Chosen Few Urbano Rar

Empathy has long been recognized as a key element of the healing professions. Yet it is not always clear how to define the concept, how to measure it, whether there are effective methods to enhance empathy, or whether empathy really helps make treatment more effective. Drawing on evolutionary research, neurological findings, developmental and psychodynamic perspectives, and systems theory, Empathy in Patient Care explains why this human quality is essential to positive health outcomes—and how it can be measured and how professionals can benefit from its enhancement.Dr. Hojat proceeds from theoretical constructs of empathy as a core ingredient of human relationships to analyze the nuanced roles it plays in the therapeutic dyad.

1. Chosen Few Urbano Rare Earth

One of the most important issues in the wine sector and prevention of adulterations of wines are discrimination of grape varieties, geographical origin of wine, and year of vintage. In this experimental research study, UV-Vis and FT-IR spectroscopic screening analytical approaches together with chemometric pattern recognition techniques were applied and compared in addressing two wine authentication problems: discrimination of (i) varietal and (ii) year of vintage of red wines produced in the same oenological region. UV-Vis and FT-IR spectra of red wines were registered for all the samples and the principal features related to chemical composition of the samples were identified. Furthermore, for the discrimination and classification of red wines a multivariate data analysis was developed.

Spectral UV-Vis and FT-IR data were reduced to a small number of principal components (PCs) using principal component analysis (PCA) and then partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) were performed in order to develop qualitative classification and regression models. The first three PCs used to build the models explained 89% of the total variance in the case of UV-Vis data and 98% of the total variance for FR-IR data. PLS-DA results show that acceptable linear regression fits were observed for the varietal classification of wines based on FT-IR data. According to the obtained LDA classification rates, it can be affirmed that UV-Vis spectroscopy works better than FT-IR spectroscopy for the discrimination of red wines according to the grape variety, while classification of wines according to year of vintage was better for the LDA based FT-IR data model.

A clear discrimination of aged wines (over six years) was observed. The proposed methodologies can be used as accessible tools for the wine identity assurance without the need for costly and laborious chemical analysis, which makes them more accessible to many laboratories. IntroductionThe consumers have been increasingly interested in information on the characteristics and the quality of wine, especially with regard to composition, nutritional properties, and origin, and for that, establishing its authenticity is one of the most important aspects in food quality and safety. Wine is one of the most susceptible products to adulteration, despite that there are specific and strict regulations protecting its authenticity.

Determination of wine authenticity by analytical methods has the purpose of confirming label declarations and is of great interest to the industry and consumers. UV-Vis Absorption Spectra AnalysisThe raw UV-Vis absorption spectra of red wines with different aging periods are visually very similar. The UV-Vis electromagnetic spectrum does indeed contain distinguishing features for wines.From the UV-Vis absorption spectra, the 250–600 nm region is of interest, being associated with volatile compounds and polyphenols originating from the grapes and the subsequent fermentation and aging process, which contain π conjugated systems with hydroxyl-phenolic groups. A strong absorption band around 280/290 nm was observed and which was associated with colorless compounds (flavanol monomers, flavanol polymers or tannins, etc.). The absorption intensity around 520 nm in the visible region of the electromagnetic spectrum is characteristic to the red coloring substances (anthocyanin compounds). The shoulders from 310 nm and 320 nm are characteristic to the galloyl group (galloylated flavanols) and acylated anthocyanins (malvidin-3-p-coumarylglucoside), respectively.By comparing all of the spectra, noticeable differences in the spectra were found among the different red wine varieties and between wines belonging to the same varieties but with different harvest years.

The peak values of absorbance varied with the variety and aging period. In concordance with , slight peak absorption wavelength shifts were observed among the different spectra of the wines with different aging periods. FT-IR Spectra AnalysisThe Fourier Transform Infrared (FT-IR) spectra of different red wine varieties and different harvest years are visually very similar. The spectra of all wines showed similar peaks. Only minor differences can be observed in specific areas of the spectra.It was observed that water and ethanol absorption peaks dominate the spectrum. The broad peak found in the 4000–3000 cm −1 region is mainly due to the stretching vibration of the O–H bond of water, alcohols, and phenols.

Other water-related absorption bands were found at around 950 and 1460 nm, which are related to the third overtone of O–H. Multivariate Statistical AnalysisIn order to objectively study if these minor visual differences are related to wine variety and the aging process, a comparative chemometric study was carried out by using UV-Vis and FT-IR spectra data.In order to avoid strong absorption of water and spectral features that are not strictly related to wine composition such as ethanol, the 4000–3000 cm −1 and 1900–1600 cm −1 spectral regions were excluded from the FT-IR data prior to performing multivariate statistical analysis. Thus, mainly the “fingerprint” region of the FT-IR spectrum was selected for further statistical analysis since absorptions in this region are due mainly to the bending and skeletal vibrations associated with phenolic compounds.Full UV-Vis and fragmented FT-IR absorption spectra of all 39 wine samples were subjected to chemometric analysis, without prior signal pretreatments.

As a result, the data matrix is arranged in 156 rows (including replicates) and 610 columns (variables) in the case of UV-Vis data and 156 rows (including replicates) and 1055 columns in the case of FT-IR data. Principal Component Analysis (PCA)In order to handle the high dimensionality and complex nature of collected UV-Vis and FT-IR spectral data, a preliminary stage of feature extraction was considered in order to compress the relevant information for our process. PCA allows the visualization of the information in the data set in a few principal components while retaining the maximum possible variability within that set.

Principal component analysis (PCA) was used to reduce the dimensionality of the spectral data to a smaller number of components, facilitating the subsequent analysis and reducing the risk of incorrect inferences. PCA was performed on the UV-Vis and FT-IR spectra of the wine samples, separately, to examine the possible grouping of samples related to wine varieties and harvest years. The differences in the proportions and the compositions of the families of the natural wine components make the discrimination between different wine varieties and harvest years possible.shows the score plots of the UV-Vis and FT-IR data on the first three principal components (PCs), explaining 89% of the total variance of the UV-Vis data and 98% of the total variance of the FT-IR data. Score plot of the first 3 principal components (PCs) derived from ( A) UV-Vis spectra of different red wine varieties and ( B) FT–IR spectra of different red wine varieties.For both, UV-Vis and FT-IR data, it was observed that the investigated red wine varieties overlapped in all plots, and thus incomplete separations between red wine varieties were observed.

Chosen Few Urbano Rare Earth

However, the best separation among red wine varieties was achieved using the FT-IR spectral data. In accordance with similar results reported by other authors, the PCA plot shows that the replicate samples are grouped in the same cluster, but without overlapping, which is due to bottle to bottle variation. For each wine variety, it is worth noting that a dispersion of plots on the first three PCs dimension spaces was observed, being associated with the different climate of the harvest years investigated in this study. In addition, the trend of separation of Mamaia variety from the other red wine varieties was evident on the plot, which demonstrated the possibility of using PCA to distinguish this variety.In the case of harvest year discrimination, the scores for each sample on the first three PCs contain 89% of the total variance in the case of UV-Vis data and 97% of the total variance in the case of FT-IR data. From the scatter plots, it could be discovered that wines of different years distributed separately in the three-dimension area. From the visual inspection of the PCA score plot of the investigated red wines, considering both UV-Vis and FT-IR spectral data, it was possible to discriminate the aged wines from the 2009, 2010, and 2011 harvest years from the rest of the other wines. As can be observed, wines aged for longer periods of time (more than six years: 2009, 2010, and 2011) showed the highest PC1 values, all of which fall in the positive area of PC1.

On the contrary, the youngest samples (from 2012 to 2017), which showed the lowest PC1 values, fall into the negative area of PC1.The eigenvectors of the first two PCs derived from the UV-Vis and FT-IR data were investigated to interpret the basis of the separation among wine varieties and harvest years. The loading values for the first two PCs obtained using UV-Vis and FT-IR data are represented in.

In the case of UV-Vis data, the greatest loading values (above 0.80) for the PC1 (A) were observed at the wavelengths higher than 350 nm. This means that practically the whole visible range is affected by the differences between the varieties and aging process, the compounds responsible for this effect being the anthocyanin compounds. From the interpretation of the eigenvectors (loading values higher than 0.70) (B), it was concluded that differences between the FT-IR spectra of red wines can be observed in the following regions: 600–900 cm −1 associated with phenolics and phenyl derivatives; 1100–1400 cm −1 associated with primary alcohols, glycerol, sugars (glucose and fructose), aromatic groups of phenolic compounds organic acids, and aldehydes, tannins, pigmented polymers ; 2000–2300 cm −1 related to alcohols, sugars, as well as compounds containing aromatic rings and organic acids. Partial Least Squares Discriminant Analysis (PLS-DA)In wine analysis, the multivariate regression methods have been widely used to build calibration and prediction models, Partial Least Squares (PLS) regression being successfully applied for the determination of anthocyanins , antioxidant activity, total phenolic, and flavonoid contents.In this study, PLS-DA models were developed using the spectral range selected previously by applying the PCA analysis, a number of 15 PCs being used to find PLS-DA models that allow the maximum separation among classes of different wine categories. The accuracy of PLS-DA models was evaluated by the slope of the regression line (R 2) and the intercept of the regression line with the vertical axis (RMSEC—Root Mean Square Error of Calibration, and RMSEV—Root Mean Square Error of Validation). A value of R 2 close to 1 indicates a linear relationship between the predicted and actual wine category.

RMSEC refers to the uncertainty of calibration while RMSEV estimates how well the method will predict wine categories for unknown samples. When the slope of the regression line was greater and the intercept was smaller, the predictive ability of the model was better.shows the results derived from the different considered samples datasets, resulting in different classification models: a model considering the wine varietal discrimination and a model considering the discrimination of wines by harvest year. As can be observed, higher values of the R 2 and smaller values of RMSEC and RMSEV were obtained considering FT-IR spectral data, compared with the UV-Vis spectral data, indicating a good prediction capability of FT-IR based regression models, for both, varietal and harvest year discrimination. The regression models developed have proven to be good enough to correlate the classification criteria of studied wine with the FT-IR spectral data, the correlation coefficient (R 2) ranging from 0.813–0.860 for wine varietal classification, and from 0.626–0.872 for vintage year classification. The calculated RMSEV values for the models ranged between 0.197–0.261 for UV-Vis data and 0.135–0.182 for FT-IR data in the case of wine varietal discrimination, while the RMSEV values for the models ranged between 0.174–0.243 for UV-Vis data and 0.108–0.184 for FT-IR data in the case of wine vintage year discrimination, indicating lower uncertainty values concerning the methods’ prediction ability when considering FT-IR data, for both, varietal and vintage year discrimination. These results suggest that wine variety and vintage year can be better estimated using FT-IR data, compared to UV-Vis data.Good values of correlation coefficient (R 2) were obtained for Merlot, Mamaia, and Pinot noire varieties (0.911, 0.918, and 0.918 respectively), with RMSEC values of 0.121, 0.115, and 0.104 and RMSEV values of 0.157, 0.151, and 0.135, which represents satisfactory statistical significant values.

Wines from 2010 and 2015 vintage years show good correlation coefficients (R 2) (0.922 and 0.900) and a lower RMSEC (0.085 and 0.114) and RMSEV (0.108 and 0.142). Linear Discriminant Analysis (LDA)LDA was applied as a supervised method in order to classify the wines according to the grape variety and harvest year. Seeing as in all four data sets the number of variables were very high compared to the number of samples, LDA was always applied working on the scores of the first principal components: (1) 5PCs; (2) 10PCs; (3) 15PCs. LDA classification matrix for the cross-validation results of red wine varieties using (1) 3PCs; (2) 5PCs; (3) 10PCs were presented in.

For each data set, the number of principal components corresponding to higher total variance was always retained. It was observed that all first 15 PCs were required to adequately discriminate among varieties, corresponding to about 64.86% total variance for the UV-Vis data, while 43.59% total variance correspond to FT-IR data. Using a cross-validation technique to the UV-Vis spectroscopic data, higher prediction abilities were for Mamaia (86.96%) and Feteasca Neagra (73.08%) wines and a lower value for Cabernet Sauvignon (42.86%) and Pinot noire (52.94%) wines. In the case of FT-IR fingerprinting technique, the results of the cross-validation technique are less favorable, with prediction abilities ranging from 24.55% in the case of Merlot wines and 61.11% in the case of Pinot noire wines.The histograms on the LDA canonical variable for the UV-Vis and FT-IR data sets showing separation between wine varieties are presented in. Scatter plot of the first two discriminant functions showing separation between wine varieties: ( A) UV-Vis data and ( B) FT-IR data.Linear correlation revealed acceptable scores for two defined discriminant factors (F1 and F2). Using cross-validation technique, the results provided a percentage of predicted membership according to the wine variety of 85.89% (54.61% F1 and 31.28% F2) using UV-Vis data and 81.50% (48.42% F1 and 33.08% F2) using FT-IR data.As presented in A, 85.89% of the samples were correctly classified using UV-Vis data, including the control wine samples, with a clear separation of Mamaia and Feteasca Neagra wines and an overlap for Pinot noire, Merlot, and Cabernet Sauvignon wines.

The first discriminant function (F1) separated mainly Feteasca Neagra and Mamaia varieties, while the second one (F2) contributed to the discrimination of Feteasca Neagra wines from Cabernet Sauvignon and Merlot wines.When FT-IR data were considered (B), no clear separation between wines according to their varietal origin was shown, and the LDA score plots presented a considerable overlapping of the wines. The classification results (81.50% of the samples correctly classified) indicated that Mamaia, Merlot, and Cabernet Sauvignon wines can be associated with the first discriminant function (F1), while Pinot noire and Feteasca Neagra wines can be associated more with the second discrimination function (F2).The comparison of the LDA results obtained from the UV-Vis and FT-IR fingerprinting techniques showed that the UV-Vis spectroscopic techniques worked better than FT-IR for the discrimination of wines according to the grape variety. The better classification using UV-Vis data compared to FT-IR data suggests that the differences among different wine varieties can be attributed to the colored phenolic compounds that absorb in the UV-Vis region of the electromagnetic spectrum.For the discrimination of wines according to the harvest year, the LDA models were developed using the 15 PCs resulted by applying the PCA for the experimental data (UV-Vis and FT-IR). Shows the score plot of the first two PCs of the LDA model, which contain 64.96% of variance for the UV-Vis data and 74.22% for the FT-IR data from which it can be observed that the grouping is similar to the PCA score plot, with a clear discrimination of aged wines from the 2009, 2010, and 2011 harvest years.

In this case, the classification of wine according to the harvest year was better for the LDA based FT-IR data model. Scatter plot of the first two discriminant functions showing separation of different harvest years: ( A) UV-Vis data and ( B) FT-IR data.The LDA models had a similar overall rate of correct classification for both, UV-Vis and FT-IR data (67.59% and 62.96%, respectively) (see ). Generally, a conclusive result for vintage classification was achieved for all investigated years, less for young wines (2016 and 2017), using UV-Vis data, and for the year 2012 using FT-IR data. The technique of cross-validation applied during the test set validation show that the proposed model appears to be a promising chemometric approach, with classification abilities higher than 70.00% for 2009, 2010, 2012, 2015, and 2017 wines using UV-Vis data and for 2011, 2013, 2014, and 2016 wines using FT-IR data, respectively. Incorrect classification of some wines can be due to the fact that the group centroids for wines produced in some years are too close to each other due to the similarity of the UV-Vis and FT-IR fingerprints.The results from this study verified that differences exist between the wines from different red wine varieties and harvest years, confirming that the UV-Vis and FT-IR spectra contain important information for discriminating among samples. Although prediction models based on quantitative chromatographic data present better performances for wine varietal and harvest year discriminations , the results achieved by using screening UV-Vis and FT-IR spectroscopies should also be encouraged, because these techniques are simple (require minimal sample preparation and no highly skilled personnel for operation), rapid, low-cost, and thus are more accessible for routine investigations.

Choosing the appropriate statistical approach for data handling, it is an important aspect for developing applicable authentication methodologies.Screening methods called also non-target methods based on spectroscopic techniques certainly represent an option accessible to many laboratories interested in the issue of wine authentication. Nevertheless, some key challenges, including guidelines and legislation that regulate both development and validation of non-targeted methodologies, the difficulty of comparing the statistical results obtained with different chemometric software, and the need to develop dedicated software that contains well-defined algorithms, should be clarified. SamplesSamples of authentic wine produced in a single area (Dobrogea region, Romania) were chosen for this study in an effort to minimize the effects due to the geographical area and winemaking, which could substantially influence the UV-Vis and FT-IR wine fingerprints. Thus, a set of thirty-nine bottles of wine made from different red grape varieties produced at SCDVV Murfatlar covering an aging period of nine years (from 2009 to 2017) were used to build the statistical models (training wine set): Cabernet Sauvignon (n = 8), Merlot (n = 8), Pinot noire (n = 6), Feteasca Neagra (n = 9), and Mamaia (n = 8). For the validation of the proposed statistical models, additional spectral acquisitions were performed, representing 25% of the total acquired spectra.The wines were produced by microvinification using a classical red wine vinification procedure and kept under similar conditions during and after the winemaking process. The samples were bottled in 750 mL glass bottles and were stored in the cellar before the analysis.

A detailed description of the investigated red wine samples and the respective notation used in this study is provided in. Spectral MeasurementsAll samples were equilibrated at a room temperature of 25–30 °C (so that highly repeatable spectral acquisition can be achieved) before spectral measurements and scanned immediately after the wine bottles were opened in order to prevent oxidation reactions. Prior to the UV-Vis and FT-IR measurements, the samples were filtrated through 0.45 µm PTFE membranes in order to remove any possible impurities or turbidity.

Further sample preparation was not needed, resulting in a significant reduction in time and costs. For each instrumental technique, three spectra were averaged for samples employed in the calibration step and one spectra for samples included in the validation step.

Samples were scanned on a single day to eliminate the instrument drift affecting a particular variety.UV-Visible Spectroscopy: The UV-Vis spectrophotometric measurements were performed using an SPECORD 250 PLUS spectrophotometer (Analytik Jena, Jena, Germany) equipped with quartz cells with 1 mm path length. Data were collected using the Win Aspect Plus Spectra Manager™ II software (Analytik Jena, Jena, Germany). The absorbance spectra were recorded in the working range 190–800 nm with a step resolution of 1 nm. Deionized water was used for the reference scan.FT-IR Spectroscopy: All spectra were collected in absorbance mode in the mid infrared (MIR) region (500–4000 cm −1) with a resolution of 4 cm −1, using an FT-IR spectrometer, Bruker ALPHA-E (Bruker Optik GmbH, Ettlingen, Germany), equipped with an ATR system (Attenuated Total Reflectance) with Eco-ZnSe crystal. The OPUS Spectroscopy Software version 7.0 was used for spectra collection and instrument diagnostics (Bruker Optik GmbH, Ettlingen, Germany). Single beam spectra of the samples were obtained and corrected against the water as background. A total of 32 scans were averaged for each spectrum.

The ZnSe crystal was carefully cleaned with ultrapure water between measurements and dried with nitrogen gas after each experiment to ensure the best possible sample spectra. A total of 500 µL of each sample were added directly in the ATR cell sample.In order to identify the main functional groups that absorb in the UV-Vis and FT-IR regions of the electromagnetic spectrum, a qualitative analysis of the main spectral regions for the investigated wines was performed by comparing with data from the literature. ConclusionsThis study proves the usefulness of UV-Vis and FT-IR screening techniques coupled with multivariate statistical analysis for red wine varietal classification and vintage year prediction. LDA applied as a classification technique on the four data matrices provided satisfactory classification results, UV-Vis spectroscopy being more appropriate for varietal discrimination of red wines, while FT-IR spectroscopy was more efficient for the prediction of wine vintage year. It was very difficult to discriminate between Cabernet Sauvignon, Merlot, and Pinot noire wines, and thus predicting different blend compositions made from these varieties becomes a challenging topic. Both UV-Vis and FT-IR spectroscopic techniques discriminate wines aged more than six years, due to the formation of new compounds during the wine maturation process.

The regression models developed have proven to be good enough to correlate the FT-IR spectral data with wine variety and harvest year. However, the similarities between some wines and the nonselective nature of UV-Vis and FT-IR techniques limit the precision of the classification models.For a reliable wine authenticity assessment process based on screening spectroscopic techniques, the development of robust spectral databases incorporating as many wine samples (covering the variation related to regional conditions, vineyard management, and winemaking practices) as possible, is encouraged.