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.

Elemental Profile in Chicken Egg Components and Associated Human Health Risk Assessment.

Egg is a food product of high nutritional quality, extensively consumed worldwide. The objectives of this study were the determination of the elemental profile in eggs (egg white, yolk, and eggshell), the estimation of the non-carcinogenic health risk associated with the presence of heavy metals in investigated egg samples, and the development of statistical models to identify the best predictors for the differentiation of egg components. The assessments were carried out in a total set of 210 samples, comprising home-produced and commercial eggs, using inductively coupled plasma mass spectrometry. The results suggested measurable differences amongst hen eggs coming from different husbandry systems. The statistical models employed in this study identified several elemental markers that can be used for discriminating between market and local producer samples. The non-carcinogenic risk related to the consumption of the analyzed egg samples was generally in the safe range for the consumers, below the maximum permitted levels set by Romanian and European legislation. Food contamination is a public health problem worldwide, and the risk associated with exposure to trace metals from food products has aroused widespread concern in human health, so assessing the heavy metal content in food products is mandatory to evaluate the health risk.

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.

The dataset for the chronology of the sedimentation in the Danube abyssal fan which records the major episodes of the late-Holocene Black Sea evolution.

Anoxic marine sediments at the confluence with large rivers are key archives for monitoring the anthropogenic impact in the environment and asses the carbon sink character of oxygen deprived waters. This data article describes the analysis methodology and the results of the deep-sea sediments sampled from the NW part of the Black Sea, using the 14C dating, stable carbon isotopes, C/N ratio, metallic traces and 210Pb and 137Cs radioactivity. For this purpose, 26 sediment samples were taken from the MN183-3 sampling point (43.925.917 N, 30.758.911 E, 658 m water depth) using a Mark II-400-type multicorer. The samples were collected during the two weeks Mare Nigrum (MN) #183 marine expedition, which took place at the beginning of September 2018, in the Romanian section of the Black Sea shelf and continental slope. These analyses were employed in the construction of a Bayesian high-resolution sedimentation model, reported in M.Ilie et al. (2022).

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.

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.

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.

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.

Geographical origin and species differentiation of Transylvanian cheese. Comparative study of isotopic and elemental profiling vs. DNA results.

The present study proposed the cheese differentiation, according to geographical production area and with respect to species (cow, sheep) of two traditional cheese specialties, (salty and ripened), produced in Transylvania, Romania. For this purpose, the elemental profile and carbon isotopic ratios (13C/12C) of cheese and extracted casein were corroborated through statistic supervised techniques to get the best discrimination markers. The manganese content, along with Rare Earth Elements (REE) concentrations, proved to be very powerful predictors, for the traditional salted cheese mainly, due to the direct influence of the local salted water. Despite that proposed techniques are not acknowledged methods for species differentiation, this approach allowed a successful discrimination of the animal species that produced the raw materials for cheese manufacturing (milk). The results generated by the developed chemometric model, for species differentiation, were compared with those obtained using Isoelectric focusing (IEF) and DNA tests. The proposed association of isotopic and elemental markers allowed a differentiation better than 92% for geographical provenance, of each investigated cheese specialties while, for species discrimination (cow vs. sheep) a percentage of 100% was obtained.

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).

Stable isotopes determination in some Romanian wines.

This paper presents a study concerning the isotopic fingerprint ((18)O and (13)C) of some wines prepared from relevant Romanian grape varieties (e.g. Feteasca Alba (FA), Feteasca Regala (FR) and Cabernet Sauvignon (CS)) obtained in different vintage years (2002, 2003, 2004, 2007 and 2008). These wines were obtained from different vineyards having a significant role in the wine market: Cotesti, Tohani, Stefanesti, Aiud, Cotnari, Bucium, Murfatlar, Bujoru, Dragasani and Valea Calugareasca. Several observations related to the dependence of isotope ratios on geographical origin and climatic conditions were drawn. The authentic wines obtained from the FA grape variety from six different vineyards showed δ(18)O values in the range of+3.28 (Cotesti region - 45 °38'N/27 °04'E) to-2.60 ‰ (Aiud region - 46 °19'N/23 °45'E). The δ(13)C values were very similar for all the samples with an average of about-26 ‰. The difference between the δ(18)O values was due to the different climatic zones, which have an influence on the δ(18)O values of wine water. For the wine variety CS obtained from the Dealu Mare-Tohani vineyard, production years 2003 and 2004, a greater difference in the δ(18)O values of wine water ranging from 1.89 (in 2004) to 5.35 ‰ (in 2003) was noted. This difference is explained by the different mean annual temperatures in 2003 and 2004.

Experimental plant for simultaneous production of (14)N and (15)N by (15)N/(14)N exchange in NO, NO(2)-HNO(3) system under pressure.

An experimental study on (14)N and (15)N simultaneous separation using the chemical exchange in NO, NO(2)-HNO(3) system under pressure is presented. The influence of the pressure and of the interstage 10 M HNO(3) flow rate on the separation of (14)N and (15)N was measured on a packed column with product and waste refluxers. At steady state and 1.8 atm (absolute), the isotopic concentration at the bottom of the separation column was 0.563 at% (15)N, and in the top of the column was 0.159 at% (15)N. The height equivalent to a theoretical plate and interstage 10 M HNO(3) flow rate values, obtained in these experimental conditions, allows the separation of (14)N highly depleted of (15)N and of (15)N at 99 at% (15)N concentration.