etc.). Changing the isotope 2H/1H gradient from "2H blood plasma > δ2H visceral organs" to "δ2H blood plasma << δ2H visceral organs" via DDW drinking increases individual adaptation by isotopic shock. The other possible mechanisms of long-term adaptation is DDW influence on the growth rate of cells, enzyme activity and cellular energetics (e.g., stimulation of the mitochondrion activity). In addition, DDW reduces the number of single-stranded DNA breaks and modifies the miRNA profile.