The development of honey recognition models with broad applicability based on the association of isotope and elemental content with ANNs.

Honey adulteration represents a worldwide problem, driven by the illicit economic gain that producers, traders, or merchants pursue. Among the falsification methods that can unfairly influence the price is the incorrect declaration of the botanical origin and harvesting year. Therefore, the present study aimed to test the potential given by the application of Artificial Neural Networks (ANNs) for developing prediction models able to assess honey botanical origin and harvesting year based on isotope and elemental fingerprints. For each classification criterion, significant focus was dedicated to the data preprocessing phase to enhance the models' prediction capability. The obtained classification performances (accuracy scores >86% during the test phase) have highlighted the efficiency of ANNs for honey authentication as well as the feasibility of applying the developed classifiers for a large-scale application, supported by their ability to recognize the correct origin despite considerable variability in botanical source, geographical origin, and harvesting period.

Nutritional Quality and Safety Assessment of Pork Meat Cuts from Romania: Fatty Acids and Elemental Profile.

In this study, the fatty acids and elemental profiles of 53 pork cut samples were determined. To offer insights into their potential health implications, we computed 18 key nutritional indices. These indices included parameters such as saturated fatty acids (SFAs), monounsaturated fatty acids (MUFAs), polyunsaturated fatty acids (PUFAs), unsaturated fatty acids (UFAs), the MUFAs/SFAs ratio, PUFAs/SFAs ratio, atherogenic index (AI), thrombogenic index (TI), the hypocholesterolemic to hypercholesterolemic ratio (h/H), health-promoting index (HPI), hypocholesterolemic index (HI), unsaturation index (UI), saturation index (SI), peroxidizability index (PI), nutritional value index (NVI), hypocholesterolemic index of fatty acids (DFAs), hypercholesterolemic index of fatty acids (OFAs), and the DFAs/OFAs ratio. These indices were calculated based on their fatty acid composition to provide comprehensive nutritional information. A health risk assessment revealed the safety and minimum health risk for the population from consuming the investigated pork cuts using the Target Hazard Quotient (THQ), Hazard Index (HI), and target cancer risk (TR). The ANOVA test showed significant differences in the levels of K, Fe, Mn, Zn, MUFAs, and AI among the pork cut samples. It was noted that by employing the correlation between the fatty acids profile, nutritional indices, and elemental concentrations and an unsupervised statistical method, such as PCA, a perfect separation from the different pork cuts could not be obtained.

Isotopic and Elemental Fingerprint of Edible Egg Parts-The Health Risk Assessment Based on Potentially Toxic Elements Content.

The present study investigated the isotopic and elemental profile (by IRMS and ICP-MS) of edible egg parts (29 egg whites and 29 yolks) mainly collected from Romania. In order to differentiate the egg white and yolk coming from different hen rearing systems (backyard and barn), Partial Least Square-Discriminant Analysis (PLS-DA) models were developed. The models' accuracies for the discrimination according to the hen growing system were 96% for egg white and 100% for egg yolk samples, respectively. Elements that proved to have the highest discrimination power for both egg white and yolk were the following: δ13C, Li, B, Mg, K, Ca, Mn, Fe, Co, Zn, Rb, Sr, Mo, Ba, La, Ce, and Pb. Nevertheless, the important compositional differentiation, in terms of essential mineral content, between the edible egg parts (egg white and egg yolk) were also pointed out. The estimated daily intake (EDI), the target hazard quotient (THQ) for Cr, Mn, Fe, Co, Ni, Cu, Zn, Se, Cd, Pb, and As, as well as the hazard index (HI) were used to assess non-carcinogenic human health risks from egg consumption. The obtained results showed no noticeable health risks related to egg consumption for humans from the point of view of the potentially toxic metals.

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.

Machine Learning Approach to Comparing Fatty Acid Profiles of Common Food Products Sold on Romanian Market.

Food composition issues represent an increasing concern nowadays, in the context of diverse food commodity varieties. The contents and types of fatty acids are a constant preoccupation among consumers because of their reflections of nutrition and health problems. This study aims to find the best tool for the rapid and reliable identification of similarities and differences among several food items from a fatty acid profile perspective. An acknowledged GC-FID method was considered, while, for a better interpretation of the analytical results, machine learning algorithms were used. It was possible to develop a recognition model able to simultaneously differentiate, with an accuracy of 79.3%, nine product types using the bagged tree ensemble model. The low number of samples or some similarities among the classes could be responsible for the wrong assignments that occurred, especially in the biscuit, wafer and instant soup classes. Better accuracies values of 95, 86.1, and 97.8% were obtained when the products were grouped into three categories: (1) sunflower oil, mayonnaise, margarine, and cream cheese; (2) biscuits, cookies, margarine, and wafers; and (3) sunflower oil, chips, and instant soup.

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.

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.

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.

Testing the limits of FT-Raman spectroscopy for wine authentication: Cultivar, geographical origin, vintage and terroir effect influence.

FT-Raman spectroscopy represents an environmentally friendly technique, suitable for the analysis of high-water content food matrices, like wines, due to its relatively weak water bending mode in the fingerprint region. Based on metabolomics applied to FT-Raman spectra, this study presents the classifications achieved for a sample set comprising 126 wines, originated from Romania and France, with respect to cultivar, geographical origin and vintage. Cultivar recognition was successfully performed among four varieties (Sauvignon, Riesling, Chardonnay, Pinot Gris) while subtle particularities exiting between the Chardonnay wines, coming from the two countries, because of terroir influences were pointed out. The obtained separations of 100% in both initial and cross-validation procedure for geographical differentiation between the two origin countries, as well as, among the three Romanian areas (Transylvania, Muntenia and Moldova) were also discussed. Apart of this, the limitations and the importance of choosing a meaningful data set, in terms of representativity for each classification criterion, are addressed in the present work.

Characterization and classification of wines according to geographical origin, vintage and specific variety based on elemental content: a new chemometric approach.

A highly informative chemometric approach using elemental data to distinguish and classify wine samples according to different criteria was successfully developed. The robust chemometric methods, such fuzzy principal component analysis (FPCA), FPCA combined with linear discriminant analysis (LDA), namely FPCA-LDA and mainly fuzzy divisive hierarchical associative-clustering (FDHAC), including also classical methods (HCA, PCA and PCA-LDA) were efficaciously applied for characterization and classification of white wines according to the geographical origin, vintage or specific variety. The correct rate of classification applying LDA was 100% in all cases, but more compact groups have been obtained for FPCA scores. A similar separation of samples resulted also when the FDHAC was employed. In addition, FDHAC offers an excellent possibility to associate each fuzzy partition of wine samples to a fuzzy set of specific characteristics, finding in this way very specific elemental contents and fuzzy markers according to the degrees of membership (DOMs).

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

This study proposes different markers associations for the discrimination of organically and conventionally grown carrots, as well as for the geographical origin differentiation. It was shown that one of the most powerful differentiation markers proved to be Mn content. Along with manganese concentrations, isotope ratios of nitrogen and a high number of Rare Earth-Elements (REEs) were able to differentiate the organically grown carrots samples in a percent of 83.3% (initial classification) and 81% (cross-validation), respectively. It was observed that some of the obtained discrimination markers were interlinked, for instance Mn content being positively correlated with some REEs (i.e. Sc, La, Ce, Pr, Nd, Lu, Th). One of the best markers that could differentiate the carrot samples grown in Transylvania, Romania, from those either grown in other side of the country or foreign samples is represented by Mn content along with another REE, particularly terbium (Tb).

Isotopic and multielemental fingerprinting of organically and conventionally grown potatoes.

Two marker combinations were used for the differentiation of organically produced from conventionally produced potatoes and also for the geographical origin identification. Fifty-seven samples (from Romanian local producers or imported) were analysed from the stable isotopic (isotope ratio mass spectrometry) and elemental profile (inductively coupled plasma mass spectrometry) point of view. In order to assess the best marker combination, both isotopic and elemental experimental results were subject to chemometric analysis. The statistical tests performed were ANOVA test, Pearson correlation, principal component analysis and linear discriminant analysis (LDA). For a more comprehensive differentiation between organic vs. conventional potato samples, LDA was applied, and 94.7 % of original cases were correctly classified and the percentage obtained in cross-validation procedure was 91.2 %. Regarding the geographic origin classification, LDA provided an initial classification of 96.5 %, while for cross-validation the percentage was 87.7. LDA found δ15N, Cd, Ca, Cu and Zn as best discrimination markers between organically and conventionally grown potatoes. The strongest predictors for Romania vs. foreign geographical areas along LDA were seen to be Ca, P, Co, Ni and δ13C.

Analysis of mineral and heavy metal content of some commercial fruit juices by inductively coupled plasma mass spectrometry.

The presence of potentially toxic elements and compounds in foodstuffs is of intense public interest and thus requires rapid and accurate methods to determine the levels of these contaminants. Inductively coupled plasma mass spectrometry is a powerful tool for the determination of metals and nonmetals in fruit juices. In this study, 21 commercial fruit juices (apple, peach, apricot, orange, kiwi, pear, pineapple, and multifruit) present on Romanian market were investigated from the heavy metals and mineral content point of view by ICP-MS. Our obtained results were compared with those reported in literature and also with the maximum admissible limit in drinking water by USEPA and WHO. For Mn the obtained values exceeded the limits imposed by these international organizations. Co, Cu, Zn, As, and Cd concentrations were below the acceptable limit for drinking water for all samples while the concentrations of Ni and Pb exceeded the limits imposed by USEPA and WHO for some fruit juices. The results obtained in this study are comparable to those found in the literature.

Isotopic and elemental determination in some romanian apple fruit juices.

H, C, O stable isotope ratios and the content of some heavy elements of 31 Romanian single-strength organic apple juices collected from four Transylvanian areas are discussed in this study. The aim of this study was to measure the ²H/¹H, ¹8O/¹6O, ¹³C/¹²C ratios of these juices and their elemental profile and to establish a database of authentic values to be used for adulteration and authenticity testing. Our results have shown mean values of δ¹8O = -4.2‰ and δDδ-46.5‰, respectively, for apples from Transylvania and at the same time the mean value of δ¹³C = -28.2‰. The content of Cd, Pb, U, Zn, As was below the acceptable limits stipulated in US-EPA standard for drinking water. Cu and Cr limits exceeded for one single juice; Ni content for some apple juices from Maramures, Alba, and Cluj was higher than the acceptable value.

FTIR, Raman, and UV-Vis spectroscopic and DFT investigations of the structure of iron-lead-tellurate glasses.

In this work, the effects of iron ion intercalations on lead-tellurate glasses were investigated via FTIR, Raman and UV-Vis spectroscopies. This homogeneous glass system has compositions xFe(2)O(3)·(100-x)[4TeO(2)·PbO(2)], where x = 0-60 mol%. The presented observations in these mechanisms show that the lead ions have a pronounced affinity towards [TeO(3)] structural units, resulting in the deformation of the Te-O-Te linkages, and leading to the intercalation of [PbO( n )] (n = 3, 4) and [FeO( n )] (n = 4, 6) entities in the [TeO(4)] chain network. The formation of negatively charged [FeO(4)](1-) structural units implies the attraction of Pb(2+) ions in order to compensate for this electrical charge. Upon increasing the Fe(2)O(3) content to 60 mol%, the network can accommodate an excess of oxygen through the formation of [FeO(6)] structural units and the conversion of [TeO(4)] into [TeO(3)] structural units. For even higher Fe(2)O(3) contents, Raman spectra indicate a greater degree of depolymerization of the vitreous network than FTIR spectra do. The bands due to the Pb-O bond vibrations are very strongly polarized and the [TeO(4)] structural units convert into [TeO(3)] units via an intermediate coordination stage termed "[TeO(3+1)]" structural units. Our UV-Vis spectroscopic data show two mechanisms: (i) the conversion of the Fe(3+) to Fe(2+) at the same time as the oxidation of Pb(2+) to Pb(+4) ions for samples with low Fe(2)O(3) contents; (ii) when the Fe(2)O(3) content is high (x ≥ 50 mol%), the Fe(2+) ions capture positive holes and are transferred to Fe(3+) ions through a photochemical reaction, while the Pb(2+) ions are formed by the reduction of Pb(4+) ions. DFT calculations show that the addition of Fe(2)O(3) to lead-tellurate glasses seems to break the axial Te-O bonds, and the [TeO(4)] structural units are gradually transformed into [TeO(3+1)]- and [TeO(3)]-type polyhedra. Analyzing these data further indicates a gradual conversion of the lead ions from covalent to ionic environment. There is then a charge transfer between the tri- and tetracoordinated tellurium atoms due to the capacity of the lead-tellurate network to form the appropriate coordination environments containing structural units of opposite charge, such as iron ions, [FeO(4)](1-).