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.

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.

Application of fuzzy algorithms in conjunction with 1 H-NMR spectroscopy to differentiate alcoholic beverages.

BACKGROUND: Recent statistics from the European Commission indicate that wine is one of the commodities most commonly subject to food fraud. In this context, the development of reliable classification models to differentiate alcoholic beverages requires, besides sensitive analytical tools, the use of the most suitable data-processing methods like those based on advanced statistical tools or artificial intelligence. RESULTS: The present study aims to establish a new, innovative approach for the differentiation of alcoholic beverages (wines and fruit distillates), which is able to increase the discrimination rate of the models that have been developed. A data dimensionality reduction step was applied to proton nuclear magnetic resonance (1 H-NMR) profiles. This stage consisted of the application of fuzzy principal component analysis (FPCA) prior to the development of classification models through discriminant analysis. The enhancement of the model's classification potential by the application of FPCA in comparison with principal component analysis (PCA) was discussed. CONCLUSION: The association of 1 H-NMR spectroscopy and an appropriate statistical approach provided a very effective tool for the differentiation of alcoholic beverages. To develop reliable metabolomic approaches for the differentiation of wines and fruit distillates, 1 H-NMR spectroscopic data were exploited in conjunction with fuzzy algorithms to reduce data dimensionality. The study proved the greater efficiency of using FPCA scores in comparison with those obtained through the widely applied PCA. The proposed approach enabled wines to be distinguished perfectly according to their geographical origins, cultivar, and vintage, and this could be used for wine classification. Moreover, 100% correctly classified samples were also achieved for the botanical and geographical differentiation of fruit distillates. © 2022 Society of Chemical Industry.

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.

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.

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.

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.

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).

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).

Optimized and validated method for simultaneous extraction, identification and quantification of flavonoids and capsaicin, along with isotopic composition, in hot peppers from different regions.

Nineteen hot pepper (Capsicum annuum L.) samples from five countries and twenty samples from Romanian producers were analyzed. Concentrations of flavonoids and capsaicin were simultaneously quantified for the first time with the method developed and validated in the present paper. δ13C, δ2H, and δ18O isotopic values were also measured. Maximum concentrations of studied compounds were detected in methanol extracts, after 12 h incubation of the samples assisted by ultrasound, at the 1:8 ratio of sample to solvent. The extraction recovery ranged from 90.60% to 115.05%. Capsaicin and four flavonoids were quantified in studied samples at different concentration ranges: capsaicin (28.23-2322.35 µg/g), vitexin (2.93-33.46 µg/g), isoquercetin (3.19-155.58 µg/g), kaempferol-3-glucoside (2.31-2462.25 µg/g) and myricetin (1.55-78.79 µg/g). The association between these analytical techniques and chemometric tools proved that kaempferol-3-glucoside is one of the strongest markers for country and maturity stage discrimination.

HCH phytoremediation potential of native plant species from a contaminated urban site in Turda, Romania.

Current physical or chemical methods used for remediation of soils contaminated with hexachlocyclohexane (HCH), leave behind significant levels of pollutants. Given the compound's volatility and persistence in the environment, sites contaminated with HCH remain a concern for the population living in nearby areas. By making use of both the recovery capacity and the pollutant uptake ability of spontaneously growing vegetation, our study aimed to identify native plant species able to cover and moreover take up the HCH left at a former lindane production unit in Turda, Romania. The results showed that dominant species across the study site like Lotus tenuis, Artemisia vulgaris or Tanacetum vulgare, were capable of taking up HCH in their tissues, according to different patterns that combined at the scale of the plant community. Regardless of the proximity of the HCH contamination hotspots, the development of the plant cover was characteristic for vegetation succession on disturbed soils of the Central European region. Finally, we conclude that plant species which grow spontaneously at the HCH contaminated site in Turda and are capable of taking up the pollutant, represent a self-sustainable and low maintenance phytomanagement approach that would allow for the reintegration of the site in the urban or industrial circuit and nevertheless would reduce the toxicity risk to the neighboring human inhabitants.

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.

The Influence of the Variety, Vineyard, and Vintage on the Romanian White Wines Quality.

The wine is one of the most consumed drinks over the world, being subjected to falsification or adulteration regarding the variety, vintage, and geographical region. In this study, the influence of different characteristics of wines (type, production year, and origin) on the total phenolic content, total flavonoids content, antioxidant activity, total sugars content, pH, and 18O/16O isotopic ratio was investigated. The differentiation of selected wines on the basis of tested parameters was investigated using chemometric techniques, such as analysis of variance, cluster analysis, and principal component analysis. The experimental results are in agreement with other outcomes and allow concluding that variety and vineyard have the major influence on the studied parameters, but, also, statistical interaction effect between year and vineyard and year and variety is observed in some cases. The obtained results have demonstrated that these parameters together with chemometric techniques show a significant potential to be used for discrimination of white wines.

Spatial and seasonal variation of organic pollutants in surface water using multivariate statistical techniques.

In this study, analysis of variance (ANOVA), cluster analysis (CA) and principal component analysis (PCA) were employed in order to evaluate the concentration profile of organic contaminants found in three main river from central Transylvania, Romania. Samples were collected from nine sampling stations, in two different sampling campaigns (wet season and dry season). Water samples were extracted using solid-phase extraction and analyzed using gas chromatography coupled with mass spectrometry (GC/MS). Twelve organic pollutants belonging to different classes were used for further interpretations. ANOVA highlighted compounds which distinguished Olt River from Mures River, and compounds that are influenced by increased river flow from the wet season. CA was applied to group the sampling stations. Three clusters were obtained, according to their organic load. PCA extracted five principal components explaining 87.330% from data set variability. Based on these results, a future monitoring study may be optimized by reducing the sampling points and compounds to those that are representative for each river, thereby reducing costs, without any information loss.