Identification of key aromatic compounds in Congou black tea by partial least-square regression with variable importance of projection scores and gas chromatography-mass spectrometry/gas chromatography-olfactometry

2018 ◽  
Vol 98 (14) ◽  
pp. 5278-5286 ◽  
Author(s):  
Shihong Mao ◽  
Changqi Lu ◽  
Meifeng Li ◽  
Yulong Ye ◽  
Xu Wei ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Gas Chromatography ◽  
Aromatic Compounds ◽  
Black Tea ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Gas Chromatography Mass Spectrometry ◽  
Least Square Regression ◽  
Gas Chromatography Olfactometry

scholarly journals Characterization of α-Glucosidase Inhibitors from Clinacanthus nutans Lindau Leaves by Gas Chromatography-Mass Spectrometry-Based Metabolomics and Molecular Docking Simulation

Molecules ◽  
2018 ◽  
Vol 23 (9) ◽  
pp. 2402 ◽  
Author(s):  
Suganya Murugesu ◽  
Zalikha Ibrahim ◽  
Qamar-Uddin Ahmed ◽  
Nik-Idris Nik Yusoff ◽  
Bisha-Fathamah Uzir ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Gas Chromatography ◽  
Molecular Docking ◽  
Acid Methyl Ester ◽  
Docking Simulation ◽  
Partial Least Square ◽  
Least Square ◽  
Gas Chromatography Mass Spectrometry ◽  
Glucosidase Inhibitors ◽  
Clinacanthus Nutans

Background: Clinacanthus nutans (C. nutans) is an Acanthaceae herbal shrub traditionally consumed to treat various diseases including diabetes in Malaysia. This study was designed to evaluate the α-glucosidase inhibitory activity of C. nutans leaves extracts, and to identify the metabolites responsible for the bioactivity. Methods: Crude extract obtained from the dried leaves using 80% methanolic solution was further partitioned using different polarity solvents. The resultant extracts were investigated for their α-glucosidase inhibitory potential followed by metabolites profiling using the gas chromatography tandem with mass spectrometry (GC-MS). Results: Multivariate data analysis was developed by correlating the bioactivity, and GC-MS data generated a suitable partial least square (PLS) model resulting in 11 bioactive compounds, namely, palmitic acid, phytol, hexadecanoic acid (methyl ester), 1-monopalmitin, stigmast-5-ene, pentadecanoic acid, heptadecanoic acid, 1-linolenoylglycerol, glycerol monostearate, alpha-tocospiro B, and stigmasterol. In-silico study via molecular docking was carried out using the crystal structure Saccharomyces cerevisiae isomaltase (PDB code: 3A4A). Interactions between the inhibitors and the protein were predicted involving residues, namely LYS156, THR310, PRO312, LEU313, GLU411, and ASN415 with hydrogen bond, while PHE314 and ARG315 with hydrophobic bonding. Conclusion: The study provides informative data on the potential α-glucosidase inhibitors identified in C. nutans leaves, indicating the plant’s therapeutic effect to manage hyperglycemia.

scholarly journals Detecting Pulmonary Oxygen Toxicity Using eNose Technology and Associations between Electronic Nose and Gas Chromatography–Mass Spectrometry Data

Metabolites ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 286
Author(s):  
Thijs T. Wingelaar ◽  
Paul Brinkman ◽  
Rianne de Vries ◽  
Pieter-Jan A.M. van Ooij ◽  
Rigo Hoencamp ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Gas Chromatography ◽  
Electronic Nose ◽  
Oxygen Toxicity ◽  
Partial Least Square ◽  
Least Square ◽  
Sensor Data ◽  
Gas Chromatography Mass Spectrometry ◽  
Exhaled Breath ◽  
Chromatography Mass Spectrometry

Exposure to oxygen under increased atmospheric pressures can induce pulmonary oxygen toxicity (POT). Exhaled breath analysis using gas chromatography–mass spectrometry (GC–MS) has revealed that volatile organic compounds (VOCs) are associated with inflammation and lipoperoxidation after hyperbaric–hyperoxic exposure. Electronic nose (eNose) technology would be more suited for the detection of POT, since it is less time and resource consuming. However, it is unknown whether eNose technology can detect POT and whether eNose sensor data can be associated with VOCs of interest. In this randomized cross-over trial, the exhaled breath from divers who had made two dives of 1 h to 192.5 kPa (a depth of 9 m) with either 100% oxygen or compressed air was analyzed, at several time points, using GC–MS and eNose. We used a partial least square discriminant analysis, eNose discriminated oxygen and air dives at 30 min post dive with an area under the receiver operating characteristics curve of 79.9% (95%CI: 61.1–98.6; p = 0.003). A two-way orthogonal partial least square regression (O2PLS) model analysis revealed an R² of 0.50 between targeted VOCs obtained by GC–MS and eNose sensor data. The contribution of each sensor to the detection of targeted VOCs was also assessed using O2PLS. When all GC–MS fragments were included in the O2PLS model, this resulted in an R² of 0.08. Thus, eNose could detect POT 30 min post dive, and the correlation between targeted VOCs and eNose data could be assessed using O2PLS.

scholarly journals Gas Chromatography-Mass Spectrometry Coupled with Multivariate Statistical Analysis to Identify the Alpha Glucosidase Inhibitors from Flesh of Salacca zalacca Fruits and Their Molecular Docking Studies

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Mohammed S. M. Saleh ◽  
Mohammad Jamshed Siddiqui ◽  
Nabil Ali Al-Mekhlafi ◽  
Hussah Abdullah Alshwyeh ◽  
Ahmed Mediani ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Gas Chromatography ◽  
Molecular Docking ◽  
Palmitic Acid ◽  
Partial Least Square ◽  
Least Square ◽  
Gas Chromatography Mass Spectrometry ◽  
Bioactive Metabolites ◽  
Inhibition Activity ◽  
Chromatography Mass Spectrometry

Fruit of salak (Salaaca zalacca) is traditionally used and commercialized as an antidiabetic agent. However, scientific evidence to prove this folk claim is quite lacking. Therefore, this research was aimed to evaluate the α-glucosidase inhibition activity of S. zalacca fruit and identify the bioactive compounds. The fruits were extracted by different ratios of ethanol and water (0, 20, 40, 60, 80, 100%, v/v) to get E0 (100% water), E20 (20% ethanol), E40 (40% ethanol), E60 (60% ethanol), E80 (80% ethanol), and E100 (100% ethanol) extracts. The extracts obtained were subjected to the α-glucosidase inhibitory assay. Gas chromatography-mass spectrometry- (GC-MS-) based metabolomics approach was used in profiling the bioactive metabolites present in the extracts. Orthogonal partial least square (OPLS) was used to correlate GC-MS data and α-glucosidase assay results to identify the possible chemical markers. All active compounds identified were subjected to molecular docking. The extracts from the S. zalacca fruit showed potent inhibition activity against α-glucosidase. The IC50 values from the α-glucosidase inhibitory assay ranged between 16 and 275 µg/ml. Overall, E60 displayed significantly higher α-glucosidase inhibition activity, while E0 showed the lowest α-glucosidase inhibition activity. Major compounds detected in S. zalacca fruits were sugars, fatty acids, and sterols, including myo-inositol, palmitic acid, stearic acid, and β-sitosterol. Moreover, the results obtained from molecular docking indicated that palmitic acid and β-sitosterol were close to the active side of the enzyme. Some of the residues that interacted include HID295, ASN259, LEU313, LYS125, PHE159, VAL216, PHE178, TYR72, TYR158, HIE315, ARG315, and PHE303. The bioassay result strongly suggests that E60 extract from S. zalacca fruits has potential α-glucosidase inhibitory activity. The hydrophobic compounds, including palmitic acid and β-sitosterol, were found to induce the α-glucosidase inhibition activity.

Modeling CO2 exchange and meteorological factors of an apple orchard using partial least square regression

2020 ◽  
Vol 27 (35) ◽  
pp. 43439-43451 ◽  
Author(s):  
Jianfeng Yang ◽  
Yumin Duan ◽  
Xiaoni Yang ◽  
Mukesh Kumar Awasthi ◽  
Huike Li ◽  
...  
Keyword(s):  
Meteorological Factors ◽  
Co2 Exchange ◽  
Apple Orchard ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Least Square Regression

QSPR MODELLING OF STABILITY CONSTANTS OF METAL-THIOSEMICARBAZONE COMPLEXES USING MULTIVARIATE REGRESSION METHODS AND ARTIFICIAL NEURAL NETWORK

2021 ◽  
Vol 36 (06) ◽  
Author(s):  
NGUYEN MINH QUANG ◽  
TRAN NGUYEN MINH AN ◽  
NGUYEN HOANG MINH ◽  
TRAN XUAN MAU ◽  
PHAM VAN TAT
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stability Constants ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Structure Property ◽  
Least Square Regression ◽  
Thiosemicarbazone Complexes ◽  
Artificial Neural

In this study, the stability constants of metal-thiosemicarbazone complexes, logb11 were determined by using the quantitative structure property relationship (QSPR) models. The molecular descriptors, physicochemical and quantum descriptors of complexes were generated from molecular geometric structure and semi-empirical quantum calculation PM7 and PM7/sparkle. The QSPR models were built by using the ordinary least square regression (QSPROLS), partial least square regression (QSPRPLS), primary component regression (QSPRPCR) and artificial neural network (QSPRANN). The best linear model QSPROLS (with k of 9) involves descriptors C5, xp9, electric energy, cosmo volume, N4, SsssN, cosmo area, xp10 and core-core repulsion. The QSPRPLS, QSPR PCR and QSPRANN models were developed basing on 9 varibles of the QSPROLS model. The quality of the QSPR models were validated by the statistical values; The QSPROLS: R2train = 0.944, Q2LOO = 0.903 and MSE = 1.035; The QSPRPLS: R2train = 0.929, R2CV = 0.938 and MSE = 1.115; The QSPRPCR: R2train = 0.934, R2CV = 0.9485 and MSE = 1.147. The neural network model QSPRANN with architecture I(9)-HL(12)-O(1) was presented also with the statistical values: R2train = 0.9723, and R2CV = 0.9731. The QSPR models also were evaluated externally and got good performance results with those from the experimental literature.

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