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The false declaration of honey authenticity requires the use of rapid and efficient analytical tools in order to be detected. In this study, the use of a green, rapid and emerging approach for food authentication, FT-Raman spectroscopy, proved to obtain reliable and efficient honey botanical and harvesting year differentiation models, when the spectroscopic data was processed by employing a supervised statistical method, namely Partial Least Squares Discriminant Analysis (PLS-DA). The peaks and bands present in the Raman spectra were discussed based on honey composition. In order to increase the efficiency of the models, different preprocessing methods were used and a variable reduction step was employed. The new authentication approach is capable of distinguishing among four botanical sources and two harvesting periods of honey with a correct prediction rate higher than 97 %. The Raman markers that proved to contribute the most to the discrimination were correlated with the honey composition.

High-latitude (∣ b ∣ > 30°) molecular clouds have virial parameters that exceed 1, but whether these clouds can form stars has not been studied systematically. Using JCMT SCUBA-2 archival data, we surveyed 70 fields that target high-latitude Planck Galactic cold clumps (HLPCs) to find dense cores with density of 10 ^5 –10 ^6 cm ^−3 and size of 1 × 10 ^21 cm ^−2 ). At an average rms of 15 mJy beam ^−1 , we detected Galactic dense cores in only one field, G6.04+36.77 (L183) while also identifying 12 extragalactic objects and two young stellar objects. Compared to the low-latitude clumps, dense cores are scarce in HLPCs. With synthetic observations, the densities of cores are constrained to be n _c ≲ 10 ^5 cm ^−3 should they exist in HLPCs. Low-latitude clumps, Taurus clumps, and HLPCs form a sequence where a higher virial parameter corresponds to a lower dense-core detection rate. If HLPCs were affected by the Local Bubble, the scarcity should favor turbulence-inhibited rather than supernova-driven star formation. Studies of the formation mechanism of the L183 molecular cloud are warranted.

The present work aimed to test the efficiency of FT-Raman spectroscopy for fruit spirits discrimination by developing differentiation models based on two approaches, namely a supervised statistical method (Partial Least Squares Discriminant Analysis), and a Machine Learning technique (Support Vector Machines). For this purpose, a data set comprising 86 Romanian distillate samples was used, which aimed to be differentiated in terms of the raw material used for production (plum, apple, pear and grape) and county of origin (Cluj, Satu Mare and Salaj). Eight distinct preprocessing methods (autoscale, mean center, variance scaling, smoothing, 1st derivative, 2nd derivative, standard normal variate and Pareto) followed by a feature selection step were applied to identify the meaningful input data based on which the most efficient classification models can be constructed. Both types of models led to accuracy scores greater than 90% in differentiating the distillate samples in terms of geographical and botanical origin.

Nowadays, in people’s perceptions, the return to roots in all aspects of life is an increasing temptation. This tendency has also been observed in the medical field, despite the availability of high-level medical services with many years of research, expertise, and trials. Equilibrium is found in the combination of the two tendencies through the inclusion of the scientific experience with the advantages and benefits provided by nature. It is well accepted that the nutritional and medicinal properties of honey are closely related to the botanical origin of the plants at the base of honey production. Despite this, people perceive honey as a natural and subsequently a simple product from a chemical point of view. In reality, honey is a very complex matrix containing more than 200 compounds having a high degree of compositional variability as function of its origin. Therefore, when discussing the nutritional and medicinal properties of honey, the importance of the geographical origin and its link to the honey’s composition, due to potential emerging contaminants such as Rare Earth Elements (REEs), should also be considered. This work offers a critical view on the use of honey as a natural superfood, in a direct relationship with its botanical and geographical origin.

We present 850 μ m thermal dust polarization observations with a resolution of 14.″4 (∼0.13 pc) toward an infrared dark cloud G16.96+0.27 using James Clerk Maxwell Telescope/POL-2. The average magnetic field orientation, which roughly agrees with the larger-scale magnetic field orientation traced by the Planck 353 GHz data, is approximately perpendicular to the filament structure. The estimated plane-of-sky magnetic field strength is ∼96 μ G and ∼60 μ G using two variants of the Davis–Chandrasekhar–Fermi methods. We calculate the virial and magnetic critical parameters to evaluate the relative importance of gravity, the magnetic field, and turbulence. The magnetic field and turbulence are both weaker than gravity, but magnetic fields and turbulence together are equal to gravity, suggesting that G16.96+0.27 is in a quasi-equilibrium state. The alignment between the magnetic field and cloud is found to have a trend moving away from perpendicularity in the dense regions, which may serve as a tracer of potential fragmentation in such quiescent filaments.

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.

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.

To improve the control and detection methods of thiabendazole (TBZ), a fungicide and parasiticide often used in food products, we investigated the performance of the SERS technique applied to frozen blueberry fruits available on the market. TBZ-treated fruit extracts provided a multiplexed SERS feature, where the SERS bands of TBZ could be distinctly recorded among the characteristic anthocyanidins from blueberries. Quantitative SERS of TBZ in a concentration range from 20 µM to 0.2 µM has been achieved in solutions. However, quantitative multiplexed SERS is challenging due to the gradually increasing spectral background of polyphenols from extracts, which covers the TBZ signal with increasing concentration. The strategy proposed here was to employ food bentonite to filter a substantial amount of flavonoids to allow a higher SERS signal-to-background recording and TBZ recognition. Using bentonite, the LOD for SERS analysis of blueberry extracts provided a detection limit of 0.09 µM. From the relative intensity of the specific SERS bands as a function of concentration, we estimated the detection capability of TBZ to be 0.0001 mg/kg in blueberry extracts, which is two orders of magnitude lower than the maximum allowed by current regulations.

Filament identification became a pivotal step in tackling fundamental problems in various fields of Astronomy. Nevertheless, existing filament identification algorithms are critically user-dependent and require individual parametrization. This study aimed to adapt the neural networks approach to elaborate on the best model for filament identification that would not require fine-tuning for a given astronomical map. First, we created training samples based on the most commonly used maps of the interstellar medium obtained by Planck and Herschel space telescopes and the atomic hydrogen all-sky survey HI4PI. We used the Rolling Hough Transform, a widely used algorithm for filament identification, to produce training outputs. In the next step, we trained different neural network models. We discovered that a combination of the Mask R-CNN and U-Net architecture is most appropriate for filament identification and determination of their orientation angles. We showed that neural network training might be performed efficiently on a relatively small training sample of only around 100 maps. Our approach eliminates the parametrization bias and facilitates filament identification and angle determination on large data sets.

The isotopic content (δ13C, δ2H, δ18O) and concentrations of 30 elements (Li, Na, Mg, P, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, As, Rb, Sr, Ag, Cd, Ba, Pb, La, Ce, Pr, Nd, Sm, Eu, Gd, and Tb) were determined in different wild and cultivated berries (raspberry, seaberry, blackberry, cranberry, and blueberry). Partial least squares discriminant analysis (PLS-DA) was applied in order to develop models for differentiating berries according to their botanical origin and growing system. δ13C, δ2H, δ18O, Li, Na, Mg, P, Ca, V, Mn, Co, Ni, Zn, As, Rb, Sr, Ba, and Eu were identified as significant elements for the differentiation of berry species, based on which an 85% PLS-DA model accuracy was obtained. Similarly, the PLS-DA model developed for the growing system differentiation correctly classified 94.4% of the cultivated berries and 77.2% of the wild ones, based on the main predictors: δ13C, δ18O, Li, Na, Ca, Cr, Mn, Ni, Rb, and Ba. The developed PLS-DA model for the discrimination of wild blueberries from cultivated ones showed excellent levels of sensitivity (100%), specificity (100%), and accuracy (100%).

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.

In this study, the stable isotope, and elemental fingerprints of 120 meat samples were determined. The Partial Last Squares-Discriminant Analysis (PLS-DA) method was applied to build classification models for chicken and pork meat samples according to the geographical origin (different Romanian regions) and the animal growing system (animals coming from yard rearing systems versus animals coming from industrial farms). The accuracy of the geographical origin differentiation model was 93.8% for chicken and 71.8% for pork meat. The principal discrimination markers for this classification were: B, Na, K, V, As, Se, Rb, Nb, Cd, Sn, δ13C, δ2H, and δ18O (for chicken meat) and B, Na, Mg, K, Ca, V, Cr, Fe, Ni, Cu, Zn, As, Rb, Sr, Nb, Mo, Sn, Sb, Ba, Pb, δ13C, δ2H, and δ18O (for pork meat). The PLS-DA models were able to differentiate the meat samples according to the animal rearing system with 100% accuracy (for pork meat) and 98% accuracy (for chicken meat), based on the main predictors: B, K, V, Cr, Mn, Fe, Cu, Zn, Se, Rb, Nb, Sn, δ13C, and δ2H (for chicken meat) and Se, Rb, Nb, Sb, Ba, Pb, and δ13C (for pork meat).

The application of artificial intelligence for the development of recognition models for food and beverages differentiation has benefited from increasing attention in recent years. For this scope, different machine learning (ML) algorithms were used in order to find the most suitable model for a certain purpose. In the present work, three ML algorithms, namely artificial neural networks (ANN), support vector machines (SVM) and k-nearest neighbors (KNN), were applied for constructing honey geographical classification models, and their performance was assessed and compared. A preprocessing step consisting of either a component reduction method or a supervised feature selection technique was applied prior to model development. The most efficient geographical differentiation models were obtained based on ANN, when a subset of features corresponding to the markers having the highest discrimination potential was used as input data. Therefore, when the samples aimed to be classified at an intercountry level, an accuracy of 95% was achieved; namely, 99% of the Romanian samples and 73% of the ones originating from other countries were correctly predicted. Promising results were also obtained for the intracountry honey discrimination; namely, the model built for classifying the Transylvanian samples from the ones produced in other Romanian regions had an 85% accuracy.

Food authenticity control represents a constant concern nowadays, and against this background, new means of food fraud detection are developed by research and control laboratories. Among the most accessible analytical methods in this regard, attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy proved to be an effective tool, being rapid, cost-effective, and not requiring solvent use. However, the generated experimental data need to be further processed in an efficient manner in order to be able to accurately assess the authenticity of a certain product. The temptation to pass some more available honey varieties as rarer ones might exist and in order to detect these types of miss labeling, we proposed in this study the development of new recognition models based on supervised chemometric models and artificial intelligence. In this way a comparison between the models’ capabilities constructed based on the association between ATR-IR spectroscopy with partial least squares discriminant analysis (PLS-DA) and support vector machines (SVM), respectively, was performed. The most efficient models for the individual botanical differentiation were developed by applying SVM on the significant spectral markers, determined through a supervised method.

Soft independent modeling class analogy (SIMCA) was applied to infrared spectra, obtained by attenuated total reflection – Fourier transform infrared (ATR - FTIR) spectroscopy, to develop discrimination models for the floral and geographical origin of honeys. Mono-floral honey samples, originated from France and Romania, were analyzed for this purpose. The meaningful spectral areas which were selected for the development of discriminating models were: 500 - 1610 and 1700 - 3000 cm−1. A second derivative and an “autoscale” processing was performed to experimental spectra before the statistical classifications. The honeys varieties were accurately classified, independently of the geographical origin of honeys, suggesting the high potential of this technique for botanical recognition. The obtained interclass distances among honey varieties were significant and the main identified discrimination markers, from FTIR spectra, are especially related to the floral origin through the pollen gathered by bees. A global geographical classification of honeys provided a correct classification with a weak interclass distance showing the model limitations for the authenticity control purposes. Nonetheless, geographical classifications inside a single class of honey variety lead to a correct classification with a significant interclass distance.

In this study, we compare the stable isotope composition of oxygen and carbon of wines from four Central and Southeastern European countries and from Argentina to study the similarities and differences in the isotope signatures and, thus, the potential of differentiation of the various wine-growing countries. We observe similar trends for wines from Austria, Slovenia, and Romania with respect to the vintages 2008 and 2009, which are absent in the Montenegrin and Argentinean samples. It is speculated that the weather develops similarly for Austria, Slovenia, and Romania, as these countries are positioned at a similar latitude and not too far away from each other (general central and eastern European weather situation), whereas Montenegro is not influenced by the latter being situated farther south and dominantly influenced by the Adriatic Sea. Investigations on further vintages are needed to test this assumption.

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.

Edible mushrooms have been recognized as a highly nutritional food for a long time, thanks to their specific flavor and texture, as well as their therapeutic effects. This study proposes a new, simple approach based on FT-IR analysis, followed by statistical methods, in order to differentiate three wild mushroom species from Romanian spontaneous flora, namely, Armillaria mellea, Boletus edulis, and Cantharellus cibarius. The preliminary data treatment consisted of data set reduction with principal component analysis (PCA), which provided scores for the next methods. Linear discriminant analysis (LDA) managed to classify 100% of the three species, and the cross-validation step of the method returned 97.4% of correctly classified samples. Only one A. mellea sample overlapped on the B. edulis group. When kNN was used in the same manner as LDA, the overall percent of correctly classified samples from the training step was 86.21%, while for the holdout set, the percent rose to 94.74%. The lower values obtained for the training set were due to one C. cibarius sample, two B. edulis, and five A. mellea, which were placed to other species. In any case, for the holdout sample set, only one sample from B. edulis was misclassified. The fuzzy c-means clustering (FCM) analysis successfully classified the investigated mushroom samples according to their species, meaning that, in every partition, the predominant species had the biggest DOMs, while samples belonging to other species had lower DOMs.

The present work aims to test the potential of the application of Artificial Neural Networks (ANNs) for food authentication. For this purpose, honey was chosen as the working matrix. The samples were originated from two countries: Romania (50) and France (53), having as floral origins: acacia, linden, honeydew, colza, galium verum, coriander, sunflower, thyme, raspberry, lavender and chestnut. The ANNs were built on the isotope and elemental content of the investigated honey samples. This approach conducted to the development of a prediction model for geographical recognition with an accuracy of 96%. Alongside this work, distinct models were developed and tested, with the aim of identifying the most suitable configurations for this application. In this regard, improvements have been continuously performed; the most important of them consisted in overcoming the unwanted phenomenon of over-fitting, observed for the training data set. This was achieved by identifying appropriate values for the number of iterations over the training data and for the size and number of the hidden layers and by introducing of a dropout layer in the configuration of the neural structure. As a conclusion, ANNs can be successfully applied in food authenticity control, but with a degree of caution with respect to the “over optimization” of the correct classification percentage for the training sample set, which can lead to an over-fitted model.

This study proposes a comparison between two analytical techniques for edible oil classification, namely gas-chromatography equipped with a flame ionization detector (GC-FID), which is an acknowledged technique for fatty acid analysis, and Raman spectroscopy, as a real time noninvasive technique. Due to the complexity of the investigated matrix, we used both methods in connection with chemometrics processing for a quick and valuable evaluation of oils. In addition to this, the possible adulteration of investigated oil varieties (sesame, hemp, walnut, linseed, sea buckthorn) with sunflower oil was also tested. In order to extract the meaningful information from the experimental data set, a supervised chemometric technique, namely linear discriminant analysis (LDA), was applied. Moreover, for possible adulteration detection, an artificial neural network (ANN) was also employed. Based on the results provided by ANN, it was possible to detect the mixture between sea buckthorn and sunflower oil.

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.

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.

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.

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.

This study investigates the association between the Functional Health Pattern Assessment Screening Tool (FHPAST) and frailty in hospitalized geriatric patients. One hundred and forty patients (mean age 78.2 years, age range 65–90) were screened for frailty using the Frail Scale during hospitalization in the geriatric unit. Among them, 57 patients were identified as prefrail (40.7%), and 83 were identified as frail (59.3%). A comparative analysis between groups in terms of the FHPAST components covering health risk, general well-being, and health promotion was performed. Correlations between FHAPST components, socio-demographic data, frailty criteria, as well as logistic regression to identify variables that better predict frailty were also sought. Frailty was mainly associated with difficulty urinating, limitations in performing activities of daily living and walking, physical discomfort, less positive feelings in controlling one’s own life, lower compliance with recommendations from the healthcare provider, and engagement in seeking healthcare services. Patients with difficulty urinating and walking had a probability of 4.38 times (OR = 4.38, CI 95% [1.20–15.94]), p = 0.025) and 65.7 times (OR = 65.7, CI 95% [19.37–223.17], p < 0.001) higher of being frail rather than prefrail. The relationship between frailty and prefrailty in hospitalized geriatric patients and components of nursing Functional Health Patterns (FHP) has yet to be explored. This study provides evidence of the most prevalent needs of frail geriatric patients in hospital settings.

Characterization of beer samples is of interest because their compositions affect the taste and stability of beer and, also, consumer health. In this work, the characterizations of 20 Romanian beers were performed by mean of Inductively Coupled Plasma Mass Spectrometry (ICP-MS) and Isotope Ratio Mass Spectrometry (IRMS) in order to trace heavy metals and isotopic content of them. Major, minor, and trace metals are important in beer fermentation since they supply the appropriate environment for yeast growth and influence yeast metabolism. Beside this, the presence of the C4 plants in the brewing process was followed. Our study has shown that the analyzed beers indicated the presence of different plant types used in brewing: C3, C3-C4 mixtures, and also C4, depending on producers. Also the trace metal content of each sample is presented and discussed in this study. A comparison of the beers quality manufactured by the same producer but bottled in different type of packaging like glass, dose, or PET was made; our results show that no compositional differences among the same beer type exist.