Hierarchical Cluster Analysis Models Research Articles

Pilot Study on the Use of Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy for Diagnosing and Characterizing Cardiac Amyloidosis.

Amyloidosis diagnosis relies on Congo red staining with immunohistochemistry and immunofluorescence for subtyping but lacks sensitivity and specificity. Laser-microdissection mass spectroscopy offers better accuracy but is complex and requires extensive sample preparation. Attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy offers a promising alternative for amyloidosis characterization. Cardiac tissue sections from nine patients with amyloidosis and 20 heart transplant recipients were analyzed using ATR-FTIR spectroscopy. Partial least squares discriminant analysis (PLS-DA), principal component analysis (PCA), and hierarchical cluster analysis (HCA) models were used to differentiate healthy post-transplant cardiac tissue from amyloidosis samples and identify amyloidosis subtypes [κ light chain (n = 1), λ light chain (n = 3), and transthyretin (n = 5)]. Leave-one-out cross-validation (LOOCV) was employed to assess the performance of the PLS-DA model. Significant spectral differences were found in the 1700-1500 cm-1 and 1300-1200 cm-1 regions, primarily related to proteins. The PLS-DA model explained 85.8% of the variance, showing clear clustering between groups. PCA in the 1712-1711 cm-1, 1666-1646 cm-1, and 1385-1383 cm-1 regions also identified two clear clusters. The PCA and the HCA model in the 1646-1642 cm-1 region distinguished κ light chain, λ light chain, and transthyretin cases. This pilot study suggests ATR-FTIR spectroscopy as a novel, non-destructive, rapid, and inexpensive tool for diagnosing and subtyping amyloidosis. This study was limited by a small dataset and variability in measurements across different instruments and laboratories. The PLS-DA model's performance may suffer from overfitting and class imbalance. Larger, more diverse datasets are needed for validation.

A comprehensive quality evaluation of Cimicifugae Rhizoma using UPLC-Q-Orbitrap-MS/MS coupled with multivariate chemometric methods.

Cimicifugae Rhizoma, known in Chinese as Shengma, is a common medicinal material in traditional Chinese medicine (TCM), mainly used for treating wind-heat headaches, sore throat, uterine prolapse, and other diseases. An approach using a combination of ultra-performance liquid chromatography (UPLC), mass spectrometry (MS), and multivariate chemometric methods was designed to assess the quality of Cimicifugae Rhizoma. All materials were crushed into powder and the powdered sample was dissolved in 70% aqueous methanol for sonicating. Chemometric methods, including hierarchical cluster analysis (HCA), principal component analysis (PCA), and orthogonal partial least squares discriminant analysis (OPLS-DA), were adopted to classify and perform a comprehensive visualization study of Cimicifugae Rhizoma. The unsupervised recognition models of HCA and PCA obtained a preliminary classification and provided a basis for classification. In addition, we constructed a supervised OPLS-DA model and established a prediction set to further validate the explanatory power of the model for the variables and unknown samples. Exploratory work research found that the samples were divided into two groups, and the differences were related to appearance traits. The correct classification of the prediction set also demonstrates a strong predictive ability of the models for new samples. Subsequently, six chemical makers were characterized by UPLC-Q-Orbitrap-MS/MS, and the content of four components was determined. The results of the content determination revealed the distribution of representative chemical markers caffeic acid, ferulic acid, isoferulic acid, and cimifugin in two classes of samples. This strategy can provide a reference for assessing the quality of Cimicifugae Rhizoma, which is significant for the clinical practice and quality control of Cimicifugae Rhizoma.

Designing Artemisinins with Antimalarial Potential, Combining Molecular Electrostatic Potential, Ligand-Heme Interaction and Multivariate Models

Artemisinins tested against W-2 strains of malaria falciparum are investigated with molecular electrostatic potential (MEP), in an attempt to identify key features of the compounds that are necessary for their activities, as well as to investigate likely interactions with the receptor in a biological process and to use that information to propose new molecules. In order to discover the best geometry involving the ligand-receptor complexes (heme) studied and help in the proposition of the new derivatives, molecular simulations of interactions between the most negative charged region around the peroxide and heme locates (the ones around the Fe2+ ion) were carried out. In addition, PCA (principal components analysis), HCA (hierarchical cluster analysis), SDA (stepwise discriminant analysis), and KNN (K-nearest neighbor) multivariate models were employed to investigate which descriptors are responsible for the classification between the higher and lower antimalarial activity of the compounds, and also this information was used to propose new potentially active molecules. The information accumulated in studies of MEP, molecular docking, and multivariate analysis supported the proposal of new structures with potential antimalarial activities. The multivariate models constructed were applied to the new structures and indicated numbers 19 and 20 as the most prominent for syntheses and biological assays.

Determination of metal contents in aromatic herbs and spices from Algeria: Chemometric approach

AbstractTrace metals (As, Cd, Co, Cr, Cu, Fe, Mg, Mn, Ni, Sr, and Zn) in acid digests of some aromatic herbs and spices (basil, fennel, laurel, mint, oregano, rosemary, thyme, black pepper, cinnamon, coriander, and cumin) were determined quantitatively using ICP‐AES method. The highest concentrations (μg g−1 dry matter) of each of the 11 investigated metals were found as follows: As (0.42) and Pb (1.6) in rosemary samples; Cd (0.1) in basil; Co (0.62), Cu (12.13), and Zn (52.26) in oregano; Cr (2.95) and Mg (3110) in fennel, Fe (494) and Ni (7.61) in cumin; Mn (192) in black pepper; and finally Sr (60.68) in mint. Some chemometric techniques such as principal component analysis (PCA), hierarchical cluster analysis (HCA), and partial least square discriminant analysis (PLS‐DA) were used on metallic concentrations data in an attempt to classify these aromatic herbs and spices. The unsupervised pattern recognition PCA and HCA models gave the same result about similarities and differences between the studied plant samples, and five clusters of similar aromatic herbs and spices samples were formed. In order to verify the results of this clustering, we used a supervised pattern recognition method called partial least square discriminant analysis (PLS‐DA). Classes were groups or clusters of similar plants obtained previously. A hierarchical model builder (HMB) based on four PLS‐DA models was used to simultaneous determination of the class of each sample. It was found that all samples were correctly classified by PLS‐DA in their original groups as determined by PCA and HCA.

A novel, non-invasive, multi-purpose and comprehensive method to date inks in real handwritten documents based on the monitoring of the dye ageing processes

Blue ink strokes belonging to 11 types of writing tools from 7 different brands were naturally aged in the darkness for 2 years and characterised by Vis-microspectrophotometry (Vis-MSP). Ink clustering and classification was performed by applying principal component analysis (PCA) and hierarchical cluster analysis (HCA) in order to subsequently assign an optimal orthogonal partial least squares (OPLS) model capable to predict the age of different inks. This method was found applicable to the exact dating of forged documents with ink strokes of up to 2 years old. The method was tested with blind samples supplied by the Forensic Science Unit of the Basque Country Police Department. Age predictions (p ​< ​0.05) with an accuracy error below 25% were obtained whenever: (i) the two ink replicas were placed inside the ellipse of the predicted score graph, (ii) the age of the ink was within the temporary application range of the OPLS model, and (iii) the ink was found to fit correctly in any of the classifications of the pen brands studied. The OPLS model was also able to detect those inks out of its temporary application range, if the ink samples were correctly clustered in the PCA model and/or classified in the HCA model with one of the pen brands studied, but one of the two ink replicas was placed outside the ellipse of the predicted score graph. Thus, these inks were temporarily above or below the application limit of the OPLS model of the corresponding pen brand.

Hydrochemical analysis and discrimination of mine water source of the Jiaojia gold mine area, China

To study the hydrochemical characteristics of main aquifers in Jiaojia gold mine area and their changing regularities, exploring the main sources of mine water, 244 water samples from the main aquifers and mine water were collected. The principal components were extracted by factor analysis and shows that with the increase of depth, the concentration of Na+ and Cl− increased significantly, the water–rock interaction becomes stronger, the water quality develops towards salinization, the total water quality, hydrodynamic conditions, and the connection between groundwater and surface water becomes worse. We extracted the main influential discriminant indexes using principal component analysis (PCA), that is, pH, MH4+, NO3−, NO2−, total hardness, TDS, Fe3+, SO42−, Cl− and F−. We weighted the extracted discriminant indexes using entropy weight method (EWM), that is in turn, 0.0002, 0.1883, 0.1272, 0.2061, 0.0680, 0.0613, 0.1461, 0.0573, 0.0844, and 0.0613. 55 water samples from each aquifer in the last 5 years were analyzed by hierarchical cluster analysis (HCA), and the relational degree between the main aquifers and mine water was judged by the distance between them; result shows that the main source of mine water is from the hanging wall of fault aquifer, followed by the footwall of fault aquifer. The mine water has little connection with bedrock weathered fractured aquifer, and almost no connection with Quaternary porous aquifer. The PCA–EWM–HCA model established in this paper provides a theoretical basis for identification of mine water inrush source and protection of underground water resources.

Portable and benchtop Raman spectrometers coupled to cluster analysis to identify quinine sulfate polymorphs in solid dosage forms and antimalarial drug quantification in solution by AuNPs-SERS with MCR-ALS.

This paper proposes for the first time: (a) a qualitative analytical method based on portable and benchtop backscattering Raman spectrometers coupled to hierarchical cluster analysis (HCA) and multivariate curve resolution - alternating least-squares (MCR-ALS) to identify two polymorphs of antimalarial quinine sulfate in commercial pharmaceutical tablets in their intact forms and (b) a quantitative analytical method based on gold nanoparticles (AuNPs) as active substrates for surface-enhanced Raman scattering (SERS) in combination with MCR-ALS to quantify quinine sulfate in commercial pharmaceutical tablets in solution. The pure concentration and spectral profiles recovered by MCR-ALS proved that both formulations present different polymorphs. These results were also confirmed by two clusters observed in the HCA model, according to their similarities within and among the samples that provided useful information about the homogeneity of different pharmaceutical manufacturing processes. AuNPs-SERS coupled to MCR-ALS was able to quantify quinine sulfate in the calibration range from 150.00 to 200.00 ng mL-1 even with the strong overlapping spectral profile of the background SERS signal, proving that it is a powerful ultrahigh sensitivity analytical method. This reduced linearity was validated throughout a large calibration range from 25.00 to 175.00 μg mL-1 used in a reference analytical method based on high performance liquid chromatography with a diode array detector (HPLC-DAD) coupled to MCR-ALS for analytical validation purposes, even in the presence of a coeluted compound. The analytical methods developed herein are fast, because second-order chromatographic data and first-order SERS spectroscopic data were obtained in less than 6 and 2 min, respectively. Concentrations of quinine sulfate were estimated with low root mean square error of prediction (RMSEP) values and a low relative error of prediction (REP%) in the range 1.8-4.5%.

Study of Brazilian Gasoline Quality Using Hydrogen Nuclear Magnetic Resonance (1H NMR) Spectroscopy and Chemometrics

The identification of gasoline adulteration by organic solvents is not an easy task, because compounds that constitute the solvents are already in gasoline composition. In this work, the use of hydrogen nuclear magnetic resonance (1H NMR) with a statistical approach for identifying gasoline adulteration by organic solvents is described. Both principal component analysis (PCA) and hierarchical cluster analysis (HCA) from NMR data of 47 commercial samples allowed the distinction between conform and nonconform samples. The 1H NMR−PCA and 1H NMR−HCA models were evaluated through the analyses of 21 intentionally adulterated samples, which showed a tendency to meet in the nonconform group with the increase of the solvent concentration.

A study on the influence of molecular properties in the psychoactivity of cannabinoid compounds

Several molecular properties are calculated for a set of 26 cannabinoid compounds with the goal of connecting the psychoactivity of the compounds with an appropriate set of calculated properties. For this purpose we used quantum chemical (the AM1 semi-empirical method) and chemometric methods. The AM1 method was employed to calculate the set of quantum chemical molecular properties and the chemometric methods were employed with the aim of selecting the most relevant properties to be correlated with psychoactivity. The chemometric methods used were Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA) and the K-Nearest Neighbor (KNN) method. The chemometric analysis showed that an electronic property (energy of LUMO), a hydrophobic property (log P), a steric property (volume of the substituent at the C4 position) and a topological property (Lovasz-Pelikan index) were the most important variables for the separation between the psychoactive and psychoinactive compounds. In order to validate our PCA, HCA and KNN results, eight new cannabinoid compounds (with known psychoactivity) were used in a prediction study and were classified correctly by the methods used in this work, indicating that our PCA, HCA and KNN models are able to predict reliable psychoactivity of cannabinoid compounds.