Chemical components determination via terahertz spectroscopic statistical analysis using microgenetic algorithm

2011 ◽  
Vol 50 (3) ◽  
pp. 034401 ◽  
Author(s):  
Yong Ma
Keyword(s):  
Statistical Analysis ◽  
Chemical Components ◽  
Microgenetic Algorithm

scholarly journals UPLC-QTOF/MS-Based Nontargeted Metabolomic Analysis of Mountain- and Garden-Cultivated Ginseng of Different Ages in Northeast China

Molecules ◽  
2018 ◽  
Vol 24 (1) ◽  
pp. 33 ◽  
Author(s):  
Hailin Zhu ◽  
Hongqiang Lin ◽  
Jing Tan ◽  
Cuizhu Wang ◽  
Han Wang ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Chemical Constituents ◽  
Octadecenoic Acid ◽  
Chemical Components ◽  
Metabolomic Analysis ◽  
Multivariate Statistical ◽  
Similar Chemical ◽  
Different Ages ◽  
First Time

Aiming at further systematically comparing the similarities and differences of the chemical components in ginseng of different ages, especially comparing the younger or the older and mountain-cultivated ginseng (MCG), 4, 5, 6-year-old cultivated ginseng (CG) and 12, 20-year-old MCG were chosen as the analytical samples in the present study. The combination of UPLC-QTOF-MSE, UNIFI platform and multivariate statistical analysis were developed to profile CGs and MCGs. By the screening analysis based on UNIFI, 126 chemical components with various structural types were characterized or tentatively identified from all the CG and MCG samples for the first time. The results showed that all the CG and MCG samples had the similar chemical composition, but there were significant differences in the contents of markers. By the metabolomic analysis based on multivariate statistical analysis, it was shown that CG4–6 years, MCG12 years and MCG20 years samples were obviously divided into three different groups, and a total of 17 potential age-dependent markers enabling differentiation among the three groups of samples were discovered. For differentiation from other two kinds of samples, there were four robust makers such as α-linolenic acid, 9-octadecenoic acid, linoleic acid and panaxydol for CG4–6 years, five robust makers including ginsenoside Re1, -Re2, -Rs1, malonylginsenoside Rb2 and isomer of malonylginsenoside Rb1 for MCG20 years, and two robust makers, 24-hydroxyoleanolic acid and palmitoleic acid, for MCG12 years were discovered, respectively. The proposed approach could be applied to directly distinguish MCG root ages, which is an important criterion for evaluating the quality of MCG. The results will provide the data for the further study on the chemical constituents of MCG.

scholarly journals Statistical Analysis of Chemical Element Compositions in Food Science: Problems and Possibilities

Molecules ◽  
2021 ◽  
Vol 26 (19) ◽  
pp. 5752
Author(s):  
Matthias Templ ◽  
Barbara Templ
Keyword(s):  
Statistical Analysis ◽  
Missing Values ◽  
Compositional Data ◽  
Chemical Elements ◽  
Chemical Components ◽  
Compositional Data Analysis ◽  
Research Fields ◽  
Log Ratio ◽  
Processing Steps

In recent years, many analyses have been carried out to investigate the chemical components of food data. However, studies rarely consider the compositional pitfalls of such analyses. This is problematic as it may lead to arbitrary results when non-compositional statistical analysis is applied to compositional datasets. In this study, compositional data analysis (CoDa), which is widely used in other research fields, is compared with classical statistical analysis to demonstrate how the results vary depending on the approach and to show the best possible statistical analysis. For example, honey and saffron are highly susceptible to adulteration and imitation, so the determination of their chemical elements requires the best possible statistical analysis. Our study demonstrated how principle component analysis (PCA) and classification results are influenced by the pre-processing steps conducted on the raw data, and the replacement strategies for missing values and non-detects. Furthermore, it demonstrated the differences in results when compositional and non-compositional methods were applied. Our results suggested that the outcome of the log-ratio analysis provided better separation between the pure and adulterated data and allowed for easier interpretability of the results and a higher accuracy of classification. Similarly, it showed that classification with artificial neural networks (ANNs) works poorly if the CoDa pre-processing steps are left out. From these results, we advise the application of CoDa methods for analyses of the chemical elements of food and for the characterization and authentication of food products.

scholarly journals Metabolomics Combined with Multivariate Statistical Analysis for Screening of Chemical Markers between Gentiana scabra and Gentiana rigescens

Molecules ◽  
2020 ◽  
Vol 25 (5) ◽  
pp. 1228
Author(s):  
Gaole Zhang ◽  
Yun Li ◽  
Wenlong Wei ◽  
Jiayuan Li ◽  
Haoju Li ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
High Performance ◽  
Ion Trap ◽  
Chemical Constituents ◽  
Chemical Components ◽  
Chemical Markers ◽  
Multivariate Statistical ◽  
Gentiana Scabra

Gentianae Radix et Rhizome (Longdan in Chinese, GRR) in Chinese Pharmacopoeia is derived from the dried roots and rhizomes of Gentiana scabra and G. rigescens, that have long been used for heat-clearing and damp-drying in the medicinal history of China. However, the characterization of the chemical components of two species and the screening of chemical markers still remain unsolved. In current research, the identification and characterization of chemical components of two species was performed using ultra-high-performance liquid chromatography (UHPLC) coupled with linear ion trap-Orbitrap (LTQ-Orbitrap) mass spectrometry. Subsequently, the chemical markers of two species were screened based on metabolomics and multivariate statistical analysis. In total, 87 chemical constituents were characterized in G. scabra (65 chemical constituents) and G. rigescens (51 chemical constituents), with 29 common chemical constituents being discovered. Thereafter, 11 differential characteristic components which could differentiate the two species were designated with orthogonal partial least squares discriminant analysis (OPLS-DA) and random forest (RF) iterative modeling. Finally, seven characteristic components identified as (+)-syringaresinol, lutonarin, trifloroside, 4-O-β-d-glu-trifloroside, 4″-O-β-d-glucopyranosy1-6′-O-(4-O-β-d-glucaffeoyl)-linearroside, macrophylloside a and scabraside were selected as the chemical markers for the recognition of two Gentiana species. It was implied that the results could distinguish the GRR derived from different botanical sources, and also be beneficial in the rational clinical use of GRR.

Statistical analysis of classification

1966 ◽  
Vol 24 ◽  
pp. 188-189
Author(s):  
T. J. Deeming
Keyword(s):  
Multivariate Analysis ◽  
Statistical Analysis ◽  
Classification Scheme ◽  
Analytical Procedure ◽  
Narrow Band ◽  
Scientific Research ◽  
Maximum Amount ◽  
Amount Of Information

If we make a set of measurements, such as narrow-band or multicolour photo-electric measurements, which are designed to improve a scheme of classification, and in particular if they are designed to extend the number of dimensions of classification, i.e. the number of classification parameters, then some important problems of analytical procedure arise. First, it is important not to reproduce the errors of the classification scheme which we are trying to improve. Second, when trying to extend the number of dimensions of classification we have little or nothing with which to test the validity of the new parameters.Problems similar to these have occurred in other areas of scientific research (notably psychology and education) and the branch of Statistics called Multivariate Analysis has been developed to deal with them. The techniques of this subject are largely unknown to astronomers, but, if carefully applied, they should at the very least ensure that the astronomer gets the maximum amount of information out of his data and does not waste his time looking for information which is not there. More optimistically, these techniques are potentially capable of indicating the number of classification parameters necessary and giving specific formulas for computing them, as well as pinpointing those particular measurements which are most crucial for determining the classification parameters.

Quantitative parallel EELS spectrum imaging developed as a new tool in AEM

1992 ◽  
Vol 50 (2) ◽  
pp. 1202-1203
Author(s):  
Gianluigi Botton ◽  
Gilles L'espérance
Keyword(s):  
Statistical Analysis ◽  
Spatial Resolution ◽  
Personal Computer ◽  
Systematic Study ◽  
Present Author ◽  
Chemical Elements ◽  
Detection Limits ◽  
Research Groups ◽  
Quantitative Performance ◽  
Spectrum Imaging

As interest for parallel EELS spectrum imaging grows in laboratories equipped with commercial spectrometers, different approaches were used in recent years by a few research groups in the development of the technique of spectrum imaging as reported in the literature. Either by controlling, with a personal computer both the microsope and the spectrometer or using more powerful workstations interfaced to conventional multichannel analysers with commercially available programs to control the microscope and the spectrometer, spectrum images can now be obtained. Work on the limits of the technique, in terms of the quantitative performance was reported, however, by the present author where a systematic study of artifacts detection limits, statistical errors as a function of desired spatial resolution and range of chemical elements to be studied in a map was carried out The aim of the present paper is to show an application of quantitative parallel EELS spectrum imaging where statistical analysis is performed at each pixel and interpretation is carried out using criteria established from the statistical analysis and variations in composition are analyzed with the help of information retreived from t/γ maps so that artifacts are avoided.

Microscopic versus process parameters in heavy-oil upgrading

1992 ◽  
Vol 50 (1) ◽  
pp. 366-367
Author(s):  
V.A. Munoz ◽  
R.J. Mikula ◽  
C. Payette ◽  
W.W. Lam
Keyword(s):  
Molecular Weight ◽  
Liquid Fuels ◽  
Chemical Components ◽  
Heavy Oils ◽  
Synthetic Fuels ◽  
High Hydrogen ◽  
Optical Texture ◽  
Polar Groups ◽  
Anisotropic Character ◽  
Planar Molecules

The transformation of high molecular weight components present in heavy oils into useable liquid fuels requires their decomposition by means of a variety of processes. The low molecular weight species produced recombine under controlled conditions to generate synthetic fuels. However, an important fraction undergo further recombination into higher molecular weight components, leading to the formation of coke. The optical texture of the coke can be related to its originating components. Those with high sulfur and oxygen content tend to produce cokes with small optical texture or fine mosaic, whereas compounds with relatively high hydrogen content are likely to produce large optical texture or domains. In addition, the structure of the parent chemical components, planar or nonplanar, determines the isotropic or anisotropic character of the coke. Planar molecules have a tendency to align in an approximately parallel arrangement to initiate the formation of the nematic mesophase leading to the formation of anisotropic coke. Nonplanar highly alkylated compounds and/or those rich in polar groups form isotropic coke. The aliphatic branches produce steric hindrance to alignment, whereas the polar groups participate in cross-linking reactions.

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