scholarly journals A study of new antimalarial artemisinins through molecular modeling and multivariate analysis

2010 ◽  
Vol 75 (11) ◽  
pp. 1533-1548 ◽  
Author(s):  
João Ferreira ◽  
Antonio Figueiredo ◽  
Jardel Barbosa ◽  
Maria Cristino ◽  
Williams Macedo ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Antimalarial Activity ◽  
External Validation ◽  
Geometry Optimization ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Partial Least Square ◽  
Least Square ◽  
Basis Set ◽  
Validation Parameters

Artemisinin and 18 derivatives with antimalarial activity against W-2 strains of Plasmodium falciparum were studied through quantum chemistry and multivariate analysis. The geometry optimization of the structures was realized with the Hartree-Fock (HF) theory and 3-21G basis set. Maps of molecular electrostatic potential (MEP) and molecular docking were used to investigate the interaction between the ligands and the receptor (heme). Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were employed to select the most important descriptors related to activity. A predictive model was generated by the Partial Least Square (PLS) method through 15 molecules and 4 used as an external validation set, which were selected in the training set, the validation parameters of which are Q2 = 0.85 and R2 = 0.86. The model included as molecular parameters, the radial distribution function, RDF060e, the hydration energy, HE, and the distance between the O1 atom from the ligand and the iron atom from heme, d(Fe-O1). Thus, the synthesis of new derivatives may follow the results of the MEP maps and the PLS analysis.

scholarly journals Authentication of Greek PDO Kalamata Table Olives: A Novel Non-Target High Resolution Mass Spectrometric Approach

Molecules ◽  
2020 ◽  
Vol 25 (12) ◽  
pp. 2919
Author(s):  
Natasa P. Kalogiouri ◽  
Reza Aalizadeh ◽  
Marilena E. Dasenaki ◽  
Nikolaos S. Thomaidis
Keyword(s):  
High Performance ◽  
Model Development ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Table Olives ◽  
Method Performance ◽  
Validation Parameters ◽  
Targeted Screening ◽  
Mass Spectrometric Approach

Food science continually requires the development of novel analytical methods to prevent fraudulent actions and guarantee food authenticity. Greek table olives, one of the most emblematic and valuable Greek national products, are often subjected to economically motivated fraud. In this work, a novel ultra-high-performance liquid chromatography–quadrupole time of flight tandem mass spectrometry (UHPLC-QTOF-MS) analytical method was developed to detect the mislabeling of Greek PDO Kalamata table olives, and thereby establish their authenticity. A non-targeted screening workflow was applied, coupled to advanced chemometric techniques such as Principal Component Analysis (PCA) and Partial Least Square Discriminant Analysis (PLS-DA) in order to fingerprint and accurately discriminate PDO Greek Kalamata olives from Kalamata (or Kalamon) type olives from Egypt and Chile. The method performance was evaluated using a target set of phenolic compounds and several validation parameters were calculated. Overall, 65 table olive samples from Greece, Egypt, and Chile were analyzed and processed for the model development and its accuracy was validated. The robustness of the chemometric model was tested using 11 Greek Kalamon olive samples that were produced during the following crop year, 2018, and they were successfully classified as Greek Kalamon olives from Kalamata. Twenty-six characteristic authenticity markers were indicated to be responsible for the discrimination of Kalamon olives of different geographical origins.

scholarly journals Gas-phase volatilomic approaches for quality control of brewing hops based on simultaneous GC-MS-IMS and machine learning

2020 ◽  
Vol 412 (26) ◽  
pp. 7085-7097
Author(s):  
Rebecca Brendel ◽  
Sebastian Schwolow ◽  
Sascha Rohn ◽  
Philipp Weller
Keyword(s):  
Machine Learning ◽  
Quality Control ◽  
Detection System ◽  
Drift Time ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Learning Tools

Abstract For the first time, a prototype HS-GC-MS-IMS dual-detection system is presented for the analysis of volatile organic compounds (VOCs) in fields of quality control of brewing hop. With a soft ionization and drift time-based ion separation in IMS and a hard ionization and m/z-based separation in MS, substance identification in the case of co-elution was improved, substantially. Machine learning tools were used for a non-targeted screening of the complex VOC profiles of 65 different hop samples for similarity search by principal component analysis (PCA) followed by hierarchical cluster analysis (HCA). Partial least square regression (PLSR) was applied to investigate the observed correlation between the volatile profile and the α-acid content of hops and resulted in a standard error of prediction of only 1.04% α-acid. This promising volatilomic approach shows clearly the potential of HS-GC-MS-IMS in combination with machine learning for the enhancement of future quality assurance of hops.

QUANTITATIVE EVALUATION OF MULTIVARIATE ANALYSIS METHODS FOR EXCITATION–EMISSION SPECTROSCOPY

2013 ◽  
Vol 25 (02) ◽  
pp. 1250027 ◽  
Author(s):  
Chun Yu ◽  
Tzu-Chien Hsiao ◽  
Chii-Wann Lin
Keyword(s):  
Multivariate Analysis ◽  
System Integration ◽  
Multivariable Analysis ◽  
Principal Component ◽  
Automatic Measurement ◽  
Partial Least Square ◽  
Least Square ◽  
Prediction Ability ◽  
Analysis Methods ◽  
The Difference

Multivariable Analysis Methods have been used widely in Spectroscopy Analysis. Partial least square (PLS) and principal component analysis (PCA) are the two most popular methods due to their excellent ability in data components analysis and results prediction. This work derived 1D/2D PLS and 1D/2D PCA based on the viewpoint of three-layer artificial neural networks, and uses theoretical proving to figure out the essences of these two methods. Two 2D experimental dataset was used to verify the calibration and prediction ability, furthermore, the similarity and dissimilarity of PLS and PCA. The finding showed that both the 1D/2D PLS and 1D/2D PCA methods use maximum covariance and minimum sum of square error to figure out the relationship between independent and dependent variables. The dissimilarity is that, weight vectors calibration of PLS is cross-correlation, and that of PCA is autocorrelation. The difference causes that the PCA method would keep more principal characters than PLS under insufficient sample among and provides better calibration ability. PLS would provide higher performance in prediction under sufficient sample among. For the needs of system implementation of spectroscopic measurement and analysis, this study designed "Multivariate Analysis Toolkits for LabVIEW" for the convenient implementation in automatic measurement system integration.

scholarly journals Application of FTIR spectroscopy for traumatic axonal injury: a possible tool for estimating injury interval

2017 ◽  
Vol 37 (4) ◽  
Author(s):  
Ji Zhang ◽  
Ping Huang ◽  
Zhenyuan Wang ◽  
Hongmei Dong
Keyword(s):  
Ftir Spectroscopy ◽  
Forensic Science ◽  
Axonal Injury ◽  
External Validation ◽  
Hierarchical Cluster ◽  
Partial Least Square ◽  
Least Square ◽  
Traumatic Axonal Injury ◽  
Post Injury ◽  
Ir Microscopy

Traumatic axonal injury (TAI) is a progressive and secondary injury following traumatic brain injury (TBI). Despite extensive investigations in the field of forensic science and neurology, no effective methods are available to estimate TAI interval between injury and death. In the present study, Fourier transform IR (FTIR) spectroscopy with IR microscopy was applied to collect IR spectra in the corpus callosum (CC) of rats subjected to TAI at 12, 24, and 72 h post-injury compared with control animals. The classification amongst different groups was visualized based on the acquired dataset using hierarchical cluster analysis (HCA) and partial least square (PLS). Furthermore, the established PLS models were used to predict injury interval of TAI in the unknown sample dataset. The results showed that samples at different time points post-injury were distinguishable from each other, and biochemical changes in protein, lipid, and carbohydrate contributed to the differences. Then, the established PLS models provided a satisfactory prediction of injury periods between different sample groups in the external validation. The present study demonstrated the great potential of FTIR-based PLS algorithm as an objective tool for estimating injury intervals of TAI in the field of forensic science and neurology.

Forensic Discrimination of Latent Fingerprints Using Laser-Induced Breakdown Spectroscopy (LIBS) and Chemometric Approaches

2018 ◽  
Vol 72 (7) ◽  
pp. 1047-1056 ◽  
Author(s):  
Jun-Ho Yang ◽  
Jack J Yoh
Keyword(s):  
Multivariate Analysis ◽  
Laser Scanning ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Laser Induced Breakdown Spectroscopy ◽  
Latent Fingerprints ◽  
Breakdown Spectroscopy ◽  
Real Time Analysis ◽  
Laser Induced Breakdown

A novel technique is reported for separating overlapping latent fingerprints using chemometric approaches that combine laser-induced breakdown spectroscopy (LIBS) and multivariate analysis. The LIBS technique provides the capability of real time analysis and high frequency scanning as well as the data regarding the chemical composition of overlapping latent fingerprints. These spectra offer valuable information for the classification and reconstruction of overlapping latent fingerprints by implementing appropriate statistical multivariate analysis. The current study employs principal component analysis and partial least square methods for the classification of latent fingerprints from the LIBS spectra. This technique was successfully demonstrated through a classification study of four distinct latent fingerprints using classification methods such as soft independent modeling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA). The novel method yielded an accuracy of more than 85% and was proven to be sufficiently robust. Furthermore, through laser scanning analysis at a spatial interval of 125 µm, the overlapping fingerprints were reconstructed as separate two-dimensional forms.

scholarly journals Changes in the milk metabolome of the giant panda (Ailuropoda melanoleuca) with time after birth: Three phases in early lactation and progressive individual differences

2015 ◽  
Author(s):  
Tong Zhang ◽  
Rong Zhang ◽  
Liang Zhang ◽  
Zhihe Zhang ◽  
Rong Hou ◽  
...  
Keyword(s):  
Giant Panda ◽  
Phase 1 ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Partial Least Square ◽  
Least Square ◽  
Ailuropoda Melanoleuca ◽  
Early Lactation ◽  
Giant Pandas ◽  
Three Phases

Ursids (bears) in general, and giant pandas in particular, are highly altricial at birth. The components of bear milks and their changes with time may be uniquely adapted to nourish relatively immature neonates, protect them from pathogens, and support the maturation of neonatal digestive physiology. Serial milk samples collected from three giant pandas in early lactation were subjected to untargeted metabolite profiling and multivariate analysis. Changes in milk metabolites with time after birth were analysed by Principal Component Analysis, Hierarchical Cluster Analysis and further supported by Orthogonal Partial Least Square-Discriminant Analysis, revealing three phases of milk maturation: days 1-6 (Phase 1), days 7-20 (Phase 2), and beyond day 20 (Phase 3). While the compositions of Phase 1 milks were essentially indistinguishable among individuals, divergences emerged during the second week of lactation. OPLS regression analysis positioned against the growth rate of one cub tentatively inferred a correlation with changes in the abundance of a trisaccharide, isoglobotriose, previously observed to be a major oligosaccharide in ursid milks. Three artificial milk formulae used to feed giant panda cubs were also analysed, and were found to differ markedly in component content from natural panda milk. These findings have implications for the dependence of the ontogeny of all species of bears, and potentially other members of the Carnivora and beyond, on the complexity and sequential changes in maternal provision of micrometabolites in the immediate period after birth.

Pemanfaatan CFSRv2 untuk Statistical Downscaling menggunakan Principal Component Regression dan Partial Least Square

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Khairunnisa Khairunnisa ◽  
Rizka Pitri ◽  
Victor P Butar-Butar ◽  
Agus M Soleh
Keyword(s):  
Statistical Downscaling ◽  
General Circulation ◽  
Principal Component Regression ◽  
Circulation Model ◽  
Principal Component ◽  
Meteorological Station ◽  
Grid Size ◽  
Partial Least Square ◽  
Least Square ◽  
Output Data

This research used CFSRv2 data as output data general circulation model. CFSRv2 involves some variables data with high correlation, so in this research is using principal component regression (PCR) and partial least square (PLS) to solve the multicollinearity occurring in CFSRv2 data. This research aims to determine the best model between PCR and PLS to estimate rainfall at Bandung geophysical station, Bogor climatology station, Citeko meteorological station, and Jatiwangi meteorological station by comparing RMSEP value and correlation value. Size used was 3×3, 4×4, 5×5, 6×6, 7×7, 8×8, 9×9, and 11×11 that was located between (-40) N - (-90) S and 1050 E -1100 E with a grid size of 0.5×0.5 The PLS model was the best model used in stastistical downscaling in this research than PCR model because of the PLS model obtained the lower RMSEP value and the higher correlation value. The best domain and RMSEP value for Bandung geophysical station, Bogor climatology station, Citeko meteorological station, and Jatiwangi meteorological station is 9 × 9 with 100.06, 6 × 6 with 194.3, 8 × 8 with 117.6, and 6 × 6 with 108.2, respectively.

Quantitative Structure-activity Relationship Analysis for Predicting Lipophilicity of Aniline Derivatives (Including some Pharmaceutical Compounds)

2019 ◽  
Vol 22 (5) ◽  
pp. 333-345
Author(s):  
Morteza Rezaei ◽  
Esmat Mohammadinasab ◽  
Tahere Momeni Esfahani
Keyword(s):  
Density Functional ◽  
Pharmaceutical Compounds ◽  
Partial Least Square ◽  
Least Square ◽  
Basis Set ◽  
Van Der Waals Volume ◽  
Relationship Analysis ◽  
Aniline Derivatives ◽  
Aqueous Solvent ◽  
Qsar Models

Background: In this study, we used a hierarchical approach to develop quantitative structureactivity relationship (QSAR) models for modeling lipophilicity of a set of 81 aniline derivatives containing some pharmaceutical compounds. Objective: The multiple linear regression (MLR), principal component regression (PCR) and partial least square regression (PLSR) methods were utilized to construct QSAR models. Materials & Methods: Quantum mechanical calculations at the density functional theory level and 6- 311++G** basis set were carried out to obtain the optimized geometry and then, the comprehensive set of molecular descriptors was computed by using the Dragon software. Genetic algorithm (GA) was applied to select suitable descriptors which have the most correlation with lipophilicity of the studied compounds. Results: It was identified that such descriptors as Barysz matrix (SEigZ), hydrophilicity factor (Hy), Moriguchi octanol-water partition coefficient (MLOGP), electrophilicity (ω/eV) van der Waals volume (vWV) and lethal concentration (LC50/molkg-1) are the best descriptors for QSAR modeling. The high correlation coefficients and the low prediction errors for MLR, PCR and PLSR methods confirmed good predictability of the three models. Conclusion: In present study, the high correlation between experimental and predicted logP values of aniline derivatives indicated the validation and the good quality of the resulting three regression methods, but MLR regression procedure was a little better than the PCR and PLSR methods. It was concluded that the studied aniline derivatives are not hydrophilic compounds and this means these compounds hardly dissolve in water or an aqueous solvent.

scholarly journals A Novel Validated Injectable Colistimethate Sodium Analysis Combining Advanced Chemometrics and Design of Experiments

Molecules ◽  
2021 ◽  
Vol 26 (6) ◽  
pp. 1546
Author(s):  
Ioanna Dagla ◽  
Anthony Tsarbopoulos ◽  
Evagelos Gikas
Keyword(s):  
Design Of Experiments ◽  
High Performance ◽  
Similarity Index ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Distance Measures ◽  
Linear Regression Models ◽  
Colistimethate Sodium

Colistimethate sodium (CMS) is widely administrated for the treatment of life-threatening infections caused by multidrug-resistant Gram-negative bacteria. Until now, the quality control of CMS formulations has been based on microbiological assays. Herein, an ultra-high-performance liquid chromatography coupled to ultraviolet detector methodology was developed for the quantitation of CMS in injectable formulations. The design of experiments was performed for the optimization of the chromatographic parameters. The chromatographic separation was achieved using a Waters Acquity BEH C8 column employing gradient elution with a mobile phase consisting of (A) 0.001 M aq. ammonium formate and (B) methanol/acetonitrile 79/21 (v/v). CMS compounds were detected at 214 nm. In all, 23 univariate linear-regression models were constructed to measure CMS compounds separately, and one partial least-square regression (PLSr) model constructed to assess the total CMS amount in formulations. The method was validated over the range 100–220 μg mL−1. The developed methodology was employed to analyze several batches of CMS injectable formulations that were also compared against a reference batch employing a Principal Component Analysis, similarity and distance measures, heatmaps and the structural similarity index. The methodology was based on freely available software in order to be readily available for the pharmaceutical industry.

scholarly journals The inflammatory profile of cerebrospinal fluid, plasma, and saliva from patients with severe neuropathic pain and healthy controls-a pilot study

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Mika Jönsson ◽  
Björn Gerdle ◽  
Bijar Ghafouri ◽  
Emmanuel Bäckryd
Keyword(s):  
Cerebrospinal Fluid ◽  
Neuropathic Pain ◽  
Pain Intensity ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Therapeutic Interventions ◽  
Healthy Controls ◽  
Inflammatory Profile

Abstract Background Neuropathic pain (NeuP) is a complex, debilitating condition of the somatosensory system, where dysregulation between pro- and anti-inflammatory cytokines and chemokines are believed to play a pivotal role. As of date, there is no ubiquitously accepted diagnostic test for NeuP and current therapeutic interventions are lacking in efficacy. The aim of this study was to investigate the ability of three biofluids - saliva, plasma, and cerebrospinal fluid (CSF), to discriminate an inflammatory profile at a central, systemic, and peripheral level in NeuP patients compared to healthy controls. Methods The concentrations of 71 cytokines, chemokines and growth factors in saliva, plasma, and CSF samples from 13 patients with peripheral NeuP and 13 healthy controls were analyzed using a multiplex-immunoassay based on an electrochemiluminescent detection method. The NeuP patients were recruited from a clinical trial of intrathecal bolus injection of ziconotide (ClinicalTrials.gov identifier NCT01373983). Multivariate data analysis (principal component analysis and orthogonal partial least square regression) was used to identify proteins significant for group discrimination and protein correlation to pain intensity. Proteins with variable influence of projection (VIP) value higher than 1 (combined with the jack-knifed confidence intervals in the coefficients plot not including zero) were considered significant. Results We found 17 cytokines/chemokines that were significantly up- or down-regulated in NeuP patients compared to healthy controls. Of these 17 proteins, 8 were from saliva, 7 from plasma, and 2 from CSF samples. The correlation analysis showed that the most important proteins that correlated to pain intensity were found in plasma (VIP > 1). Conclusions Investigation of the inflammatory profile of NeuP showed that most of the significant proteins for group separation were found in the less invasive biofluids of saliva and plasma. Within the NeuP patient group it was also seen that proteins in plasma had the highest correlation to pain intensity. These preliminary results indicate a potential for further biomarker research in the more easily accessible biofluids of saliva and plasma for chronic peripheral neuropathic pain where a combination of YKL-40 and MIP-1α in saliva might be of special interest for future studies that also include other non-neuropathic pain states.

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