Classification of wine distillates using multivariate statistical methods based on their direct GC-MS analysis

2015 ◽  
Vol 69 (3) ◽  
Author(s):  
Ivan Špánik ◽  
Luboš Čirka ◽  
Pavel Májek
Keyword(s):  
Geographical Origin ◽  
Direct Injection ◽  
Principal Component ◽  
Processing Technology ◽  
Time Interval ◽  
Multivariate Statistical ◽  
Ms Analysis ◽  
Peak Areas ◽  
Chromatographic Peaks

AbstractThis work describes a novel methodology for the recognition of brandies based on direct injection of a raw sample followed by GC-MS analysis. Direct injection was chosen for its simplicity and the fact that the composition of the samples analysed remains unchanged compared to original brandy. The repeatability of the analytical procedure was evaluated by a comparison of the peak areas for randomly selected compounds obtained from 10 parallel measurements. A novel chemometric procedure was investigated in order to separate the samples studied on the basis of their geographical origin, processing technology or maturation time. In this procedure, a principal component analysis was applied to full chromatograms to select the time interval that shows the significant differences between the samples studied. It was shown that the chromatogram recorded at 36-39 min bore the maximal differences, hence it could be used to classify the brandy samples. The chromatographic peaks found within this time interval were identified and their peak areas determined. These compounds could be used as specific markers for determining geographical origin or processing technology.

scholarly journals Metabolomics-based authentication of wines according to grape variety

2019 ◽  
Vol 37 (No. 4) ◽  
pp. 239-245 ◽  
Author(s):  
Leos Uttl ◽  
Kamila Hurkova ◽  
Vladimir Kocourek ◽  
Jana Pulkrabova ◽  
Monika Tomaniova ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Statistical Models ◽  
High Performance ◽  
Geographical Origin ◽  
Principal Component ◽  
White Wine ◽  
Tandem Mass ◽  
Grape Variety ◽  
Varietal Identification

In 2008, the European Commission highlighted the risk of wine mislabelling regarding the geographical origin and varietal identification. While analytical methods for the identification of wine by geographical origin exist, a reliable strategy for authentication of wine variety is still missing. Here, we investigate the suitability of the metabolomic fingerprinting of ethyl acetate wine extracts, using ultra-high-performance liquid chromatography coupled to high-resolution tandem mass spectrometry. In total, 43 white wine samples (three varieties) were analysed within our study. The generated data were processed by principal component analysis and then by partial least squares discriminant analysis. The resulting statistical models were validated and assessed according to their R2 (cum) and Q2 (cum) parameters. The most promising models were based on positive ionisation data, enabling successful classification of 92% of wine samples.

scholarly journals Wine Traceability Using Chemical Analysis, Isotopic Parameters, and Sensory Profiles

Beverages ◽  
2018 ◽  
Vol 4 (3) ◽  
pp. 54 ◽  
Author(s):  
Federica Bonello ◽  
Maria Cravero ◽  
Valentina Dell’Oro ◽  
Christos Tsolakis ◽  
Aldo Ciambotti
Keyword(s):  
Statistical Analysis ◽  
Geographical Origin ◽  
Central Italy ◽  
Principal Component ◽  
Climatic Conditions ◽  
Sensory Profile ◽  
Red Wines ◽  
Multivariate Statistical ◽  
Sensory Parameters ◽  
Sensory Profiles

NMR/IRMS techniques are now widely used to assess the geographical origin of wines. The sensory profile of a wine is also an interesting method of characterizing its origin. This study aimed at elaborating chemical, isotopic, and sensory parameters by means of statistical analysis. The data were determined in some Italian white wines—Verdicchio and Fiano—and red wines—Refosco dal Peduncolo Rosso and Nero d’Avola—produced from grapes grown in two different regions with different soil and climatic conditions during the years 2009–2010. The grapes were cultivated in Veneto (northwest Italy) and Marches (central Italy). The results show that the multivariate statistical analysis PCA (Principal Component Analysis) of all the data can be a useful tool to characterize the vintage and identify the origin of wines produced from different varieties. Moreover, it could discriminate wines of the same variety produced in regions with different soil and climatic conditions.

scholarly journals Metabolomics Approach for Discrimination and Quality Control of Natural and Commercial Fallopia multiflora Products in Vietnam

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Nguyen Thi Thoa ◽  
Nguyen Hai Dang ◽  
Do Hoang Giang ◽  
Nguyen Thi Thu Minh ◽  
Nguyen Tien Dat
Keyword(s):  
Clustering Analysis ◽  
Principal Component ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Fda Guidance ◽  
Promising Tool ◽  
Precision And Accuracy ◽  
And Control

A precise HPLC-DAD-based quantification together with the metabolomics statistical method was developed to distinguish and control the quality of Fallopia multiflora, a popular medicinal material in Vietnam. Multivariate statistical methods such as hierarchical clustering analysis and principal component analysis were utilized to compare and discriminate six natural and twelve commercial samples. 2,3,4′,5-Tetrahydroxystilbene 2-O-β-D-glucopyranoside (THSG) (1), emodin (4), and the new compound 6-hydroxymusizin 8-O-α-D-apiofuranosyl-(1⟶6)-β-D-glucopyranoside (5) could be considered as important markers for classification of F. multiflora. Furthermore, seven phenolics were quantified that the variation in the contents of selected metabolites revealed the differences in the quality of natural and commercial samples. Recovery of the compounds from the analytes was more than 98%, while the limits of detection (LOD) and the limits of quantitation (LOQ) ranged from 0.5 to 6.6 μg/ml and 1.5 to 19.8 μg/ml, respectively. The linearity, LOD, LOQ, precision, and accuracy satisfied the criteria FDA guidance on bioanalytical methods. Overall, this method is a promising tool for discrimination and quality assurance of F. multiflora products.

scholarly journals Multivariate statistical treatment of plant extract compositional data: Average mass scan of the total ion chromatogram (AMS) approach

2013 ◽  
Vol 11 (1) ◽  
pp. 85-99
Author(s):  
Polina Blagojevic ◽  
Niko Radulovic
Keyword(s):  
Clustering Analysis ◽  
Compositional Data ◽  
Statistical Treatment ◽  
Principal Component ◽  
Chemical Compositions ◽  
Agglomerative Hierarchical Clustering ◽  
Multivariate Statistical ◽  
Time Saving ◽  
Average Mass ◽  
Peak Areas

It was recently confirmed that relative abundances of m/z values of the average mass scan of the total GC chromatograms (AMS) are suitable variables for multivariate statistical comparison (MVA) of essential oils. These are even more applicable, reliable and faster than the traditionally used variables-percentages (peak areas) of individual oil constituents. Herein, we have explored if AMS-derived variables are appropriate for MVA comparison of plant solvent extract compositional data. To achieve this, average mass scans of the total GC chromatograms and chemical compositions (relative percentages) of eight diethyl ether extracts (six different species; samples were analyzed using GC-FID and GC-MS; data from the literature) were separately compared using two MVA methods: agglomerative hierarchical clustering analysis and principal component analysis. The obtained results strongly suggest that MVA of complex volatile mixtures (GC-MS analyzable fractions of plant solvent extracts), using the corresponding AMS, could be considered as a promising time saving tool for easy and reliable comparison purposes. The AMS approach gives comparable or even better results than the traditional method.

Evaluation of colon cancer elements contents in serum using statistical methods

2007 ◽  
Vol 61 (5) ◽  
Author(s):  
D. Milde ◽  
J. Macháček ◽  
V. Stužka
Keyword(s):  
Statistical Methods ◽  
Tnm Classification ◽  
Principal Component ◽  
Absorption Spectrometry ◽  
Acid Mixture ◽  
Multivariate Statistical Methods ◽  
Serum Samples ◽  
Multivariate Statistical ◽  
Colorectal Cancer Patients

AbstractClassification of normal and different cancer groups (TNM classification) with univariate and multivariate statistical methods according to the contents of Cu, Fe, Mn, Se, and Zn in blood serum is discussed. All serum samples were digested by acid mixture in a microwave mineralization unit prior to the analysis by atomic absorption spectrometry. Results show that univariate methods can distinguish normal and cancer groups. Level of selenium evaluated as arithmetic mean with its standard deviation in colorectal cancer patients was (42.61 ± 23.76) µg L−1. Retransformed mean was used to evaluate levels of managanese (11.99 ± 1.71) µg L−1, copper (1.05 ± 0.06) mg L−1, zinc (2.14 ± 0.21) mg L−1, and iron (1.82 ± 0.22) mg L−1. Conclusions of multivariate statistical procedures (principal component analysis, hierarchical, and k-means clustering) do not correlate very well with the division of serum samples according to the TNM classification.

scholarly journals Geochemical classification of groundwater using multivariate statistical analysis in Latvia

2016 ◽  
Vol 47 (4) ◽  
pp. 799-813 ◽  
Author(s):  
Inga Retike ◽  
Andis Kalvans ◽  
Konrads Popovs ◽  
Janis Bikse ◽  
Alise Babre ◽  
...  
Keyword(s):  
Trace Elements ◽  
Statistical Methods ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Ion Chemistry ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Gypsum Dissolution ◽  
Major Ion

Multivariate statistical methods – principal component analysis (PCA) and hierarchical cluster analysis (HCA) – are applied to identify geochemically distinct groundwater groups in the territory of Latvia. The main processes observed to be responsible for groundwater chemical composition are carbonate and gypsum dissolution, fresh and saltwater mixing and ion exchange. On the basis of major ion concentrations, eight clusters (C1–C8) are identified. C6 is interpreted as recharge water not in equilibrium with most sediment forming minerals. Water table aquifers affected by diffuse agricultural influences are found in C3. Groundwater in C4 reflects brine or seawater admixture and gypsum dissolution in C5. C7 and C2 belong to typical bicarbonate groundwater resulting from calcite and dolomite weathering. Extremely low Cl− and SO42− are observed in C8 and described as pre-industrial groundwater or a solely carbonate weathering result. Finally, C1 seems to be a poorly defined subgroup resulting from mixing between other groups. This research demonstrates the validity of applying multivariate statistical methods (PCA and HCA) on major ion chemistry to distribute characteristic trace elements in each cluster even when incomplete records of trace elements are present.

scholarly journals Evaluation of effect factors on water quality of Karun River in downstream and lake of the Gotvand-e-Olya Dam (SW Iran)

2019 ◽  
Vol 9 (7) ◽  
Author(s):  
M. Jahangiry Fard ◽  
H. Amanipoor ◽  
S. Battaleb-Looie ◽  
K. Ghanemi
Keyword(s):  
Water Quality ◽  
Saline Water ◽  
Principal Component ◽  
Time Interval ◽  
Time Sampling ◽  
Multivariate Statistical ◽  
Local Conditions ◽  
Ionic Ratios ◽  
Reverse Ion Exchange ◽  
Sw Iran

Abstract Outcrop of Gachsaran evaporative formation in the lake of Gotvand-e-Olya Dam in SW IRAN has posed a major challenge in terms of water quality. In the present study, multivariate statistical analysis, ionic ratios, and Piper diagram were utilized to investigate the effect of formation dissolution on water quality. Sampling was performed two times with a time interval of 6 months. The result showed that the types of downstream samples are Cl–Na and Cl–Ca, which are affected by the dissolution of Gachsaran Formation and reverse ion exchange. Due to the transmission of the saline water to the depth and layering of reservoir, the water types in the upper levels of dam’s lake are (SO4–Ca and HCO3–Ca) and (Cl–Ca and SO4–Ca) upon the first and second sampling, respectively. In both times, the clustering of the EC, TDS, Na, and Cl parameters demonstrates the effect of halite dissolution on water quality in downstream and lake of the dam. At the first sampling, the SO4 and HCO3 parameters are in one cluster that shows increasing calcareous formation dissolution. At the second time, the grouping of the Ca and SO4 parameters shows the effect of gypsum and anhydrite dissolution on water quality. In both sampling times, the stations are grouped based on distance to the dam such that upstream stations are in one cluster. Results of principal component analysis show that data of the first-time sampling are summarized into two factors that show the effect of the formation dissolution and rainfall effect on water quality, respectively. At the second-time sampling, studied parameters are summarized in one factor. Local conditions of the studied area indicate the dominant effect of formation dissolution on water quality. The results of ionic ratios confirm the effect of geological formation on water quality in the lake and downstream of the dam. Due to the water layering, in terms of salinity in the dam’s lake, the rate and discharge of water outflow of the dam also affect the water quality in downstream.

scholarly journals Use of energy-dispersive X-ray fluorescence combined with chemometric modelling to classify honey according to botanical variety and geographical origin

2019 ◽  
Vol 412 (2) ◽  
pp. 463-472 ◽  
Author(s):  
Yiannis Fiamegos ◽  
Catalina Dumitrascu ◽  
Michele Ghidotti ◽  
Maria Beatriz de la Calle Guntiñas
Keyword(s):  
Geographical Origin ◽  
External Validation ◽  
Principal Component ◽  
Energy Dispersive ◽  
Sample Treatment ◽  
Classification Models ◽  
Botanical Variety ◽  
X Ray ◽  
Food Commodities

AbstractHoney is one of the food commodities most frequently affected by fraud. Although addition of extraneous sugars is the most common type of fraud, analytical methods are also needed to detect origin masking and misdescription of botanical variety. In this work, multivariate analysis of the content of certain macro- and trace elements, determined by energy-dispersive X-ray fluorescence (ED-XRF) without any type of sample treatment, were used to classify honeys according to botanical variety and geographical origin. Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were used to create classification models for nine different botanical varieties—orange, robinia, lavender, rosemary, thyme, lime, chestnut, eucalyptus and manuka—and seven different geographical origins—Italy, Romania, Spain, Portugal, France, Hungary and New Zealand. Although characterised by 100% sensitivity, PCA models lacked specificity. The PLS-DA models constructed for specific combinations of botanical variety-country (BV-C) allowed the successful classification of honey samples, which was verified by external validation samples.

scholarly journals Rapid Quantitative Analysis of Ethanol and Prediction of Methanol Content in Traditional Fruit Brandies from Romania, using FTIR Spectroscopy and Chemometrics

2013 ◽  
Vol 41 (1) ◽  
pp. 143 ◽  
Author(s):  
Teodora Emilia COLDEA ◽  
Carmen SOCACIU ◽  
Florinela FETEA ◽  
FloricuÅ£a RANGA ◽  
Raluca Maria POP ◽  
...  
Keyword(s):  
Ftir Spectroscopy ◽  
Partial Least Squares Regression ◽  
Geographical Origin ◽  
Reference Method ◽  
Principal Component ◽  
Statistical Correlation ◽  
Good Prediction ◽  
Least Squares Regression ◽  
Peak Areas ◽  
Ftir Technique

Fourier Transform Infrared (FTIR) spectroscopy in combination with chemometrics were applied for the quality control of 26 fruit brandies made by traditional technology in Romania. Firstly, for the identification and quantitative evaluation of methanol and ethanol in such samples, 4 mixtures of methanol and ethanol standard solutions and a spiked sample were fingerprinted in the 1200 - 950 cm-1 FTIR spectral range, identifying specific wavelength of 1020 and 1112 for methanol, 1047 and 1087 for ethanol. Then, the FTIR spectra of all brandy samples in the range 3500 - 750 cm-1 was registered and the methanol and ethanol concentrations were determined according to the previous calibration. By PCA (Principal component analysis) of FTIR areas (1170 -1000 cm-1), the variability and discrimination between samples was possible, discriminating between the biological and geographical origin of brandy samples. Based on peak areas and intensities, it was predicted the concentration of methanol in all samples, using Partial least squares regression (PLS). The correlation between FTIR and the reference method (GC-FID) was well correlated and significant (p<0.05). It was demostrated that FTIR technique offer a good prediction and statistical correlation with GC-FID technique for methanol quantification.

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