scholarly journals Pattern recognition methods as supplementary techniques for identification of salicylamide — cyclodextrins inclusion complexes

2012 ◽  
Vol 10 (5) ◽  
pp. 1534-1546 ◽  
Author(s):  
Ewa Kosecka-Judin ◽  
Marek Wesolowski ◽  
Dominik Paukszta
Keyword(s):  
Pattern Recognition ◽  
Inclusion Complexes ◽  
Solid Phase ◽  
Principal Component ◽  
Phase Solubility ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Pattern Recognition Methods ◽  
X Ray Scattering

AbstractThe objective of this study was to learn whether or not the pattern recognition methods, such as agglomerative cluster analysis (CA) and principal component analysis (PCA), can be used as supplementary techniques for identification of salicylamide (SAA) inclusion complexes with β-cyclodextrin (β-CD) and 2-hydroxypropyl-β-cyclodextrin (HP-β-CD). To do this, phase-solubility of SAA in the presence of the cyclodextrins was studied by the Higuchi-Connors method, which showed that the cyclodextrins enhanced the solubility of SAA in water as compared to that of the drug. Next, the solid phase complexes of the drug with β-CD and HP-β-CD were prepared by using the coprecipitation, precipitation-evaporation, and kneading methods. Identification of the inclusion complexes was performed by using thermal analysis, infrared spectroscopy, and wide angle X-ray scattering. Two multivariate statistical methods, CA and PCA, were used as the supplementary techniques for identification of the inclusion complexes. The results of the statistical analysis have shown that CA and PCA are helpful for interpretation of the thermoanalytical and spectral data. Moreover, these methods enabled proper classification of the products in all doubtful cases. They can be used as supplementary techniques to verify the conclusions of the above-mentioned standard methods.

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.

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 Characterization of the Volatile Profile of Cultivated and Wild-Type Italian Celery (Apium graveolens L.) Varieties by HS-SPME/GC-MS

2021 ◽  
Vol 11 (13) ◽  
pp. 5855
Author(s):  
Samantha Reale ◽  
Valter Di Cecco ◽  
Francesca Di Donato ◽  
Luciano Di Martino ◽  
Aurelio Manzi ◽  
...  
Keyword(s):  
Solid Phase ◽  
Principal Component ◽  
Apium Graveolens ◽  
Gas Chromatography Mass Spectrometry ◽  
Bioactive Metabolites ◽  
Chemical Components ◽  
Volatile Profile ◽  
Wild Type ◽  
Multivariate Statistical ◽  
Clear Differentiation

Celery (Apium graveolens L.) is a vegetable belonging to the Apiaceae family that is widely used for its distinct flavor and contains a variety of bioactive metabolites with healthy properties. Some celery ecotypes cultivated in specific territories of Italy have recently attracted the attention of consumers and scientists because of their peculiar sensorial and nutritional properties. In this work, the volatile profiles of white celery “Sedano Bianco di Sperlonga” Protected Geographical Indication (PGI) ecotype, black celery “Sedano Nero di Torricella Peligna” and wild-type celery were investigated using head-space solid-phase microextraction combined with gas-chromatography/mass spectrometry (HS-SPME/GC-MS) and compared to that of the common ribbed celery. Exploratory multivariate statistical analyses were conducted using principal component analysis (PCA) on HS-SPME/GC-MS patterns, separately collected from celery leaves and petioles, to assess similarity/dissimilarity in the flavor composition of the investigated varieties. PCA revealed a clear differentiation of wild-type celery from the cultivated varieties. Among the cultivated varieties, black celery “Sedano Nero di Torricella Peligna” exhibited a significantly different composition in volatile profile in both leaves and petioles compared to the white celery and the prevalent commercial variety. The chemical components of aroma, potentially useful for the classification of celery according to the variety/origin, were identified.

Evaluation of graphical and multivariate statistical methods for classification of water chemistry data

2002 ◽  
Vol 10 (4) ◽  
pp. 455-474 ◽  
Author(s):  
Cüneyt Güler ◽  
Geoffrey D. Thyne ◽  
John E. McCray ◽  
Keith A. Turner
Keyword(s):  
Water Chemistry ◽  
Statistical Methods ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Chemistry Data

Classification and Prediction of Goldfish Population and Water Quality Using a Potentiometric E-Tongue

2017 ◽  
Vol 60 (4) ◽  
pp. 1037-1044
Author(s):  
Zhenbo Wei ◽  
Yu Zhao ◽  
Jun Wang
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Pattern Recognition ◽  
Comprehensive Evaluation ◽  
Back Propagation ◽  
Correlation Coefficients ◽  
Principal Component ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Pattern Recognition Methods

Abstract. In this study, a potentiometric E-tongue was employed for comprehensive evaluation of water quality and goldfish population with the help of pattern recognition methods. Four water quality parameters, i.e., pH and concentrations of dissolved oxygen (DO), nitrite (NO2-N), and ammonium (NH3-N), were tested by conventional analysis methods. The differences in water quality parameters between samples were revealed by two-way analysis of variance (ANOVA). The cultivation days and goldfish population were classified well by principal component analysis (PCA) and canonical discriminant analysis (CDA), and the distribution of each sample was clearer in CDA score plots than in PCA score plots. The cultivation days, goldfish population, and water parameters were predicted by a T-S fuzzy neural network (TSFNN) and back-propagation artificial neural network (BPANN). BPANN performed better than TSFNN in the prediction, and all fitting correlation coefficients were >0.90. The results indicated that the potentiometric E-tongue coupled with pattern recognition methods could be applied as a rapid method for the determination and evaluation of water quality and goldfish population. Keywords: Classify, E-tongue, Goldfish water, Prediction.

scholarly journals Classification of Wines from Five Spanish Origin Denominations by Aromatic Compound Analysis

2010 ◽  
Vol 93 (6) ◽  
pp. 1916-1922 ◽  
Author(s):  
Cecilia Sáenz ◽  
Trinidad Cedráenzn ◽  
Susana Cabredo
Keyword(s):  
Discriminant Analysis ◽  
Solid Phase ◽  
Principal Component ◽  
Decanoic Acid ◽  
Multivariate Statistical ◽  
Ethyl Hexanoate ◽  
La Mancha ◽  
Diethyl Succinate ◽  
Ethyl Octanoate

Abstract Wine is a complex matrix in which aroma compounds play an important role in the characterization of the flavor pattern of a given wine. Twelve volatile compounds were determined in 244 samples of Spanish red wines from different denominations of origin: Rioja, Navarra, Valdepeas, La Mancha, and Cariena. The samples were analyzed by GC using headspace solid-phase microextraction. The concentration (mg/mL) intervals obtained were 3-methyl-butyl acetate (3.9 to 116), 3-methyl-1-butanol (93 to 724), ethyl hexanoate (0.8 to 39), 1-hexanol (0.3 to 6.7), ethyl octanoate (1.4 to 41), diethyl succinate (0.2 to 13), 2-phenyl ethyl acetate (0 to 5.3), hexanoic acid (0 to 8.3), geraniol (0 to 3.0), 2-phenylethanol (1.5 to 56), octanoic acid (0 to 20), and decanoic acid (0 to 3.3). Wines were classified by multivariate statistical methods: principal component analysis, and lineal discriminant analysis. A correct differentiation among wines according to their origin was obtained by lineal discriminant analysis.

scholarly journals Physicochemical Properties of Honey from Marche, Central Italy: Classification of Unifloral and Multifloral Honeys by Multivariate Analysis

2014 ◽  
Vol 9 (11) ◽  
pp. 1934578X1400901 ◽  
Author(s):  
Cristina Truzzi ◽  
Silvia Illuminati ◽  
Anna Annibaldi ◽  
Carolina Finale ◽  
Monica Rossetti ◽  
...  
Keyword(s):  
Electrical Conductivity ◽  
Physicochemical Properties ◽  
Robinia Pseudoacacia ◽  
Central Italy ◽  
Physicochemical Characterization ◽  
Castanea Sativa ◽  
Principal Component ◽  
Pattern Recognition Methods ◽  
Multifloral Honey

The purpose of this study was the physicochemical characterization and classification of Italian honey from Marche Region with a chemometric approach. A total of 135 honeys of different botanical origins [acacia ( Robinia pseudoacacia L.), chestnut ( Castanea sativa), coriander ( Coriandrum sativum L.), lime ( Tilia spp.), sunflower ( Helianthus annuus L.), Metcalfa honeydew and multifloral honey] were considered. The average results of electrical conductivity (0.14 – 1.45 mS cm−1), pH (3.89 – 5.42), free acidity (10.9 – 39.0 meqNaOH kg−1), lactones (2.4 – 4.5 meqNaOH kg−1), total acidity (14.5 – 40.9 meqNaOH kg−1), proline (229–665 mg kg−1) and 5-(hydroxy-methyl)-2-furaldehyde (0.6–3.9 mg kg−1) content show wide variability among the analysed honey types, with statistically significant differences between the different honey types. Pattern recognition methods such as principal component analysis and discriminant analysis were performed in order to find a relationship between variables and types of honey and to classify honey on the basis of its physicochemical properties. The variables of electrical conductivity, acidity (free, lactones), pH and proline content exhibited higher discriminant power and provided enough information for the classification and distinction of unifloral honey types, but not for the classification of multifloral honey (100% and 85% of samples correctly classified, respectively).

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