scholarly journals Quality Evaluation of Phellodendri Chinensis Cortex by Fingerprint–Chemical Pattern Recognition

Molecules ◽  
2018 ◽  
Vol 23 (9) ◽  
pp. 2307 ◽  
Author(s):  
Xuexiao Cao ◽  
Lili Sun ◽  
Di Li ◽  
Guangjiao You ◽  
Meng Wang ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
High Performance ◽  
Quality Evaluation ◽  
Herbal Medicines ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Maximum Thickness

Phellodendri Chinensis Cortex (PCC) and Phellodendri Amurensis Cortex (PAC) are increasingly being used as traditional herbal medicines, but they are often mistaken for each other. In this study, the fingerprints of PCC from six different geographical sources were obtained by high-performance liquid chromatography, and multivariate chemometric methods were used for comprehensive analysis. Two unsupervised pattern recognition models (principal component analysis and hierarchical cluster analysis) and a supervised pattern recognition model (partial least squares discriminant analysis) were established on the basis of the chemical composition and physical traits of PCC and PAC. PCC and PAC were found to be distinguishable by these methods. The PCC category was divisible into two categories, one with more crude cork and a maximum thickness of ~1.5 mm, and the other with less net crude cork and a maximum thickness of 0.5 mm. According to the model established by partial least squares discriminant analysis (PLS-DA), the important chemical marker berberine hydrochloride was obtained and analyzed quantitatively. From these results combined with chemometric and content analyses, the preliminary classification standards for phellodendron were established as three grades: superior, first-order and mixed. Compared with the traditional identification methods of thin layer chromatography identification and microscopic identification, our method for quality evaluation is relatively simple. It provides a basis and reference for identification of PCC and enables establishment of grade standards. It also could be applied in quality control for compound preparations containing PCC.

scholarly journals Human Melanoma-Cell Metabolic Profiling: Identification of Novel Biomarkers Indicating Metastasis

2020 ◽  
Vol 21 (7) ◽  
pp. 2436 ◽  
Author(s):  
Mariangela Kosmopoulou ◽  
Aikaterini F. Giannopoulou ◽  
Aikaterini Iliou ◽  
Dimitra Benaki ◽  
Aristeidis Panagiotakis ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
High Resolution ◽  
Least Squares ◽  
Partial Least Squares ◽  
Cell Lines ◽  
High Performance ◽  
Principal Component ◽  
Metastatic Tumor ◽  
Human Melanoma ◽  
Melanoma Metastasis

Melanoma is the most aggressive type of skin cancer, leading to metabolic rewiring and enhancement of metastatic transformation. Efforts to improve its early and accurate diagnosis are largely based on preclinical models and especially cell lines. Hence, we herein present a combinational Nuclear Magnetic Resonance (NMR)- and Ultra High Performance Liquid Chromatography-High-Resolution Tandem Mass Spectrometry (UHPLC-HRMS/MS)-mediated untargeted metabolomic profiling of melanoma cells, to landscape metabolic alterations likely controlling metastasis. The cell lines WM115 and WM2664, which belong to the same patient, were examined, with WM115 being derived from a primary, pre-metastatic, tumor and WM2664 clonally expanded from lymph-node metastases. Metabolite samples were analyzed using NMR and UHPLC-HRMS. Multivariate statistical analysis of high resolution NMR and MS (positive and negative ionization) results was performed by Principal Component Analysis (PCA), Partial Least Squares-Discriminant Analysis (PLS-DA) and Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA), while metastasis-related biomarkers were determined on the basis of VIP lists, S-plots and Student’s t-tests. Receiver Operating Characteristic (ROC) curves of NMR and MS data revealed significantly differentiated metabolite profiles for each cell line, with WM115 being mainly characterized by upregulated levels of phosphocholine, choline, guanosine and inosine. Interestingly, WM2664 showed notably increased contents of hypoxanthine, myo-inositol, glutamic acid, organic acids, purines, pyrimidines, AMP, ADP, ATP and UDP(s), thus indicating the critical roles of purine, pyrimidine and amino acid metabolism during human melanoma metastasis.

scholarly journals Correlation of Volatile Compounds and Sensory Attributes of Chinese Traditional Sweet Fermented Flour Pastes Using Hierarchical Cluster Analysis and Partial Least Squares-Discriminant Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Meigui Huang ◽  
Yulin Li ◽  
Ping Zhan ◽  
Ping Liu ◽  
Honglei Tian ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Discriminant Analysis ◽  
Least Squares ◽  
Volatile Compounds ◽  
Partial Least Squares ◽  
Hierarchical Cluster Analysis ◽  
Solid Phase ◽  
Hierarchical Cluster ◽  
Sensory Attributes ◽  
Volatile Components

The aroma compositions, sensory attributes, and their correlations of various traditional Chinese sweet fermented flour pastes (SFFPs) were investigated. SFFPs, including LEEJ, LEEH, and XH6, showed high overall acceptance scores of 8.00, 8.21, and 7.50, respectively. Ninety-six volatile compounds were detected using solid-phase microextraction gas chromatography mass spectrometry. Hierarchical cluster analysis grouped SFFPs into three clusters according to their concentrations and compositions of volatile components. Partial least squares-discriminant analysis showed that volatile compounds, including ethyl phenylacetate, 5-methyl furfural, amyl cinnamal, ethyl myristate, decyl aldehyde, 1-phenylethyl acetate, 1-octen-3-ol, 3-buten-2-ol, butanoic acid, and caproaldehyde, were highly negatively correlated with saltiness, sourness, and bitterness, while they were positively correlated with sweetness, umami, richness, and acceptance. The obvious correlation between flavor profiles and sensory attributes could help online monitoring of SFFPs’ flavor quality during production.

scholarly journals Harvest year effects on Apulian EVOOs evaluated by1H NMR based metabolomics

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2740 ◽  
Author(s):  
Chiara R. Girelli ◽  
Laura Del Coco ◽  
Paride Papadia ◽  
Sandra A. De Pascali ◽  
Francesco P. Fanizzi
Keyword(s):  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Metabolic Profile ◽  
Principal Component ◽  
Summer Rainfall ◽  
Weather Conditions ◽  
Genetic Identification ◽  
Mahalanobis Distances ◽  
Olive Trees

Nine hundred extra virgin olive oils (EVOO) were extracted from individual olive trees of four olive cultivars (Coratina, Cima di Mola, Ogliarola, Peranzana), originating from the provinces of Bari and Foggia (Apulia region, Southern Italy) and collected during two consecutive harvesting seasons (2013/14 and 2014/15). Following genetic identification of individual olive trees, a detailed Apulian EVOO NMR database was built using 900 oils samples obtained from 900 cultivar certified single trees. A study on the olive oil lipid profile was carried out by statistical multivariate analysis (Principal Component Analysis, PCA, Partial Least-Squares Discriminant Analysis, PLS-DA, Orthogonal Partial Least-Squares Discriminant Analysis, OPLS-DA). Influence of cultivar and weather conditions, such as the summer rainfall, on the oil metabolic profile have been evaluated. Mahalanobis distances andJ2criterion have been measured to assess the quality of resulting scores clusters for each cultivar in the two harvesting campaigns. The four studied cultivars showed non homogeneous behavior. Notwithstanding the geographical spread and the wide number of samples, Coratina showed a consistent behavior of its metabolic profile in the two considered harvests. Among the other three Peranzana showed the second more consistent behavior, while Cima di Mola and Ogliarola having the biggest change over the two years.

Comparison and Discrimination of Artemisia argyi and Artemisia lavandulifolia by Gas Chromatography-Mass Spectrometry-Based Metabolomic Approach

2019 ◽  
Vol 102 (6) ◽  
pp. 1814-1821 ◽  
Author(s):  
Long Guo ◽  
Dan Zhang ◽  
Lei Wang ◽  
Zijing Xue ◽  
Mei Guo ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Least Squares ◽  
Volatile Compounds ◽  
Partial Least Squares ◽  
Herbal Medicines ◽  
Principal Component ◽  
Component Analysis ◽  
Chemical Markers ◽  
Artemisia Argyi ◽  
Metabolomic Approach

Abstract Background: Artemisia argyi and A. lavandulifolia are two morphologically similar herbal medicines derived from the Artemisia genus. Although the two Artemisia herbs have been used as herbal medicines for a long time, studies on their phytochemicals and bioactive compositions are still limited, and no research has been devoted to compare the volatile compounds in A. argyi and A. lavandulifolia. Objective: To compare the volatile constituents in A. argyi and A. lavandulifolia and to explore chemical markers for discrimination and quality evaluation of the two Artemisia herbal medicines. Methods: A GC-MS-based metabolomic approach was employed to compare and discriminate A. argyi and A. lavandulifolia from the aspect of volatile compounds. Multivariate statistical methods, including principal component analysis and orthogonal partial least-squares discriminate analysis, were applied to explore chemical markers for discrimination of the two Artemisia herbal medicines. Results: Thirty volatile compounds were identified, and the chemical profiles of volatile compounds in A. argyi and A. lavandulifolia were quite similar. Principal component analysis and orthogonal partial least-squares discrimination analysis results indicated that the two Artemisia herbal medicines could be distinguished effectively from each other. Ten volatile compounds were selected as potential chemical markers for discrimination of the two Artemisia herbal medicines. Conclusions: The GC-MS-based metabolomics could be an acceptable strategy for comparison and discrimination of A. argyi and A. lavandulifolia as well as authentication of herbal medicines derived from other closely related species. Highlights: GC-MS based metabolomic approach was firstly applied to compare and discriminate Artemisia argyi and Artemisia lavandulifolia.

scholarly journals Analysis of the procedures used in the forensic evaluation of illegal substances: a statistical approach to interpreting reports

2019 ◽  
Vol 8 (1) ◽  
pp. 7-23
Author(s):  
Aline Thaís Bruni ◽  
Ricardo Luís Yoshida ◽  
Arthur Serra Lopes Ferreira ◽  
Jesus Antonio Velho ◽  
Bruno Spinosa De Martinis ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Hierarchical Cluster Analysis ◽  
Labor Demand ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Component Analysis ◽  
Final Report

ResumoEste estudo utilizou ferramentas estatísticas para avaliar laudos forenses sobre substâncias ilegais. Avaliamos variáveis quanto às características da análise e abordamos a metodologia empregada pelos peritos. Perguntas baseadas no que é necessário para esclarecer a lei foram formuladas. Analisamos 1008 documentos oficiais de diferentes jurisdições, divididos em 504 conjuntos compostos por um laudo preliminar e um laudo definitivo para cada caso. Os laudos foram examinados por uma equação empírica formulada para fornecer um parâmetro denominado “Report Relevance” (Relevância do Laudo), que teve por finalidade classificar cada documento de acordo com uma pontuação relacionada à quantidade de informação contida. A validação do método foi realizada por análise multivariada de dados: Análise de Componentes Principais (Principal Component Analysis, PCA), Análise de Agrupamentos Hierárquicos (Hierarchical Cluster Analysis, HCA), Soft Independent Modeling of Class Analogy (SIMCA) e Mínimos Quadrados Parciais (Partial Least Squares, PLS). A análise quantitativa mostrou que os documentos foram bem produzidos, com boa qualidade, uma vez que a Relevância do Laudo apresentou valores em torno de 0,74 ± 0,08 para aqueles provenientes da Polícia Estadual. Em comparação, os documentos provenientes da Polícia Federal obtiveram valores em torno de 0,87 ± 0,05. Fatores que podem explicar essas diferenças e as melhores pontuações para os laudos federais incluem maior investimento em tecnologia e treinamento de pessoal, e menor demanda de mão-de-obra e rotina. Para ambas as forças policiais, alguns aspectos poderiam ser melhorados, como imagens das evidências coletadas ou procedimentos analíticos laboratoriais. Finalmente, a metodologia neste estudo pode ser adaptada para ser usada em outros tipos de investigação forense.Palavras-chave: Substâncias Ilícitas, Procedimentos Periciais, Análise MultivariadaAbstractThis study used statistical tools to evaluate forensic reports on illegal substances. We evaluated variables regarding the characteristics of the analysis and we addressed the methodology employed by the experts. Questions based on what is required to clarify the law were formulated. We have parsed 1008 official documents from different jurisdictions, divided into 504 sets comprised of a preliminary and a final report for each case. The reports were examined by an empirical equation formulated to provide a parameter called “Report Relevance”, which intended to classify each report according to a score related to the amount of information it contained.  The validation of the method was performed by multivariate data analysis: Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), Soft Independent Modeling of Class Analogy (SIMCA) and Partial Least Squares (PLS). Quantitative analysis showed that the expert documents were well produced, with good quality, since the Report Relevance showed values around 0.74 ± 0.08 for the reports from the State Police. By comparison, reports from the Federal Police obtained scores around 0.87 ± 0.05. Factors that might explain these differences and the better scores for the Federal reports include increased investment in technology and training of staff, and a lower labor demand and routine. For both police forces, some aspects could be improved, such as images of the collected evidence or laboratory analytical procedures. Finally, the methodology in this study can be adapted to be used in other kinds of forensic investigation.Keywords: Illegal Substances, Expertise Procedures, Multivariate Analysis.  

scholarly journals Quality Evaluation of Mahonia bealei (Fort.) Carr. Using Supercritical Fluid Chromatography with Chemical Pattern Recognition

Molecules ◽  
2019 ◽  
Vol 24 (20) ◽  
pp. 3684 ◽  
Author(s):  
Yang Huang ◽  
Zhengjin Jiang ◽  
Jue Wang ◽  
Guo Yin ◽  
Kun Jiang ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Supercritical Fluid ◽  
Supercritical Fluid Chromatography ◽  
Quality Evaluation ◽  
Predictive Ability ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Chemical Pattern ◽  
Mahonia Bealei ◽  
Chemical Pattern Recognition

Mahonia bealei (Fort.) Carr. (M. bealei) plays an important role in the treatment of many diseases. In the present study, a comprehensive method combining supercritical fluid chromatography (SFC) fingerprints and chemical pattern recognition (CPR) for quality evaluation of M. bealei was developed. Similarity analysis, hierarchical cluster analysis (HCA), principal component analysis (PCA) were applied to classify and evaluate the samples of wild M. bealei, cultivated M. bealei and its substitutes according to the peak area of 11 components but an accurate classification could not be achieved. PLS-DA was then adopted to select the characteristic variables based on variable importance in projection (VIP) values that responsible for accurate classification. Six characteristics peaks with higher VIP values (≥1) were selected for building the CPR model. Based on the six variables, three types of samples were accurately classified into three related clusters. The model was further validated by a testing set samples and predication set samples. The results indicated the model was successfully established and predictive ability was also verified satisfactory. The established model demonstrated that the developed SFC coupled with PLS-DA method showed a great potential application for quality assessment of M. bealei.

Moving Window Partial Least-Squares Discriminant Analysis for Identification of Different Kinds of Bezoar Samples by near Infrared Spectroscopy and Comparison of Different Pattern Recognition Methods

2007 ◽  
Vol 15 (5) ◽  
pp. 291-297 ◽  
Author(s):  
Hai-Yan Fu ◽  
Shuang-Yan Huan ◽  
Lu Xu ◽  
Li-Juan Tang ◽  
Jian-Hui Jiang ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Geographical Origin ◽  
Quality Analysis ◽  
Moving Window ◽  
Linear Discriminant ◽  
Pattern Recognition Methods

Moving window partial least-squares (MWPLS) regression was coupled with near infrared (NIR) spectra as an interval selection method to improve the performance of partial least squares discriminant analysis (PLSDA) models. This method was applied to the identification of artificial bezoar, natural bezoar and artificial bezoar in natural bezoar and compared with some traditional pattern recognition methods, such as principal component analysis (PCA), linear discriminant analysis (LDA) and PLSDA. The introduction of MWPLS enhanced the performance of PLSDA model. The results obtained showed that moving window partial least-squares discriminant analysis (MWPLSDA) can extract wavelength intervals with useful information and build simple yet effective classification models that can significantly improve the classification accuracy. Then MWPLSDA was used to identify natural bezoar by geographical origin; a promising result was achieved. The work showed that MWPLSDA could be a promising method for quality analysis and discrimination of chinese medical herbs according to geographical origin.

scholarly journals Multivariate statistical analysis of tropane alkaloids in Anisodus tanguticus (Maxim.) Pascher from different regions to trace geographical origins

2021 ◽  
Author(s):  
Chen Chen ◽  
Jingjing Li ◽  
Feng Xiong ◽  
Bo Wang ◽  
Yuanming Xiao ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Quantitative Analysis ◽  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Geographical Origin ◽  
Principal Component ◽  
Tropane Alkaloids ◽  
Component Analysis ◽  
Tibet Plateau

Abstract Anisodus tanguticus (Maxim.) Pascher is an important Tibetan folk medicine and the source of tropane alkaloids (TAs) grown in Qinghai-Tibet Plateau. There are marked differences in quality of A. tanguticus from geographic areas. The aim of present research was to establish a method for the quantitative analysis of TAs coupled with chemometrics analysis to trace geographical origins. Qualitative analysis of TAs in A. tanguticus was carried out using ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry and quantitative analysis of TAs in different plant organs from different geographical origin was achieved. Contents of TAs were subjected to the principal component analysis, and orthogonal partial least-squares discriminant analysis. The contents of the three marker compounds (anisodamine, anisodine and atropine) in the roots and acrial parts of A. tanguticus were positive correlated and varied significantly from different geographical origins. Principal component analysis, and orthogonal partial least-squares discriminant analysis results showed excellent discrimination between different geographical origin of A. tanguticus. This study could provide comprehensive evaluation and further utilization of A. tanguticus resources.

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